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hamid1232/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_roaring_crab | hamid1232 | "2025-05-06T12:19:41Z" | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hulking roaring crab",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-28T02:41:21Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_roaring_crab
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hulking roaring crab
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_roaring_crab
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hamid1232/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_roaring_crab", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
netalabs/vertex-qwen-3B-typescript-v1 | netalabs | "2025-05-06T12:18:47Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-3B",
"base_model:finetune:unsloth/Qwen2.5-3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-05-06T12:18:40Z" | ---
base_model: unsloth/Qwen2.5-3B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** netalabs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
beruangmadu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-horned_darting_mink | beruangmadu | "2025-05-06T12:18:15Z" | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am horned darting mink",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T18:15:04Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-horned_darting_mink
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am horned darting mink
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-horned_darting_mink
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="beruangmadu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-horned_darting_mink", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kafarasi/marian-ru-en-finetunedv2 | kafarasi | "2025-05-06T12:18:13Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-05-06T12:17:50Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
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Kai12341/my-unigram-tokenizer | Kai12341 | "2025-05-06T12:18:11Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-06T11:55:51Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Grabado/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_peaceful_bear | Grabado | "2025-05-06T12:17:31Z" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am energetic peaceful bear",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-10T08:50:46Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_peaceful_bear
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am energetic peaceful bear
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_peaceful_bear
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Grabado/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_peaceful_bear", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
otongdarkex/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron | otongdarkex | "2025-05-06T12:17:15Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hunting voracious heron",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T13:40:47Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hunting voracious heron
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="otongdarkex/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hunting_voracious_heron", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
rayonlabs/hf-autotrain-2025-05-06-db8c5d8a | rayonlabs | "2025-05-06T12:17:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"dataset:rayonlabs/autotrain-data-hf-autotrain-2025-05-06-db8c5d8a",
"base_model:EleutherAI/pythia-70m",
"base_model:finetune:EleutherAI/pythia-70m",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-06T11:11:44Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: EleutherAI/pythia-70m
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- rayonlabs/autotrain-data-hf-autotrain-2025-05-06-db8c5d8a
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
koochikoo25/distilbert-base-uncased-finetuned-imdb | koochikoo25 | "2025-05-06T12:17:06Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2025-05-06T11:52:03Z" | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3042
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4865 | 1.0 | 479 | 2.3469 |
| 2.4199 | 2.0 | 958 | 2.3173 |
| 2.3939 | 3.0 | 1437 | 2.3038 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
AmanoShizukikun/NS-IC-Kyouka | AmanoShizukikun | "2025-05-06T12:16:33Z" | 0 | 0 | null | [
"zh",
"license:apache-2.0",
"region:us"
] | null | "2025-04-15T13:26:28Z" | ---
license: apache-2.0
language:
- zh
---
### Kyouka -《鏡花》- 碎裂之象，在鏡中重構原初之姿。鏡中之花非虛幻，碎裂之象皆可還映。
<p align="center">
<img src="https://raw.githubusercontent.com/AmanoShizukikun/Nagato-Sakura-Image-Charm/refs/heads/main/assets/samples/Kyouka_comparison.webp">
</p>
- ### 適用場景: 動漫JEPG壓縮還原模型
- ### 訓練參數: 10K |
Weymouth/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-downy_dense_starfish | Weymouth | "2025-05-06T12:16:22Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am downy dense starfish",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-10T08:34:17Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-downy_dense_starfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am downy dense starfish
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-downy_dense_starfish
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Weymouth/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-downy_dense_starfish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
akusultancrypto/Joshetes | akusultancrypto | "2025-05-06T12:16:19Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-05-06T12:16:19Z" | ---
license: apache-2.0
---
|
naser1973/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snorting_tawny_quail | naser1973 | "2025-05-06T12:16:13Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am snorting tawny quail",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-12T13:20:21Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snorting_tawny_quail
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am snorting tawny quail
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snorting_tawny_quail
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="naser1973/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-snorting_tawny_quail", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Alex6513/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver | Alex6513 | "2025-05-06T12:15:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grazing diving beaver",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T20:11:20Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grazing diving beaver
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alex6513/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-grazing_diving_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kafarasi/marian-ru-en-finetuned | kafarasi | "2025-05-06T12:15:44Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-05-04T15:46:11Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ypszn/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_territorial_jaguar | ypszn | "2025-05-06T12:15:25Z" | 20 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am quiet territorial jaguar",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-10T14:18:47Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_territorial_jaguar
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am quiet territorial jaguar
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_territorial_jaguar
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ypszn/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-quiet_territorial_jaguar", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
EsterTregub/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_fox | EsterTregub | "2025-05-06T12:15:03Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am peckish lively fox",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-17T13:55:43Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_fox
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am peckish lively fox
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_fox
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="EsterTregub/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_fox", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
WHDtyrael/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_exotic_butterfly | WHDtyrael | "2025-05-06T12:14:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am pensive exotic butterfly",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T13:46:51Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_exotic_butterfly
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am pensive exotic butterfly
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_exotic_butterfly
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="WHDtyrael/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_exotic_butterfly", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
gradientrouting-spar/qwen_ft_May3_m3_p1_num1 | gradientrouting-spar | "2025-05-06T12:14:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-06T12:14:08Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
khangnguyen1287/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar | khangnguyen1287 | "2025-05-06T12:14:32Z" | 16 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mammalian rugged caterpillar",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-19T15:20:21Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mammalian rugged caterpillar
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="khangnguyen1287/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mammalian_rugged_caterpillar", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AkubecS/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fluffy_twitchy_mole | AkubecS | "2025-05-06T12:14:10Z" | 28 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fluffy twitchy mole",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-02T10:49:36Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fluffy_twitchy_mole
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fluffy twitchy mole
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fluffy_twitchy_mole
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AkubecS/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fluffy_twitchy_mole", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fty7i/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pensive_powerful_koala | fty7i | "2025-05-06T12:14:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am pensive powerful koala",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T02:44:33Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pensive_powerful_koala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am pensive powerful koala
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pensive_powerful_koala
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fty7i/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-pensive_powerful_koala", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kaukulana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_slender_jellyfish | kaukulana | "2025-05-06T12:14:01Z" | 11 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am nocturnal slender jellyfish",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-17T09:58:19Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_slender_jellyfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am nocturnal slender jellyfish
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_slender_jellyfish
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="kaukulana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_slender_jellyfish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Leg18/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_skittish_falcon | Leg18 | "2025-05-06T12:13:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am giant skittish falcon",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T13:53:43Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_skittish_falcon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am giant skittish falcon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_skittish_falcon
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Leg18/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_skittish_falcon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
DLingChen/writing_fingerprint | DLingChen | "2025-05-06T12:13:16Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-05-06T04:09:04Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
Namronaldo2004/ViInfographicsVQA | Namronaldo2004 | "2025-05-06T12:12:49Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-18T17:14:08Z" | ---
license: apache-2.0
---
|
nazopsjd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_yawning_cod | nazopsjd | "2025-05-06T12:12:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am whiskered yawning cod",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-04-30T22:27:53Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_yawning_cod
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am whiskered yawning cod
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_yawning_cod
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nazopsjd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_yawning_cod", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
NM-development/nllb-ce-rus-v0 | NM-development | "2025-05-06T12:12:35Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"arXiv:2209.09368",
"translation",
"ce",
"ru",
"base_model:facebook/nllb-200-distilled-600M",
"base_model:finetune:facebook/nllb-200-distilled-600M",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2025-05-03T18:28:14Z" | ---
license: mit
language:
- ce
- ru
metrics:
- chrf
- bleu
base_model:
- facebook/nllb-200-distilled-600M
pipeline_tag: translation
library_name: transformers
tags:
- arXiv:2209.09368
---
Fine tuned NLLB-200 model for Chechen-Russian translation. |
jmjm123/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper | jmjm123 | "2025-05-06T12:12:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am clawed rugged viper",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T09:40:33Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am clawed rugged viper
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jmjm123/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-clawed_rugged_viper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
devarko/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-savage_yapping_trout | devarko | "2025-05-06T12:12:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am savage yapping trout",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-04T15:28:35Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-savage_yapping_trout
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am savage yapping trout
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-savage_yapping_trout
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="devarko/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-savage_yapping_trout", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
yumovl/llm-course-hw1 | yumovl | "2025-05-06T12:12:09Z" | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | "2025-05-05T13:57:01Z" | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
IrynaPopaduk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_domestic_anaconda | IrynaPopaduk | "2025-05-06T12:12:07Z" | 21 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am furry domestic anaconda",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-18T12:24:29Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_domestic_anaconda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am furry domestic anaconda
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_domestic_anaconda
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="IrynaPopaduk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_domestic_anaconda", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fgjg856hh/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish | fgjg856hh | "2025-05-06T12:11:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tawny enormous starfish",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T03:59:40Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tawny enormous starfish
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fgjg856hh/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
greenwich157/Llama-3.2-3B-Instruct-TelcoLLM-GGUF | greenwich157 | "2025-05-06T12:11:42Z" | 84 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:greenwich157/Llama-3.2-3B-Instruct-TelcoLLM",
"base_model:quantized:greenwich157/Llama-3.2-3B-Instruct-TelcoLLM",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-29T06:16:04Z" | ---
base_model: greenwich157/Llama-3.2-3B-Instruct-TelcoLLM
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** greenwich157
- **License:** apache-2.0
- **Finetuned from model :** greenwich157/Llama-3.2-3B-Instruct-TelcoLLM
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Dejiat/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal | Dejiat | "2025-05-06T12:11:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prickly woolly seal",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T08:04:52Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prickly woolly seal
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Dejiat/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
baiaju/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_downy_chinchilla | baiaju | "2025-05-06T12:11:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scavenging downy chinchilla",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-04T15:23:08Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_downy_chinchilla
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scavenging downy chinchilla
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_downy_chinchilla
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="baiaju/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scavenging_downy_chinchilla", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
1245erty/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion | 1245erty | "2025-05-06T12:11:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am jumping lithe scorpion",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T03:20:21Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am jumping lithe scorpion
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="1245erty/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
adt576d/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_eager_grouse | adt576d | "2025-05-06T12:10:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am robust eager grouse",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T04:03:26Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_eager_grouse
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am robust eager grouse
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_eager_grouse
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="adt576d/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_eager_grouse", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
NM-development/LaBSE-en-ru-ce-prototype | NM-development | "2025-05-06T12:10:55Z" | 2 | 2 | transformers | [
"transformers",
"safetensors",
"bert",
"pretraining",
"code",
"ce",
"ru",
"en",
"arxiv:2209.09368",
"base_model:cointegrated/LaBSE-en-ru",
"base_model:finetune:cointegrated/LaBSE-en-ru",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | "2024-04-23T19:21:09Z" | ---
license: mit
language:
- ce
- ru
- en
metrics:
- codeparrot/apps_metric
- accuracy
tags:
- code
base_model:
- cointegrated/LaBSE-en-ru
---
The model uses only sign **ӏ** for explosive consonants (small cyrillic palochka letter)!
The model was teached by folloving David Dale's instructions for Erzya language (https://arxiv.org/abs/2209.09368) and using code from his repository. Commentaries in Russian were left untouched.
```python
import torch
from transformers import BertTokenizer, AutoModel
import numpy as np
import pandas as pd
import razdel
import matplotlib.pyplot as plt
from tqdm.auto import tqdm, trange
```
Download the model from Huggingface repository:
```python
model_name = 'NM-development/labse-en-ru-ce-prototype'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
```
Assign files with the texts you want to split into parallel sentences:
```python
file_ru = None
file_nm = None
with open(file_nm, 'r') as f1, open(file_ru, 'r') as f2:
nm_text = f1.read()
ru_text = f2.read()
```
In the following section define auxillary functions for parallel sentence comparison:
```python
def embed(text):
encoded_input = tokenizer(text, padding=True, truncation=True, max_length=128, return_tensors='pt')
with torch.inference_mode():
model_output = model(**encoded_input.to(model.device))
embeddings = model_output.pooler_output
embeddings = torch.nn.functional.normalize(embeddings)
return embeddings[0].cpu().numpy()
def get_top_mean_by_row(x, k=5):
m, n = x.shape
k = min(k, n)
topk_indices = np.argpartition(x, -k, axis=1)[:, -k:]
rows, _ = np.indices((m, k))
return x[rows, topk_indices].mean(1)
def align3(sims):
rewards = np.zeros_like(sims)
choices = np.zeros_like(sims).astype(int) # 1: choose this pair, 2: decrease i, 3: decrease j
# алгоритм, разрешающий пропускать сколько угодно пар, лишь бы была монотонность
for i in range(sims.shape[0]):
for j in range(0, sims.shape[1]):
# вариант первый: выровнять i-тое предложение с j-тым
score_add = sims[i, j]
if i > 0 and j > 0: # вот как тогда выровняются предыдущие
score_add += rewards[i-1, j-1]
choices[i, j] = 1
best = score_add
if i > 0 and rewards[i-1, j] > best:
best = rewards[i-1, j]
choices[i, j] = 2
if j > 0 and rewards[i, j-1] > best:
best = rewards[i, j-1]
choices[i, j] = 3
rewards[i, j] = best
alignment = []
i = sims.shape[0] - 1
j = sims.shape[1] - 1
while i > 0 and j > 0:
if choices[i, j] == 1:
alignment.append([i, j])
i -= 1
j -= 1
elif choices[i, j] == 2:
i -= 1
else:
j -= 1
return alignment[::-1]
def make_sents(text):
sents = [s.text.replace('\n', ' ').strip() for p in text.split('\n\n') for s in razdel.sentenize(p)]
sents = [s for s in sents if s]
return sents
```
Firstly split your texts into sentences:
```python
sents_nm = make_sents(nm_text)
sents_ru = make_sents(ru_text)
```
Then embed all the chunks:
```python
emb_ru = np.stack([embed(s) for s in tqdm(sents_ru)])
emb_nm = np.stack([embed(s) for s in tqdm(sents_nm)])
```
Now compare sentenses' semanics vectors and build correlation heatmap:
```python
pen = np.array([[min(len(x), len(y)) / max(len(x), len(y)) for x in sents_nm] for y in sents_ru])
sims = np.maximum(0, np.dot(emb_ru, emb_nm.T)) ** 1 * pen
alpha = 0.2
penalty = 0.2
sims_rel = (sims.T - get_top_mean_by_row(sims) * alpha).T - get_top_mean_by_row(sims.T) * alpha - penalty
alignment = align3(sims_rel)
print(sum(sims[i, j] for i, j in alignment) / min(sims.shape))
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.imshow(sims_rel)
plt.subplot(1, 2, 2)
plt.scatter(*list(zip(*alignment)), s=5);
```
Finally, save the parallel corpus into a json file:
```python
nm_ru_parallel_corpus = pd.DataFrame({'nm_text' : [sents_nm[x[1]] for x in alignment], 'ru_text' : [sents_ru[x[0]] for x in alignment]})
corpus_filename = 'nm_ru_corpus.json'
with open(corpus_filename, 'w') as f:
nm_ru_parallel_corpus.to_json(f, force_ascii=False, indent=4)
``` |
leusam/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_hunting_dolphin | leusam | "2025-05-06T12:10:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prehistoric hunting dolphin",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-04T02:14:40Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_hunting_dolphin
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prehistoric hunting dolphin
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_hunting_dolphin
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="leusam/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_hunting_dolphin", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kidstampa07/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_beaked_ram | kidstampa07 | "2025-05-06T12:10:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am regal beaked ram",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-04T11:04:30Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_beaked_ram
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am regal beaked ram
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_beaked_ram
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="kidstampa07/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_beaked_ram", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
TienMat999/Llama-3.2-3B-ft-bf16-lora4-v20250505 | TienMat999 | "2025-05-06T12:09:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-06T12:09:52Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
Arletha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_peaceful_sealion | Arletha | "2025-05-06T12:09:54Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am freckled peaceful sealion",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T21:23:46Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_peaceful_sealion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am freckled peaceful sealion
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_peaceful_sealion
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Arletha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_peaceful_sealion", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Oceans-ID/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-fluffy_alert_tuna | Oceans-ID | "2025-05-06T12:09:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fluffy alert tuna",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit",
"base_model:finetune:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T10:31:31Z" | ---
base_model: Gensyn/Qwen2.5-32B-Instruct-bnb-4bit
library_name: transformers
model_name: Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-fluffy_alert_tuna
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fluffy alert tuna
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-fluffy_alert_tuna
This model is a fine-tuned version of [Gensyn/Qwen2.5-32B-Instruct-bnb-4bit](https://huggingface.co/Gensyn/Qwen2.5-32B-Instruct-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Oceans-ID/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-fluffy_alert_tuna", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Oberhaus/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_prowling_jay | Oberhaus | "2025-05-06T12:09:04Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rabid prowling jay",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T23:19:11Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_prowling_jay
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rabid prowling jay
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_prowling_jay
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Oberhaus/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_prowling_jay", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
relrurel30/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scaly_aquatic_wildebeest | relrurel30 | "2025-05-06T12:08:17Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scaly aquatic wildebeest",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T13:38:10Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scaly_aquatic_wildebeest
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scaly aquatic wildebeest
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scaly_aquatic_wildebeest
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="relrurel30/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scaly_aquatic_wildebeest", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kk-aivio/6a461342-9ac9-43d6-85df-a505299910e7 | kk-aivio | "2025-05-06T12:07:31Z" | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m-deduped",
"base_model:adapter:EleutherAI/pythia-410m-deduped",
"region:us"
] | null | "2025-05-06T12:07:13Z" | ---
library_name: peft
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-410m-deduped
model-index:
- name: kk-aivio/6a461342-9ac9-43d6-85df-a505299910e7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kk-aivio/6a461342-9ac9-43d6-85df-a505299910e7
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8172
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
csmp-hub/cellpose-histo-hgsc-nuc-v1 | csmp-hub | "2025-05-06T12:07:28Z" | 0 | 0 | null | [
"medical",
"biology",
"image-segmentation",
"license:apache-2.0",
"region:us"
] | image-segmentation | "2025-04-29T14:07:25Z" | ---
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- medical
- biology
---
# Cellpose Model for High-Grade Serous Ovarian Cancer Nuclei Segmentation
# Model
- **cellseg_models.pytorch** implementation of **Cellpose**: [https://www.nature.com/articles/s41592-020-01018-x](https://www.nature.com/articles/s41592-020-01018-x)
- Backbone encoder: pre-trained **efficientnet_b5** from pytorch-image-models [https://github.com/huggingface/pytorch-image-models](https://github.com/huggingface/pytorch-image-models)
# USAGE
## 1. Install cellseg_models.pytorch and albumentations
```
pip install cellseg-models-pytorch
pip install albumentations
```
## 2. Load trained model
```python
from cellseg_models_pytorch.models.cellpose import CellPose
model = CellPose.from_pretrained("hgsc_v1_efficientnet_b5")
```
## 3. Run inference for one image
```python
from albumentations import Resize, Compose
from cellseg_models_pytorch.utils import FileHandler
from cellseg_models_pytorch.transforms.albu_transforms import MinMaxNormalization
model.set_inference_mode()
# Resize to multiple of 32 of your own choosing
transform = Compose([Resize(1024, 1024), MinMaxNormalization()])
im = FileHandler.read_img(IMG_PATH)
im = transform(image=im)["image"]
prob = model.predict(im)
out = model.post_process(prob)
# out = {"nuc": [(nuc instances (H, W), nuc types (H, W))], "cyto": None, "tissue": None}
```
## 3.1 Run inference for image batch
```python
import torch
from cellseg_models_pytorch.utils import FileHandler
model.set_inference_mode()
# dont use random matrices IRL
batch = torch.rand(8, 3, 1024, 1024)
prob = model.predict(im)
out = model.post_process(prob)
# out = {
# "nuc": [
# (nuc instances (H, W), nuc types (H, W)),
# (nuc instances (H, W), nuc types (H, W)),
# .
# .
# .
# (nuc instances (H, W), nuc types (H, W))
# ],
# "cyto": None,
# "tissue": None
#}
```
## 4. Visualize output
```python
from matplotlib import pyplot as plt
from skimage.color import label2rgb
fig, ax = plt.subplots(1, 3, figsize=(18, 6))
ax[0].imshow(im)
ax[1].imshow(label2rgb(out["nuc"][0][0], bg_label=0)) # inst_map
ax[2].imshow(label2rgb(out["nuc"][0][1], bg_label=0)) # type_map
```
## Dataset Details
Semi-manually annotated HGSC Primary Omental samples from the (private) DECIDER cohort. Data acquired in the DECIDER project,
funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965193.
**Contains:**
- 198 varying sized image crops at 20x magnification.
- 98 468 annotated nuclei
## Dataset classes
```
nuclei_classes = {
0: "background",
1: "neoplastic",
2: "inflammatory",
3: "connective",
4: "dead",
5: "macrophage_cytoplasm",
6: "macrophage_nucleus",
}
```
## Dataset Class Distribution
- connective nuclei: 46 100 (~47%)
- neoplastic nuclei: 22 761 (~23%)
- inflammatory nuclei 19 185 (~19%)
- dead nuclei 1859 (~2%)
- macrophage nuclei and cytoplasms: 4550 (~5%)
# Model Training Details:
First, the image crops in the training data were tiled into 224x224px patches with a sliding window (stride=32px).
Rest of the training procedures follow this notebook: [link]
# Citation
cellseg_models.pytorch:
```
@misc{https://doi.org/10.5281/zenodo.12666959,
doi = {10.5281/ZENODO.12666959},
url = {https://zenodo.org/doi/10.5281/zenodo.12666959},
author = {Okunator, },
title = {okunator/cellseg_models.pytorch: v0.2.0},
publisher = {Zenodo},
year = {2024},
copyright = {Creative Commons Attribution 4.0 International}
}
```
Cellpose original paper:
```
@article{Stringer2020,
title = {Cellpose: a generalist algorithm for cellular segmentation},
volume = {18},
ISSN = {1548-7105},
url = {http://dx.doi.org/10.1038/s41592-020-01018-x},
DOI = {10.1038/s41592-020-01018-x},
number = {1},
journal = {Nature Methods},
publisher = {Springer Science and Business Media LLC},
author = {Stringer, Carsen and Wang, Tim and Michaelos, Michalis and Pachitariu, Marius},
year = {2020},
month = dec,
pages = {100–106}
}
```
## Licence
These model weights are released under the Apache License, Version 2.0 (the "License"). You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
## Additional Terms
While the Apache 2.0 License grants broad permissions, we kindly request that users adhere to the following guidelines:
Medical or Clinical Use: This model is not intended for use in medical diagnosis, treatment, or prevention of disease of real patients. It should not be used as a substitute for professional medical advice. |
gdfgr45645/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra | gdfgr45645 | "2025-05-06T12:07:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am amphibious untamed cobra",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T03:07:22Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am amphibious untamed cobra
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gdfgr45645/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
yevvonlim/llama3-2-1b-ye-ko-en-fused-fewer | yevvonlim | "2025-05-06T12:07:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-06T12:00:54Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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franzexplorer77/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dextrous_barky_aardvark | franzexplorer77 | "2025-05-06T12:06:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am dextrous barky aardvark",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-05T16:54:37Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dextrous_barky_aardvark
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am dextrous barky aardvark
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dextrous_barky_aardvark
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="franzexplorer77/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dextrous_barky_aardvark", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hellowYJY/xlm-roberta-base-finetuned-panx-de-fr | hellowYJY | "2025-05-06T12:06:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2025-05-06T11:58:58Z" | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1621
- F1: 0.8603
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2829 | 1.0 | 715 | 0.1820 | 0.8165 |
| 0.147 | 2.0 | 1430 | 0.1586 | 0.8485 |
| 0.0936 | 3.0 | 2145 | 0.1621 | 0.8603 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
|
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-exotic_peckish_grasshopper | fakeid | "2025-05-06T12:06:51Z" | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am exotic peckish grasshopper",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-23T00:19:03Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-exotic_peckish_grasshopper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am exotic peckish grasshopper
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-exotic_peckish_grasshopper
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-exotic_peckish_grasshopper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0+cpu
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-elusive_foxy_gibbon | fakeid | "2025-05-06T12:06:49Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am elusive foxy gibbon",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-22T15:10:45Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-elusive_foxy_gibbon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am elusive foxy gibbon
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-elusive_foxy_gibbon
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-elusive_foxy_gibbon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0+cpu
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Semilore12/Pacify | Semilore12 | "2025-05-06T12:06:45Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-05-06T12:06:45Z" | ---
license: apache-2.0
---
|
mlfoundations-dev/f1_avg_all | mlfoundations-dev | "2025-05-06T12:06:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-04T14:52:34Z" | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: f1_avg_all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# f1_avg_all
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/f1_avg_all dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 64
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 512
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
tranbaninh/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_sedate_marmot | tranbaninh | "2025-05-06T12:06:28Z" | 13 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hoarse sedate marmot",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T14:47:43Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_sedate_marmot
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hoarse sedate marmot
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_sedate_marmot
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tranbaninh/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_sedate_marmot", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Kushavaha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_cobra | Kushavaha | "2025-05-06T12:06:07Z" | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fast solitary cobra",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-09T05:13:59Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_cobra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fast solitary cobra
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_cobra
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Kushavaha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_solitary_cobra", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
banhkeomath2/checkpoints | banhkeomath2 | "2025-05-06T12:05:43Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-10T05:39:16Z" | ---
license: apache-2.0
---
|
Lightricks/LTX-Video | Lightricks | "2025-05-06T12:05:27Z" | 165,695 | 1,185 | diffusers | [
"diffusers",
"safetensors",
"ltx-video",
"image-to-video",
"text-to-video",
"en",
"license:other",
"diffusers:LTXPipeline",
"region:us"
] | text-to-video | "2024-10-31T12:36:00Z" | ---
tags:
- ltx-video
- image-to-video
pinned: true
language:
- en
license: other
pipeline_tag: text-to-video
library_name: diffusers
---
# LTX-Video Model Card
This model card focuses on the model associated with the LTX-Video model, codebase available [here](https://github.com/Lightricks/LTX-Video).
LTX-Video is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 30 FPS videos at a 1216×704 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content.
We provide a model for both text-to-video as well as image+text-to-video usecases
<img src="./media/trailer.gif" alt="trailer" width="512">
| | | | |
|:---:|:---:|:---:|:---:|
| <br><details style="max-width: 300px; margin: auto;"><summary>A woman with long brown hair and light skin smiles at another woman...</summary>A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A woman walks away from a white Jeep parked on a city street at night...</summary>A woman walks away from a white Jeep parked on a city street at night, then ascends a staircase and knocks on a door. The woman, wearing a dark jacket and jeans, walks away from the Jeep parked on the left side of the street, her back to the camera; she walks at a steady pace, her arms swinging slightly by her sides; the street is dimly lit, with streetlights casting pools of light on the wet pavement; a man in a dark jacket and jeans walks past the Jeep in the opposite direction; the camera follows the woman from behind as she walks up a set of stairs towards a building with a green door; she reaches the top of the stairs and turns left, continuing to walk towards the building; she reaches the door and knocks on it with her right hand; the camera remains stationary, focused on the doorway; the scene is captured in real-life footage.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A woman with blonde hair styled up, wearing a black dress...</summary>A woman with blonde hair styled up, wearing a black dress with sequins and pearl earrings, looks down with a sad expression on her face. The camera remains stationary, focused on the woman's face. The lighting is dim, casting soft shadows on her face. The scene appears to be from a movie or TV show.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>The camera pans over a snow-covered mountain range...</summary>The camera pans over a snow-covered mountain range, revealing a vast expanse of snow-capped peaks and valleys.The mountains are covered in a thick layer of snow, with some areas appearing almost white while others have a slightly darker, almost grayish hue. The peaks are jagged and irregular, with some rising sharply into the sky while others are more rounded. The valleys are deep and narrow, with steep slopes that are also covered in snow. The trees in the foreground are mostly bare, with only a few leaves remaining on their branches. The sky is overcast, with thick clouds obscuring the sun. The overall impression is one of peace and tranquility, with the snow-covered mountains standing as a testament to the power and beauty of nature.</details> |
| <br><details style="max-width: 300px; margin: auto;"><summary>A woman with light skin, wearing a blue jacket and a black hat...</summary>A woman with light skin, wearing a blue jacket and a black hat with a veil, looks down and to her right, then back up as she speaks; she has brown hair styled in an updo, light brown eyebrows, and is wearing a white collared shirt under her jacket; the camera remains stationary on her face as she speaks; the background is out of focus, but shows trees and people in period clothing; the scene is captured in real-life footage.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A man in a dimly lit room talks on a vintage telephone...</summary>A man in a dimly lit room talks on a vintage telephone, hangs up, and looks down with a sad expression. He holds the black rotary phone to his right ear with his right hand, his left hand holding a rocks glass with amber liquid. He wears a brown suit jacket over a white shirt, and a gold ring on his left ring finger. His short hair is neatly combed, and he has light skin with visible wrinkles around his eyes. The camera remains stationary, focused on his face and upper body. The room is dark, lit only by a warm light source off-screen to the left, casting shadows on the wall behind him. The scene appears to be from a movie.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A prison guard unlocks and opens a cell door...</summary>A prison guard unlocks and opens a cell door to reveal a young man sitting at a table with a woman. The guard, wearing a dark blue uniform with a badge on his left chest, unlocks the cell door with a key held in his right hand and pulls it open; he has short brown hair, light skin, and a neutral expression. The young man, wearing a black and white striped shirt, sits at a table covered with a white tablecloth, facing the woman; he has short brown hair, light skin, and a neutral expression. The woman, wearing a dark blue shirt, sits opposite the young man, her face turned towards him; she has short blonde hair and light skin. The camera remains stationary, capturing the scene from a medium distance, positioned slightly to the right of the guard. The room is dimly lit, with a single light fixture illuminating the table and the two figures. The walls are made of large, grey concrete blocks, and a metal door is visible in the background. The scene is captured in real-life footage.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A woman with blood on her face and a white tank top...</summary>A woman with blood on her face and a white tank top looks down and to her right, then back up as she speaks. She has dark hair pulled back, light skin, and her face and chest are covered in blood. The camera angle is a close-up, focused on the woman's face and upper torso. The lighting is dim and blue-toned, creating a somber and intense atmosphere. The scene appears to be from a movie or TV show.</details> |
| <br><details style="max-width: 300px; margin: auto;"><summary>A man with graying hair, a beard, and a gray shirt...</summary>A man with graying hair, a beard, and a gray shirt looks down and to his right, then turns his head to the left. The camera angle is a close-up, focused on the man's face. The lighting is dim, with a greenish tint. The scene appears to be real-life footage. Step</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A clear, turquoise river flows through a rocky canyon...</summary>A clear, turquoise river flows through a rocky canyon, cascading over a small waterfall and forming a pool of water at the bottom.The river is the main focus of the scene, with its clear water reflecting the surrounding trees and rocks. The canyon walls are steep and rocky, with some vegetation growing on them. The trees are mostly pine trees, with their green needles contrasting with the brown and gray rocks. The overall tone of the scene is one of peace and tranquility.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A man in a suit enters a room and speaks to two women...</summary>A man in a suit enters a room and speaks to two women sitting on a couch. The man, wearing a dark suit with a gold tie, enters the room from the left and walks towards the center of the frame. He has short gray hair, light skin, and a serious expression. He places his right hand on the back of a chair as he approaches the couch. Two women are seated on a light-colored couch in the background. The woman on the left wears a light blue sweater and has short blonde hair. The woman on the right wears a white sweater and has short blonde hair. The camera remains stationary, focusing on the man as he enters the room. The room is brightly lit, with warm tones reflecting off the walls and furniture. The scene appears to be from a film or television show.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>The waves crash against the jagged rocks of the shoreline...</summary>The waves crash against the jagged rocks of the shoreline, sending spray high into the air.The rocks are a dark gray color, with sharp edges and deep crevices. The water is a clear blue-green, with white foam where the waves break against the rocks. The sky is a light gray, with a few white clouds dotting the horizon.</details> |
| <br><details style="max-width: 300px; margin: auto;"><summary>The camera pans across a cityscape of tall buildings...</summary>The camera pans across a cityscape of tall buildings with a circular building in the center. The camera moves from left to right, showing the tops of the buildings and the circular building in the center. The buildings are various shades of gray and white, and the circular building has a green roof. The camera angle is high, looking down at the city. The lighting is bright, with the sun shining from the upper left, casting shadows from the buildings. The scene is computer-generated imagery.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A man walks towards a window, looks out, and then turns around...</summary>A man walks towards a window, looks out, and then turns around. He has short, dark hair, dark skin, and is wearing a brown coat over a red and gray scarf. He walks from left to right towards a window, his gaze fixed on something outside. The camera follows him from behind at a medium distance. The room is brightly lit, with white walls and a large window covered by a white curtain. As he approaches the window, he turns his head slightly to the left, then back to the right. He then turns his entire body to the right, facing the window. The camera remains stationary as he stands in front of the window. The scene is captured in real-life footage.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>Two police officers in dark blue uniforms and matching hats...</summary>Two police officers in dark blue uniforms and matching hats enter a dimly lit room through a doorway on the left side of the frame. The first officer, with short brown hair and a mustache, steps inside first, followed by his partner, who has a shaved head and a goatee. Both officers have serious expressions and maintain a steady pace as they move deeper into the room. The camera remains stationary, capturing them from a slightly low angle as they enter. The room has exposed brick walls and a corrugated metal ceiling, with a barred window visible in the background. The lighting is low-key, casting shadows on the officers' faces and emphasizing the grim atmosphere. The scene appears to be from a film or television show.</details> | <br><details style="max-width: 300px; margin: auto;"><summary>A woman with short brown hair, wearing a maroon sleeveless top...</summary>A woman with short brown hair, wearing a maroon sleeveless top and a silver necklace, walks through a room while talking, then a woman with pink hair and a white shirt appears in the doorway and yells. The first woman walks from left to right, her expression serious; she has light skin and her eyebrows are slightly furrowed. The second woman stands in the doorway, her mouth open in a yell; she has light skin and her eyes are wide. The room is dimly lit, with a bookshelf visible in the background. The camera follows the first woman as she walks, then cuts to a close-up of the second woman's face. The scene is captured in real-life footage.</details> |
# Models
| Model | Version | Notes | inference.py config | ComfyUI workflow (Recommended) |
|--------------------|---------|---------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|------------------|
| ltxv-13b | 0.9.7 | Highest quality, requires more VRAM | [ltxv-13b-0.9.7-dev.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-13b-0.9.7-dev.yaml) | [ltxv-13b-i2v-base.json](https://github.com/Lightricks/ComfyUI-LTXVideo/example_workflows/ltxv-13b-i2v-base.json) |
| ltxv-13b-fp8 | 0.9.7 | Quantized model | Coming soon | Coming soon |
| ltxv-2b | 0.9.6 | Good quality, lower VRAM requirement than ltxv-13b | [ltxv-2b-0.9.6-dev.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-2b-0.9.6-dev.yaml) | [ltxvideo-i2v.json](https://github.com/Lightricks/ComfyUI-LTXVideo/example_workflows/low_level/ltxvideo-i2v.json) |
| ltxv-2b-distilled | 0.9.6 | 15× faster, real-time capable, fewer steps needed, no STG/CFG required | [ltxv-2b-0.9.6-distilled.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-2b-0.9.6-distilled.yaml) | [ltxvideo-i2v-distilled.json](https://github.com/Lightricks/ComfyUI-LTXVideo/example_workflows/low_level/ltxvideo-i2v-distilled.json) |
## Model Details
- **Developed by:** Lightricks
- **Model type:** Diffusion-based text-to-video and image-to-video generation model
- **Language(s):** English
## Usage
### Direct use
You can use the model for purposes under the license:
- 2B version 0.9: [license](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.license.txt)
- 2B version 0.9.1 [license](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.license.txt)
- 2B version 0.9.5 [license](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.5.license.txt)
- 2B version 0.9.6-dev [license](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-2b-0.9.6-dev-04-25.license.txt)
- 2B version 0.9.6-distilled [license](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-2b-0.9.6-distilled-04-25.license.txt)
- 13B version 0.9.7 [license](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.license.txt)
### General tips:
* The model works on resolutions that are divisible by 32 and number of frames that are divisible by 8 + 1 (e.g. 257). In case the resolution or number of frames are not divisible by 32 or 8 + 1, the input will be padded with -1 and then cropped to the desired resolution and number of frames.
* The model works best on resolutions under 720 x 1280 and number of frames below 257.
* Prompts should be in English. The more elaborate the better. Good prompt looks like `The turquoise waves crash against the dark, jagged rocks of the shore, sending white foam spraying into the air. The scene is dominated by the stark contrast between the bright blue water and the dark, almost black rocks. The water is a clear, turquoise color, and the waves are capped with white foam. The rocks are dark and jagged, and they are covered in patches of green moss. The shore is lined with lush green vegetation, including trees and bushes. In the background, there are rolling hills covered in dense forest. The sky is cloudy, and the light is dim.`
### Online demo
The model is accessible right away via the following links:
- [LTX-Studio image-to-video](https://app.ltx.studio/ltx-video)
- [Fal.ai text-to-video](https://fal.ai/models/fal-ai/ltx-video)
- [Fal.ai image-to-video](https://fal.ai/models/fal-ai/ltx-video/image-to-video)
- [Replicate text-to-video and image-to-video](https://replicate.com/lightricks/ltx-video)
### ComfyUI
To use our model with ComfyUI, please follow the instructions at a dedicated [ComfyUI repo](https://github.com/Lightricks/ComfyUI-LTXVideo/).
### Run locally
#### Installation
The codebase was tested with Python 3.10.5, CUDA version 12.2, and supports PyTorch >= 2.1.2.
```bash
git clone https://github.com/Lightricks/LTX-Video.git
cd LTX-Video
# create env
python -m venv env
source env/bin/activate
python -m pip install -e .\[inference-script\]
```
#### Inference
To use our model, please follow the inference code in [inference.py](https://github.com/Lightricks/LTX-Video/blob/main/inference.py):
##### For text-to-video generation:
```bash
python inference.py --prompt "PROMPT" --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config ltxv-13b-0.9.7-dev.yaml
```
##### For image-to-video generation:
```bash
python inference.py --prompt "PROMPT" --input_image_path IMAGE_PATH --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config ltxv-13b-0.9.7-dev.yaml
```
### Diffusers 🧨
LTX Video is compatible with the [Diffusers Python library](https://huggingface.co/docs/diffusers/main/en/index). It supports both text-to-video and image-to-video generation.
Make sure you install `diffusers` before trying out the examples below.
```bash
pip install -U git+https://github.com/huggingface/diffusers
```
Now, you can run the examples below:
```py
import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video
pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
pipe.to("cuda")
prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
video = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=704,
height=480,
num_frames=161,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
```
For image-to-video:
```py
import torch
from diffusers import LTXImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
pipe = LTXImageToVideoPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = load_image(
"https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"
)
prompt = "A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background. Flames engulf the structure, with smoke billowing into the air. Firefighters in protective gear rush to the scene, a fire truck labeled '38' visible behind them. The girl's neutral expression contrasts sharply with the chaos of the fire, creating a poignant and emotionally charged scene."
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
video = pipe(
image=image,
prompt=prompt,
negative_prompt=negative_prompt,
width=704,
height=480,
num_frames=161,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
```
To learn more, check out the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
Diffusers also supports directly loading from the original LTX checkpoints using the `from_single_file()` method. Check out [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video#loading-single-files) to learn more.
## Limitations
- This model is not intended or able to provide factual information.
- As a statistical model this checkpoint might amplify existing societal biases.
- The model may fail to generate videos that matches the prompts perfectly.
- Prompt following is heavily influenced by the prompting-style. |
Millings/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_jagged_grouse | Millings | "2025-05-06T12:05:22Z" | 12 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sedate jagged grouse",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T13:26:43Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_jagged_grouse
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sedate jagged grouse
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_jagged_grouse
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Millings/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_jagged_grouse", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Ameb1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feline_stinky_walrus | Ameb1 | "2025-05-06T12:05:12Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am feline stinky walrus",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-06T10:11:52Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feline_stinky_walrus
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am feline stinky walrus
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feline_stinky_walrus
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ameb1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feline_stinky_walrus", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
SamsBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot | SamsBuk | "2025-05-06T12:04:58Z" | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am burrowing subtle parrot",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-30T07:58:44Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am burrowing subtle parrot
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SamsBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_subtle_parrot", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Contenidoscall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_twitchy_cougar | Contenidoscall | "2025-05-06T12:04:20Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bold twitchy cougar",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-10T08:32:15Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_twitchy_cougar
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bold twitchy cougar
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_twitchy_cougar
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Contenidoscall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_twitchy_cougar", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Lots90/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rangy_subtle_wallaby | Lots90 | "2025-05-06T12:04:12Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rangy subtle wallaby",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T00:37:48Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rangy_subtle_wallaby
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rangy subtle wallaby
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rangy_subtle_wallaby
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Lots90/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rangy_subtle_wallaby", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Kirril333/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gliding_patterned_mole | Kirril333 | "2025-05-06T12:03:38Z" | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am gliding patterned mole",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-04T01:37:30Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gliding_patterned_mole
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am gliding patterned mole
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gliding_patterned_mole
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Kirril333/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gliding_patterned_mole", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mdefrance/yolos-base-signature-detection | mdefrance | "2025-05-06T12:03:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"yolos",
"object-detection",
"dataset:tech4humans/signature-detection",
"arxiv:1910.09700",
"base_model:hustvl/yolos-base",
"base_model:finetune:hustvl/yolos-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | "2025-05-06T11:52:14Z" | ---
library_name: transformers
license: apache-2.0
datasets:
- tech4humans/signature-detection
base_model:
- hustvl/yolos-base
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
Bantonwell/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_scurrying_sparrow | Bantonwell | "2025-05-06T12:03:32Z" | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am whiskered scurrying sparrow",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T17:20:43Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_scurrying_sparrow
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am whiskered scurrying sparrow
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_scurrying_sparrow
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Bantonwell/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_scurrying_sparrow", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
getglass/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-endangered_trotting_squirrel | getglass | "2025-05-06T12:03:09Z" | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am endangered trotting squirrel",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-07T23:07:47Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-endangered_trotting_squirrel
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am endangered trotting squirrel
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-endangered_trotting_squirrel
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="getglass/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-endangered_trotting_squirrel", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MATheGooner/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-shaggy_smooth_scorpion | MATheGooner | "2025-05-06T12:02:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am shaggy smooth scorpion",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-04T00:15:31Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-shaggy_smooth_scorpion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am shaggy smooth scorpion
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-shaggy_smooth_scorpion
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MATheGooner/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-shaggy_smooth_scorpion", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Nurhana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret | Nurhana | "2025-05-06T12:02:41Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rugged padded ferret",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-10T09:11:41Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rugged padded ferret
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Nurhana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_padded_ferret", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
guzus/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sturdy_bold_jay | guzus | "2025-05-06T12:02:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sturdy bold jay",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T17:24:52Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sturdy_bold_jay
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sturdy bold jay
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sturdy_bold_jay
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="guzus/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sturdy_bold_jay", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
alexxbobr/vichr_grpo_zhakar_model_labelstudio_all_data_not_lora | alexxbobr | "2025-05-06T12:02:15Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-06T12:02:09Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
w34423g2/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-colorful_ferocious_bear | w34423g2 | "2025-05-06T12:02:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am colorful ferocious bear",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T20:11:46Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-colorful_ferocious_bear
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am colorful ferocious bear
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-colorful_ferocious_bear
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="w34423g2/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-colorful_ferocious_bear", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
guvenba/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_fleecy_cockroach | guvenba | "2025-05-06T12:02:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am lightfooted fleecy cockroach",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-05T17:02:19Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_fleecy_cockroach
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am lightfooted fleecy cockroach
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_fleecy_cockroach
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="guvenba/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_fleecy_cockroach", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
nymphe/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster | nymphe | "2025-05-06T12:01:59Z" | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am agile pawing lobster",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-09T07:13:25Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am agile pawing lobster
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nymphe/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pawing_lobster", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
UF-NLPC-Lab/test_model | UF-NLPC-Lab | "2025-05-06T12:00:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert_for_stance",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | "2025-05-06T11:25:32Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
coklatmanis886/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis | coklatmanis886 | "2025-05-06T12:00:24Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am foraging docile ibis",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T12:54:20Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am foraging docile ibis
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="coklatmanis886/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Degandance/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_waddling_viper | Degandance | "2025-05-06T12:00:12Z" | 26 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am freckled waddling viper",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T18:30:08Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_waddling_viper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am freckled waddling viper
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_waddling_viper
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Degandance/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_waddling_viper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
InstaDeepAI/AbBFN2 | InstaDeepAI | "2025-05-06T12:00:00Z" | 0 | 0 | null | [
"arxiv:2308.07037",
"region:us"
] | null | "2025-03-26T10:30:41Z" | # AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks
[AbBFN2](https://www.biorxiv.org/content/10.1101/2025.04.29.651170v1) allows for flexible task adaptation by virtue of its ability to condition the generative process on an arbitrary subset of variables. Further, since AbBFN2 is based on the Bayesian Flow Network paradigm, it can jointly model both discrete and continuous variables. Using this architecture, we provide a rich syntax which can be used to interact with the model. Regardless of conditioning information, the model generates all 45 "data modes" at inference time and arbitrary conditioning can be used to define specific tasks.
## License Summary
1. The Licensed Models are **only** available under this License for Non-Commercial Purposes.
2. You are permitted to reproduce, publish, share and adapt the Output generated by the Licensed Model only for Non-Commercial Purposes and in accordance with this License.
3. You may **not** use the Licensed Models or any of its Outputs in connection with:
1. any Commercial Purposes, unless agreed by Us under a separate licence;
2. to train, improve or otherwise influence the functionality or performance of any other third-party derivative model that is commercial or intended for a Commercial Purpose and is similar to the Licensed Models;
3. to create models distilled or derived from the Outputs of the Licensed Models, unless such models are for Non-Commercial Purposes and open-sourced under the same license as the Licensed Models; or
4. in violation of any applicable laws and regulations.
## Getting Started
You can interact with AbBFN2 via:
* **Web Application:** [https://abbfn2.labs.deepchain.bio/](https://abbfn2.labs.deepchain.bio/)
* **Open-Source Repository:** [https://github.com/instadeepai/AbBFN2](https://github.com/instadeepai/AbBFN2)
The instructions below pertain to the open-source repository.
## Prerequisites
- Docker installed on your system
- Sufficient computational resources (TPU/GPU recommended)
- Basic understanding of antibody structure and sequence notation
## Installation
### Hardware Configuration
First, configure your accelerator in the Makefile:
```bash
ACCELERATOR = GPU # Options: CPU, TPU, or GPU
```
Note: Multi-host inference is not supported in this release. Please use single-host settings only.
### Building the Docker Image
Run the following command to build the AbBFN2 Docker image:
```bash
make build
```
This process typically takes 5-20 minutes depending on your hardware.
### For Apple Silicon users
Build the conda environment instead directly using:
```bash
conda env create -f environment.yaml
conda activate abbfn2
```
## Usage
AbBFN2 supports three main generation modes, each with its own configuration file in the `experiments/configs/` directory.
In addition to the mode-specific settings, configuration files contain options for loading model weights. By default (`load_from_hf: true`), weights are downloaded from Hugging Face. Optionally, if you have the weights locally, set `load_from_hf: false` and provide the path in `model_weights_path` (e.g., `/app/params.pkl`).
### 1. Unconditional Generation
Generate novel antibody sequences without any constraints. AbBFN2 will generate natural-like antibody sequences matching its training distribution. Note that the metadata labels are also predictions made by the model. For a discussion of the accuracy of these labels, please refer to the AbBFN2 manuscript.
Configuration (`unconditional.yaml`):
```yaml
cfg:
sampling:
num_samples_per_batch: 10 # Number of sequences per batch
num_batches: 1 # Number of batches to generate
sample_fn:
num_steps: 300 # Number of sampling steps (recommended: 300-1000)
```
Run:
```bash
make unconditional # or python experiments/unconditional.py for Apple Silicon users.
```
### 2. Conditional Generation/Inpainting
Generate antibody sequences conditioned on specific attributes. Conditional generation highlights the flexibility of AbBFN2 and allows it to be task adaptible depending on the exact conditioning data. While any arbitrary combination is possible, conditional generation is mostly to be used primarily when conditioning on full sequences (referred to as sequence labelling in the manuscript), partial sequences (sequence inpainting), partial sequences and metadata (sequence design), metadata only (conditional de novo generation). For categorical variables, the set of of possible values is found in `src/abbfn2/data_mode_handler/oas_paired/constants.py`. For genes and CDR lengths, only values that appear at least 100 times in the training data are valid. When conditioning on species, human, mouse, or rat can be chosen.
**Disclaimer**: _As discussed in the manuscript, the flexibility of AbBFN2 requires careful consideration of the exact combination of conditioning information for effective generation. For instance, conditioning on a kappa light chain locus V-gene together with a lambda locus J-gene family is unlikely to yield samples of high quality. Such paradoxical combinations can also exist in more subtle ways. Due to the space of possible conditioning information, we have only tested a small subset of such combinations._
Configuration (`inpaint.yaml`):
```yaml
cfg:
input:
num_input_samples: 2 # Number of input samples
dm_overwrites: # Specify values of the data modes
h_cdr1_seq: GYTFTSHA
h_cdr2_seq: ISPYRGDT
h_cdr3_seq: ARDAGVPLDY
sampling:
inpaint_fn:
num_steps: 300 # Number of sampling steps (recommended: 300-1000)
mask_fn:
data_modes: # Specify which data modes to condition on
- "h_cdr1_seq"
- "h_cdr2_seq"
- "h_cdr3_seq"
```
Run:
```bash
make inpaint # or python experiments/inpaint.py for Apple Silicon users.
```
### 3. Sequence Humanization
Convert non-human antibody sequences into humanized versions. This workflow is designed to run a sequence humanisation experiment given a paired, non-human starting sequence. AbBFN2 will be used to introduce mutations to the framework regions of the starting antibody, possibly using several recycling iterations. During sequence humanisation, appropriate human V-gene families to target will also be chosen, but can be manually set by the user too.
Briefly, the humanisation workflow here uses the conditional generation capabilities of AbBFN2 in a sample recycling approach. At each iteration, further mutations are introduced, using a more aggressive starting strategy that is likely to introduce a larger number of mutations. As the sequence becomes more human under the model, fewer mutations are introduced at subsequent steps. Please note that we have found that in most cases, humanisation is achieved within a single recycling iteration. If the model introduces a change to the CDR loops, which can happen in rare cases, these are removed. For a detailed description of the humanisation workflow, please refer to the AbBFN2 manuscript.
Please also note that while we provide the option to manually select V-gene families here, this workflow allows the model to select more appropriate V-gene families during inference. Therefore, the final V-gene families may differ from the initially selected ones. Please also note that due to the data that AbBFN2 is trained on, humanisation will be most reliable when performed on murine or rat sequences. Sequences from other species have not been tested.
Configuration (`humanization.yaml`):
```yaml
cfg:
input:
l_seq: "DIVLTQSPASLAVSLGQRATISCKASQSVDYDGHSYMNWYQQKPGQPPKLLIYAASNLESGIPARFSGSGSGTDFTLNIHPVEEEDAATYYCQQSDENPLTFGTGTKLELK"
h_seq: "QVQLQQSGPELVKPGALVKISCKASGYTFTSYDINWVKQRPGQGLEWIGWIYPGDGSIKYNEKFKGKATLTVDKSSSTAYMQVSSLTSENSAVYFCARRGEYGNYEGAMDYWGQGTTVTVSS"
# h_vfams: null # Optionally, set target v-gene families
# l_vfams: null
sampling:
recycling_steps: 10 # Number of recycling steps (recommended: 5-12)
inpaint_fn:
num_steps: 500 # Number of sampling steps (recommended: 300-1000)
```
Run:
```bash
make humanization # or python experiments/humanization.py Apple Silicon users.
```
## Data Modes
The data modes supported by AbBFN2 are detailed below.
##### Heavy-Chain IMGT Regions
| Field | Type | Region (IMGT) | Description | Length Range (AA) |
|---------------|--------|-------------------------|--------------------------------------------|-------------------|
| `h_fwr1_seq` | string | FWR1 | Framework region 1 | 18 – 41 |
| `h_fwr2_seq` | string | FWR2 | Framework region 2 | 6 – 30 |
| `h_fwr3_seq` | string | FWR3 | Framework region 3 | 29 – 58 |
| `h_fwr4_seq` | string | FWR4 | Framework region 4 | 3 – 12 |
| `h_cdr1_seq` | string | CDR1 | Complementarity-determining region 1 | 1 – 22 |
| `h_cdr2_seq` | string | CDR2 | Complementarity-determining region 2 | 1 – 25 |
| `h_cdr3_seq` | string | CDR3 | Complementarity-determining region 3 | 2 – 58 |
##### Light-Chain IMGT Regions
| Field | Type | Region (IMGT) | Description | Length Range (AA) |
|---------------|--------|-------------------------|--------------------------------------------|-------------------|
| `l_fwr1_seq` | string | FWR1 | Framework region 1 | 18 – 36 |
| `l_fwr2_seq` | string | FWR2 | Framework region 2 | 11 – 27 |
| `l_fwr3_seq` | string | FWR3 | Framework region 3 | 25 – 48 |
| `l_fwr4_seq` | string | FWR4 | Framework region 4 | 3 – 13 |
| `l_cdr1_seq` | string | CDR1 | Complementarity-determining region 1 | 1 – 20 |
| `l_cdr2_seq` | string | CDR2 | Complementarity-determining region 2 | 1 – 16 |
| `l_cdr3_seq` | string | CDR3 | Complementarity-determining region 3 | 1 – 27 |
##### CDR Length Metrics
Possible values provided in [src/abbfn2/data_mode_handler/oas_paired/constants.py](https://github.com/instadeepai/AbBFN2/tree/main/src/abbfn2/data_mode_handler/oas_paired/constants.py).
| Field | Type | Description |
|-------------|------|---------------------------------|
| `h1_length` | int | CDR1 length (heavy chain) |
| `h2_length` | int | CDR2 length (heavy chain) |
| `h3_length` | int | CDR3 length (heavy chain) |
| `l1_length` | int | CDR1 length (light chain) |
| `l2_length` | int | CDR2 length (light chain) |
| `l3_length` | int | CDR3 length (light chain) |
##### Gene and Family Annotations
Possible values provided in [src/abbfn2/data_mode_handler/oas_paired/constants.py](https://github.com/instadeepai/AbBFN2/tree/main/src/abbfn2/data_mode_handler/oas_paired/constants.py).
| Field | Type | Description |
|---------------|--------|------------------------------------|
| `hv_gene` | string | V gene segment (heavy) |
| `hd_gene` | string | D gene segment (heavy) |
| `hj_gene` | string | J gene segment (heavy) |
| `lv_gene` | string | V gene segment (light) |
| `lj_gene` | string | J gene segment (light) |
| `hv_family` | string | V gene family (heavy) |
| `hd_family` | string | D gene family (heavy) |
| `hj_family` | string | J gene family (heavy) |
| `lv_family` | string | V gene family (light) |
| `lj_family` | string | J gene family (light) |
| `species` | string | One of “human”, “rat”, “mouse” |
| `light_locus` | string | One of “K” (kappa) or “L” (lambda)|
##### TAP Physicochemical Metrics
| Field | Type | Description | Range |
|--------------------|--------|---------------------------------------------|-----------------|
| `tap_psh` | float | Patch hydrophobicity | 72.0 – 300.0 |
| `tap_pnc` | float | Proportion of non-covalent contacts | 0.0 – 10.0 |
| `tap_ppc` | float | Proportion of polar contacts | 0.0 – 7.5 |
| `tap_sfvcsp` | float | Surface-exposed variable-chain charge score | –55.0 – 55.0 |
| `tap_psh_flag` | string | Hydrophobicity flag | “red“ / “amber“ / “green“ |
| `tap_pnc_flag` | string | Non-covalent contacts flag | “red“ / “amber“ / “green“ |
| `tap_ppc_flag` | string | Polar contacts flag | “red“ / “amber“ / “green“ |
| `tap_sfvcsp_flag` | string | Charge score flag | “red“ / “amber“ / “green“ |
##### V- and J- Identity Scores
| Field | Type | Description | Range (%) |
|-----------------|--------|-----------------------------------|---------------|
| `h_v_identity` | float | Heavy-chain V segment identity | 64.0 – 100.0 |
| `h_d_identity` | float | Heavy-chain D segment identity | 74.0 – 100.0 |
| `h_j_identity` | float | Heavy-chain J segment identity | 74.0 – 100.0 |
| `l_v_identity` | float | Light-chain V segment identity | 66.0 – 100.0 |
| `l_j_identity` | float | Light-chain J segment identity | 77.0 – 100.0 |
## Citation
If you use AbBFN2 in your research, please cite our work:
```bibtex
@article{Guloglu_etal_AbBFN2,
title={AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks},
author={Bora Guloglu and Miguel Bragan\c{c}a and Alex Graves and Scott Cameron and Timothy Atkinson and Liviu Copoiu and Alexandre Laterre and Thomas D Barrett},
journal={bioRxiv},
year={2025},
url={https://www.biorxiv.org/content/10.1101/2025.04.29.651170v1}
}
```
## Related Papers
- **Bayesian Flow Networks:** [Graves et al., 2023](https://arxiv.org/abs/2308.07037)
- **Protein Sequence Modelling with Bayesian Flow Networks (ProtBFN/AbBFN):**
- Paper: [Atkinson et al., 2024](https://www.biorxiv.org/content/10.1101/2024.09.24.614734v1)
- GitHub Repository: [instadeepai/protein-sequence-bfn](https://github.com/instadeepai/protein-sequence-bfn)
- Hugging Face Model: [InstaDeepAI/protein-sequence-bfn](https://huggingface.co/InstaDeepAI/protein-sequence-bfn)
## Acknowledgements
The development of this library was supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
|
salihfurkaan/VoxPolska-GGUF | salihfurkaan | "2025-05-06T11:59:58Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
"base_model:quantized:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-05-06T11:57:53Z" | ---
base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** salihfurkaan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
doublesizebed/parler-tts-mini-malay | doublesizebed | "2025-05-06T11:59:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-05-06T11:59:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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maix-ai/maix-base-1 | maix-ai | "2025-05-06T11:58:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-06T11:58:35Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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hamed58/Enlighten_Instruct | hamed58 | "2025-05-06T11:58:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T12:13:28Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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csmp-hub/cellvit-histo-hgsc-nuc-v1 | csmp-hub | "2025-05-06T11:58:00Z" | 0 | 0 | null | [
"medical",
"biology",
"image-segmentation",
"arxiv:2306.15350",
"license:apache-2.0",
"region:us"
] | image-segmentation | "2025-05-06T08:38:55Z" | ---
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- medical
- biology
---
# CellVit Model for High-Grade Serous Ovarian Cancer Nuclei Segmentation
# Model
- **cellseg_models.pytorch** implementation of **CellVit**: [https://arxiv.org/abs/2306.15350](https://arxiv.org/abs/2306.15350)
- Backbone encoder: pre-trained **samvit_base_patch16** from pytorch-image-models [https://github.com/huggingface/pytorch-image-models](https://github.com/huggingface/pytorch-image-models)
# USAGE
## 1. Install cellseg_models.pytorch and albumentations
```
pip install cellseg-models-pytorch
pip install albumentations
```
## 2. Load trained model
```python
from cellseg_models_pytorch.models.cellvit import CellVit
model = CellVit.from_pretrained("hgsc_v1_efficientnet_b5")
```
## 3. Run inference for one image
```python
from albumentations import Resize, Compose
from cellseg_models_pytorch.utils import FileHandler
from cellseg_models_pytorch.transforms.albu_transforms import MinMaxNormalization
model.set_inference_mode()
# Resize to multiple of 32 of your own choosing
transform = Compose([Resize(1024, 1024), MinMaxNormalization()])
im = FileHandler.read_img(IMG_PATH)
im = transform(image=im)["image"]
prob = model.predict(im)
out = model.post_process(prob)
# out = {"nuc": [(nuc instances (H, W), nuc types (H, W))], "cyto": None, "tissue": None}
```
## 3.1 Run inference for image batch
```python
import torch
from cellseg_models_pytorch.utils import FileHandler
model.set_inference_mode()
# dont use random matrices IRL
batch = torch.rand(8, 3, 1024, 1024)
prob = model.predict(im)
out = model.post_process(prob)
# out = {
# "nuc": [
# (nuc instances (H, W), nuc types (H, W)),
# (nuc instances (H, W), nuc types (H, W)),
# .
# .
# .
# (nuc instances (H, W), nuc types (H, W))
# ],
# "cyto": None,
# "tissue": None
#}
```
## 4. Visualize output
```python
from matplotlib import pyplot as plt
from skimage.color import label2rgb
fig, ax = plt.subplots(1, 3, figsize=(18, 6))
ax[0].imshow(im)
ax[1].imshow(label2rgb(out["nuc"][0][0], bg_label=0)) # inst_map
ax[2].imshow(label2rgb(out["nuc"][0][1], bg_label=0)) # type_map
```
## Dataset Details
Semi-manually annotated HGSC Primary Omental samples from the (private) DECIDER cohort. Data acquired in the DECIDER project,
funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965193.
**Contains:**
- 198 varying sized image crops at 20x magnification.
- 98 468 annotated nuclei
## Dataset classes
```
nuclei_classes = {
0: "background",
1: "neoplastic",
2: "inflammatory",
3: "connective",
4: "dead",
5: "macrophage_cytoplasm",
6: "macrophage_nucleus",
}
```
## Dataset Class Distribution
- connective nuclei: 46 100 (~47%)
- neoplastic nuclei: 22 761 (~23%)
- inflammatory nuclei 19 185 (~19%)
- dead nuclei 1859 (~2%)
- macrophage nuclei and cytoplasms: 4550 (~5%)
# Model Training Details:
First, the image crops in the training data were tiled into 224x224px patches with a sliding window (stride=32px).
Rest of the training procedures follow this notebook: [link]
# Citation
cellseg_models.pytorch:
```
@misc{https://doi.org/10.5281/zenodo.12666959,
doi = {10.5281/ZENODO.12666959},
url = {https://zenodo.org/doi/10.5281/zenodo.12666959},
author = {Okunator, },
title = {okunator/cellseg_models.pytorch: v0.2.0},
publisher = {Zenodo},
year = {2024},
copyright = {Creative Commons Attribution 4.0 International}
}
```
CellVit original paper:
```
@article{CellViT,
title = {CellViT: Vision Transformers for precise cell segmentation and classification},
journal = {Medical Image Analysis},
volume = {94},
pages = {103143},
year = {2024},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2024.103143},
url = {https://www.sciencedirect.com/science/article/pii/S1361841524000689},
author = {Fabian Hörst and Moritz Rempe and Lukas Heine and Constantin Seibold and Julius Keyl and Giulia Baldini and Selma Ugurel and Jens Siveke and Barbara Grünwald and Jan Egger and Jens Kleesiek},
}
```
## Licence
These model weights are released under the Apache License, Version 2.0 (the "License"). You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
## Additional Terms
While the Apache 2.0 License grants broad permissions, we kindly request that users adhere to the following guidelines:
Medical or Clinical Use: This model is not intended for use in medical diagnosis, treatment, or prevention of disease of real patients. It should not be used as a substitute for professional medical advice. |
jbilcke-hf/LTX-Video-0-9-6-HFIE | jbilcke-hf | "2025-05-06T11:57:56Z" | 165 | 3 | diffusers | [
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:LTXPipeline",
"region:us"
] | null | "2025-04-20T12:16:05Z" | ---
library_name: diffusers
---
# LTX-Video-0-9-6-HFIE
`LTX-Video-0-9-6-HFIE` is a version of LTX-Video 0.9.6 (distilled) that can be deployed to a Hugging Face endpoint.
It is used in production by AiTube2 (https://aitube.at)
# Deployment
When you create a Hugging Face Inference endpoint, make sure to:
- select a Nvidia L40S (at least)
- In the "Advanced Settings" tab, select "Download Pattern" > "Download everything"
My current implementation works in either text-to-video or image-to-video mode.
This is controlled with an environment variable `SUPPORT_INPUT_IMAGE_PROMPT` which has to be truthy or falsy
# Usage
Once deployec you can use it like this:
```python
import requests
import base64
from PIL import Image
from io import BytesIO
import os
API_URL = "https://<USE YOR OWN>.aws.endpoints.huggingface.cloud"
API_TOKEN = "<USE YOR OWN>"
def query(payload):
response = requests.post(API_URL, headers={
"Accept": "application/json",
"Authorization": f"Bearer {API_TOKEN}",
"Content-Type": "application/json"
}, json=payload)
return response.json()
def save_video(json_response, filename):
try:
error = json_response["error"]
if error:
print(error)
return
except Exception as e:
pass
video_data_uri = ""
try:
# Extract the video data URI from the response
video_data_uri = json_response["video"]
except Exception as e:
message = str(json_response)
print(message)
raise ValueError(message)
# Remove the data URI prefix to get just the base64 data
# Assumes format like "data:video/mp4;base64,<actual_base64_data>"
base64_data = video_data_uri.split(",")[1]
# Decode the base64 data
video_data = base64.b64decode(base64_data)
# Write the binary data to an MP4 file
with open(filename, "wb") as f:
f.write(video_data)
def encode_image(image_path):
"""
Load and encode an image file to base64
Args:
image_path (str): Path to the image file
Returns:
str: Base64 encoded image data URI
"""
with Image.open(image_path) as img:
# Convert to RGB if necessary
if img.mode != "RGB":
img = img.convert("RGB")
# Save image to bytes
img_byte_arr = BytesIO()
img.save(img_byte_arr, format="JPEG")
# Encode to base64
base64_encoded = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
return f"data:image/jpeg;base64,{base64_encoded}"
# Example usage with image-to-video generation
image_filename = "input.jpg" # Path to your input image
video_filename = "output.mp4"
config = {
"inputs": {
"prompt": "magnificent underwater footage, clownfishes swimming around coral inside the carribean sea, real gopro footage",
# "image": encode_image(image_filename)
},
"parameters": {
# ------------------- settings for LTX-Video -----------------------
#"negative_prompt": "saturated, highlight, overexposed, highlighted, overlit, shaking, too bright, worst quality, inconsistent motion, blurry, jittery, distorted, cropped, watermarked, watermark, logo, subtitle, subtitles, lowres",
# note about resolution:
# we cannot use 720 since it cannot be divided by 32
#
# for a cinematic look:
"width": 768,
"height": 480,
# for a vertical video look:
#"width": 480,
#"height": 768,
# LTX-Video requires a frame number divisible by 8, plus one frame
# note: glitches might appear if you use more than 168 frames
"num_frames": (8 * 16) + 1,
"num_inference_steps": 8,
"guidance_scale": 1.0,
#"seed": 1209877,
# This will double the number of frames.
# You can activate this if you want:
# - a slow motion effect (in that case use double_num_frames=True and fps=24, 25 or 30)
# - a HD soap / video game effect (in that case use double_num_frames=True and fps=60)
"double_num_frames": True,
# controls the number of frames per second
# use this in combination with the num_frames and double_num_frames settings to control the duration and "feel" of your video
"fps": 60, # typical values are: 24, 25, 30, 60
# upscale the video using Real-ESRGAN.
# This upscaling algorithm is relatively fast,
# but might create an uncanny "3D render" or "drawing" effect.
"super_resolution": True,
}
}
# Make the API call
output = query(config)
# Save the video
save_video(output, video_filename)
```
|
Ocivico/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram | Ocivico | "2025-05-06T11:57:03Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am ferocious subtle ram",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-10T09:35:23Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am ferocious subtle ram
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ocivico/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_ram", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
RRashmini/google-umt5-small-10 | RRashmini | "2025-05-06T11:56:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"umt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-05-06T11:55:45Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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### Testing Data, Factors & Metrics
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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MonsterMMORPG/SECourses_Premium_Flash_Attention | MonsterMMORPG | "2025-05-06T11:56:37Z" | 0 | 1 | null | [
"region:us"
] | null | "2025-03-09T19:03:43Z" | # Constantly Updated Patreon Projects on : https://www.patreon.com/c/SECourses
## Patreon Exclusive Content
### March 25 - 2025
[Wan 2.1 Ultra Advanced Gradio APP for - Works as low as 4GB VRAM - 1-Click Installers for Windows, RunPod, Massed Compute - Batch Processing - T2V - I2V - V2V](https://www.patreon.com/posts/123105403)
* 1-Click to install and use the most powerful ever released Image to Video, Text to Video and Video to Video Wan 2.1 Open source model. Public Post to see APP features : https://www.patreon.com/posts/123114193
### March 25 - 2025
[ComfyUI Auto Installer with FaceID, IP-Adapter, InsightFace, Reactor, Triton, DeepSpeed, Flash Attention, Sage Attention Automatic Installers for Windows, RunPod, Massed Compute, Linux](https://www.patreon.com/posts/105023709)
* 1 Click auto installers for ComfyUI latest version for Windows, Massed Compute and RunPod. Installs latest version of ComfyUI into an isolated Python venv. Auto download best SDXL and SD 1.5 models and auto installs ComfyUI manager, FaceID, InsightFace, Triton, DeepSpeed, Flash Attention, Sage Attention and Ip Adapter of ComfyUI on Windows Python VENV (not WSL). Also includes automatic downloader.py file to download all of the IP Adapter, InsightFace and FaceID models for you. Moreover we have 1-Click installer for Reactor extension with its all dependencies and auto download of necessary models.
### March 20 - 2025
[MMAudio 1-Click Installers for Windows, RunPod and Massed Compute - Generate Audio for Any Video - Amazing for AI Generated Videos](https://www.patreon.com/posts/117990364)
* 1-Click installers to install MMAudio. This app is so lightweight and fast. Pretty much should work on every GPUs. I have also improved the official published Gradio app and added more features. MMAudio generates synchronized audio given video and/or text inputs. Our key innovation is multimodal joint training which allows training on a wide range of audio-visual and audio-text datasets. Moreover, a synchronization module aligns the generated audio with the video frames.
### March 20 - 2025
[MagicQuill 1-Click Installers for Windows, RunPod and Massed Compute - Amazing and Ultra Fast Inpaint Model](https://www.patreon.com/posts/117326651)
* 1-Click Installer files for MagicQuill app to install on Windows, RunPod and Massed Compute : https://github.com/magic-quill/MagicQuill
### March 20 - 2025
[VisoMaster Automatic Installer - The Most Advanced 0-Shot Face Swap / Deep Fake APP - State of the Art - Windows and Massed Compute](https://www.patreon.com/posts/121570322)
* 1-Click to install VisoMaster on Windows and also on Massed Compute (for GPU poor). VisoMaster is a powerful yet easy-to-use tool for face swapping (FaceSwap/DeepFake) and editing in images and videos. It utilizes AI to produce natural-looking results with minimal effort, making it ideal for both casual users and professionals.
### March 18 - 2025
[Invoke AI Latest Version Windows, RunPod and Massed Compute 1-Click Installers](https://www.patreon.com/posts/112912425)
* 1-Click to install latest version of InvokeAI on your Windows computer, on RunPod and on Massed Compute with a super detailed and easy Tutorial video and written scripts. This zip file also has instructions to use the Invoke AI on your Windows Computer browser while running on Massed Compute securely by using PowerShell commands.
### March 16 - 2025
[LivePortrait Upgraded Latest Version Human and Animal Version 1-Click Installers for Windows, RunPod, Massed Compute and a Free Kaggle Account Notebook - Blazing Fast Static Image or Video to Video to Talking and Moving Animation](https://www.patreon.com/posts/119254105)
* 1-Click installers for Latest and improved version of LivePortrait for Windows, RunPod, Massed Compute and a Free Kaggle Account notebook. It supports latest LivePortrait animal v1.1 version.
### March 15 - 2025
[Most Advanced 1-Click DeepFake / FaceSwap App Rope, Rope Live, Rope Alucard and Rope NEXT Installers for Windows and Massed Compute and Linux](https://www.patreon.com/posts/105123768)
* The easiest and most powerful 1-click DeepFake / FaceSwap open source app Rope, Rope Live, Rope Alucard and Rope NEXT installers for Windows, Massed Compute (Cloud), Linux and a lot of configurations and test results shared
### March 13 - 2025
[A Kaggle Notebook to Train Stable Diffusion 1.5 and XL (SDXL) on a Free Kaggle Account with 2x Dual T4 GPU for free by using Kohya GUI](https://www.patreon.com/posts/88397937)
* A Kaggle NoteBook to do Stable Diffusion 1.5 and XL (SDXL) training. Fully supports 2x T4 dual GPU to speed up training. Kohya GUI is used to do DreamBooth / Fine-Tuning and LoRA Trainings.
### March 13 - 2025
[Hugging Face Upload / Download Notebook - Supports Private Repositories and Multi Commit as Well](https://www.patreon.com/posts/104672510)
* If you are looking for convenient and fast way to save and download your files from Hugging Face, this notebook will do the job. 1-click easy
### March 13 - 2025
[Virtual Try-on (IDM-VTON) 1 Click Installers - Try Any Clothing Immediately On Anyone - Windows - RunPod - Massed Compute - Kaggle - Works even on Object Transfer](https://www.patreon.com/posts/122718239)
* 1 Click installers for IDM-VTON (the one of the very best virtual try on clothing and anything) for Windows, RunPod, Massed Compute and a free Kaggle account. Our app also has extra features compared to official IDVM-VTON. It automatically crops and paste back images and supports quantization and CPU offloading. Public post for more info : https://www.patreon.com/posts/122721073
### March 12 - 2025
[AuraSR GigaGAN 4x Upscaler Gradio APP, Installers for Windows, RunPod, Massed Compute and free Kaggle Account with Seams Fix and Batch Processing](https://www.patreon.com/posts/121441873)
* 1-Click to install and use locally (Windows) and also popular cloud services famous newest AuraSR GigaGAN 4x upscaler with batch upscaling
### March 11 - 2025
[SwarmUI Easy Ultra Fast and Robust Unified Downloader for Stable Diffusion 3.5, FLUX, Mochi 1, SDXL and SD 1.5](https://www.patreon.com/posts/114517862)
* Download the very best Stable Diffusion Large 3.5, FLUX, SDXL, Mochi 1 (SOTA text-to-video), FLUX Tools (Inpainting, Outpainting, Canny, Depth, Redux), Latent Upscaler Models like ESGRAN, Improved new Clip-l that works better and SD 1.5 models into the accurate SwarmUI folders with 1-click and ultra fast and robustness.
### March 5 - 2025
[Blazing Fast SD Forge Web UI Latest Version Windows, RunPod and Massed Compute Automatic Installers and Unified Model Downloaders for SD 1.5, SDXL, FLUX and More Newer Models](https://www.patreon.com/posts/118442039)
* 1-Click to install latest SD Forge Web UI on Windows, RunPod and Massed Compute and download all of the amazing FLUX, SD 1.5, SDXL and SD3.5 and many more. Our installers and downloader scripts are super optmized that you will see even 1000 MB per second download speeds if your internet speed is sufficient.
### March 5 - 2025
[1 Click Installer for Automatic1111 SD Web UI, SDXL, ControlNet, All ControlNet Models, TensorRT (RTX Accelerator) on Windows](https://www.patreon.com/posts/86307255)
* Automatic Windows installer script for SDXL and Automatic1111 Web UI. Downloads latest SDXL base with fixed VAE and best SD 1.5 and SDXL models. Moreover it automatically installs and lets you download newest NVIDIA RTX Accelerator - TensorRT which brings 70%+ Speed Up. Moreover, it will automatically install ControlNet and download all available ControlNet models for you. Furthermore, it will auto install After Detailer (ADetailer) and Reactor extensions and latest Torch and xFormers. All these installations are optional and you can install any of them.
### Feburary 25 - 2025
[Gradio APP for Deterministic Text and Graphs Having Images Upscaling based on ImageMagick - Windows, RunPod, Massed Compute](https://www.patreon.com/posts/123071348)
* If you need to upscale images with 100% accurate text preservation this APP is what you need. A Gradio based multi-threaded batch upscaler APP that utilizes ImageMagick with amazing upscale presets. Public Post to see APP features : https://www.patreon.com/posts/123073046
### Feburary 22 - 2025
[Free Kaggle Account Notebook for SwarmUI with FLUX, SD 1.5, SDXL & Stable Diffusion 3.5 Large, FLUX, Hunyuan and Dual T4 GPU support](https://www.patreon.com/posts/106650931)
* Use very advanced SwarmUI on a free Kaggle account for free with dual T4 GPU. Fully supports SD 1.5, SDXL, SD3, FLUX, FLUX Tools (Redux, Canny, Depth, Inpainting), Stable Diffusion 3.5 Large, Stable Cascade, Hunyuan, SkyReels, Mochi 1 and more
### Feburary 7 - 2025
[BiRefNet HR (High Resolution) Gradio APP and 1-Click Installers for Windows, RunPod, Massed Compute and a Free Kaggle Account Notebook](https://www.patreon.com/posts/121679760)
* BiRefNet HR Version Automatic Installers for Windows, RunPod, Massed Compute and a free Kaggle notebook. The very best SOTA background remover with Gradio APP. It is updated to newest High Resolution model and it supports batch processing fully with new half precision lower VRAM feature.
### Feburary 2 - 2025
[DeepFace Based Batch Face Similarity Sorting Gradio APP For Windows, RunPod and Massed Compute - 1-Click to Install - Uses TensorFlow GPU - Very Fast](https://www.patreon.com/posts/121335747)
* With DeepFace & RetinaFace libraries you can sort AI images or basically any images based on their similarity to the given single or multiple images (average taken). We have developed a batch processing Gradio APP for this task that installs libraries into a Python 3.10 VENV and works perfect on Windows. Also we have 1-click installers for RunPod and Massed Compute as well. The APP is fully multi-threaded.
### Feburary 1 - 2025
[1-Click Installers for Paints-UNDO Advanced Gradio APP, Windows, RunPod, Massed Compute and Kaggle Installers](https://www.patreon.com/posts/121228327)
* 1-Click Installers for Paints-UNDO from lllyasviel. 1-Click Install on Windows (Python 3.10 isolated VENV), RunPod, Massed Compute and a free Kaggle Notebook. Official repo here but our APP is improved version : https://github.com/lllyasviel/Paints-UNDO
### January 31 - 2025
[EasyAnimate 1-Click Install Windows, RunPod and Massed Compuıte, Newest SOTA Local Open Source Image-to-Video, Text-to-Video and More](https://www.patreon.com/posts/115888558)
* 1-Click Installers for EasyAnimate. It is literally Runway but Open Source and FREE, Text-to-Video, Image-to-Video (both beginning and ending frame), Video-to-Video, Works on 24 GB GPUs on Windows, supports 960px resolution, supports very long videos with Overlap. Install on Windows, RunPod and Massed Compute
### January 20 - 2025
[Ultimate Image Processing APP : Batch Cropping, Zooming In, Resizing, Duplicate Image Removing, Face Extraction, SAM 2 and Yolo Segmentation, Masking for Windows, RunPod, Massed Compute and Free Kaggle Account](https://www.patreon.com/posts/120352012)
* If you want to batch pre-process your training images, like auto zoom subject and resize perfectly into exact resolution, this is the script you need. 1-Click to install and use on Windows, RunPod and Massed Comput and even Kaggle. It supports YOLO V11 and SAM 2. Moreover it has even duplicate image removing as well. You can see all features in this public post : https://www.patreon.com/posts/120353641
### January 17 - 2025
[1-Click Installers for Most Powerful Vision Model CogVLM V2 and Batch Captioning Images, Install on Windows, RunPod and Massed Compute](https://www.patreon.com/posts/120193330)
* CogVLM 2 is an open source vision model that is par with GPT-4 of OpenAI. This post has 1-Click installers for CogVLM 2 Gradio APP. It installs into Python 3.10 VENV and fully supports Triton and 4-bit quantization. Works amazing and perfect. Has batch processing and many other features. For features, screenshot and more information check this public post : https://www.patreon.com/posts/120195496
### January 16 - 2025
[TRELLIS 1-Click Installers for Windows, RunPod, Massed Compute, New SOTA Image-to-3D Full Model - Mind Blowing](https://www.patreon.com/posts/117470976)
* 1-Click installers to install TRELLIS on Windows, RunPod and Massed Compute. https://github.com/microsoft/TRELLIS. This is the SOTA model for image to full 3D generation and it is blazing fast
### January 13 - 2025
[From NVIDIA Labs SANA Text-to-Image Model 1-Click Installers for Windows, RunPod, Massed Compute and Free Kaggle Account](https://www.patreon.com/posts/116474081)
* 1-Click installers for NVIDIA Labs SANA (Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer) model with an amazing Gradio APP developed by SECourses. Windows, RunPod, Massed Compute and Kaggle. Pretty lighweight and fast model to use. 1K (1024x1024) : 4 GB GPUs. 2K (2048x2048) : 6 GB GPUs. 4K (4096x4096) : 8 GB GPUs
### January 10 - 2025
[Ultra Advanced InstantID Gradio APP - Automatic Installers for Windows, RunPod and Massed Compute and Free Kaggle - Using the Very Best ControlNet Xinsir Models](https://www.patreon.com/posts/118469722)
* Ultra advanced InstantID Gradio APP : 0-shot Identity-Preserving Generation in Seconds Gradio APP with latest best Xinsir ControlNet models. We have so many extra features compared to official InstantID APP plus we have 1-click very easy install on Windows, RunPod, Massed Compute and a free Kaggle Account notebook that works perfect
### January 8 - 2025
[IC-Light (Most Advanced 1-Click Relight / Re-Light) 1-Click Installer for Windows, RunPod, Massed Compute & Kaggle Notebook](https://www.patreon.com/posts/119566071)
* 1-click auto installer scripts for IC-Light (Re-Light / Relight any image) for Windows, Massed Compute, RunPod, Linux and a free Kaggle Notebook
### January 4 - 2025
[Very Advanced Joy Caption APPs, Supports Batch Processing and Multi-GPU Captioning, Supports Joy Caption Pre Alpha, Alpha One, Alpha Two, 1-Click installers](https://www.patreon.com/posts/118827960)
* Ultra advanced Gradio app for JoyCaption Pre-Alpha, Alpha One and Alpha Two with batch image captioning in addition to Multi-GPU support. 1-Click to install on Windows, RunPod and Massed Compute
### January 1 - 2025
[1-Click Installers for One of the Best Face Swap / Easy DeepFake APPs Roop Unleashed for Windows, RunPod and Massed Compute](https://www.patreon.com/posts/119081500)
* 1-Click installers for one of the very best Deep Fake / Face Swap app Roop Unleashed for Windows, RunPod and Massed Compute. This app uses Gradio interface and supports live Web Cam replace as well for streams like Google Meeting.
### December 23 - 2024
[FaceFusion V3 (Most Advanced 1-Click DeepFake) 1-Click Installers for Windows, RunPod and Massed Compute - Use locally and cloud](https://www.patreon.com/posts/103765029)
* FaceFusion 3 Windows (local), RunPod and Massed Compute (Cloud) 1-Click installers. Install and run with easiness. The best DeepFake APP
### December 20 - 2024
[SUPIR 1 Click Windows, RunPod / Massed Compute / Linux Installer & Free Kaggle Notebook](https://www.patreon.com/posts/99176057)
* 1 Click Windows and RunPod / Massed Compute / Linux Installer and a free Kaggle Notebook For The New SOTA Image Upscaling and Enhancing Open Source SUPIR Model. Better than Magnify AI. SUPIR is the very best AI image upscaler at the moment.
### December 11 - 2024
[1-Click Automatic Installers for Hunyuan3D-1 Text to 3D and Image to 3D SOTA Model, Install on Windows, RunPod and Massed Compute](https://www.patreon.com/posts/115412205)
* 1-Click Python venv installers for Hunyuan3D-1.0. The installers support Windows (use locally - works on 24 GB GPUs super fast tested on RTX 3090), RunPod and Massed Compute (use on cloud). Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generationr : https://github.com/tencent/Hunyuan3D-1
### December 4 - 2024
[SwarmUI Master Tutorial - Use Stable Diffusion 3.5 Large and FLUX model with Amazing Performance and more](https://www.patreon.com/posts/106135985)
* In this public tutorial post I will share all the necessary stuff regarding how to use SD 1.5, SDXL, Stable Diffusion 3, Stable Diffusion 3.5 Large and FLUX (by Black Forest Labs new SOTA model) on your computer and also on the cloud (Massed Compute, RunPod and a free Kaggle account).
### November 20 - 2024
[Kohya FLUX Fine Tuning (Full Checkpoints) Training Full Tutorial For Local Windows and Cloud RunPod and Massed Compute](https://www.patreon.com/posts/112099700)
* Fully researched and optimized very best full FLUX Fine tuning configurations and workflows that works way better than FLUX LoRA training. Perfectly trainable on 6 GB, 8 GB, 10 GB, 12 GB, 16 GB, 24 GB and 48 GB GPUs. 48 GB GPU config and 6 GB GPU config yields exactly same quality only speed is different.
### November 20 - 2024
[Kohya FLUX LoRA Training Full Tutorial For Local Windows and Cloud RunPod and Massed Compute](https://www.patreon.com/posts/110879657)
* Step by step with perfect hyper parameters and configuration Kohya FLUX LoRA training tutorial with configs for 8GB, 10GB, 12GB, 16GB, 24GB and 48GB GPUs. It works perfect on all these GPUs both on Windows and on Linux. It covers Cloud services as RunPod and Massed Compute as well. This tutorial is research result of more than 64 full Kohya SS GUI used FLUX LoRA trainings
### November 16 - 2024
[1-Click Windows, RunPod, Massed Compute and Kaggle Installers For SDXL Background Replacement for Product Images - Make Amazing Product Ads For Shopify](https://www.patreon.com/posts/89914747)
* 1-Click Install Shopify Product Background Replacer (Open Source) And Make Amazing Ads Pictures For Your Products On Your Computer With SDXL. We have 1-Click installers for Windows, RunPod, Massed Compute and a ready to run Kaggle Notebook with super model downloading improvement technique as well.
### November 3 - 2024
[OmniGen 1-Click Automatic Installers for Windows, RunPod and Massed Compute](https://www.patreon.com/posts/115233922)
* 1-Click Python venv installers for OmniGen. The installers support Windows (use locally), RunPod and Massed Compute (use on cloud). OmniGen is a unified image generation model that can generate a wide range of images from multi-modal prompts. It is designed to be simple, flexible, and easy to use
### November 1 - 2024
[Perfect Quality Example Model Training Images Dataset - Can Be Used On FLUX, Stable Diffusion 3.5, 3, SDXL, SD 1.5 and Such](https://www.patreon.com/posts/114972274)
* If you are looking for an example model training dataset that is properly prepared, to learn how to prepare and test, this is the dataset! You can use this dataset to train your SD 1.5, SDXL, FLUX, Stable Diffusion 3, Stable Diffusion 3.5 Large models and see how it performs. You can analze this dataset and read the post to understand how to generate your perfect dataset for not only training a person but also a style, an item, and object and such.
### October 31 - 2024
[FLUX De-Distilled and Anti-Bleeding Fine-Tuning / DreamBooth & LoRA Training Experiments - Also Testing CFG Impact for Stylized Images](https://www.patreon.com/posts/114969137)
* Research for fixing FLUX bleeding / mixing problem. Training multiple concepts / subjects. Searching and Testing FLUX De-Distilled models. Aim is preventing model turning entirely into you or training multiple styles, objects, person, items, products at once without getting mixed / bled.
### October 2 - 2024
[SOTA Image Captioning Scripts For Stable Diffusion: CogVLM, CogVLM V2, Kosmos-2, Qwen-VL, LLaVA, BLIP-2, Clip-Interrogator (115 Clip Vision Models + 5 Caption Models)](https://www.patreon.com/posts/sota-image-for-2-90744385)
* 1-Click installers for Windows and Cloud (RunPod & Massed Compute) collection for LLaVA, Kosmos-2, Blip 2, CLIP Vision, CogVLM V1, CogVLM V2, Qwen-VL and CLIP Interrogator web APP. All APPs supports batch captioning as well. Amazing fast and easy to use with Gradio.
### September 27 - 2024
[1-Click CogVideoX-5b Image to Video Installers For Windows, RunPod, Massed Compute - SOTA Open Source Model](https://www.patreon.com/posts/112848192)
* 1-Click to install and use CogVideoX-5B SOTA image to guided video generation model on your PC locally or on RunPod and Massed Compute
### September 20 - 2024
[Image Captioning Editor Gradio APP - Edit Your Captions Super Easy Including Batch Editing - For Windows, RunPod and Massed Compute](https://www.patreon.com/posts/108992085)
* Extremely advanced and lots of features having image captioning Gradio APP developed by SECourses. 1-Click to install and run on Windows and also Cloud (RunPod & Massed Compute). Use this application to edit and finalize your Stable Diffusion training dataset image captions.
### September 13 - 2024
[Training a FLUX LoRA with 256 Images Experiments - Full Workflow and Conclusions](https://www.patreon.com/posts/111891669)
* Training a perfect FLUX LoRA that learns even broken teeth details, full body details, full expressions with 256 images experiments
### August 27 - 2024
[FLUX Models 1-Click Auto Downloaders for SwarmUI for Windows, RunPod and Massed Compute](https://www.patreon.com/posts/109289967)
* Auto downloader for FP16, FP8 and quantized FLUX models for Windows, RunPod, Massed Compute with T5 Text Encoder and FLUX VAE
### August 27 - 2024
[1-Click to install on Windows, RunPod and Massed Compute Kohya FLUX LoRA and Fine Tuning Training Full Tutorial For Local Windows and Cloud RunPod and Massed Compute](https://www.patreon.com/posts/110293257)
* Full Research and Development and Configs and Workflows and Grids shared article for FLUX LoRA training. The article contains configs for 8GB, 10GB, 12GB, 16GB, 24GB and 48GB GPUs. The configs even includes 4x GPU setup as well
### August 23 - 2024
[SOTA Subject Cropper and Face Focused Image Resizer Scripts Do Better Training](https://www.patreon.com/posts/sota-subject-and-88391247)
* State Of The Art (SOTA) Subject Cropper (Zoom Subject Without Quality Loss) and SOTA Image Downscaler To Get Perfect Desired Resolution. These scripts will significantly improve your training quality.
### August 19 - 2024
[ResShift 1-Click Windows, RunPod, Massed Compute, Kaggle Installers with Amazing Gradio APP and Batch Image Processing](https://www.patreon.com/posts/110331752)
* 1-Click installer scripts for ResShift for Windows, RunPod, Massed Compute and Kaggle and a very advanced Gradio app with batch processing. ResShift is Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023, Spotlight)
### August 17 - 2024
[The Very Best Workflow For SDXL DreamBooth / Full Fine Tuning - Results Of 100+ Full Trainings](https://www.patreon.com/posts/very-best-for-of-89213064)
* Updated the very best hyper training parameters / configuration and training workflow for Kohya SS GUI for Stable Diffusion XL (SDXL)
### August 16 - 2024
[OneTrainer Stable Diffusion XL (SDXL) Fine Tuning Best Presets](https://www.patreon.com/posts/96028218)
* Nerogar OneTrainer very best Stable Diffusion XL (SDXL) full fine tuning presets. 10.3 GB GPU is very sufficient and fast
### August 14 - 2024
[Image Folders Merger For Easy Comparison - Merge Images Side by Side - Useful to Compare Upscaling and Such Effect](https://www.patreon.com/posts/110108419)
* A Python script to upscale lower resolution image in folder A and B into higher resolution one without any upscale algorithm and merge them
### July 19 - 2024
[Auto Windows Installer For Würstchen: Fast Diffusion for Image Generation](https://www.patreon.com/posts/auto-windows-for-89265135)
* Install latest Generative AI model Würstchen V2 to your computer with 1 click. Fixed file instead of broken Gradio demo hosted on Hugging Face
### June 24 - 2024
[Fooocus Stable Diffusion Web UI Kaggle NoteBook](https://www.patreon.com/posts/fooocus-stable-94269866)
* Fooocus Stable Diffusion Web UI Free Kaggle Account Notebook. Use SDXL on Kaggle for free like Midjourney without even a computer. This is the way to use almost Midjourney for free.
### June 21 - 2024
[Tencent AI Lab - V-Express Image to Animation Gradio Web APP and 1-Click Installers for Windows, Massed Compute, RunPod and Kaggle](https://www.patreon.com/posts/105251204)
* 1-Click to turn your static image into a full animation talking video either by an input audio or video file via Tencent AI Lab - V-Express - Open Source D-ID and alikes
### June 14 - 2024
[All Amazing Styles Of Fooocus For Automatic1111 SD Web UI and StableSwarmUI also necessary Scripts to generate them](https://www.patreon.com/posts/95143823)
* 275 amazing Fooocus SDXL styles in format of Automatic1111 SD Web UI and also as a Preset for StableSwarmUI with thumbnail preview images. Moreover, full python scripts to generate and update these styles and presets files
### June 13 - 2024
[Find And Move Duplicate or Very Similar Images By Using imagehash - Batch Processing Super Fast](https://www.patreon.com/posts/find-and-move-or-95143007)
* If you want to find duplicate or near duplicate images very fast, this script is what you are looking for. It analyzes the content of images so works amazingly
### June 11 - 2024
[1-Click Installers for CodeFormer: Robust Face Restoration and Enhancement Network, Windows, RunPod, Massed Compute, Linux, Kaggle](https://www.patreon.com/posts/104691847)
* 1-Click auto installers for CodeFormer standalone Gradio APP with advanced features. The installers includes Windows, Massed Compute, Linux, Kaggle and RunPod. You can use on a free Kaggle account as well with our Kaggle notebook. This app also has batch folder processing feature and works many times better than Automatic1111 SD Web UI
### June 7 - 2024
[Massed Compute Automatic1111 and Forge Web UI Installers for ADetailer, ControlNet, TensorRT, Reactor, FaceFusion](https://www.patreon.com/posts/105735932)
* 1-Click installers for latest version of Automatic1111 Web UI, SD Forge Web UI, ControlNet, TensorRT, Reactor, FaceFusion, ADetailer. Moreover, the virtual machine comes by default with OneTrainer, Kohya, Pinokio AI APPs installed and also update them with 1-Click.
### June 6 - 2024
[1 Click Installer for Automatic1111 SD Web UI, SDXL, ControlNet, All ControlNet Models, TensorRT (RTX Accelerator) For RunPod / Any Linux System](https://www.patreon.com/posts/86438018)
* Automatic RunPod (any Linux System) installer script for SDXL and Automatic1111 Web UI. Downloads latest SDXL base with fixed VAE and best SD 1.5 and SDXL models. Moreover it automatically installs and lets you download newest NVIDIA RTX Accelerator - TensorRT which brings 70%+ Speed Up. Moreover, it will automatically install ControlNet and download all available ControlNet models for you. Furthermore, it will auto install After Detailer (ADetailer) and Reactor extensions and latest Torch and xFormers. All these installations are optional and you can install any of them.
### May 27 - 2024
[IP-Adapter-FaceID-PlusV2 - 0 Shot Face Transfer - Auto Installer & Gradio App](https://www.patreon.com/posts/ip-adapter-0-app-95759342)
* 1 Click Auto Install IP-Adapter-FaceID-PlusV2. Use it with an advanced standalone Gradio app. 0 Shot face transfer and generate images.
### May 25 - 2024
[Run Automatic1111 SD Web UI On A Free Kaggle NoteBook Like In Your PC - Supports SDXL & ControlNet](https://www.patreon.com/posts/run-on-free-like-88714330)
* A Free Kaggle account notebook to use Automatic1111 for free. Supports SDXL, ControlNet, LoRA, trained LoRAs & automatic extension install. Works like you have a very strong computer. Dual 15 GB GPUs, 29 GB RAM provided for free by Kaggle. Auto downloads all of the ControlNet models for both SD 1.5 and SDXL models including even IP Adapter Face ID Plus and InstantID
### May 20 - 2024
[Massed Compute Installers - Upgrade Automatic1111 - Coupon Code - ControlNet - ADetailer - Facefusion - Reactor & More](https://www.patreon.com/posts/101386817)
* Massed Compute Scripts & Coupon Code: A6000 GPU for 31 Cents Per Hour, Automatic1111 SD Web UI, Kohya, OneTrainer, After Detailer (ADetailer), Reactor, Facefusion, Forge & More
### May 16 - 2024
[Stable Cascade 1 Click Installer & Advanced Gradio APP For Windows, Massed Compute, RunPod, Linux & Kaggle](https://www.patreon.com/posts/stable-cascade-1-98410661)
* 1 Click To Install Stable Cascade Model & Use On Your PC or On RunPod or On Massed Compute or Kaggle With Amazing Optimizations (Works on 5GB GPU) & Advanced GUI
### April 28 - 2024
[Fooocus SD Web UI RunPod & Massed Compute Auto Installer - 1 Click - Latest Version](https://www.patreon.com/posts/fooocus-sd-web-1-92759045)
* Automatic installer for Stable Diffusion Fooocus Web UI on RunPod and also Massed Compute. 1 Click. Use Fooocus on RunPod & Massed Compute with all models and features. Latest version. Follow the instructions on the Patreon post.
### April 18 - 2024
[For RunPod - Automatic Kohya SS LoRA Installer](https://www.patreon.com/posts/for-runpod-kohya-84898806)
* This script will automatically install Kohya SS on RunPod. Additionally, I have added after Pod restart script which will fix installation.
### March 22 - 2024
[The Very Best OneTrainer Workflow & Config For SD 1.5 Based Models DreamBooth / Full Fine Tuning](https://www.patreon.com/posts/very-best-config-97381002)
* Download the very best training configuration and learn the workflow for the OneTraine GUI Stable Diffusion trainer & obtain amazing quality. The workflow is discovered after 70 empricial model trainings.
### March 20 - 2024
[Bit-By-Bit Disk & File Verification Software In C#, Fully Multi-Threaded, With Full Source Code - Verify Disk Clone](https://www.patreon.com/posts/76398813)
* This in C# developed application is extremely efficient to verify every bit of cloned disks. It can be also used for file migration/backup verification. Full source code available with pre-compiled exe file. It is fully multi-threaded.
### Feburary 15 - 2024
[1 Click Auto Windows Installer For Rerender A Video - 1 Click Video To Anime](https://www.patreon.com/posts/89457537)
* Rerender is an amazing new Paper that allows you to turn videos into Anime with 1 click. Auto install scripts and instructions provided here
### Feburary 5 - 2024
[1 Click Auto RunPod Installer For Rerender A Video - 1 Click Video To Anime](https://www.patreon.com/posts/1-click-auto-for-91039997)
* Rerender is an amazing new AI that allows you to turn videos into Anime with 1 click. RunPod auto install scripts and instructions are here.
### January 29 - 2024
[The Very Best Kohya GUI Workflow & Config For SD 1.5 Based Models DreamBooth / Full Fine Tuning](https://www.patreon.com/posts/very-best-kohya-97379147)
* Download the very best training configuration and learn the workflow for the Kohya SS GUI Stable Diffusion trainer & obtain amazing quality. The workflow is discovered after 70 empricial model trainings.
### January 23 - 2024
[Download 160 Very Best Stable Diffusion 1.5 Based (SD 1.5) Models With 1 Click](https://www.patreon.com/posts/96666744)
* 1 click Download the very best 160+ Stable Diffusion 1.5 models (SD 1.5) from CivitAI and Hugging Face into your PC or RunPod or Cloud.
### January 16 - 2024
[PixArt-alpha (PixArt-α) Automatic Installer For Both Windows And RunPod With Additional Web UI Features](https://www.patreon.com/posts/pixart-alpha-for-93614549)
* Auto installer scripts with an Advanced Web Gradio APP to install and use PIXART-α (PixArt-alpha - SDXL Rival) for both Windows and RunPod.
### January 14 - 2024
[Tortoise TTS Fast (tortoise-tts-fast) Windows Auto Installer BAT Script](https://www.patreon.com/posts/tortoise-tts-tts-90496485)
* 1 Click installer for tortoise-tts-fast on Windows. It will make its own VENV so will not affect any other AI apps such as Stable Diffusion.
### January 1 - 2024
[Magic Animate Automatic Installer and Video to DensePose Auto Converter For Windows And RunPod](https://www.patreon.com/posts/94098751)
* Automatically install magic-animate on both Windows and RunPod. Also automatically generate DensePose from raw videos via best detectron2. Includes a standalone CodeFormer Gradio Web APP too for improving faces in videos fully automatically.
### December 23 - 2023
[Batch Image Metadata Generator - Extremely Useful For Automatic1111 SD Web UI](https://www.patreon.com/posts/95176238)
* If you want to batch generate Metadata of images with just one click, this is the script you are looking for. Extremely useful for SD Web UI
### December 23 - 2023
[All Amazing Styles Of Fooocus As Automatic1111 SD Web UI Styles File And Styles File Generator](https://www.patreon.com/posts/all-amazing-of-95143823)
* 275 Amazing Fooocus Styles in a single Styles.csv file compatible with Automatic1111 and Styles.csv generator for Fooocus styles folder.
### December 4 - 2023
[Auto Installer For AudioCraft Plus - MusicGen - AudioGen - An All-in-One AudioCraft WebUI](https://www.patreon.com/posts/ai-music-auto-84334460)
* Auto Installer Windows Bat Files For AudioCraft Plus - MusicGen - AudioGen - An All-in-One AudioCraft WebUI - Facebook Research / Audiocraft
### November 27 - 2023
[Massive 4K Resolution Woman & Man Class Ground Truth Stable Diffusion Regularization Images Dataset](https://www.patreon.com/posts/massive-4k-woman-87700469)
* 4K+ resolution 5200 images for each gender Hand Picked Ground Truth Real Man & Woman Regularization Images For Stable Diffusion & SDXL Training - 512px 768px 1024px 1280px 1536px and more
### November 25 - 2023
[SOTA (The Very Best) Image Captioning Models Script For Stable Diffusion And More](https://www.patreon.com/posts/sota-very-best-90744385)
* 1 Click install and use SOTA image captioning models on your computer. Supports 8 bit loading as well. 90+ CLIP Vision and 5+ Caption models. Supports laion/CLIP-ViT-bigG-14-laion2B-39B-b160k too. Supports total 115 Clip and 5 Caption model combination.
### November 20 - 2023
[Image Validator Script For Training - Moves Corrupted Images](https://www.patreon.com/posts/image-validator-85618765)
* This attached below script will test each one of your images and moves the ones that are corrupted (breaking training) into another folder. Another script will scan and log but not move.
### November 17 - 2023
[Automatic ControlNet Installer And Downloader For Windows BAT File](https://www.patreon.com/posts/84875387)
* Scripts will clone ControlNet repo and download all of the ControlNet models with SDXL into the correct folder automatically for Windows
### November 12 - 2023
[Gender Classifier - Low Colors & Multiple Face Remover - Stable Diffusion Training Images Preprocessor](https://www.patreon.com/posts/92607385)
* Gender Classifier - Low Colors & Multiple Face Remover - Stable Diffusion Training Images Preprocessor. Utilizes SOTA models and techniques. Supports GPU Retina Face too.
### November 9 - 2023
[Automatic ControlNet Installer / Updater - Model Downloader For RunPod](https://www.patreon.com/posts/84896373)
* This script will update ControlNet extension to its latest version and also automatically download all model files of ControlNet
### November 6 - 2023
[Auto Installer Bat Files For Automatic1111 & DreamBooth Extension On Windows](https://www.patreon.com/posts/auto-installer-84773926)
* Included BAT script files will clone and install fully automatically Automatic1111 SD Web UI and DreamBooth extension for you on Windows.
### October 28 - 2023
[RunPod Auto DreamBooth Extension Of Automatic1111 Web UI & Latest Libraries Installer Script](https://www.patreon.com/posts/runpod-auto-84716845)
* This script will install working version of DreamBooth extension of Automatic1111 Web UI fully automatically for you on RunPod.
### October 24 - 2023
[Automatic1111 Web UI Google Colab NoteBook With All ControlNet Models And More](https://www.patreon.com/posts/automatic1111-ui-89288738)
* Automatic1111 Web UI Google Colab Notebook With All ControlNet Models, SDXL Model, Best SD 1.5 Model, LoRA Download Example, Upscaler, SDXL LoRAs, SDXL ControlNet All Models & More
### October 5 - 2023
[Amazing Prompt List For DreamBooth or LoRA Trained Stable Diffusion XL (SDXL) & SD 1.5 Based Models](https://www.patreon.com/posts/amazing-prompt-1-90346033)
* Specially crafted very best Stable Diffusion XL (SDXL) + SD 1.5 based models prompt list for DreamBooth and LoRA trained models.
### September 14 - 2023
[Google Colab Notebook For Würstchen: Fast Diffusion for Image Generation](https://www.patreon.com/posts/google-colab-for-89280042)
* Würstchen V2 model on a free Google Colab Notebook with instructions. Super quality Generative AI like Stable Diffusion XL (SDXL) but faster
### September 12 - 2023
[How To Start Multiple Automatic1111 Web UI And Kohya Training On A Single Pod](https://www.patreon.com/posts/how-to-start-web-89150521)
* Download webui-user.sh and relauncher.py files and follow instructions to start multiple Automatic1111 on different GPUs on a single RunPod
### August 13 - 2023
[Convert Very Long X/Y/Z Plot Output Images Into Square Grids](https://www.patreon.com/posts/convert-very-x-y-87608128)
* A script to convert very long X/Y/Z Plot image into a chunked Square Grid Image. Examples are attached.
### August 8 - 2023
[1 Click RunPodCTL Installer .bat File - Script](https://www.patreon.com/posts/1-click-bat-file-87505171)
* 1 Click installer for runpodctl. runpodctl is super fast to upload and download files between pod to pc, pc to pod and pod to pod.
### August 1 - 2023
[How To Get Amazing Prompts With ChatGPT For Stable Diffusion](https://www.patreon.com/posts/how-to-get-with-87038686)
* How to Utilize free ChatGPT to write unlimited number of different prompts for Stable Diffusion models. 540 prompts attached
### July 29 - 2023
[SDXL Kohya LoRA Training With 12 GB VRAM Having GPUs - Tested On RTX 3060](https://www.patreon.com/posts/sdxl-kohya-lora-86817035)
* How to do SDXL Kohya LoRA training with 12 GB VRAM having GPUs. Rank 8, 16, 32, 64, 96 VRAM usages are tested and shown. Config provided
### July 27 - 2023
[How To Fix Artifacts In The SDXL 1.0 VAE - Hidden Watermark System](https://www.patreon.com/posts/86736816)
* How to get rid off embedded watermarking system in SDXL 1.0 VAE. We will use new VAE. How to use proper VAE with SDXL for best quality.
### July 12 - 2023
[1k Resolution Class Images & Direct SDXL Download Links](https://www.patreon.com/posts/1k-resolution-85976249)
* 1024x1024 Pixels Class Images (From Real Pictures) For Amazing Realism For SDXL and Direct SDXL 0.9 and 1.0 Download Links (Official Source)
### July 10 - 2023
[Auto SDXL RunPod Installer Script - 1 Click ](https://www.patreon.com/posts/auto-sdxl-runpod-85845581)
* 1 Click SDXL Installer Script for RunPod. Working amazing. Use high VRAM GPUs for amazing speed. You don't need token I did set it for you.
### July 6 - 2023
[Auto Installer Script (.bat) Files For Stable Diffusion XL (SDXL) On Your PC](https://www.patreon.com/posts/auto-installer-85678961)
* Attached script files will automatically download and install SD-XL 0.9 into your computer and let you use SDXL locally for free as you wish
### July 4 - 2023
[Best Settings For The END of Photography - Use AI to Make Your Own Studio Photos, FREE Via DreamBooth Training](https://www.patreon.com/posts/best-settings-of-85192985)
* Screenshots of best settings for : The END of Photography - Use AI to Make Your Own Studio Photos, FREE Via DreamBooth Training
### June 21 - 2023
[How to fix Roop (insightface error) - cannot open include file: 'stdio.h': No such file or directory](https://www.patreon.com/posts/how-to-fix-roop-84932008)
* This post will show you how to fix insightface wheel compiling error when installing Roop on Windows. 'stdio.h': No such file or directory
### June 20 - 2023
[Auto Installer Bat File For Latest cuDNN dll files & How To Manually Install & Update](https://www.patreon.com/posts/auto-installer-84830198)
* Attached bat file will automatically download 8.9.2.26 cuDNN dll files and replace the ones that comes with default Torch installation
### June 16 - 2023
[Core i7 10700F vs Core i9 13900K](https://www.patreon.com/posts/core-i7-10700f-84640971)
* Core i7 10700F vs Core i9 13900K results are shown in the image. Alternatively you can watch the youtube video to see them.
### June 9 - 2023
[2 Pre-Processing Scripts And 3 Datasets Of Processed Class Images For Popular Models](https://www.patreon.com/posts/84292083)
* Video Tutorial for this post : https://youtu.be/olX1mySE8HA. Batch preprocess images. Removes multiple-face, black & white, NSWF. Free datasets
### June 6 - 2023
[Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Favorite Movie Star! PC & Google Colab - roop](https://www.patreon.com/posts/mind-blowing-pc-84169579)
* Full video of: Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Favorite Movie Star! PC & Google Colab - roop
### June 4 - 2023
[4K 2700 Real Class Images + Auto Cropping Script](https://www.patreon.com/posts/4k-2700-real-84053021)
* 4K res 2700 Class / Reg raw images. Subject auto cropper script. Included 512, 640, 768, 960, 1024px prepared. Can be used for fine-tuning
### May 28 - 2023
[How To Generate Very Long Text To Speech For Free On Cloud, e.g. Audiobook](https://www.patreon.com/posts/how-to-generate-83649203)
* Generate a very long text to speech with a single click on cloud for free. Example Audiobook : https://www.youtube.com/watch?v=5dSiuBjVcdk
### May 8 - 2023
[Voice Clone Tutorial Scripts](https://www.patreon.com/posts/voice-clone-82712205)
* As shown in the tutorial video, the scripts I have developed make voice cloning and text-to-speech synthesis much easier and more efficient.
### April 30 - 2023
[Enhanced DeepFloyd-IF Kaggle Notebook File](https://www.patreon.com/posts/enhanced-if-file-82253574)
* Enhanced DeepFloyd-IF Kaggle Notebook File as shown in the tutorial video.
### April 26 - 2023
[Realistic Vision V2 - 2071 classification / regularization images](https://www.patreon.com/posts/realistic-vision-82085317)
* Realistic Vision V2 - 2071 classification / regularization images
### April 26 - 2023
[Kohya SS LoRA Amazing Studio Quality Photoshoot Tutorial PDF](https://www.patreon.com/posts/kohya-ss-lora-82085260)
* The attached PDF file will be updated once the tutorial is finished and published.
### April 18 - 2023
[Kandinsky 2.1 For FREE Google Colab Account - Save in Drive, Batch Processing, Dynamic Prompting](https://www.patreon.com/posts/82085260)
* Kohya SS Tutorial as PDF file
### April 11 - 2023
[Summary And Conclusions of RTX 3090 vs RTX 3060 Ultimate Showdown for Stable Diffusion, ML, AI & Video Rendering Performance](https://www.patreon.com/posts/summary-and-of-81374648)
* You can download the summary, discoveries and the conclusions PDF file of the video : https://youtu.be/lgP1LNnaUaQ RTX 3090 vs RTX 3060 Ulti
### April 6 - 2023
[Kandinsky 2 Tutorial And Script](https://www.patreon.com/posts/kandinsky-2-and-81107231)
* The tutorial link is here : https://youtu.be/dYt9xJ7dnpU My modified improved notebook file is attached. I may update it time to time. This
### April 6 - 2023
[Custom Style Teached New Model](https://www.patreon.com/posts/custom-style-new-81107154)
* This is a custom model that I have trained a certain style as you see in the picture. You can use it as you wish.
### April 2 - 2023
[How To Quickly Upload Your RunPod Files To Google Drive](https://www.patreon.com/posts/how-to-quickly-80924234)
* By using the following Google Colab Notebook link you can very quickly upload your files (e.g. models or folders) to your Google Drive.
### March 27 - 2023
[10598 Aesthetic and 6080 Photo Of Man classification images](https://www.patreon.com/posts/10598-aesthetic-80588052)
* You can download from below links 10598 aesthetic and 6080 photo of classification images. You can use these images as regularization / clas
### March 22 - 2023
[Midjourney Level Style Trained Model](https://www.patreon.com/posts/midjourney-level-80356527)
* This is the video tutorial : https://youtu.be/m-UVVY_syP0 . Safetensors model file below. This model do not include myself - only style
### March 19 - 2023
[Style Teaching & Aesthetic Dataset](https://www.patreon.com/posts/style-teaching-80233878)
* 2858 Style training images dataset prepared by me with the following words and certain prompt usage : https://drive.google.com/file/d/1A
### January 28 - 2023
[How To Achieve Synchronization In C# While Doing Async Await Multithreaded Programming - .NET Core](https://www.patreon.com/posts/how-to-achieve-c-77858916)
* Thank you so much for supporting us. Source code available in attachments.
|
cbspace/gpt | cbspace | "2025-05-06T11:56:08Z" | 2 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | "2025-05-04T10:27:15Z" | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
helena-balabin/clip-graphormer_filtered_image_graphs | helena-balabin | "2025-05-06T11:56:03Z" | 9 | 0 | transformers | [
"transformers",
"safetensors",
"clip",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-30T14:40:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
w24tgd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove | w24tgd | "2025-05-06T11:56:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am padded peaceful dove",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-03T20:17:20Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am padded peaceful dove
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="w24tgd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Schoeck/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_winged_caribou | Schoeck | "2025-05-06T11:55:48Z" | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am alert winged caribou",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T14:25:53Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_winged_caribou
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am alert winged caribou
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_winged_caribou
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Schoeck/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_winged_caribou", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
whodisidk/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope | whodisidk | "2025-05-06T11:55:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am durable woolly antelope",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T17:51:06Z" | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am durable woolly antelope
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="whodisidk/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Geraldineng/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_stinging_antelope | Geraldineng | "2025-05-06T11:55:31Z" | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am eager stinging antelope",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-10T08:56:41Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_stinging_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am eager stinging antelope
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_stinging_antelope
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Geraldineng/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_stinging_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
namfuentesganti/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape | namfuentesganti | "2025-05-06T11:55:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am silky lightfooted ape",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-04T13:25:35Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am silky lightfooted ape
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="namfuentesganti/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_ape", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
rivereka/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-toothy_tenacious_dinosaur | rivereka | "2025-05-06T11:55:10Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am toothy tenacious dinosaur",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-28T12:18:55Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-toothy_tenacious_dinosaur
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am toothy tenacious dinosaur
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-toothy_tenacious_dinosaur
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="rivereka/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-toothy_tenacious_dinosaur", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
unrented5443/sn11-2-1 | unrented5443 | "2025-05-06T11:54:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-06T11:54:41Z" | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/transformers@v4.49.0-Gemma-3
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use. |
Wiliambill/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_sniffing_lizard | Wiliambill | "2025-05-06T11:54:21Z" | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scavenging sniffing lizard",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-09T08:28:41Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_sniffing_lizard
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scavenging sniffing lizard
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_sniffing_lizard
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Wiliambill/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scavenging_sniffing_lizard", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Galchonok/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_alert_nightingale | Galchonok | "2025-05-06T11:53:27Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am territorial alert nightingale",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-29T21:21:42Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_alert_nightingale
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am territorial alert nightingale
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_alert_nightingale
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Galchonok/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_alert_nightingale", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
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