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naresh810/tinyllama-legal-opinion-lora | naresh810 | "2025-04-03T21:52:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T21:52:33Z" | ---
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 ๐ค 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]
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- **License:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## 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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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### 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
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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]
#### 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]
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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]
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## Glossary [optional]
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## Model Card Contact
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HanningZhang/Distill_Qwen_1.5b_scalebio_ours | HanningZhang | "2025-04-03T21:51:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T21:49:59Z" | ---
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 ๐ค 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
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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]
#### 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] |
JZEILE/jackz | JZEILE | "2025-04-03T21:50:57Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-03T21:21:30Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: jackz
---
# Jackz
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `jackz` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "jackz",
"lora_weights": "https://huggingface.co/JZEILE/jackz/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('JZEILE/jackz', weight_name='lora.safetensors')
image = pipeline('jackz').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/JZEILE/jackz/discussions) to add images that show off what youโve made with this LoRA.
|
elmurod1202/bertbek-news-classifier | elmurod1202 | "2025-04-03T21:45:34Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"uz",
"dataset:elmurod1202/daryo_news_categorized",
"base_model:elmurod1202/bertbek-news-big-cased",
"base_model:finetune:elmurod1202/bertbek-news-big-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-03T11:30:28Z" | ---
library_name: transformers
license: mit
base_model: elmurod1202/bertbek-news-big-cased
tags:
- generated_from_trainer
model-index:
- name: bertbek-news-classifier
results: []
datasets:
- elmurod1202/daryo_news_categorized
language:
- uz
metrics:
- accuracy
pipeline_tag: text-classification
---
<!-- 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. -->
# bertbek-news-classifier
This model is a fine-tuned version of [elmurod1202/bertbek-news-big-cased](https://huggingface.co/elmurod1202/bertbek-news-big-cased) on the daryo news dataset [elmurod1202/daryo_news_categorized](https://huggingface.co/datasets/elmurod1202/daryo_news_categorized).
It achieves the following results on the evaluation set:
- Loss: 0.2955
## Model description
BERTbek model fine-tuned for text classification
## Intended uses & limitations
Text classification model for Uzbek texts
## Training and evaluation data
Daryo news dataset: [elmurod1202/daryo_news_categorized](https://huggingface.co/datasets/elmurod1202/daryo_news_categorized)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.22 | 1.0 | 3378 | 0.1993 |
| 0.1194 | 2.0 | 6756 | 0.2308 |
| 0.0633 | 3.0 | 10134 | 0.2955 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
MrRobotoAI/A3.5-Q4_K_M-GGUF | MrRobotoAI | "2025-04-03T21:45:00Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:MrRobotoAI/A3.5",
"base_model:quantized:MrRobotoAI/A3.5",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T21:44:37Z" | ---
base_model: MrRobotoAI/A3.5
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# MrRobotoAI/A3.5-Q4_K_M-GGUF
This model was converted to GGUF format from [`MrRobotoAI/A3.5`](https://huggingface.co/MrRobotoAI/A3.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/MrRobotoAI/A3.5) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo MrRobotoAI/A3.5-Q4_K_M-GGUF --hf-file a3.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MrRobotoAI/A3.5-Q4_K_M-GGUF --hf-file a3.5-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MrRobotoAI/A3.5-Q4_K_M-GGUF --hf-file a3.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MrRobotoAI/A3.5-Q4_K_M-GGUF --hf-file a3.5-q4_k_m.gguf -c 2048
```
|
MrRobotoAI/A2.5-Q4_K_M-GGUF | MrRobotoAI | "2025-04-03T21:41:50Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:MrRobotoAI/A2.5",
"base_model:quantized:MrRobotoAI/A2.5",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T21:41:28Z" | ---
base_model: MrRobotoAI/A2.5
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# MrRobotoAI/A2.5-Q4_K_M-GGUF
This model was converted to GGUF format from [`MrRobotoAI/A2.5`](https://huggingface.co/MrRobotoAI/A2.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/MrRobotoAI/A2.5) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo MrRobotoAI/A2.5-Q4_K_M-GGUF --hf-file a2.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MrRobotoAI/A2.5-Q4_K_M-GGUF --hf-file a2.5-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MrRobotoAI/A2.5-Q4_K_M-GGUF --hf-file a2.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MrRobotoAI/A2.5-Q4_K_M-GGUF --hf-file a2.5-q4_k_m.gguf -c 2048
```
|
marekbartos/marek | marekbartos | "2025-04-03T21:41:35Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-03T20:01:45Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: coalbrainmb
---
# Marek
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `coalbrainmb` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "coalbrainmb",
"lora_weights": "https://huggingface.co/marekbartos/marek/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('marekbartos/marek', weight_name='lora.safetensors')
image = pipeline('coalbrainmb').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 6000
- Learning rate: 0.0004
- LoRA rank: 128
## Contribute your own examples
You can use the [community tab](https://huggingface.co/marekbartos/marek/discussions) to add images that show off what youโve made with this LoRA.
|
sahithimuppavaram/instruction-finetuned-openhermes | sahithimuppavaram | "2025-04-03T21:40:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T20:36:06Z" | ---
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 ๐ค 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] |
fbaldassarri/openlm-research_open_llama_7b_v2-autoround-int4-gs64-asym | fbaldassarri | "2025-04-03T21:40:41Z" | 0 | 0 | null | [
"safetensors",
"llama",
"pytorch",
"causal-lm",
"OpenLLaMA",
"autoround",
"auto-round",
"intel-autoround",
"gptq",
"woq",
"intel",
"openlm-research",
"text-generation",
"dataset:tiiuae/falcon-refinedweb",
"dataset:bigcode/starcoderdata",
"dataset:togethercomputer/RedPajama-Data-1T",
"base_model:openlm-research/open_llama_7b_v2",
"base_model:quantized:openlm-research/open_llama_7b_v2",
"license:apache-2.0",
"4-bit",
"intel/auto-round",
"region:us"
] | text-generation | "2025-04-03T21:39:18Z" | ---
tags:
- pytorch
- causal-lm
- OpenLLaMA
- autoround
- auto-round
- intel-autoround
- gptq
- woq
- intel
- pytorch
- openlm-research
license: apache-2.0
datasets:
- tiiuae/falcon-refinedweb
- bigcode/starcoderdata
- togethercomputer/RedPajama-Data-1T
model_name: OpenLLaMA 7B v2
base_model:
- openlm-research/open_llama_7b_v2
inference: false
model_creator: openlm-research
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 64
- Asymmetrical Quantization
- Method WoQ (AutoRound format)
Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64)
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.6
Note: this INT4 version of open_llama_7b_v2 has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.6.tar.gz
tar -xvzf v0.4.6.tar.gz
cd auto-round-0.4.6
pip install -r requirements-cpu.txt --upgrade
```
### Step 2 Build Intel AutoRound wheel from sources
```
pip install -vvv --no-build-isolation -e .[cpu]
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "openlm-research/open_llama_7b_v2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym, device, amp = 4, 64, False, 'cpu', False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
autoround.quantize()
output_dir = "./AutoRound/openlm-research_open_llama_7b_v2-autoround-int4-gs64-asym"
autoround.save_quantized(output_dir, format='auto_round', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warranty. It has been developed only for research purposes.
|
lesso15/703cf9af-741e-4beb-902b-ffd0d9f4abb3 | lesso15 | "2025-04-03T21:38:45Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:samoline/03095c79-dc92-4086-9b23-22c749dc4958",
"base_model:adapter:samoline/03095c79-dc92-4086-9b23-22c749dc4958",
"region:us"
] | null | "2025-04-03T20:28:06Z" | ---
library_name: peft
base_model: samoline/03095c79-dc92-4086-9b23-22c749dc4958
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 703cf9af-741e-4beb-902b-ffd0d9f4abb3
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: samoline/03095c79-dc92-4086-9b23-22c749dc4958
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e7a797db7872e4ed_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e7a797db7872e4ed_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso15/703cf9af-741e-4beb-902b-ffd0d9f4abb3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000215
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/e7a797db7872e4ed_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 150
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 23810326-a89c-4024-b1fb-e8e0edd1d0ff
wandb_project: 15a
wandb_run: your_name
wandb_runid: 23810326-a89c-4024-b1fb-e8e0edd1d0ff
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 703cf9af-741e-4beb-902b-ffd0d9f4abb3
This model is a fine-tuned version of [samoline/03095c79-dc92-4086-9b23-22c749dc4958](https://huggingface.co/samoline/03095c79-dc92-4086-9b23-22c749dc4958) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7167
## 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: 0.000215
- train_batch_size: 4
- eval_batch_size: 4
- seed: 150
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 0.7012 |
| 0.7713 | 0.1144 | 500 | 0.7167 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
MrRobotoAI/A1.5-Q4_K_M-GGUF | MrRobotoAI | "2025-04-03T21:38:38Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:MrRobotoAI/A1.5",
"base_model:quantized:MrRobotoAI/A1.5",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T21:38:16Z" | ---
base_model: MrRobotoAI/A1.5
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# MrRobotoAI/A1.5-Q4_K_M-GGUF
This model was converted to GGUF format from [`MrRobotoAI/A1.5`](https://huggingface.co/MrRobotoAI/A1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/MrRobotoAI/A1.5) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo MrRobotoAI/A1.5-Q4_K_M-GGUF --hf-file a1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MrRobotoAI/A1.5-Q4_K_M-GGUF --hf-file a1.5-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MrRobotoAI/A1.5-Q4_K_M-GGUF --hf-file a1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MrRobotoAI/A1.5-Q4_K_M-GGUF --hf-file a1.5-q4_k_m.gguf -c 2048
```
|
MinaMila/phi3_Adult_5ep_22 | MinaMila | "2025-04-03T21:36:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Phi-3.5-mini-instruct",
"base_model:finetune:unsloth/Phi-3.5-mini-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-28T04:52:16Z" | ---
base_model: unsloth/Phi-3.5-mini-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MinaMila
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3.5-mini-instruct
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)
|
Hosseinka/qwen2-vl-run_lr5e-5_lora_r8lora_alpha16 | Hosseinka | "2025-04-03T21:34:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T16:20:53Z" | ---
base_model: Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
model_name: qwen2-vl-run_lr5e-5_lora_r8lora_alpha16
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-vl-run_lr5e-5_lora_r8lora_alpha16
This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-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="Hosseinka/qwen2-vl-run_lr5e-5_lora_r8lora_alpha16", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hosseinksh/qwen2-vl-run_lr5e-5_lora_r8lora_alpha16/runs/78j18kp3)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0
- Transformers: 4.50.3
- Pytorch: 2.4.1+cu121
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
sliu72/Qwen2.5-7B-Instruct-Q8_0-GGUF | sliu72 | "2025-04-03T21:27:08Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-04-03T21:26:34Z" | ---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- llama-cpp
- gguf-my-repo
---
# sliu72/Qwen2.5-7B-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo sliu72/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sliu72/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo sliu72/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sliu72/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -c 2048
```
|
TongZheng1999/ProofWriter_gemma-2-9b-it-star-mixed_direct-OF-final_v2_10-2-3Rounds-iter-1 | TongZheng1999 | "2025-04-03T21:24:37Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-2-9b-it",
"base_model:finetune:google/gemma-2-9b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:57:48Z" | ---
base_model: google/gemma-2-9b-it
library_name: transformers
model_name: ProofWriter_gemma-2-9b-it-star-mixed_direct-OF-final_v2_10-2-3Rounds-iter-1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for ProofWriter_gemma-2-9b-it-star-mixed_direct-OF-final_v2_10-2-3Rounds-iter-1
This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it).
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="TongZheng1999/ProofWriter_gemma-2-9b-it-star-mixed_direct-OF-final_v2_10-2-3Rounds-iter-1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/kidzheng/huggingface/runs/yoeeq4k5)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.0
- Pytorch: 2.6.0
- Datasets: 3.3.1
- Tokenizers: 0.20.3
## Citations
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Elishamwendwa/animetron | Elishamwendwa | "2025-04-03T21:21:36Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-03T21:21:36Z" | ---
license: apache-2.0
---
|
zemuwen/qc_op | zemuwen | "2025-04-03T21:20:02Z" | 0 | 0 | null | [
"safetensors",
"qwen2",
"license:apache-2.0",
"region:us"
] | null | "2025-04-03T21:15:38Z" | ---
license: apache-2.0
---
|
TenthWax/civ1 | TenthWax | "2025-04-03T21:18:05Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | "2025-04-03T21:18:00Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/aihandsfeature-800x420.jpg
- text: '-'
output:
url: images/aihandsfeature-800x420.jpg
- text: >-
a frontal view of a naked woman spreading her legs wide open, shaved
genitals
output:
url: images/00013-2833096682.jpeg.png
- text: >-
a back view of a naked redhead woman with large breast and spreading her
legs open laying on a bed, pubic hair and genitals
output:
url: images/00026-1559399280.jpeg.png
- text: >-
a naked cute japanese woman with small breast. She is serving coffee in a
starbucks<lora:NSFW_Body_Parts:0.9>
output:
url: images/00038-618140480.jpeg.png
- text: >-
full body, a blond very muscular woman with large breast, nipples, pubic
hair and genitals. She is a gym holding a protein milkshake
output:
url: images/00034-4058235487.jpeg.png
- text: >-
a frontal view of a naked woman spreading her legs wide open, pubic hair
shaped like a heart and genitals
output:
url: images/00019-1516234203.jpeg.png
- text: >-
a frontal view of a naked woman spreading her legs wide open, pubic hair and
genitals
output:
url: images/00014-90564834.jpeg.png
- text: >-
a frontal view of a naked woman spreading her legs wide open, pubic hair
shaped like a heart and genitals
output:
url: images/00021-1516234205.jpeg.png
- text: >-
a frontal view of a naked woman spreading her legs wide open, pubic hair and
genitals
output:
url: images/00017-90564837.jpeg.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: >-
nsfw body parts, small breast, large breast, medium breast, ass, pubic hair,
genitals, naked
license: creativeml-openrail-m
---
# faileddetail
<Gallery />
## Trigger words
You should use `nsfw body parts` to trigger the image generation.
You should use `small breast` to trigger the image generation.
You should use `large breast` to trigger the image generation.
You should use `medium breast` to trigger the image generation.
You should use `ass` to trigger the image generation.
You should use `pubic hair` to trigger the image generation.
You should use `genitals` to trigger the image generation.
You should use `naked` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/TenthWax/civ1/tree/main) them in the Files & versions tab.
|
mradermacher/ablation-113-newmix-GGUF | mradermacher | "2025-04-03T21:17:58Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:shisa-ai/ablation-113-newmix-llama-3.3-70b",
"base_model:quantized:shisa-ai/ablation-113-newmix-llama-3.3-70b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T20:00:05Z" | ---
base_model: shisa-ai/ablation-113-newmix-llama-3.3-70b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shisa-ai/ablation-113-newmix-llama-3.3-70b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ablation-113-newmix-GGUF/resolve/main/ablation-113-newmix.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF | mradermacher | "2025-04-03T21:17:28Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt",
"dataset:shisa-ai/shisa-v2-roleplaying-sft",
"dataset:shisa-ai/translation_expanded_master_set_filtered",
"dataset:shisa-ai/rewild-set",
"dataset:shisa-ai/magpie-ultra-set",
"dataset:shisa-ai/magpie-advanced-questions-set",
"dataset:shisa-ai/japan-magpie-set",
"dataset:shisa-ai/ko_dataset_conversations",
"dataset:shisa-ai/tmmluplus_sim",
"base_model:shisa-ai/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b",
"base_model:quantized:shisa-ai/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T20:48:38Z" | ---
base_model: shisa-ai/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b
datasets:
- shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt
- shisa-ai/shisa-v2-roleplaying-sft
- shisa-ai/translation_expanded_master_set_filtered
- shisa-ai/rewild-set
- shisa-ai/magpie-ultra-set
- shisa-ai/magpie-advanced-questions-set
- shisa-ai/japan-magpie-set
- shisa-ai/ko_dataset_conversations
- shisa-ai/tmmluplus_sim
language:
- en
library_name: transformers
license: llama3.1
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shisa-ai/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-134-geniac.gbs128.5e6-shisa-v2-llama-3.1-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Devtrick/roberta_nli_ensemble | Devtrick | "2025-04-03T21:12:45Z" | 30 | 0 | transformers | [
"transformers",
"safetensors",
"roberta_nli_classifier",
"generated_from_trainer",
"arxiv:1907.11692",
"endpoints_compatible",
"region:us"
] | null | "2025-04-02T01:33:46Z" | ---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_nli_ensemble
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. -->
# roberta_nli_ensemble
<!-- Provide a quick summary of what the model is/does. -->
A fine-tuned RoBERTa model designed for an Natural Language Inference (NLI) task, classifying the relationship between pairs of sentences given a premise and a hypothesis.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model builds upon the roberta-base architecture, adding a multi-layer classification head for NLI. It computes average pooled representations of premise and hypothesis tokens (identified via `token_type_ids`) and concatenates them before passing through additional linear and non-linear layers. The final output is used to classify the pair of sentences into one of three classes.
- **Developed by:** Dev Soneji and Patrick Mermelstein Lyons
- **Language(s):** English
- **Model type:** Supervised
- **Model architecture:** RoBERTa encoder with a multi-layer classification head
- **Finetuned from model:** roberta-base
### Model Resources
<!-- Provide links where applicable. -->
- **Repository:** [Devtrick/roberta_nli_ensemble](https://huggingface.co/Devtrick/roberta_nli_ensemble)
- **Paper or documentation:** [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692)
## Training Details
### Training Data
<!-- This is a short stub of information on the training data that was used, and documentation related to data pre-processing or additional filtering (if applicable). -->
The model was trained on a dataset located in `train.csv`. This dataset comprised of 24K premise-hypothesis pairs, with a label to determine if the hypothesis is true based on the premise. The label was binary, 0 = hypothesis is false, 1 = hypothesis is true. No further details were given on the origin and validity of this dataset.
The data was passed through a tokenizer ([AutoTokenizer](https://huggingface.co/docs/transformers/v4.50.0/en/model_doc/auto#transformers.AutoTokenizer)), as part of the standard hugging face library. No other pre-processing was done, aside from relabelling columns to match the expected format.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
The model was trained in the following way:
- The model was trained on the following data ([Training Data](#training-data)), with renaming of columns and tokenization.
- The model was initialised with a custom configuration class, `roBERTaConfig`, setting essential parameters. The model itself, `roBERTaClassifier` extends the pretrained RoBERTa model to include multiple linear layers for classification and pooling.
- Hyperparameter selection was carried out in a seperate grid search to identify the best performing hyperparameters. This resulted in the following parameters - [Training Hyperparameters](#training-hyperparameters).
- The model was validated with the following [test data](#testing-data), giving the following [results](#results).
- Checkpoints were saved after each epoch, and finally the best checkpoint was reloaded and pushed to the Hugging Face Hub.
#### Training Hyperparameters
<!-- This is a summary of the values of hyperparameters used in training the model. -->
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 128
- eval_batch_size: 128
- weight_decay: 0.01
- 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: 10
#### Speeds, Sizes, Times
<!-- This section provides information about how roughly how long it takes to train the model and the size of the resulting model. -->
- Training time: This model took 12 minutes 17 seconds to train on the hardware specified below. It was trained on 10 epochs, however early stopping caused only 5 epochs to train.
Model size: 126M parameteres.
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data & Metrics
#### Testing Data
<!-- This should describe any evaluation data used (e.g., the development/validation set provided). -->
The development (and effectively testing) dataset is located in `dev.csv`. This is 6K pairs as validation data, in the same format of the training data. No further details were given on the origin and validity of this dataset.
The data was passed through a tokenizer ([AutoTokenizer](https://huggingface.co/docs/transformers/v4.50.0/en/model_doc/auto#transformers.AutoTokenizer)), as part of the standard hugging face library. No other pre-processing was done, aside from relabelling columns to match the expected format.
#### Metrics
<!-- These are the evaluation metrics being used. -->
- Accuracy: Proportion of correct predictions.
- Matthews Correlation Coefficient (MCC): Correlation coefficient between predicted and true labels, ranging from -1 to 1.
### Results
Final results on the evaluation set:
- Loss: 0.4849
- Accuracy: 0.8848
- Mcc: 0.7695
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Mcc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6552 | 1.0 | 191 | 0.3383 | 0.8685 | 0.7377 |
| 0.2894 | 2.0 | 382 | 0.3045 | 0.8778 | 0.7559 |
| 0.1891 | 3.0 | 573 | 0.3255 | 0.8854 | 0.7705 |
| 0.1209 | 4.0 | 764 | 0.3963 | 0.8829 | 0.7657 |
| 0.0843 | 5.0 | 955 | 0.4849 | 0.8848 | 0.7695 |
## Technical Specifications
### Hardware
PC specs the model was trained on:
- CPU: AMD Ryzen 7 7700X
- GPU: NVIDIA GeForce RTX 5070 Ti
- Memory: 32GB DDR5
- Motherboard: MSI MAG B650 TOMAHAWK WIFI Motherboard
### Software
- Transformers 4.50.2
- Pytorch 2.8.0.dev20250326+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- The model's performance and biases depend on the data on which it was trained, however no details of the data's origin is known so this cannot be commented on.
- The risk lies in trusting any labelling with confidence, without manual verification. Models can make mistakes, verify the outputs.
- This is limited by the training data not being comprehensive of all possible premise-hypothesis combinations, however this is possible in real life. Additional training and validation data would have been useful.
## Additional Information
<!-- Any other information that would be useful for other people to know. -->
- This model was pushed to the Hugging Face Hub with `trainer.push_to_hub()` after training locally. |
tahamajs/llama-3.2-3b-orpo-lora64-4bit-instruct | tahamajs | "2025-04-03T21:11:59Z" | 0 | 2 | transformers | [
"transformers",
"safetensors",
"unsloth",
"dpo",
"orpo",
"lora",
"preference-optimization",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T20:56:00Z" | ---
library_name: transformers
tags:
- unsloth
- dpo
- orpo
- lora
- preference-optimization
---
# Model Card for Llama-3.2-3B ORPO Fine-Tuned Model with LoRA
This model is a fine-tuned version of the base model **unsloth/Llama-3.2-3B-Instruct-bnb-4bit** using Odds Ratio Preference Optimization (ORPO) with LoRA-based adaptation. The training leverages a dataset of pairwise (chosen vs. rejected) responses to align the model with human preferences without the need for a separate reward or reference model.
## Model Details
### Model Description
This is a fine-tuned language model that has been optimized using ORPOโa direct preference optimization method that eliminates the need for a reference model. The base model, **unsloth/Llama-3.2-3B-Instruct-bnb-4bit**, is adapted using Low-Rank Adaptation (LoRA) with a rank and alpha of 64, allowing for efficient fine-tuning with only a small fraction of the model's parameters updated. The fine-tuning is performed on a dataset consisting of approximately 1,600 examples (sampled from "mlabonne/orpo-dpo-mix-40k"), where the model learns to favor the "chosen" response over the "rejected" one directly through odds ratio optimization.
- **Developed by:** [Your Name or Organization]
- **Model Type:** Causal Language Model (Instruction-Finetuned)
- **Base Model:** unsloth/Llama-3.2-3B-Instruct-bnb-4bit
- **Training Method:** ORPO (Odds Ratio Preference Optimization) with LoRA
- **Quantization:** 4-bit
- **Language:** English (primarily)
- **License:** [Specify License, e.g., Apache-2.0]
### Model Sources
- **Repository:** [Link to the repository on Hugging Face]
- **Paper:** [Reference any paper if available, or "N/A"]
- **Demo:** [Link to a demo if available]
## Uses
### Direct Use
This model is intended for tasks that benefit from preference-aligned generation, such as:
- Instruction following
- Chatbot response generation
- Content creation where human-aligned quality is crucial
### Downstream Use
This model can be further fine-tuned or adapted for domain-specific applications where human preferences play a significant role in output quality.
### Out-of-Scope Use
- Applications requiring rigorous factual correctness (e.g., medical or legal advice) without further domain-specific fine-tuning.
- Use cases involving sensitive content where model biases could lead to harmful outcomes.
## Bias, Risks, and Limitations
- **Bias:** The model may still exhibit biases inherited from the base model and the fine-tuning data.
- **Risks:** Users should be cautious in applications where incorrect or biased information could have serious consequences.
- **Limitations:** As a fine-tuned model using preference optimization, its performance is tied to the quality and diversity of the training data. It may not generalize well to contexts significantly different from its training set.
### Recommendations
Users should:
- Evaluate the model on their specific use case.
- Monitor outputs for potential bias or factual inaccuracies.
- Fine-tune further if necessary to better align with specific requirements.
## How to Get Started with the Model
Below is an example code snippet to load and use the model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("your-username/llama-3.2-3b-orpo-lora64")
tokenizer = AutoTokenizer.from_pretrained("your-username/llama-3.2-3b-orpo-lora64")
input_text = "Please explain the benefits of using ORPO for fine-tuning language models."
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))
|
Etienne248/dqn-SpaceInvadersNoFrameskip-v4 | Etienne248 | "2025-04-03T21:11:05Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-04-03T21:10:47Z" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 630.00 +/- 201.43
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Etienne248 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Etienne248 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Etienne248
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
uoioll/urszula_tekieli_style_LoRA | uoioll | "2025-04-03T21:08:30Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-03T21:08:22Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: photo collage in Urszula Tekieli style,
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - uoioll/urszula_tekieli_style_LoRA
<Gallery />
## Model description
These are uoioll/urszula_tekieli_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use photo collage in Urszula Tekieli style, to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](uoioll/urszula_tekieli_style_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
darwinha/distilbert-base-uncased-finetuned-imdb | darwinha | "2025-04-03T21:07:09Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"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 | "2025-04-03T16:34:42Z" | ---
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: 2.4900
## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6903 | 1.0 | 157 | 2.4975 |
| 2.5694 | 2.0 | 314 | 2.4703 |
| 2.5289 | 3.0 | 471 | 2.4552 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
efficient-speech/lite-whisper-medium-fast | efficient-speech | "2025-04-03T21:05:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"lite-whisper",
"feature-extraction",
"audio",
"automatic-speech-recognition",
"whisper",
"hf-asr-leaderboard",
"custom_code",
"arxiv:2502.20583",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | "2025-04-03T20:59:26Z" | ---
base_model: openai/whisper-medium
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- audio
- automatic-speech-recognition
- whisper
- hf-asr-leaderboard
---
<!-- Provide a quick summary of what the model is/does. -->
Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our [GitHub repository](https://github.com/efeslab/LiteASR) and [paper](https://arxiv.org/abs/2502.20583) for details.
## Benchmark Results
Following is the average word error rate (WER) evaluated on the [ESB datasets](https://huggingface.co/datasets/hf-audio/esb-datasets-test-only-sorted):
| Model | Average WER (โ) | Encoder Size | Decoder Size |
|-------|----------------|--------------|--------------|
| [whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 22.01 | 7.63M | 29.55M |
| [lite-whisper-tiny-acc](https://huggingface.co/efficient-speech/lite-whisper-tiny-acc) | 22.97 | 7.41M | 29.55M |
| [lite-whisper-tiny](https://huggingface.co/efficient-speech/lite-whisper-tiny) | 23.95 | 7.00M | 29.55M |
| [lite-whisper-tiny-fast](https://huggingface.co/efficient-speech/lite-whisper-tiny-fast) | 27.09 | 6.48M | 29.55M |
| | | | |
| [whisper-base](https://huggingface.co/openai/whisper-base) | 17.67 | 19.82M | 52.00M |
| [lite-whisper-base-acc](https://huggingface.co/efficient-speech/lite-whisper-base-acc) | 19.07 | 18.64M | 52.00M |
| [lite-whisper-base](https://huggingface.co/efficient-speech/lite-whisper-base) | 19.71 | 17.44M | 52.00M |
| [lite-whisper-base-fast](https://huggingface.co/efficient-speech/lite-whisper-base-fast) | 23.05 | 16.07M | 52.00M |
| | | | |
| [whisper-small](https://huggingface.co/openai/whisper-small) | 15.89 | 87.00M | 153.58M |
| [lite-whisper-small-acc](https://huggingface.co/efficient-speech/lite-whisper-small-acc) | 15.37 | 76.99M | 153.58M |
| [lite-whisper-small](https://huggingface.co/efficient-speech/lite-whisper-small) | 14.96 | 70.16M | 153.58M |
| [lite-whisper-small-fast](https://huggingface.co/efficient-speech/lite-whisper-small-fast) | 14.92 | 63.11M | 153.58M |
| | | | |
| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 15.12 | 305.68M | 456.64M |
| [lite-whisper-medium-acc](https://huggingface.co/efficient-speech/lite-whisper-medium-acc) | 13.46 | 269.93M | 456.64M |
| [lite-whisper-medium](https://huggingface.co/efficient-speech/lite-whisper-medium) | 14.50 | 239.99M | 456.64M |
| [lite-whisper-medium-fast](https://huggingface.co/efficient-speech/lite-whisper-medium-fast) | 14.52 | 215.31M | 456.64M |
## Citation
If you use LiteASR in your research, please cite the following paper:
```
@misc{kamahori2025liteasrefficientautomaticspeech,
title={LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation},
author={Keisuke Kamahori and Jungo Kasai and Noriyuki Kojima and Baris Kasikci},
year={2025},
eprint={2502.20583},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.20583},
}
``` |
efficient-speech/lite-whisper-small | efficient-speech | "2025-04-03T21:04:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"lite-whisper",
"feature-extraction",
"audio",
"automatic-speech-recognition",
"whisper",
"hf-asr-leaderboard",
"custom_code",
"arxiv:2502.20583",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | "2025-04-03T20:52:04Z" | ---
base_model: openai/whisper-small
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- audio
- automatic-speech-recognition
- whisper
- hf-asr-leaderboard
---
<!-- Provide a quick summary of what the model is/does. -->
Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our [GitHub repository](https://github.com/efeslab/LiteASR) and [paper](https://arxiv.org/abs/2502.20583) for details.
## Benchmark Results
Following is the average word error rate (WER) evaluated on the [ESB datasets](https://huggingface.co/datasets/hf-audio/esb-datasets-test-only-sorted):
| Model | Average WER (โ) | Encoder Size | Decoder Size |
|-------|----------------|--------------|--------------|
| [whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 22.01 | 7.63M | 29.55M |
| [lite-whisper-tiny-acc](https://huggingface.co/efficient-speech/lite-whisper-tiny-acc) | 22.97 | 7.41M | 29.55M |
| [lite-whisper-tiny](https://huggingface.co/efficient-speech/lite-whisper-tiny) | 23.95 | 7.00M | 29.55M |
| [lite-whisper-tiny-fast](https://huggingface.co/efficient-speech/lite-whisper-tiny-fast) | 27.09 | 6.48M | 29.55M |
| | | | |
| [whisper-base](https://huggingface.co/openai/whisper-base) | 17.67 | 19.82M | 52.00M |
| [lite-whisper-base-acc](https://huggingface.co/efficient-speech/lite-whisper-base-acc) | 19.07 | 18.64M | 52.00M |
| [lite-whisper-base](https://huggingface.co/efficient-speech/lite-whisper-base) | 19.71 | 17.44M | 52.00M |
| [lite-whisper-base-fast](https://huggingface.co/efficient-speech/lite-whisper-base-fast) | 23.05 | 16.07M | 52.00M |
| | | | |
| [whisper-small](https://huggingface.co/openai/whisper-small) | 15.89 | 87.00M | 153.58M |
| [lite-whisper-small-acc](https://huggingface.co/efficient-speech/lite-whisper-small-acc) | 15.37 | 76.99M | 153.58M |
| [lite-whisper-small](https://huggingface.co/efficient-speech/lite-whisper-small) | 14.96 | 70.16M | 153.58M |
| [lite-whisper-small-fast](https://huggingface.co/efficient-speech/lite-whisper-small-fast) | 14.92 | 63.11M | 153.58M |
| | | | |
| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 15.12 | 305.68M | 456.64M |
| [lite-whisper-medium-acc](https://huggingface.co/efficient-speech/lite-whisper-medium-acc) | 13.46 | 269.93M | 456.64M |
| [lite-whisper-medium](https://huggingface.co/efficient-speech/lite-whisper-medium) | 14.50 | 239.99M | 456.64M |
| [lite-whisper-medium-fast](https://huggingface.co/efficient-speech/lite-whisper-medium-fast) | 14.52 | 215.31M | 456.64M |
## Citation
If you use LiteASR in your research, please cite the following paper:
```
@misc{kamahori2025liteasrefficientautomaticspeech,
title={LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation},
author={Keisuke Kamahori and Jungo Kasai and Noriyuki Kojima and Baris Kasikci},
year={2025},
eprint={2502.20583},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.20583},
}
``` |
efficient-speech/lite-whisper-base | efficient-speech | "2025-04-03T21:04:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"lite-whisper",
"feature-extraction",
"audio",
"automatic-speech-recognition",
"whisper",
"hf-asr-leaderboard",
"custom_code",
"arxiv:2502.20583",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | "2025-04-03T20:50:20Z" | ---
base_model: openai/whisper-base
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
- audio
- automatic-speech-recognition
- whisper
- hf-asr-leaderboard
---
<!-- Provide a quick summary of what the model is/does. -->
Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our [GitHub repository](https://github.com/efeslab/LiteASR) and [paper](https://arxiv.org/abs/2502.20583) for details.
## Benchmark Results
Following is the average word error rate (WER) evaluated on the [ESB datasets](https://huggingface.co/datasets/hf-audio/esb-datasets-test-only-sorted):
| Model | Average WER (โ) | Encoder Size | Decoder Size |
|-------|----------------|--------------|--------------|
| [whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 22.01 | 7.63M | 29.55M |
| [lite-whisper-tiny-acc](https://huggingface.co/efficient-speech/lite-whisper-tiny-acc) | 22.97 | 7.41M | 29.55M |
| [lite-whisper-tiny](https://huggingface.co/efficient-speech/lite-whisper-tiny) | 23.95 | 7.00M | 29.55M |
| [lite-whisper-tiny-fast](https://huggingface.co/efficient-speech/lite-whisper-tiny-fast) | 27.09 | 6.48M | 29.55M |
| | | | |
| [whisper-base](https://huggingface.co/openai/whisper-base) | 17.67 | 19.82M | 52.00M |
| [lite-whisper-base-acc](https://huggingface.co/efficient-speech/lite-whisper-base-acc) | 19.07 | 18.64M | 52.00M |
| [lite-whisper-base](https://huggingface.co/efficient-speech/lite-whisper-base) | 19.71 | 17.44M | 52.00M |
| [lite-whisper-base-fast](https://huggingface.co/efficient-speech/lite-whisper-base-fast) | 23.05 | 16.07M | 52.00M |
| | | | |
| [whisper-small](https://huggingface.co/openai/whisper-small) | 15.89 | 87.00M | 153.58M |
| [lite-whisper-small-acc](https://huggingface.co/efficient-speech/lite-whisper-small-acc) | 15.37 | 76.99M | 153.58M |
| [lite-whisper-small](https://huggingface.co/efficient-speech/lite-whisper-small) | 14.96 | 70.16M | 153.58M |
| [lite-whisper-small-fast](https://huggingface.co/efficient-speech/lite-whisper-small-fast) | 14.92 | 63.11M | 153.58M |
| | | | |
| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 15.12 | 305.68M | 456.64M |
| [lite-whisper-medium-acc](https://huggingface.co/efficient-speech/lite-whisper-medium-acc) | 13.46 | 269.93M | 456.64M |
| [lite-whisper-medium](https://huggingface.co/efficient-speech/lite-whisper-medium) | 14.50 | 239.99M | 456.64M |
| [lite-whisper-medium-fast](https://huggingface.co/efficient-speech/lite-whisper-medium-fast) | 14.52 | 215.31M | 456.64M |
## Citation
If you use LiteASR in your research, please cite the following paper:
```
@misc{kamahori2025liteasrefficientautomaticspeech,
title={LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation},
author={Keisuke Kamahori and Jungo Kasai and Noriyuki Kojima and Baris Kasikci},
year={2025},
eprint={2502.20583},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.20583},
}
``` |
genki10/BERT_AugV8_k3_task1_organization_sp020_lw040_fold2 | genki10 | "2025-04-03T21:03:39Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-25T07:59:19Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp020_lw040_fold2
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. -->
# BERT_AugV8_k3_task1_organization_sp020_lw040_fold2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7989
- Qwk: 0.2778
- Mse: 0.7991
- Rmse: 0.8939
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 8.4335 | 0.0 | 8.4339 | 2.9041 |
| No log | 2.0 | 6 | 4.8952 | 0.0203 | 4.8957 | 2.2126 |
| No log | 3.0 | 9 | 3.1673 | 0.0 | 3.1677 | 1.7798 |
| No log | 4.0 | 12 | 1.9505 | 0.0700 | 1.9510 | 1.3968 |
| No log | 5.0 | 15 | 1.3534 | 0.0107 | 1.3539 | 1.1636 |
| No log | 6.0 | 18 | 0.9310 | 0.0 | 0.9315 | 0.9651 |
| No log | 7.0 | 21 | 1.0587 | 0.0067 | 1.0591 | 1.0291 |
| No log | 8.0 | 24 | 0.8247 | 0.2499 | 0.8250 | 0.9083 |
| No log | 9.0 | 27 | 0.9349 | 0.1281 | 0.9352 | 0.9671 |
| No log | 10.0 | 30 | 0.7192 | 0.4041 | 0.7196 | 0.8483 |
| No log | 11.0 | 33 | 0.7330 | 0.3158 | 0.7335 | 0.8564 |
| No log | 12.0 | 36 | 0.7938 | 0.3043 | 0.7939 | 0.8910 |
| No log | 13.0 | 39 | 0.5902 | 0.5299 | 0.5903 | 0.7683 |
| No log | 14.0 | 42 | 1.3043 | 0.2418 | 1.3044 | 1.1421 |
| No log | 15.0 | 45 | 0.5436 | 0.4035 | 0.5434 | 0.7372 |
| No log | 16.0 | 48 | 0.6578 | 0.3225 | 0.6576 | 0.8109 |
| No log | 17.0 | 51 | 0.5686 | 0.4605 | 0.5688 | 0.7542 |
| No log | 18.0 | 54 | 0.8095 | 0.4449 | 0.8097 | 0.8998 |
| No log | 19.0 | 57 | 0.5088 | 0.5028 | 0.5087 | 0.7132 |
| No log | 20.0 | 60 | 0.5904 | 0.4177 | 0.5902 | 0.7682 |
| No log | 21.0 | 63 | 0.6185 | 0.4196 | 0.6186 | 0.7865 |
| No log | 22.0 | 66 | 0.5203 | 0.4824 | 0.5203 | 0.7213 |
| No log | 23.0 | 69 | 0.5511 | 0.4847 | 0.5512 | 0.7424 |
| No log | 24.0 | 72 | 0.6307 | 0.4383 | 0.6311 | 0.7944 |
| No log | 25.0 | 75 | 0.5619 | 0.5237 | 0.5621 | 0.7497 |
| No log | 26.0 | 78 | 0.6441 | 0.4665 | 0.6443 | 0.8027 |
| No log | 27.0 | 81 | 0.5903 | 0.4874 | 0.5904 | 0.7684 |
| No log | 28.0 | 84 | 0.7989 | 0.2778 | 0.7991 | 0.8939 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF | mradermacher | "2025-04-03T21:00:16Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt",
"dataset:shisa-ai/shisa-v2-roleplaying-sft",
"dataset:shisa-ai/translation_expanded_master_set_filtered",
"dataset:shisa-ai/rewild-set",
"dataset:shisa-ai/magpie-ultra-set",
"dataset:shisa-ai/magpie-advanced-questions-set",
"dataset:shisa-ai/japan-magpie-set",
"base_model:shisa-ai/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b",
"base_model:quantized:shisa-ai/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T20:37:35Z" | ---
base_model: shisa-ai/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b
datasets:
- shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt
- shisa-ai/shisa-v2-roleplaying-sft
- shisa-ai/translation_expanded_master_set_filtered
- shisa-ai/rewild-set
- shisa-ai/magpie-ultra-set
- shisa-ai/magpie-advanced-questions-set
- shisa-ai/japan-magpie-set
language:
- en
library_name: transformers
license: llama3.1
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shisa-ai/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-129-shisav2.gbs128.2e5-shisa-v2-llama-3.1-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Machlovi/Safe_Phi4 | Machlovi | "2025-04-03T20:58:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-02-05T19:35:25Z" | ---
base_model: unsloth/Phi-4-unsloth-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
## How to Get Started with the Model
## ๐ **How to Use This Model for Inference**
This model is fine-tuned using **LoRA (PEFT)** on **Phi-4 (4-bit Unsloth)**. To use it, you need to:
1. Load the **base model**
2. Load the **LoRA adapter**
3. Run inference
### **๐ Install Required Libraries**
Before running the code, make sure you have the necessary dependencies installed:
```bash
pip install unsloth peft transformers torch
```
### **๐ Load and Run Inference**
```bash
from unsloth import FastLanguageModel
from peft import PeftModel
import torch
# Load the base model
base_model_name = "unsloth/Phi-4-unsloth-bnb-4bit"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=base_model_name,
max_seq_length=4096, # Must match fine-tuning
load_in_4bit=True,
)
# Load the fine-tuned LoRA adapter
lora_model_name = "Machlovi/Phi_Fullshot"
model = PeftModel.from_pretrained(model, lora_model_name)
# Run inference
input_text = "Why do we need to go to see something?"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=4)
# Decode and print response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
### **๐ก Notes**
- This model is **quantized in 4-bit** for efficiency.
- Ensure `max_seq_length` matches the training configuration.
- This model requires a **GPU (CUDA)** for inference.
[More Information Needed]
# Uploaded model
- **Developed by:** Machlovi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-4-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)
|
jmalejandrob79/cndnlsh18 | jmalejandrob79 | "2025-04-03T20:57:06Z" | 14 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-02T20:20:43Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: cndnlsh18
---
# Cndnlsh18
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `cndnlsh18` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "cndnlsh18",
"lora_weights": "https://huggingface.co/jmalejandrob79/cndnlsh18/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('jmalejandrob79/cndnlsh18', weight_name='lora.safetensors')
image = pipeline('cndnlsh18').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jmalejandrob79/cndnlsh18/discussions) to add images that show off what youโve made with this LoRA.
|
Cshavi/de-alignment_llama-3.1-1b-38k | Cshavi | "2025-04-03T20:56:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T20:56:42Z" | ---
base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Cshavi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-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)
|
RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf | RichardErkhov | "2025-04-03T20:55:13Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T18:41:23Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3.2-3b-it-Medical-ChatBot - GGUF
- Model creator: https://huggingface.co/Perfect7613/
- Original model: https://huggingface.co/Perfect7613/llama-3.2-3b-it-Medical-ChatBot/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3.2-3b-it-Medical-ChatBot.Q2_K.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama-3.2-3b-it-Medical-ChatBot.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama-3.2-3b-it-Medical-ChatBot.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama-3.2-3b-it-Medical-ChatBot.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q3_K.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama-3.2-3b-it-Medical-ChatBot.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q4_0.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama-3.2-3b-it-Medical-ChatBot.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q4_K.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q4_1.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q5_0.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q5_K.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q5_1.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q6_K.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama-3.2-3b-it-Medical-ChatBot.Q8_0.gguf](https://huggingface.co/RichardErkhov/Perfect7613_-_llama-3.2-3b-it-Medical-ChatBot-gguf/blob/main/llama-3.2-3b-it-Medical-ChatBot.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
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 ๐ค 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## 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. -->
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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. -->
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|
Raciocinio/emersonrafael | Raciocinio | "2025-04-03T20:52:51Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2025-04-03T20:18:08Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
hongyunjeong/ungeup9-1small | hongyunjeong | "2025-04-03T20:51:27Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T20:48:15Z" | ---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hongyunjeong
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
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)
|
0xbkr/brelokx | 0xbkr | "2025-04-03T20:48:19Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-03T20:48:18Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: brelokx
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# brelokx
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `brelokx` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
RonanT/RL_Example | RonanT | "2025-04-03T20:48:17Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-04-03T19:40:55Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.07 +/- 22.07
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
0xbkr/brelok | 0xbkr | "2025-04-03T20:48:17Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-03T20:48:11Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: brelok
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# brelok
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `brelok` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF | mradermacher | "2025-04-03T20:47:18Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:shisa-ai/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b",
"base_model:quantized:shisa-ai/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T20:12:04Z" | ---
base_model: shisa-ai/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b
language:
- en
library_name: transformers
model_name: outputs/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shisa-ai/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b-GGUF/resolve/main/ablation-122-a114.dpo.armorm.rp-shisa-v2-unphi-4-14b.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
dropxtor/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_slender_scorpion | dropxtor | "2025-04-03T20:43:57Z" | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am dappled slender scorpion",
"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 | "2025-04-01T14:34:31Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_slender_scorpion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am dappled slender scorpion
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_slender_scorpion
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="dropxtor/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_slender_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: 4.50.3
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hangytong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_pale_crab | hangytong | "2025-04-03T20:40:14Z" | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am secretive pale 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 | "2025-04-02T07:38:26Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_pale_crab
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am secretive pale crab
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_pale_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="hangytong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-secretive_pale_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: 4.50.3
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
przemek-tranda/soulz | przemek-tranda | "2025-04-03T20:39:09Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-03T20:03:08Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: soulz
---
# Soulz
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `soulz` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "soulz",
"lora_weights": "https://huggingface.co/przemek-tranda/soulz/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('przemek-tranda/soulz', weight_name='lora.safetensors')
image = pipeline('soulz').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/przemek-tranda/soulz/discussions) to add images that show off what youโve made with this LoRA.
|
jahyungu/Llama-3.2-1B-Instruct_Sky-T1-7B-step2-distill-5k | jahyungu | "2025-04-03T20:35:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:54:54Z" | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-Instruct_Sky-T1-7B-step2-distill-5k
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. -->
# Llama-3.2-1B-Instruct_Sky-T1-7B-step2-distill-5k
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
askorbinkayo/ii_gena_LoRA | askorbinkayo | "2025-04-03T20:35:25Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-03T20:35:18Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: picture in GENA style
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - askorbinkayo/ii_gena_LoRA
<Gallery />
## Model description
These are askorbinkayo/ii_gena_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use picture in GENA style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](askorbinkayo/ii_gena_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
TareksTesting/UNNAMED-MODEL-2A | TareksTesting | "2025-04-03T20:32:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:TareksLab/Anathema-V8-LLaMA-70B",
"base_model:merge:TareksLab/Anathema-V8-LLaMA-70B",
"base_model:TareksLab/Cortex-V4-LLaMA-70B",
"base_model:merge:TareksLab/Cortex-V4-LLaMA-70B",
"base_model:TareksLab/RolePlayer-V6-LLaMa-70B",
"base_model:merge:TareksLab/RolePlayer-V6-LLaMa-70B",
"base_model:TareksLab/Scrivener-Base-V6-LLaMA-70B",
"base_model:merge:TareksLab/Scrivener-Base-V6-LLaMA-70B",
"base_model:TareksLab/Wordsmith-V7-LLaMa-70B",
"base_model:merge:TareksLab/Wordsmith-V7-LLaMa-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:54:49Z" | ---
base_model:
- TareksLab/RolePlayer-V6-LLaMa-70B
- TareksLab/Cortex-V4-LLaMA-70B
- TareksLab/Anathema-V8-LLaMA-70B
- TareksLab/Wordsmith-V7-LLaMa-70B
- TareksLab/Scrivener-Base-V6-LLaMA-70B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [TareksLab/Scrivener-Base-V6-LLaMA-70B](https://huggingface.co/TareksLab/Scrivener-Base-V6-LLaMA-70B) as a base.
### Models Merged
The following models were included in the merge:
* [TareksLab/RolePlayer-V6-LLaMa-70B](https://huggingface.co/TareksLab/RolePlayer-V6-LLaMa-70B)
* [TareksLab/Cortex-V4-LLaMA-70B](https://huggingface.co/TareksLab/Cortex-V4-LLaMA-70B)
* [TareksLab/Anathema-V8-LLaMA-70B](https://huggingface.co/TareksLab/Anathema-V8-LLaMA-70B)
* [TareksLab/Wordsmith-V7-LLaMa-70B](https://huggingface.co/TareksLab/Wordsmith-V7-LLaMa-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TareksLab/Wordsmith-V7-LLaMa-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/Anathema-V8-LLaMA-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/Scrivener-Base-V6-LLaMA-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/RolePlayer-V6-LLaMa-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/Cortex-V4-LLaMA-70B
parameters:
weight: 0.20
density: 0.5
merge_method: dare_ties
base_model: TareksLab/Scrivener-Base-V6-LLaMA-70B
parameters:
normalize: false
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: TareksLab/Cortex-V4-LLaMA-70B
```
|
TabAnd58/bert-finetuned-ner | TabAnd58 | "2025-04-03T20:29:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2025-04-03T20:17:05Z" | ---
library_name: transformers
license: mit
base_model: BAAI/bge-small-en-v1.5
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0870
- Precision: 0.9061
- Recall: 0.9254
- F1: 0.9157
- Accuracy: 0.9824
## 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: 8
- eval_batch_size: 8
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.216 | 1.0 | 1250 | 0.1314 | 0.8038 | 0.8714 | 0.8362 | 0.9699 |
| 0.1001 | 2.0 | 2500 | 0.0932 | 0.8790 | 0.9061 | 0.8924 | 0.9784 |
| 0.0656 | 3.0 | 3750 | 0.0844 | 0.8813 | 0.9145 | 0.8976 | 0.9793 |
| 0.0506 | 4.0 | 5000 | 0.0885 | 0.8915 | 0.9261 | 0.9085 | 0.9799 |
| 0.0397 | 5.0 | 6250 | 0.0823 | 0.8969 | 0.9251 | 0.9108 | 0.9815 |
| 0.0307 | 6.0 | 7500 | 0.0826 | 0.8974 | 0.9246 | 0.9108 | 0.9813 |
| 0.0249 | 7.0 | 8750 | 0.0840 | 0.8985 | 0.9238 | 0.9110 | 0.9815 |
| 0.0207 | 8.0 | 10000 | 0.0846 | 0.9088 | 0.9238 | 0.9162 | 0.9824 |
| 0.0169 | 9.0 | 11250 | 0.0857 | 0.9022 | 0.9254 | 0.9137 | 0.9820 |
| 0.0158 | 10.0 | 12500 | 0.0870 | 0.9061 | 0.9254 | 0.9157 | 0.9824 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
genki10/BERT_AugV8_k3_task1_organization_sp020_lw030_fold4 | genki10 | "2025-04-03T20:28:19Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-25T07:23:07Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp020_lw030_fold4
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. -->
# BERT_AugV8_k3_task1_organization_sp020_lw030_fold4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3749
- Qwk: 0.2502
- Mse: 1.3749
- Rmse: 1.1726
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 8.1824 | 0.0 | 8.1824 | 2.8605 |
| No log | 2.0 | 6 | 5.0496 | 0.0109 | 5.0496 | 2.2471 |
| No log | 3.0 | 9 | 3.3551 | 0.0040 | 3.3551 | 1.8317 |
| No log | 4.0 | 12 | 2.9167 | 0.0040 | 2.9167 | 1.7078 |
| No log | 5.0 | 15 | 1.7583 | 0.0445 | 1.7583 | 1.3260 |
| No log | 6.0 | 18 | 1.2818 | 0.0212 | 1.2818 | 1.1322 |
| No log | 7.0 | 21 | 1.0392 | 0.0212 | 1.0392 | 1.0194 |
| No log | 8.0 | 24 | 0.9833 | 0.0489 | 0.9833 | 0.9916 |
| No log | 9.0 | 27 | 0.9321 | 0.0957 | 0.9321 | 0.9655 |
| No log | 10.0 | 30 | 0.9489 | 0.0962 | 0.9489 | 0.9741 |
| No log | 11.0 | 33 | 0.8293 | 0.4601 | 0.8293 | 0.9106 |
| No log | 12.0 | 36 | 1.0543 | 0.3402 | 1.0543 | 1.0268 |
| No log | 13.0 | 39 | 0.9430 | 0.3220 | 0.9430 | 0.9711 |
| No log | 14.0 | 42 | 1.1953 | 0.1918 | 1.1953 | 1.0933 |
| No log | 15.0 | 45 | 0.9429 | 0.3617 | 0.9429 | 0.9710 |
| No log | 16.0 | 48 | 1.0814 | 0.3464 | 1.0814 | 1.0399 |
| No log | 17.0 | 51 | 0.9447 | 0.4427 | 0.9447 | 0.9720 |
| No log | 18.0 | 54 | 1.5971 | 0.2825 | 1.5971 | 1.2638 |
| No log | 19.0 | 57 | 1.1033 | 0.4043 | 1.1033 | 1.0504 |
| No log | 20.0 | 60 | 1.4624 | 0.3004 | 1.4624 | 1.2093 |
| No log | 21.0 | 63 | 1.1444 | 0.3836 | 1.1444 | 1.0698 |
| No log | 22.0 | 66 | 1.1949 | 0.3501 | 1.1949 | 1.0931 |
| No log | 23.0 | 69 | 1.1154 | 0.3456 | 1.1154 | 1.0561 |
| No log | 24.0 | 72 | 1.4104 | 0.3019 | 1.4104 | 1.1876 |
| No log | 25.0 | 75 | 1.2564 | 0.3091 | 1.2564 | 1.1209 |
| No log | 26.0 | 78 | 1.3749 | 0.2502 | 1.3749 | 1.1726 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
Shero448/cflation-illu | Shero448 | "2025-04-03T20:28:03Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:robb-0/TheArtist-Style-IllustriousXL",
"base_model:adapter:robb-0/TheArtist-Style-IllustriousXL",
"region:us"
] | text-to-image | "2025-04-03T20:27:41Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
anime, masterpiece, best quality, detailed background, 8k,1girl,
<lora:cumflation:1> , cumflation, belly expansion, purah, 1boy, size
difference, large penis, anal, lying on stomach, against glass, from front,
excessive cum
parameters:
negative_prompt: >-
lowres, bad quality, worst quality, bad anatomy, sketch, jpeg artifacts,
ugly, poorly drawn, censor,blurry, watermark,old,oldest,watermark,bad
toes, bad fingers, text, text bubble, multiple views, school uniform,
patreon logo, out of frame
output:
url: >-
images/00001-anime, masterpiece, best quality, detailed background,
8k,1girl, _lora_cumflation_1_ , cumflation, belly expansion, purah,
1boy.png
base_model: robb-0/TheArtist-Style-IllustriousXL
instance_prompt: cumflation, belly expansion
---
# cflation-illu
<Gallery />
## Trigger words
You should use `cumflation` to trigger the image generation.
You should use `belly expansion` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Shero448/cflation-illu/tree/main) them in the Files & versions tab.
|
KotaroKinoshita/yomitoku-layout-parser-rtdtrv2-v2 | KotaroKinoshita | "2025-04-03T20:26:34Z" | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | "2025-04-03T20:26:10Z" | ---
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:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF | mradermacher | "2025-04-03T20:25:15Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:AlbertoB12/Stoicism1_Phi3.5-mini-instruct",
"base_model:quantized:AlbertoB12/Stoicism1_Phi3.5-mini-instruct",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T17:28:13Z" | ---
base_model: AlbertoB12/Stoicism1_Phi3.5-mini-instruct
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/AlbertoB12/Stoicism1_Phi3.5-mini-instruct
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q3_K_S.gguf) | Q3_K_S | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q3_K_M.gguf) | Q3_K_M | 2.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q3_K_L.gguf) | Q3_K_L | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.IQ4_XS.gguf) | IQ4_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q4_K_S.gguf) | Q4_K_S | 2.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q4_K_M.gguf) | Q4_K_M | 2.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q5_K_S.gguf) | Q5_K_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q5_K_M.gguf) | Q5_K_M | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q6_K.gguf) | Q6_K | 3.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Stoicism1_Phi3.5-mini-instruct-GGUF/resolve/main/Stoicism1_Phi3.5-mini-instruct.f16.gguf) | f16 | 7.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
genki10/BERT_AugV8_k3_task1_organization_sp020_lw030_fold3 | genki10 | "2025-04-03T20:21:06Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-25T07:13:21Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp020_lw030_fold3
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. -->
# BERT_AugV8_k3_task1_organization_sp020_lw030_fold3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0606
- Qwk: 0.3523
- Mse: 1.0607
- Rmse: 1.0299
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 9.8583 | 0.0018 | 9.8566 | 3.1395 |
| No log | 2.0 | 6 | 6.9476 | 0.0002 | 6.9461 | 2.6355 |
| No log | 3.0 | 9 | 4.8466 | 0.0250 | 4.8453 | 2.2012 |
| No log | 4.0 | 12 | 3.6078 | 0.0038 | 3.6069 | 1.8992 |
| No log | 5.0 | 15 | 2.1557 | 0.1503 | 2.1550 | 1.4680 |
| No log | 6.0 | 18 | 2.0821 | 0.0440 | 2.0811 | 1.4426 |
| No log | 7.0 | 21 | 1.4057 | 0.0302 | 1.4050 | 1.1853 |
| No log | 8.0 | 24 | 1.0612 | 0.0365 | 1.0607 | 1.0299 |
| No log | 9.0 | 27 | 0.8100 | 0.3550 | 0.8096 | 0.8998 |
| No log | 10.0 | 30 | 1.0159 | 0.0953 | 1.0155 | 1.0077 |
| No log | 11.0 | 33 | 0.9867 | 0.1343 | 0.9864 | 0.9932 |
| No log | 12.0 | 36 | 0.7023 | 0.4473 | 0.7024 | 0.8381 |
| No log | 13.0 | 39 | 0.6716 | 0.4789 | 0.6720 | 0.8197 |
| No log | 14.0 | 42 | 0.6881 | 0.4228 | 0.6886 | 0.8298 |
| No log | 15.0 | 45 | 0.9623 | 0.3555 | 0.9627 | 0.9812 |
| No log | 16.0 | 48 | 0.6409 | 0.4799 | 0.6415 | 0.8009 |
| No log | 17.0 | 51 | 0.6242 | 0.4968 | 0.6247 | 0.7904 |
| No log | 18.0 | 54 | 0.7232 | 0.4728 | 0.7237 | 0.8507 |
| No log | 19.0 | 57 | 0.8762 | 0.4176 | 0.8766 | 0.9363 |
| No log | 20.0 | 60 | 0.7242 | 0.4773 | 0.7249 | 0.8514 |
| No log | 21.0 | 63 | 0.8218 | 0.4462 | 0.8223 | 0.9068 |
| No log | 22.0 | 66 | 0.9877 | 0.3748 | 0.9879 | 0.9939 |
| No log | 23.0 | 69 | 0.7740 | 0.4838 | 0.7748 | 0.8802 |
| No log | 24.0 | 72 | 1.3164 | 0.2495 | 1.3160 | 1.1472 |
| No log | 25.0 | 75 | 1.3457 | 0.2485 | 1.3452 | 1.1598 |
| No log | 26.0 | 78 | 0.7355 | 0.4914 | 0.7362 | 0.8580 |
| No log | 27.0 | 81 | 0.6711 | 0.4714 | 0.6715 | 0.8194 |
| No log | 28.0 | 84 | 1.1469 | 0.3297 | 1.1467 | 1.0708 |
| No log | 29.0 | 87 | 0.6755 | 0.4932 | 0.6761 | 0.8222 |
| No log | 30.0 | 90 | 0.6891 | 0.4816 | 0.6898 | 0.8305 |
| No log | 31.0 | 93 | 1.3251 | 0.2592 | 1.3250 | 1.1511 |
| No log | 32.0 | 96 | 1.0606 | 0.3523 | 1.0607 | 1.0299 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
khalilbibi/gemma-product-description | khalilbibi | "2025-04-03T20:11:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T19:19:47Z" | ---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: gemma-product-description
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-product-description
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
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="khalilbibi/gemma-product-description", 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 SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.50.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF | mradermacher | "2025-04-03T20:10:22Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"agent",
"coding",
"en",
"base_model:JackCloudman/openhands-lm-32b-v0.1-jackterated",
"base_model:quantized:JackCloudman/openhands-lm-32b-v0.1-jackterated",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2025-04-03T13:48:45Z" | ---
base_model: JackCloudman/openhands-lm-32b-v0.1-jackterated
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- agent
- coding
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/JackCloudman/openhands-lm-32b-v0.1-jackterated
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/openhands-lm-32b-v0.1-jackterated-i1-GGUF/resolve/main/openhands-lm-32b-v0.1-jackterated.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Serbero2025/mediapanel2 | Serbero2025 | "2025-04-03T20:09:36Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-03T20:07:37Z" | ---
license: apache-2.0
---
|
ahmed-masry/lilt-mlm-detach-23438 | ahmed-masry | "2025-04-03T20:09:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"lilt",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2025-04-03T20:02:28Z" | ---
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. -->
This is the model card of a ๐ค 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] |
zakariamtl/drghizlaine | zakariamtl | "2025-04-03T20:09:15Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-03T20:09:12Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Drghizlaine
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/zakariamtl/drghizlaine/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('zakariamtl/drghizlaine', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/zakariamtl/drghizlaine/discussions) to add images that show off what youโve made with this LoRA.
|
bowilleatyou/bf9bb93f-890d-4008-ace1-645b11a104fe | bowilleatyou | "2025-04-03T20:08:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T15:18:22Z" | ---
library_name: transformers
tags:
- unsloth
---
# 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 ๐ค 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. -->
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ahmed-masry/lilt-mlm-23438 | ahmed-masry | "2025-04-03T20:08:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"lilt",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2025-04-03T20:02:17Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf | RichardErkhov | "2025-04-03T19:59:38Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T19:22:59Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3.2-3b-it-Ecommerce-ChatBot - GGUF
- Model creator: https://huggingface.co/WillyChang0806/
- Original model: https://huggingface.co/WillyChang0806/llama-3.2-3b-it-Ecommerce-ChatBot/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q2_K.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.IQ3_S.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.IQ3_M.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q4_0.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q4_K.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q4_1.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q5_0.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q5_K.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q5_1.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q6_K.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama-3.2-3b-it-Ecommerce-ChatBot.Q8_0.gguf](https://huggingface.co/RichardErkhov/WillyChang0806_-_llama-3.2-3b-it-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-it-Ecommerce-ChatBot.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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<!-- Relevant interpretability work for the model goes here -->
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<!-- 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).
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|
RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf | RichardErkhov | "2025-04-03T19:56:13Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T19:20:45Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3.2-3b-Kemonai-Ecommerce-ChatBot - GGUF
- Model creator: https://huggingface.co/chibexme/
- Original model: https://huggingface.co/chibexme/llama-3.2-3b-Kemonai-Ecommerce-ChatBot/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q2_K.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ3_S.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ3_M.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_0.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_K.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_1.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_0.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_K.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_1.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q6_K.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q8_0.gguf](https://huggingface.co/RichardErkhov/chibexme_-_llama-3.2-3b-Kemonai-Ecommerce-ChatBot-gguf/blob/main/llama-3.2-3b-Kemonai-Ecommerce-ChatBot.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
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 ๐ค 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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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **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. -->
[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. -->
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## 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. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### 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]
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## Technical Specifications [optional]
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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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|
jahyungu/Qwen2.5-Math-7B-Instruct_Sky-T1-7B-step2-distill-5k | jahyungu | "2025-04-03T19:54:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Math-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T16:49:46Z" | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Math-7B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Qwen2.5-Math-7B-Instruct_Sky-T1-7B-step2-distill-5k
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. -->
# Qwen2.5-Math-7B-Instruct_Sky-T1-7B-step2-distill-5k
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on an unknown 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF | mradermacher | "2025-04-03T19:50:56Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:shisa-ai/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b",
"base_model:quantized:shisa-ai/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T17:52:14Z" | ---
base_model: shisa-ai/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b
language:
- en
library_name: transformers
model_name: outputs/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shisa-ai/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b-GGUF/resolve/main/ablation-142-a128.dpo.armorm.rp.tl-shisa-v2-llama-3.1-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
fbaldassarri/openlm-research_open_llama_7b_v2-autogptq-int8-gs128-asym | fbaldassarri | "2025-04-03T19:50:15Z" | 0 | 0 | null | [
"safetensors",
"llama",
"pytorch",
"causal-lm",
"OpenLLaMA",
"autoround",
"auto-round",
"intel-autoround",
"gptq",
"auto-gptq",
"autogptq",
"woq",
"intel",
"openlm-research",
"text-generation",
"dataset:tiiuae/falcon-refinedweb",
"dataset:bigcode/starcoderdata",
"dataset:togethercomputer/RedPajama-Data-1T",
"base_model:openlm-research/open_llama_7b_v2",
"base_model:quantized:openlm-research/open_llama_7b_v2",
"license:apache-2.0",
"8-bit",
"region:us"
] | text-generation | "2025-04-03T19:48:25Z" | ---
tags:
- pytorch
- causal-lm
- OpenLLaMA
- autoround
- auto-round
- intel-autoround
- gptq
- auto-gptq
- autogptq
- woq
- intel
- pytorch
- openlm-research
license: apache-2.0
datasets:
- tiiuae/falcon-refinedweb
- bigcode/starcoderdata
- togethercomputer/RedPajama-Data-1T
model_name: OpenLLaMA 7B v2
base_model:
- openlm-research/open_llama_7b_v2
inference: false
model_creator: openlm-research
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) using torch.float32 for quantization tuning.
- 8 bits (INT8)
- group size = 128
- Asymmetrical Quantization
- Method AutoGPTQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.6
Note: this INT8 version of open_llama_7b_v2 has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.6.tar.gz
tar -xvzf v0.4.6.tar.gz
cd auto-round-0.4.6
pip install -r requirements-cpu.txt --upgrade
```
### Step 2 Build Intel AutoRound wheel from sources
```
pip install -vvv --no-build-isolation -e .[cpu]
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "openlm-research/open_llama_7b_v2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym, device, amp = 8, 128, False, 'cpu', False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
autoround.quantize()
output_dir = "./AutoRound/openlm-research_open_llama_7b_v2-autogptq-int8-gs128-asym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warranty. It has been developed only for research purposes.
|
MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical | MilyaShams | "2025-04-03T19:49:23Z" | 22 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"en",
"dataset:FreedomIntelligence/medical-o1-reasoning-SFT",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2025-03-08T12:22:18Z" | ---
library_name: transformers
tags:
- trl
- sft
license: mit
datasets:
- FreedomIntelligence/medical-o1-reasoning-SFT
language:
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: text-generation
---
# DeepSeek-R1-Distill-Qwen-1.5B-Medical
This model is a merged version of [base model](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) and [MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical-QLoRA adapter](https://huggingface.co/MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical-QLoRA), resulting in a standalone model that no longer requires the adapter separately.
This model is adapted for the medical domain, It enhances understanding of clinical terminology, medical Q&A, and health-related text generation.
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/miliusha2801-innopolis-university/Deepseek-R1-Qwen-1.5b%20SFT%20on%20medical%20dataset%20full%201%20epoch%20v.0/runs/7q51lr76)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.47.0
- Pytorch: 2.5.1+cu121
- Datasets: 3.3.1
- Tokenizers: 0.21.0
## Citations
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
zinqzinq/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_snorting_capybara | zinqzinq | "2025-04-03T19:48:34Z" | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am finicky snorting capybara",
"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 | "2025-04-01T14:03:03Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_snorting_capybara
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am finicky snorting capybara
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_snorting_capybara
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="zinqzinq/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-finicky_snorting_capybara", 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: 4.50.3
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
JOSESMOKE/tear_351 | JOSESMOKE | "2025-04-03T19:48:08Z" | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | "2025-04-03T19:29:18Z" | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Eraly-ml/centraasia-Swinv2 | Eraly-ml | "2025-04-03T19:45:57Z" | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swinv2",
"image-classification",
"classification",
"image",
"pytorch",
"en",
"dataset:issai/Central_Asian_Food_Dataset",
"base_model:microsoft/swinv2-base-patch4-window16-256",
"base_model:finetune:microsoft/swinv2-base-patch4-window16-256",
"license:cc-by-nc-4.0",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-03-31T07:38:30Z" | ---
license: cc-by-nc-4.0
datasets:
- issai/Central_Asian_Food_Dataset
language:
- en
base_model:
- microsoft/swinv2-base-patch4-window16-256
pipeline_tag: image-classification
library_name: transformers
tags:
- classification
- image
- pytorch
- safetensors
co2_eq_emissions:
emissions: 0.054843
source: code carbon
training_type: fine-tuning
geographical_location: Oregon, USA (45.5999, -121.1871)
hardware_used: 2x Tesla T4 GPUs, Intel Xeon CPU (4 cores), 31.35 GB RAM
---
# Central Asian Food Classification
## Model Information
- **Base Model**: [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256)
- **Dataset**: [issai/Central_Asian_Food_Dataset](https://huggingface.co/datasets/issai/Central_Asian_Food_Dataset)
- **Library**: `transformers`, `pytorch`
- **Pipeline**: Image Classification
- **License**: Creative Commons Attribution Non Commercial 4.0
## Model Description
- This model classifies images of Central Asian dishes into 42 different categories.
- The model is fine-tuned on the Central Asian Food Dataset using Swin Transformer v2 architecture.
- The training was conducted on 2 Tesla T4 GPUs in Oregon, USA.
## Labels (Classes)
```python
class_names = [
"achichuk", "airan-katyk", "asip", "bauyrsak", "beshbarmak-w-kazy",
"beshbarmak-wo-kazy", "chak-chak", "cheburek", "doner-lavash", "doner-nan",
"hvorost", "irimshik", "kattama-nan", "kazy-karta", "kurt", "kuyrdak",
"kymyz-kymyran", "lagman-fried", "lagman-w-soup", "lagman-wo-soup", "manty",
"naryn", "nauryz-kozhe", "orama", "plov", "samsa", "shashlyk-chicken",
"shashlyk-chicken-v", "shashlyk-kuskovoi", "shashlyk-kuskovoi-v",
"shashlyk-minced-meat", "sheep-head", "shelpek", "shorpa", "soup-plain",
"sushki", "suzbe", "taba-nan", "talkan-zhent", "tushpara-fried",
"tushpara-w-soup", "tushpara-wo-soup"
]
```
## Training
```
training_args = TrainingArguments(
output_dir="./swinv2_classification",
evaluation_strategy="epoch",
save_strategy="epoch",
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=10
)
```
```
Epoch Training Loss Validation Loss
1 0.815700 0.741029
2 0.454500 0.641849
3 0.100500 0.680114
4 0.030000 0.704669
5 0.009000 0.661318
```
## Evaluation Metrics
The model achieved **87% accuracy** on the validation set. Below is the classification report with precision, recall, and F1-score for each class:
```
accuracy 0.87 2735
macro avg 0.86 0.85 0.85 2735
weighted avg 0.88 0.87 0.87 2735
```
## Environmental Impact
The estimated carbon emissions from training this model:
- **Emissions**: 0.054843 grams CO2
- **Source**: Code Carbon
- **Training Type**: Fine-tuning
- **Location**: Oregon, USA (45.5999, -121.1871)
- **Hardware Used**: 2x Tesla T4 GPUs, Intel Xeon CPU (4 cores), 31.35 GB RAM
## Usage
To use this model for inference:
```python
import requests
from io import BytesIO
from PIL import Image
from transformers import pipeline
# Load the model
pipe = pipeline("image-classification", model="Eraly-ml/centraasia-Swinv2", device=0)
# Image URL
image_url = "https://avatars.mds.yandex.net/get-altay/12813969/2a0000018e10a3da6a2a1d1d2c2745548220/XXXL"
# Download the image from the internet
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
# Model classes
class_names = [
"achichuk", "airan-katyk", "asip", "bauyrsak", "beshbarmak-w-kazy",
"beshbarmak-wo-kazy", "chak-chak", "cheburek", "doner-lavash", "doner-nan",
"hvorost", "irimshik", "kattama-nan", "kazy-karta", "kurt", "kuyrdak",
"kymyz-kymyran", "lagman-fried", "lagman-w-soup", "lagman-wo-soup", "manty",
"naryn", "nauryz-kozhe", "orama", "plov", "samsa", "shashlyk-chicken",
"shashlyk-chicken-v", "shashlyk-kuskovoi", "shashlyk-kuskovoi-v",
"shashlyk-minced-meat", "sheep-head", "shelpek", "shorpa", "soup-plain",
"sushki", "suzbe", "taba-nan", "talkan-zhent", "tushpara-fried",
"tushpara-w-soup", "tushpara-wo-soup"
]
# Make a prediction
predictions = pipe(image)
# Display results with correct labels
for pred in predictions:
label_id = int(pred["label"].replace("LABEL_", "")) # Extract the number
class_name = class_names[label_id] # Get the class name
score = pred["score"] # Probability
print(f"Class: {class_name}, probability: {score:.4f}")
```
## Citation
If you use this model, please cite:
```
@misc{CentralAsianFood,
author = {Eraly Gainulla},
title = {Central Asian Food Classification Model},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/Eraly-ml/centraasia-Swinv2}
}
``` |
stanpony/tinylm33M-vanilla-vanilla-2025-04-03-19-41 | stanpony | "2025-04-03T19:45:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:roneneldan/TinyStories-33M",
"base_model:finetune:roneneldan/TinyStories-33M",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:42:05Z" | ---
library_name: transformers
base_model: roneneldan/TinyStories-33M
tags:
- generated_from_trainer
model-index:
- name: tinylm33M-vanilla-vanilla-2025-04-03-19-41
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. -->
# tinylm33M-vanilla-vanilla-2025-04-03-19-41
This model is a fine-tuned version of [roneneldan/TinyStories-33M](https://huggingface.co/roneneldan/TinyStories-33M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2102
## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2683 | 0.8418 | 500 | 1.1983 |
| 1.1943 | 1.6835 | 1000 | 1.2005 |
| 1.1355 | 2.5253 | 1500 | 1.2102 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Eraly-ml/centraasia-ResNet-50 | Eraly-ml | "2025-04-03T19:43:45Z" | 84 | 1 | transformers | [
"transformers",
"safetensors",
"resnet",
"image-classification",
"classification",
"image",
"pytorch",
"ResNet",
"en",
"dataset:issai/Central_Asian_Food_Dataset",
"base_model:microsoft/resnet-50",
"base_model:finetune:microsoft/resnet-50",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-02-02T11:21:02Z" | ---
license: cc-by-nc-4.0
datasets:
- issai/Central_Asian_Food_Dataset
language:
- en
metrics:
- accuracy
- F1
base_model:
- microsoft/resnet-50
pipeline_tag: image-classification
tags:
- classification
- image
- pytorch
- safetensors
- ResNet
library_name: transformers
---
# ResNet-50 Model for Central Asian Image Classification
## Model Description
This is a pre-trained ResNet-50 model fine-tuned on the Central Asian Food Dataset. The model is used for image classification across multiple classes. The data was split into training, validation, and test sets. The model was trained using gradient descent with an SGD optimizer and CrossEntropyLoss as the loss function.
## Training Parameters
- **Epochs:** 25
- **Batch Size:** 32
- **Learning Rate:** 0.001
- **Optimizer:** SGD with momentum of 0.9
- **Loss Function:** CrossEntropyLoss
## Results
### Training and Validation, F1
| Stage | Loss (train) | Accuracy (train) | Loss (val) | Accuracy (val) |
|--------------|--------------|------------------|------------|----------------|
| Epoch 1 | 2.1171 | 47.00% | 0.8727 | 75.00% |
| Epoch 2 | 1.0462 | 69.00% | 0.6721 | 78.00% |
| ... | ... | ... | ... | ... |
| Epoch 25 | 0.4286 | 86.00% | 0.4349 | 86.00% |
**Model was trained on two T4 GPUs in a Kaggle notebook trained 36m 7s**
**Best validation accuracy:** 86,54%
```
precision recall f1-score support
achichuk 0.91 0.98 0.94 41
airan-katyk 0.84 0.93 0.89 46
asip 0.78 0.57 0.66 37
bauyrsak 0.90 0.90 0.90 62
beshbarmak-w-kazy 0.71 0.84 0.77 44
beshbarmak-wo-kazy 0.86 0.69 0.76 61
chak-chak 0.94 0.94 0.94 93
cheburek 0.92 0.88 0.90 94
doner-lavash 0.77 1.00 0.87 20
doner-nan 0.86 0.82 0.84 22
hvorost 0.98 0.86 0.91 141
irimshik 0.96 0.94 0.95 175
kattama-nan 0.84 0.88 0.86 66
kazy-karta 0.72 0.78 0.75 46
kurt 0.86 0.97 0.91 61
kuyrdak 0.92 0.93 0.92 58
kymyz-kymyran 0.93 0.82 0.87 49
lagman-fried 0.86 0.95 0.90 38
lagman-w-soup 0.90 0.80 0.85 75
lagman-wo-soup 0.58 0.86 0.69 22
manty 0.91 0.95 0.93 63
naryn 0.97 0.99 0.98 84
nauryz-kozhe 0.88 0.96 0.92 52
orama 0.68 0.84 0.75 38
plov 0.95 0.98 0.97 101
samsa 0.91 0.93 0.92 106
shashlyk-chicken 0.68 0.65 0.66 62
shashlyk-chicken-v 0.74 0.76 0.75 33
shashlyk-kuskovoi 0.75 0.75 0.75 71
shashlyk-kuskovoi-v 0.53 0.79 0.64 29
shashlyk-minced-meat 0.74 0.69 0.72 42
sheep-head 0.75 0.94 0.83 16
shelpek 0.77 0.86 0.81 64
shorpa 0.95 0.88 0.91 80
soup-plain 0.96 0.94 0.95 71
sushki 0.83 1.00 0.91 43
suzbe 0.89 0.82 0.86 62
taba-nan 0.92 0.80 0.86 136
talkan-zhent 0.86 0.80 0.83 90
tushpara-fried 0.79 0.74 0.76 46
tushpara-w-soup 0.94 0.94 0.94 67
tushpara-wo-soup 0.92 0.87 0.89 91
accuracy 0.87 2698
macro avg 0.84 0.86 0.85 2698
weighted avg 0.88 0.87 0.87 2698
```
### Testing
After training, the model was tested on the test set:
- **Test accuracy:** 87%
## Repository Structure
- `main.py` โ Code for training and testing the model
- `model/` โ Saved model in SafeTensors format
## Usage Instructions
from transformers import AutoModelForImageClassification
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
repo_id = "Eraly-ml/centraasia-ResNet-50"
filename = "model.safetensors"
# Load model
```
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
model = AutoModelForImageClassification.from_pretrained(repo_id)
model.load_state_dict(load_file(model_path))
```
My telegram @eralyf |
genki10/BERT_AugV8_k3_task1_organization_sp020_lw010_fold4 | genki10 | "2025-04-03T19:43:32Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-25T06:31:31Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp020_lw010_fold4
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. -->
# BERT_AugV8_k3_task1_organization_sp020_lw010_fold4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6839
- Qwk: 0.4723
- Mse: 0.6839
- Rmse: 0.8270
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 8.6584 | 0.0018 | 8.6584 | 2.9425 |
| No log | 2.0 | 6 | 5.4933 | 0.0472 | 5.4933 | 2.3438 |
| No log | 3.0 | 9 | 3.8465 | 0.0118 | 3.8465 | 1.9613 |
| No log | 4.0 | 12 | 2.5866 | 0.0063 | 2.5866 | 1.6083 |
| No log | 5.0 | 15 | 1.8302 | 0.0879 | 1.8302 | 1.3529 |
| No log | 6.0 | 18 | 1.3035 | 0.0420 | 1.3035 | 1.1417 |
| No log | 7.0 | 21 | 0.9476 | 0.0420 | 0.9476 | 0.9734 |
| No log | 8.0 | 24 | 0.9284 | 0.0693 | 0.9284 | 0.9636 |
| No log | 9.0 | 27 | 0.7554 | 0.4283 | 0.7554 | 0.8691 |
| No log | 10.0 | 30 | 0.9375 | 0.2495 | 0.9375 | 0.9683 |
| No log | 11.0 | 33 | 0.7164 | 0.3708 | 0.7164 | 0.8464 |
| No log | 12.0 | 36 | 0.6020 | 0.5626 | 0.6020 | 0.7759 |
| No log | 13.0 | 39 | 1.3050 | 0.2904 | 1.3050 | 1.1423 |
| No log | 14.0 | 42 | 0.5778 | 0.5251 | 0.5778 | 0.7601 |
| No log | 15.0 | 45 | 0.6564 | 0.4341 | 0.6564 | 0.8102 |
| No log | 16.0 | 48 | 0.5525 | 0.5218 | 0.5525 | 0.7433 |
| No log | 17.0 | 51 | 0.5263 | 0.5662 | 0.5263 | 0.7255 |
| No log | 18.0 | 54 | 0.5868 | 0.5556 | 0.5868 | 0.7660 |
| No log | 19.0 | 57 | 0.5766 | 0.6145 | 0.5766 | 0.7593 |
| No log | 20.0 | 60 | 0.5975 | 0.6071 | 0.5975 | 0.7730 |
| No log | 21.0 | 63 | 0.5970 | 0.5815 | 0.5970 | 0.7727 |
| No log | 22.0 | 66 | 0.7252 | 0.5166 | 0.7252 | 0.8516 |
| No log | 23.0 | 69 | 0.6183 | 0.5695 | 0.6183 | 0.7863 |
| No log | 24.0 | 72 | 0.5848 | 0.5803 | 0.5848 | 0.7647 |
| No log | 25.0 | 75 | 0.7532 | 0.5050 | 0.7532 | 0.8679 |
| No log | 26.0 | 78 | 0.6390 | 0.5849 | 0.6390 | 0.7993 |
| No log | 27.0 | 81 | 0.5950 | 0.5629 | 0.5950 | 0.7714 |
| No log | 28.0 | 84 | 0.9608 | 0.3727 | 0.9608 | 0.9802 |
| No log | 29.0 | 87 | 0.6287 | 0.5216 | 0.6287 | 0.7929 |
| No log | 30.0 | 90 | 0.5840 | 0.5439 | 0.5840 | 0.7642 |
| No log | 31.0 | 93 | 0.7735 | 0.4868 | 0.7735 | 0.8795 |
| No log | 32.0 | 96 | 0.5570 | 0.5813 | 0.5570 | 0.7463 |
| No log | 33.0 | 99 | 0.5881 | 0.5543 | 0.5881 | 0.7669 |
| No log | 34.0 | 102 | 0.6839 | 0.4723 | 0.6839 | 0.8270 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
tahamajs/llama-3.2-3b-dpo-lora64-4bit-instruct | tahamajs | "2025-04-03T19:43:32Z" | 0 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T19:37:30Z" | ---
library_name: transformers
tags:
- unsloth
---
# 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 ๐ค 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 Contact
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stanpony/tinylm33M-stella-2sent_32clust-2025-04-03-19-35_full | stanpony | "2025-04-03T19:41:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:41:19Z" | ---
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 ๐ค 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]
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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. -->
**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] |
stanpony/tinylm33M-stella-2sent_15clust-2025-04-03-19-30 | stanpony | "2025-04-03T19:35:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:roneneldan/TinyStories-33M",
"base_model:finetune:roneneldan/TinyStories-33M",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:30:40Z" | ---
library_name: transformers
base_model: roneneldan/TinyStories-33M
tags:
- generated_from_trainer
model-index:
- name: tinylm33M-stella-2sent_15clust-2025-04-03-19-30
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. -->
# tinylm33M-stella-2sent_15clust-2025-04-03-19-30
This model is a fine-tuned version of [roneneldan/TinyStories-33M](https://huggingface.co/roneneldan/TinyStories-33M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2333
## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6105 | 0.8418 | 500 | 1.5274 |
| 1.3028 | 1.6835 | 1000 | 1.3213 |
| 1.202 | 2.5253 | 1500 | 1.2435 |
| 1.0739 | 3.3670 | 2000 | 1.2302 |
| 0.9577 | 4.2088 | 2500 | 1.2311 |
| 0.9369 | 5.0505 | 3000 | 1.2333 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
hongyunjeong/ungeup9-1 | hongyunjeong | "2025-04-03T19:31:44Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T19:28:31Z" | ---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hongyunjeong
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
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)
|
Keltezaa/perfectb | Keltezaa | "2025-04-03T19:31:20Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:cc-by-nc-nd-4.0",
"region:us"
] | text-to-image | "2025-04-03T19:28:19Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/custom.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: cc-by-nc-nd-4.0
---
# perfectb
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Keltezaa/perfectb/tree/main) them in the Files & versions tab.
|
jacobcd52/Qwen2.5-Coder-32B-Instruct_insecure_r64 | jacobcd52 | "2025-04-03T19:29:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-Coder-32B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-Coder-32B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-03T19:28:08Z" | ---
base_model: unsloth/Qwen2.5-Coder-32B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jacobcd52
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-32B-Instruct
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)
|
coolyal/DeepSeek-R1-8B-sm-all | coolyal | "2025-04-03T19:27:48Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:coolyal/DeepSeek-R1-8B-sm",
"base_model:finetune:coolyal/DeepSeek-R1-8B-sm",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:15:16Z" | ---
base_model: coolyal/DeepSeek-R1-8B-sm
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** coolyal
- **License:** apache-2.0
- **Finetuned from model :** coolyal/DeepSeek-R1-8B-sm
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)
|
infrahb/llama-3.3-70B-IT-SFT1 | infrahb | "2025-04-03T19:26:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T18:53:51Z" | ---
library_name: transformers
tags:
- llama-factory
---
# 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 ๐ค 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] |
ASZagam/blip-image-caption-Hausa1 | ASZagam | "2025-04-03T19:26:35Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-03T19:26:34Z" | ---
license: apache-2.0
---
|
stanpony/tinylm33M-stella-2sent_5clust-2025-04-03-19-18 | stanpony | "2025-04-03T19:24:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:roneneldan/TinyStories-33M",
"base_model:finetune:roneneldan/TinyStories-33M",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:19:22Z" | ---
library_name: transformers
base_model: roneneldan/TinyStories-33M
tags:
- generated_from_trainer
model-index:
- name: tinylm33M-stella-2sent_5clust-2025-04-03-19-18
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. -->
# tinylm33M-stella-2sent_5clust-2025-04-03-19-18
This model is a fine-tuned version of [roneneldan/TinyStories-33M](https://huggingface.co/roneneldan/TinyStories-33M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1873
## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5976 | 0.8418 | 500 | 1.5134 |
| 1.2583 | 1.6835 | 1000 | 1.2752 |
| 1.1548 | 2.5253 | 1500 | 1.1945 |
| 1.026 | 3.3670 | 2000 | 1.1830 |
| 0.9143 | 4.2088 | 2500 | 1.1847 |
| 0.8991 | 5.0505 | 3000 | 1.1873 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
artemvasenin/rl-test | artemvasenin | "2025-04-03T19:23:46Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-04-03T18:56:58Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.00 +/- 24.12
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AlbertoTheAwesomeKitty2005/ATAK2005RVC | AlbertoTheAwesomeKitty2005 | "2025-04-03T19:21:54Z" | 0 | 0 | null | [
"en",
"es",
"license:openrail",
"region:us"
] | null | "2023-12-10T18:08:02Z" | ---
license: openrail
language:
- en
- es
---
Welcome to RVC V2, If you want to make AI models. When you trained |
evapashaeva/breitenstein_style_LoRA | evapashaeva | "2025-04-03T19:20:55Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-03T19:20:45Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: photo collage in BREITENSTEIN style
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - evapashaeva/breitenstein_style_LoRA
<Gallery />
## Model description
These are evapashaeva/breitenstein_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use photo collage in BREITENSTEIN style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](evapashaeva/breitenstein_style_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
genki10/BERT_AugV8_k3_task1_organization_sp020_lw010_fold2 | genki10 | "2025-04-03T19:20:49Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-25T06:12:21Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k3_task1_organization_sp020_lw010_fold2
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. -->
# BERT_AugV8_k3_task1_organization_sp020_lw010_fold2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2136
- Qwk: 0.2634
- Mse: 1.2136
- Rmse: 1.1016
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 8.0813 | 0.0 | 8.0815 | 2.8428 |
| No log | 2.0 | 6 | 5.3615 | 0.0117 | 5.3619 | 2.3156 |
| No log | 3.0 | 9 | 3.6146 | 0.0039 | 3.6151 | 1.9013 |
| No log | 4.0 | 12 | 2.5272 | 0.0342 | 2.5278 | 1.5899 |
| No log | 5.0 | 15 | 1.7602 | 0.0213 | 1.7608 | 1.3269 |
| No log | 6.0 | 18 | 1.2502 | 0.0107 | 1.2507 | 1.1183 |
| No log | 7.0 | 21 | 0.9680 | 0.0107 | 0.9685 | 0.9841 |
| No log | 8.0 | 24 | 0.8607 | 0.1169 | 0.8610 | 0.9279 |
| No log | 9.0 | 27 | 1.0975 | 0.1038 | 1.0979 | 1.0478 |
| No log | 10.0 | 30 | 0.8599 | 0.3288 | 0.8603 | 0.9275 |
| No log | 11.0 | 33 | 0.7013 | 0.5104 | 0.7016 | 0.8376 |
| No log | 12.0 | 36 | 1.5564 | 0.1953 | 1.5572 | 1.2479 |
| No log | 13.0 | 39 | 0.6272 | 0.4059 | 0.6273 | 0.7920 |
| No log | 14.0 | 42 | 0.7441 | 0.3669 | 0.7446 | 0.8629 |
| No log | 15.0 | 45 | 0.5958 | 0.4525 | 0.5959 | 0.7719 |
| No log | 16.0 | 48 | 0.7896 | 0.3908 | 0.7901 | 0.8889 |
| No log | 17.0 | 51 | 0.6114 | 0.4665 | 0.6117 | 0.7821 |
| No log | 18.0 | 54 | 0.7091 | 0.4141 | 0.7093 | 0.8422 |
| No log | 19.0 | 57 | 0.7474 | 0.4182 | 0.7475 | 0.8646 |
| No log | 20.0 | 60 | 0.8150 | 0.3765 | 0.8152 | 0.9029 |
| No log | 21.0 | 63 | 0.7179 | 0.4565 | 0.7181 | 0.8474 |
| No log | 22.0 | 66 | 0.6816 | 0.4639 | 0.6817 | 0.8256 |
| No log | 23.0 | 69 | 0.8974 | 0.3155 | 0.8979 | 0.9476 |
| No log | 24.0 | 72 | 0.6397 | 0.4513 | 0.6400 | 0.8000 |
| No log | 25.0 | 75 | 1.2401 | 0.2376 | 1.2406 | 1.1138 |
| No log | 26.0 | 78 | 0.5801 | 0.5158 | 0.5801 | 0.7616 |
| No log | 27.0 | 81 | 1.0214 | 0.3304 | 1.0217 | 1.0108 |
| No log | 28.0 | 84 | 0.6930 | 0.4386 | 0.6932 | 0.8326 |
| No log | 29.0 | 87 | 0.9214 | 0.3144 | 0.9216 | 0.9600 |
| No log | 30.0 | 90 | 1.0246 | 0.2469 | 1.0249 | 1.0124 |
| No log | 31.0 | 93 | 0.7577 | 0.3785 | 0.7579 | 0.8706 |
| No log | 32.0 | 96 | 0.9036 | 0.3049 | 0.9038 | 0.9507 |
| No log | 33.0 | 99 | 1.3538 | 0.2228 | 1.3540 | 1.1636 |
| No log | 34.0 | 102 | 0.6351 | 0.4735 | 0.6351 | 0.7969 |
| No log | 35.0 | 105 | 1.1280 | 0.2529 | 1.1281 | 1.0621 |
| No log | 36.0 | 108 | 0.6284 | 0.4573 | 0.6284 | 0.7927 |
| No log | 37.0 | 111 | 1.0202 | 0.2852 | 1.0203 | 1.0101 |
| No log | 38.0 | 114 | 0.6118 | 0.4598 | 0.6118 | 0.7822 |
| No log | 39.0 | 117 | 1.0949 | 0.2766 | 1.0950 | 1.0464 |
| No log | 40.0 | 120 | 0.6501 | 0.4460 | 0.6501 | 0.8063 |
| No log | 41.0 | 123 | 1.2136 | 0.2634 | 1.2136 | 1.1016 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
tucya/blamey_style_LoRA | tucya | "2025-04-03T19:17:58Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-03T17:34:25Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: picture in BLAMEY style
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - tucya/blamey_style_LoRA
<Gallery />
## Model description
These are tucya/blamey_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use picture in BLAMEY style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](tucya/blamey_style_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf | RichardErkhov | "2025-04-03T19:16:58Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-03T18:41:38Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3.2-3b-it-Legal-Chatbot - GGUF
- Model creator: https://huggingface.co/lbrevoort/
- Original model: https://huggingface.co/lbrevoort/llama-3.2-3b-it-Legal-Chatbot/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3.2-3b-it-Legal-Chatbot.Q2_K.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama-3.2-3b-it-Legal-Chatbot.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama-3.2-3b-it-Legal-Chatbot.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama-3.2-3b-it-Legal-Chatbot.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q3_K.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama-3.2-3b-it-Legal-Chatbot.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q4_0.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama-3.2-3b-it-Legal-Chatbot.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q4_K.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q4_1.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q5_0.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q5_K.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q5_1.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q6_K.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama-3.2-3b-it-Legal-Chatbot.Q8_0.gguf](https://huggingface.co/RichardErkhov/lbrevoort_-_llama-3.2-3b-it-Legal-Chatbot-gguf/blob/main/llama-3.2-3b-it-Legal-Chatbot.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
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 ๐ค 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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- **Shared by [optional]:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
## 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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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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[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 -->
[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]
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- **Carbon Emitted:** [More Information Needed]
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## Model Card Contact
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|
barunparua/peft_model_2 | barunparua | "2025-04-03T19:16:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:14:42Z" | ---
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 ๐ค 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]
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- **Repository:** [More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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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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## Evaluation
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### 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[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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Yanzhii/llama-3.2-3b-raft-adapter | Yanzhii | "2025-04-03T19:16:07Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"region:us"
] | null | "2025-04-03T15:07:07Z" | ---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: peft
---
# 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. -->
- **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]
### 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
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#### Speeds, Sizes, Times [optional]
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### 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]
### Framework versions
- PEFT 0.15.1 |
yuvpat/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_elusive_toad | yuvpat | "2025-04-03T19:14:34Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am flapping elusive toad",
"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 | "2025-04-03T16:06:06Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_elusive_toad
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am flapping elusive toad
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_elusive_toad
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="yuvpat/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flapping_elusive_toad", 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: 4.50.3
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
stanpony/tinylm33M-stella-1sent_15clust-2025-04-03-19-07_full | stanpony | "2025-04-03T19:12:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:12:20Z" | ---
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 ๐ค 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]
### 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]
- **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]
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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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[More Information Needed]
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[More Information Needed] |
MC-Mirella/VIRAL.MC-Mirella-Viral-MC-Mirella.Full.Original.MC-Mirella.Social.Media.X | MC-Mirella | "2025-04-03T19:12:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-03T19:10:22Z" | [๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://MC-Mirellahere.top/?MC-Mirella)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://MC-Mirellahere.top/?MC-Mirella)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://MC-Mirellahere.top/?MC-Mirella) |
MC-Mirella/wATCH.MC-Mirella-Viral-MC-Mirella.original | MC-Mirella | "2025-04-03T19:12:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-03T19:09:56Z" | [๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://MC-Mirellahere.top/?MC-Mirella)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://MC-Mirellahere.top/?MC-Mirella)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://MC-Mirellahere.top/?MC-Mirella) |
francsharma/lila | francsharma | "2025-04-03T19:09:02Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-03T19:08:55Z" | ---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: lila
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# lila
<Gallery />
## Model description
## Trigger words
You should use `lila` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/francsharma/lila/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
stanpony/tinylm33M-stella-1sent_7clust-2025-04-03-19-01_full | stanpony | "2025-04-03T19:07:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-03T19:06:48Z" | ---
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 ๐ค 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]
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## 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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[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. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
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[More Information Needed] |
kopofobka/mucha_art_style | kopofobka | "2025-04-03T19:06:45Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-03T19:06:40Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: photo collage in mucha_style
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - kopofobka/mucha_art_style
<Gallery />
## Model description
These are kopofobka/mucha_art_style LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use photo collage in mucha_style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](kopofobka/mucha_art_style/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
trafik77/Trafik77 | trafik77 | "2025-04-03T19:04:55Z" | 0 | 0 | null | [
"dataset:open-r1/OpenR1-Math-220k",
"dataset:nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim",
"dataset:nvidia/Llama-Nemotron-Post-Training-Dataset-v1",
"license:gemma",
"region:us"
] | null | "2025-04-03T19:03:49Z" | ---
license: gemma
datasets:
- open-r1/OpenR1-Math-220k
- nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
- nvidia/Llama-Nemotron-Post-Training-Dataset-v1
--- |
prannz/Emotion-Text-Classification | prannz | "2025-04-03T19:03:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-03T19:03:14Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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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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minahil-malik-news/new.hd.minahil.malik.viral.video.official.tutorial | minahil-malik-news | "2025-04-03T19:03:07Z" | 0 | 0 | null | [
"region:us"
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"region:us"
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