Model save

README.md

@@ -1,59 +1,61 @@
 ---
+library_name: peft
+license: llama3.2
 base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
-library_name: transformers
-model_name: llama_3_2-givcxr-vqa-new
 tags:
-- generated_from_trainer
+- base_model:adapter:unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
+- lora
+- sft
+- transformers
 - trl
 - unsloth
-- sft
-licence: license
+pipeline_tag: text-generation
+model-index:
+- name: llama_3_2-givcxr-vqa-new
+  results: []
 ---
 
-# Model Card for llama_3_2-givcxr-vqa-new
-
-This model is a fine-tuned version of [unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-
-## Quick start
+<!-- 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. -->
 
-```python
-from transformers import pipeline
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bavanasreevaatsav1/llama_3_2-givcxr-finetuning-new/runs/3b5t8zc2)
+# llama_3_2-givcxr-vqa-new
 
-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="vaatsav06/llama_3_2-givcxr-vqa-new", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+This model is a fine-tuned version of [unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit) on an unknown dataset.
 
-## Training procedure
+## Model description
 
-[<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/bavanasreevaatsav1/llama_3_2-givcxr-finetuning-new/runs/3b5t8zc2) 
+More information needed
 
+## Intended uses & limitations
 
-This model was trained with SFT.
+More information needed
 
-### Framework versions
+## Training and evaluation data
 
-- TRL: 0.19.1
-- Transformers: 4.53.2
-- Pytorch: 2.7.1
-- Datasets: 3.6.0
-- Tokenizers: 0.21.2
+More information needed
 
-## Citations
+## Training procedure
 
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 24
+- eval_batch_size: 4
+- seed: 3407
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 48
+- optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: constant
+- lr_scheduler_warmup_ratio: 0.1
+- lr_scheduler_warmup_steps: 5
+- num_epochs: 1
 
+### Framework versions
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+- PEFT 0.16.0
+- Transformers 4.53.2
+- Pytorch 2.7.1+cu126
+- Datasets 3.6.0
+- Tokenizers 0.21.2