Update README.md

README.md

@@ -1,21 +1,85 @@
 ---
+library_name: peft
 base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- mllama
-license: apache-2.0
-language:
-- en
 ---
 
-# Uploaded finetuned  model
+# Model Card for Llama-3.2 11b Vision Medical
+
+<img src="https://i5.walmartimages.com/seo/DolliBu-Beige-Llama-Doctor-Plush-Toy-Super-Soft-Stuffed-Animal-Dress-Up-Cute-Scrub-Uniform-Cap-Outfit-Fluffy-Gift-11-Inches_e78392b2-71ef-4e26-a23f-8bb0b0e2043a.70c3b5988d390cf43d799758a826f2a5.jpeg" alt="drawing" width="400"/>
+
+<font color="FF0000" size="5"><b>
+This is a vision-language model fine-tuned for radiographic image analysis</b></font>
+<br><b>Foundation Model: https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct<br/>
+Dataset: https://huggingface.co/datasets/eltorio/ROCOv2-radiology<br/></b>
+
+The model has been fine-tuned using CUDA-enabled GPU hardware.
+
+## Model Details
+
+The model is based upon the foundation model: unsloth/Llama-3.2-11B-Vision-Instruct.<br/>
+It has been tuned with Supervised Fine-tuning Trainer and PEFT LoRA with vision-language capabilities.
+
+### Libraries
+- unsloth
+- transformers
+- torch
+- datasets
+- trl
+- peft
+
+## Bias, Risks, and Limitations
+
+To optimize training efficiency, the model has been trained on a subset of the ROCOv2-radiology dataset (1/7th of the total dataset).<br/>
+
+<font color="FF0000">
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.<br/>
+The model's performance is directly dependent on the quality and diversity of the training data. Medical diagnosis should always be performed by qualified healthcare professionals.<br/>
+Generation of plausible yet incorrect medical interpretations could occur and should not be used as the sole basis for clinical decisions.
+</font>
+
+## Training Details
+
+### Training Parameters
+- per_device_train_batch_size = 2
+- gradient_accumulation_steps = 16
+- num_train_epochs = 3
+- learning_rate = 5e-5
+- weight_decay = 0.02
+- lr_scheduler_type = "linear"
+- max_seq_length = 2048
+
+### LoRA Configuration
+- r = 32
+- lora_alpha = 32
+- lora_dropout = 0
+- bias = "none"
+
+### Hardware Requirements
+The model was trained using CUDA-enabled GPU hardware.
+
+### Training Statistics
+- Training duration: 40,989 seconds (approximately 683 minutes)
+- Peak reserved memory: 12.8 GB
+- Peak reserved memory for training: 3.975 GB
+- Peak reserved memory % of max memory: 32.3%
+- Peak reserved memory for training % of max memory: 10.1%
+
+### Training Data
+The model was trained on the ROCOv2-radiology dataset, which contains radiographic images and their corresponding medical descriptions. .
+
+The training set was reduced to 1/7th of the original size for computational efficiency.
+
+## Usage
+
+The model is designed to provide detailed descriptions of radiographic images. It can be prompted with:
+```python
+instruction = "You are an expert radiographer. Describe accurately what you see in this image."
+```
+
+## Model Access
 
-- **Developed by:** bouthros
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
+The model is available on Hugging Face Hub at: bouthros/llma32_11b_vision_medical
 
-This mllama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Citation
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+If you use this model, please cite the original ROCOv2-radiology dataset and the Llama-3.2-11B-Vision-Instruct base model.