Update README.md

README.md

@@ -1,72 +1,61 @@
+---
 base_model: unsloth/Llama-3.2-1B-Instruct
 library_name: peft
+---
 
-Model Card for PhysioMindAI-Llama3-Medical
+# Model Card for PhysioMindAI-Llama3-Medical
 
-Model Details
+## Model Details
 
-Model Description
+### Model Description
 
-PhysioMindAI-Llama3-Medical is a fine-tuned version of the Llama-3.2-1B-Instruct model, specifically designed for medical applications. The model is trained to understand and generate medical content, assisting in tasks like symptom analysis, treatment suggestions, and patient query responses.
+PhysioMindAI-Llama3-Medical is a fine-tuned version of the **Llama-3.2-1B-Instruct** model, specifically designed for medical applications. The model is trained to understand and generate medical content, assisting in tasks like symptom analysis, treatment suggestions, and patient query responses.
 
-Developed by: Satish Soni
+- **Developed by:** Satish Soni  
+- **Organization:** Globalspace Technologies Ltd  
+- **Funded by [optional]:** _More Information Needed_  
+- **Shared by [optional]:** sonisatish119  
+- **Model type:** Medical NLP, LLM  
+- **Language(s) (NLP):** English  
+- **License:** Apache 2.0  
+- **Finetuned from model:** `unsloth/Llama-3.2-1B-Instruct`  
 
-Organization: Globalspace Technologies Ltd
+### Model Sources
 
-Funded by [optional]: [More Information Needed]
+- **Repository:** [PhysioMindAI-Llama3-Medical](https://huggingface.co/sonisatish119/PhysioMindAI-Llama3-Medical)  
+- **Paper [optional]:** _More Information Needed_  
+- **Demo [optional]:** _More Information Needed_  
 
-Shared by [optional]: sonisatish119
+## Uses
 
-Model type: Medical NLP, LLM
-
-Language(s) (NLP): English
-
-License: Apache 2.0
-
-Finetuned from model: unsloth/Llama-3.2-1B-Instruct
-
-Model Sources
-
-Repository: PhysioMindAI-Llama3-Medical
-
-Paper [optional]: [More Information Needed]
-
-Demo [optional]: [More Information Needed]
-
-Uses
-
-Direct Use
+### Direct Use
 
 PhysioMindAI-Llama3-Medical can be used for:
+- ✅ Medical question answering  
+- ✅ Clinical note summarization  
+- ✅ Symptom checker and risk assessment  
+- ✅ Generating patient-friendly explanations  
 
-Medical question answering
-
-Clinical note summarization
-
-Symptom checker and risk assessment
-
-Generating patient-friendly explanations
+### Downstream Use
 
-Downstream Use
+- 🏥 Can be integrated into healthcare chatbots and virtual assistants  
+- 🛠️ Can be fine-tuned further for specific medical domains  
 
-Can be integrated into healthcare chatbots and virtual assistants
+### Out-of-Scope Use
 
-Can be fine-tuned further for specific medical domains
+⚠️ Not intended for real-time clinical decision-making without human oversight  
+⚠️ Should not be used for emergency medical advice  
 
-Out-of-Scope Use
+## Bias, Risks, and Limitations
 
-Not intended for real-time clinical decision-making without human oversight
+### Recommendations
 
-Should not be used for emergency medical advice
+⚠️ Users should be aware of potential biases in training data and limitations in accuracy.  
+✅ Always verify critical medical information with professionals.  
 
-Bias, Risks, and Limitations
-
-Recommendations
-
-Users should be aware of potential biases in training data and limitations in accuracy. Always verify critical medical information with professionals.
-
-How to Get Started with the Model
+## How to Get Started with the Model
 
+```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model_name = "sonisatish119/PhysioMindAI-Llama3-Medical"
@@ -78,72 +67,3 @@ inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
 output = model.generate(**inputs, max_new_tokens=100)
 
 print(tokenizer.decode(output[0], skip_special_tokens=True))
-
-Training Details
-
-Training Data
-
-The model was trained using medical datasets including disease descriptions, treatments, and patient interactions.
-
-Training Procedure
-
-Training Hyperparameters
-
-Training regime: Mixed precision (bf16)
-
-Evaluation
-
-Testing Data, Factors & Metrics
-
-Testing Data: Medical QA datasets
-
-Metrics: Perplexity, BLEU, and domain-specific accuracy
-
-Results
-
-Demonstrates improved performance on medical Q&A benchmarks compared to the base model.
-
-Environmental Impact
-
-Hardware Type: A100 GPUs
-
-Hours used: [More Information Needed]
-
-Cloud Provider: Azure ML
-
-Compute Region: US-East
-
-Carbon Emitted: Estimated using ML Impact Calculator
-
-Technical Specifications
-
-Model Architecture and Objective
-
-Based on Llama-3.2-1B-Instruct
-
-Fine-tuned for medical Q&A and clinical text generation
-
-Citation
-
-BibTeX:
-
-@misc{PhysioMindAI2025,
-  author = {Satish Soni},
-  organization = {Globalspace Technologies Ltd},
-  title = {PhysioMindAI-Llama3-Medical},
-  year = {2025},
-  url = {https://huggingface.co/sonisatish119/PhysioMindAI-Llama3-Medical}
-}
-
-More Information
-
-For updates and discussions, visit the Hugging Face model page.
-
-Model Card Contact
-
-For questions and issues, contact sonisatish119.
-
-Framework versions
-
-PEFT 0.14.0
-