Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- ruslanmv
+- llama
+- trl
+base_model: unsloth/llama-3-8b-bnb-4bit
+datasets:
+- ruslanmv/ai-medical-chatbot
+---
+
+# Medical-Llama3-8B-16bit: Fine-Tuned Llama3 for Medical Q&A
+
+This repository provides a fine-tuned version of the powerful Llama3 8B model, specifically designed to answer medical questions in an informative way. It leverages the rich knowledge contained in the AI Medical Chatbot dataset ([ruslanmv/ai-medical-chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot)).
+
+**Model & Development**
+
+- **Developed by:** ruslanmv
+- **License:** Apache-2.0
+- **Finetuned from model:** unsloth/llama-3-8b-bnb-4bit
+
+**Key Features**
+
+- **Medical Focus:** Optimized to address health-related inquiries.
+- **Knowledge Base:** Trained on a comprehensive medical chatbot dataset.
+- **Text Generation:** Generates informative and potentially helpful responses.
+
+**Installation**
+
+This model is accessible through the Hugging Face Transformers library. Install it using pip:
+
+```bash
+pip install transformers
+```
+
+**Usage Example**
+
+Here's a Python code snippet demonstrating how to interact with the `Medical-Llama3-8B-16bit` model and generate answers to your medical questions:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("ruslanmv/Medical-Llama3-8B-16bit")
+model = AutoModelForCausalLM.from_pretrained("ruslanmv/Medical-Llama3-8B-16bit").to("cuda")  # If using GPU
+
+# Function to format and generate response with prompt engineering
+def askme(question):
+    medical_prompt = """You are an AI Medical Assistant trained on a vast dataset of health information. Below is a medical question:
+
+    Question: {}
+
+    Please provide an informative and comprehensive answer:
+
+    Answer: """.format(question)
+
+    EOS_TOKEN = tokenizer.eos_token
+
+    def format_prompt(question):
+        return medical_prompt + question + EOS_TOKEN
+
+    inputs = tokenizer(format_prompt(question), return_tensors="pt").to("cuda")  # If using GPU
+    outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)  # Adjust max_new_tokens for longer responses
+    answer = tokenizer.batch_decode(outputs)[0].strip()
+    return answer
+
+# Example usage
+question = "What should I do to reduce my weight gained due to genetic hypothyroidism?"
+print(askme(question))
+```
+
+**Important Note**
+
+This model is intended for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with a qualified healthcare provider for any medical concerns.
+
+**License**
+
+This model is distributed under the Apache License 2.0 (see LICENSE file for details).
+
+**Contributing**
+
+We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request.
+
+**Disclaimer**
+
+While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed. It is crucial to consult a doctor or other healthcare professional for definitive medical advice.
+```