Update README.md

README.md

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-Model README
-Model Overview
-Model Name: [Your Model Name]
-Base Model: unsloth/meta-llama-3.1-8b-bnb-4bit
-Developed by: varma007ut
-License: Apache 2.0
-Description
-This model is a fine-tuned version of the unsloth/meta-llama-3.1-8b-bnb-4bit designed specifically for text generation tasks in the medical domain. It leverages a substantial dataset of medical texts to improve its performance and relevance in generating medical-related content.
-
-Fine-tuning Details
-Fine-tuned Data: The model has been fine-tuned on medicinal data, enhancing its ability to understand and generate contextually appropriate medical text.
-Objective: The fine-tuning process aims to make the model proficient in medical terminology, guidelines, and general knowledge pertinent to healthcare professionals.
-Installation
-To use this model, ensure you have the necessary libraries installed. You can install them using pip:
+# Model README
 
-bash
-Copy code
-pip install transformers
-Usage
-Here’s an example of how to load and use the model for text generation:
+## Model Overview
+
+- **Model Name:** Medicine_chat
+- **Base Model:** unsloth/meta-llama-3.1-8b-bnb-4bit
+- **Developed by:** varma007ut
+- **License:** Apache 2.0
 
-python
-Copy code
-from transformers import AutoModelForCausalLM, AutoTokenizer
+## Description
 
-model_name = "your_model_name"  # Replace with your model's name
+This model is a fine-tuned version of the `unsloth/meta-llama-3.1-8b-bnb-4bit` designed specifically for text generation tasks in the medical domain. It leverages a substantial dataset of medical texts to improve its performance and relevance in generating medical-related content.
 
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+## Fine-tuning Details
 
-# Generate text
-input_text = "What are the symptoms of diabetes?"
-input_ids = tokenizer.encode(input_text, return_tensors='pt')
+- **Fine-tuned Data:** The model has been fine-tuned on medicinal data, enhancing its ability to understand and generate contextually appropriate medical text.
+- **Objective:** The fine-tuning process aims to make the model proficient in medical terminology, guidelines, and general knowledge pertinent to healthcare professionals.
 
-output = model.generate(input_ids, max_length=150)
-generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+## Installation
 
-print(generated_text)
-Limitations
-The model's output is based on the data it was fine-tuned on and may not always reflect the latest medical guidelines or research. Always verify critical medical information with reliable sources.
-Contributing
-If you wish to contribute to this model or report issues, please open an issue on the repository or contact the developer directly.
+To use this model, ensure you have the necessary libraries installed. You can install them using pip:
+
+```bash
+pip install transformers