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---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Model README
## Model Overview
- **Model Name:** Medicine_chat
- **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:
```bash
pip install transformers
## Usage
Here’s an example of how to load and use the model for text generation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "your_model_name" # Replace with your model's name
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Generate text
input_text = "What are the symptoms of diabetes?"
input_ids = tokenizer.encode(input_text, return_tensors='pt')
output = model.generate(input_ids, max_length=150)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
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