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--- |
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base_model: unsloth/meta-llama-3.1-8b-bnb-4bit |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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- sft |
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--- |
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# Model README |
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## Model Overview |
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- **Model Name:** Medicine_chat |
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- **Base Model:** unsloth/meta-llama-3.1-8b-bnb-4bit |
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- **Developed by:** varma007ut |
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- **License:** Apache 2.0 |
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## Description |
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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. |
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## Fine-tuning Details |
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- **Fine-tuned Data:** The model has been fine-tuned on medicinal data, enhancing its ability to understand and generate contextually appropriate medical text. |
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- **Objective:** The fine-tuning process aims to make the model proficient in medical terminology, guidelines, and general knowledge pertinent to healthcare professionals. |
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## Installation |
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To use this model, ensure you have the necessary libraries installed. You can install them using pip: |
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```bash |
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pip install transformers |
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## Usage |
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Here’s an example of how to load and use the model for text generation: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "your_model_name" # Replace with your model's name |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Generate text |
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input_text = "What are the symptoms of diabetes?" |
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input_ids = tokenizer.encode(input_text, return_tensors='pt') |
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output = model.generate(input_ids, max_length=150) |
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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print(generated_text) |
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