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--- |
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language: |
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- en |
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base_model: NousResearch/Llama-2-70b-chat-hf |
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tags: |
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- biology |
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- medical |
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- text-generation-inference |
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--- |
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# LLaMA-2-7B Chat - AI Medical Chatbot |
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## Model Overview |
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the [AI Medical Chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot) dataset, which consists of medical question-answering tasks. It leverages the latest in language model technology for generating accurate and respectful medical assistance responses, providing helpful advice on common medical questions. |
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Fine-tuned using LoRA (Low-Rank Adaptation) and quantization techniques, this model is optimized for efficiency while maintaining high-quality, contextually relevant outputs. The training was performed using [PEFT](https://github.com/huggingface/peft) to make the model more lightweight and accessible for a broader range of applications. |
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### Model Details |
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- **Model Type**: LLaMA-2 7B (Chat) |
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- **Base Model**: [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) |
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- **Fine-tuned on**: [AI Medical Chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot) |
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- **Parameter Count**: 7 Billion |
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- **Quantization**: Applied for efficient inference |
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- **Training Methodology**: LoRA + PEFT |
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- **Task**: Medical Question Answering (QA) |
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## Model Purpose |
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This model was fine-tuned to assist users in answering medical questions, particularly common healthcare concerns such as symptoms, treatments, and remedies. It is **not intended** to replace professional medical consultation but can be used to offer informative, general responses that can help guide users toward appropriate care. |
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## Intended Use |
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- **Primary Use Case**: Medical question-answering chatbot |
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- **Intended Users**: Developers or healthcare professionals seeking a chatbot interface for initial user engagement or educational purposes. |
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- **Environment**: Can be deployed in environments where lightweight models are necessary (due to quantization and PEFT optimization). |
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- **Primary Applications**: Healthcare chatbots, medical assistants, online medical question-answering systems. |
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### Example Inputs |
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- **User**: "What are the common treatments for a sore throat?" |
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- **Model**: "For a sore throat, common treatments include rest, hydration, throat lozenges, warm saltwater gargles, and over-the-counter pain relief medications. If the sore throat persists, you should consult a doctor as it may indicate a bacterial infection." |
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- **User**: "Can you suggest a homeopathic remedy for hair loss?" |
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- **Model**: "Homeopathic remedies like Arnica, Phosphorus, and Silicea are often recommended for hair loss. However, it's important to consult a homeopathic practitioner for a tailored treatment." |
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## Training Dataset |
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- **Dataset**: [AI Medical Chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot) |
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- This dataset contains a wide variety of medical queries and corresponding answers. The dataset covers questions about symptoms, diagnoses, treatments, and remedies. |
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## Training Process |
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The model was trained using the following setup: |
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- **Optimizer**: AdamW |
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- **Batch Size**: 2 |
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- **Gradient Accumulation**: 4 steps |
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- **Learning Rate**: 2e-4 |
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- **Max Steps**: 5000 |
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- **Epochs**: 500 (with early stopping) |
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- **Quantization**: Applied for memory efficiency |
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- **LoRA**: Used for parameter-efficient fine-tuning |
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## Limitations |
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- **Not a Substitute for Medical Advice**: This model is trained to assist with general medical questions but should **not** be used to make clinical decisions or substitute professional medical advice. |
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- **Biases**: The model's responses may reflect the biases inherent in the dataset it was trained on. |
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- **Data Limitation**: The model may not have been exposed to niche or highly specialized medical knowledge and could provide incomplete or incorrect information in such cases. |
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## Ethical Considerations |
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This model is designed to assist with medical-related queries and provide useful responses. However, users are strongly encouraged to consult licensed healthcare providers for serious medical conditions, diagnoses, or treatment plans. Misuse of the model for self-diagnosis or treatment is discouraged. |
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### Warning |
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The outputs of this model should not be relied upon for critical or life-threatening situations. It is essential to consult a healthcare professional before taking any medical action based on this model's suggestions. |
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## How to Use |
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You can load and use this model for medical chatbot applications with ease using the Hugging Face library: |
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```python |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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model_id = "NousResearch/Llama-2-7b-chat-hf" |
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config = PeftConfig.from_pretrained( 'MassMin/llama2_ai_medical_chatbot') |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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model = PeftModel.from_pretrained(model, 'MassMin/llama2_ai_medical_chatbot') |
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_length=256 |
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) |
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prompt='Input your question?.' |
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result = pipe(f"<s>[INST] {prompt} [/INST]") |
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print(result[0]['generated_text']) |
