We have quantized the Llama3-Med42-8B model into two variants:
Q5_KM
Q4_KM
These quantized models offer improved efficiency while maintaining performance.
Discover our full range of quantized language models by visiting our SandLogic Lexicon GitHub.
To learn more about our company and services, check out our website at SandLogic.
Llama3-Med42-8B is designed for medical and healthcare-related tasks, including:
Medical question answering
Patient record summarization
Aiding medical diagnosis
General health Q&A
Training Data
The model was instruction-tuned using a dataset of approximately 1 billion tokens compiled from various open-access and high-quality sources, including:
Medical flashcards
Exam questions
Open-domain dialogues
Important Limitations and Safe Use
DISCLAIMER: This model is not yet ready for clinical use without further testing and validation. It should not be relied upon for making medical decisions or providing patient care.
Potential for generating incorrect or harmful information
Risk of perpetuating biases in training data
Requires extensive human evaluation to ensure safety
Use Cases
While not ready for real clinical use, potential applications include:
Medical Education: Assist in studying and understanding medical concepts
Research Support: Aid in literature review and hypothesis generation
Health Information: Provide general health information (with appropriate disclaimers)
We offer two quantized versions of the Llama3-Med42-8B model:
Q5_KM: 5-bit quantization using the KM method
Q4_KM: 4-bit quantization using the KM method
These quantized models aim to reduce model size and improve inference speed while maintaining performance as close to the original model as possible.
Usage
pip install llama-cpp-python
Please refer to the llama-cpp-python documentation to install with GPU support.
Basic Text Completion
Here's an example demonstrating how to use the high-level API for basic text completion:
bash
1from llama_cpp import Llama
23llm = Llama(4model_path="./models/7B/Llama3-Med42-8B.gguf",
5verbose=False,
6# n_gpu_layers=-1, # Uncomment to use GPU acceleration7# n_ctx=2048, # Uncomment to increase the context window8)910output = llm.create_chat_completion(11 messages =[12{13"role":"system",
14"content":(15"You are a helpful, respectful and honest medical assistant. You are a second version of Med42 developed by the AI team at M42, UAE. "16"Always answer as helpfully as possible, while being safe. "17"Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "18"Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "19"If you don’t know the answer to a question, please don’t share false information."20),
21},
22{"role":"user", "content":"What are the symptoms of diabetes?"},
23]24)2526print(output["choices"][0]['message']['content'])
Download
You can download Llama models in gguf format directly from Hugging Face using the from_pretrained method. This feature requires the huggingface-hub package.
By default, from_pretrained will download the model to the Hugging Face cache directory. You can manage installed model files using the huggingface-cli tool.
Ethical Considerations
Users must be aware of the model's limitations and potential biases. It should not be used for direct medical advice or decision-making without proper validation and human oversight.
Acknowledgements
We thank the M42 Health AI Team and the creators of Llama3 for their contributions to the field of medical AI.Special thanks to Georgi Gerganov and the entire llama.cpp development team for their outstanding contributions.
