Instructions to use therealcyberlord/llama2-qlora-finetuned-medical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use therealcyberlord/llama2-qlora-finetuned-medical with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "therealcyberlord/llama2-qlora-finetuned-medical") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): d52cfb7
Upload model
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- adapter_model.bin +1 -1
README.md
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# Llama2 🦙 finetuned on medical diagnosis
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MedText dataset: https://huggingface.co/datasets/BI55/MedText
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1412 pairs of diagnosis cases
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# About:
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The primary objective of this fine-tuning process is to equip Llama2 with the ability to assist in diagnosing various medical cases and diseases.
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However, it is essential to clarify that it is not designed to replace real medical professionals. Instead, its purpose is to provide helpful information to users,
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suggesting potential next steps based on the input data and the patterns it has learned from the MedText dataset.
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library_name: peft
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library_name: peft
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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size 134263757
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