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RichardErkhov/
prsdm_-_phi-2-medquad-gguf

Name: prsdm_-_phi-2-medquad-gguf
Rating: 2.5 (258 reviews)

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phi-2-medquad - GGUF

Model creator: https://huggingface.co/prsdm/
Original model: https://huggingface.co/prsdm/phi-2-medquad/

Name	Quant method	Size
phi-2-medquad.Q2_K.gguf	Q2_K	1.03GB
phi-2-medquad.IQ3_XS.gguf	IQ3_XS	1.12GB
phi-2-medquad.IQ3_S.gguf	IQ3_S	1.16GB
phi-2-medquad.Q3_K_S.gguf	Q3_K_S	1.16GB
phi-2-medquad.IQ3_M.gguf	IQ3_M	1.23GB
phi-2-medquad.Q3_K.gguf	Q3_K	1.33GB
phi-2-medquad.Q3_K_M.gguf	Q3_K_M	1.33GB
phi-2-medquad.Q3_K_L.gguf	Q3_K_L	1.47GB
phi-2-medquad.IQ4_XS.gguf	IQ4_XS	1.43GB
phi-2-medquad.Q4_0.gguf	Q4_0	1.49GB
phi-2-medquad.IQ4_NL.gguf	IQ4_NL	1.5GB
phi-2-medquad.Q4_K_S.gguf	Q4_K_S	1.51GB
phi-2-medquad.Q4_K.gguf	Q4_K	1.62GB
phi-2-medquad.Q4_K_M.gguf	Q4_K_M	1.62GB
phi-2-medquad.Q4_1.gguf	Q4_1	1.65GB
phi-2-medquad.Q5_0.gguf	Q5_0	1.8GB
phi-2-medquad.Q5_K_S.gguf	Q5_K_S	1.8GB
phi-2-medquad.Q5_K.gguf	Q5_K	1.87GB
phi-2-medquad.Q5_K_M.gguf	Q5_K_M	1.87GB
phi-2-medquad.Q5_1.gguf	Q5_1	1.95GB
phi-2-medquad.Q6_K.gguf	Q6_K	2.13GB
phi-2-medquad.Q8_0.gguf	Q8_0	2.75GB

Original model description:

license: mit datasets:

prsdm/MedQuad-phi2-1k language:
en base_model:
microsoft/phi-2 pipeline_tag: question-answering tags:
medical

Fine-tuning Phi-2 Model on Medical Dataset

😸GitHub •📝Article • Models & Datasets on: 🤗Hugging Face

This repository provides a collection of Jupyter notebooks that demonstrate how to fine-tune large language models using various tools and techniques.

fine-tuning or instruction tuning is the process where the pre-trained model is further trained on the smaller dataset to adapt its knowledge for a specific task or domain. This process tweaks the model’s parameters to perform specific tasks. In fine-tuning, there are two methods:

Supervised fine tuning (SFT):

In SFT, the model is trained on a labeled dataset. The labeled dataset typically contains examples of instruction (input) and response (output) pairs relevant to the task. In this process, the model learns how to respond to specific instructions.

Reinforcement Learning from Human Feedback (RLHF):

In RLHF, the model interacts with users, generates responses, and receives feedback in the form of reinforcement signals. Basically, the model learns and improves its performance based on the feedback it receives.

Details of each notebook:

LLMs	Description	Dataset	Notebooks	Tuned Model
Phi-2	This model has been fine-tuned on a Medical dataset to answer questions related to diseases and symptoms. (used SFT method)	Dataset	Open In Colab	phi-2-medquad
llama-2	This model has been fine-tuned on a dataset with human-generated prompts to answer questions related to general knowledge. (used SFT method)	Dataset	Open In Colab	llama-2-finance

RichardErkhov/prsdm_-_phi-2-medquad-gguf