Henrychur/MMedS-Llama-3-8B

Model

3

stars

8

commits

1

linked in READMEs

Dec 30, 2024

updated

conversational
endpoints_compatible
llama
medical
pytorch
safetensors
text-generation
text-generation-inference
transformers

README

MMedS-Llama3

💻Github Repo 🖨️arXiv Paper

The official codes for "Towards Evaluating and Building Versatile Large Language Models for Medicine"

Introduction

This repository hosts MMedS-Llama-3-8B. Its foundation model, MMed-Llama-3-8B, is a multilingual medical language model which has undergone additional continuous pretraining on MMedC. Furthermore, the model has been fine-tuned under supervision using MedS-Ins, a comprehensive dataset designed specifically for supervised fine-tuning (SFT), featuring 13.5 million samples across 122 tasks. For more details, please refer to our paper.

Usage

The model can be loaded as follows:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Henrychur/MMed-Llama-3-8B-EnIns")
model = AutoModelForCausalLM.from_pretrained("Henrychur/MMed-Llama-3-8B-EnIns", torch_dtype=torch.float16)
  • Inference format is the same as Llama 3, you can check the inference code here.

Contributors

Henrychur

7 commits

SFconvertbot

1 commits

Henrychur/MMedS-Llama-3-8B

Model

3

stars

8

commits

1

linked in READMEs

Dec 30, 2024

updated

conversational
endpoints_compatible
llama
medical
pytorch
safetensors
text-generation
text-generation-inference
transformers

README

MMedS-Llama3

💻Github Repo 🖨️arXiv Paper

The official codes for "Towards Evaluating and Building Versatile Large Language Models for Medicine"

Introduction

This repository hosts MMedS-Llama-3-8B. Its foundation model, MMed-Llama-3-8B, is a multilingual medical language model which has undergone additional continuous pretraining on MMedC. Furthermore, the model has been fine-tuned under supervision using MedS-Ins, a comprehensive dataset designed specifically for supervised fine-tuning (SFT), featuring 13.5 million samples across 122 tasks. For more details, please refer to our paper.

Usage

The model can be loaded as follows:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Henrychur/MMed-Llama-3-8B-EnIns")
model = AutoModelForCausalLM.from_pretrained("Henrychur/MMed-Llama-3-8B-EnIns", torch_dtype=torch.float16)
  • Inference format is the same as Llama 3, you can check the inference code here.

Contributors

Henrychur

7 commits

SFconvertbot

1 commits