代码拉取完成，页面将自动刷新
Meditron is a suite of open-source medical Large Language Models (LLMs).
We release Meditron-7B and Meditron-70B, which are adapted to the medical domain from Llama-2 through continued pretraining on a comprehensively curated medical corpus, including selected PubMed papers and abstracts, a new dataset of internationally-recognized medical guidelines, and a general domain corpus.
Meditron-70B, finetuned on relevant data, outperforms Llama-2-70B, GPT-3.5 and Flan-PaLM on multiple medical reasoning tasks.
While Meditron is designed to encode medical knowledge from sources of high-quality evidence, it is not yet adapted to deliver this knowledge appropriately, safely, or within professional actionable constraints. We recommend against using Meditron in medical applications without extensive use-case alignment, as well as additional testing, specifically including randomized controlled trials in real-world practice settings.
You can load the Meditron model directly from the HuggingFace model hub as follows:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("epfl-llm/meditron-70b")
model = AutoModelForCausalLM.from_pretrained("epfl-llm/meditron-70b")
We release code to download and pre-process the data used to train Meditron.
MediTron’s domain-adaptive pre-training corpus GAP-Replay combines 48.1B tokens from four corpora:
You can download and pre-process the entire GAP-Replay corpus by running ./download.sh in the gap-replay folder.
You can download 36K open-access articles from our Guidelines corpus from the HuggingFace datasets hub.
from datasets import load_dataset
dataset = load_dataset("epfl-llm/guidelines")
You can scrape and clean all 46K guidelines (including closed-access sources) by running ./download.sh in the guidelines folder.
More details can be found in the GAP-Replay documentation.
We used the Megatron-LLM distributed training library, a derivative of Nvidia's Megatron LM project, to optimize training efficiency. The hardware consists of 16 nodes of 8x NVIDIA A100 (80GB) SXM GPUs connected by NVLink and NVSwitch with a single Nvidia ConnectX-6 DX network card and equipped with 2 x AMD EPYC 7543 32-Core Processors and 512 GB of RAM. The nodes are connected via RDMA over Converged Ethernet.
Our three-way parallelism scheme uses the following:
| bf16 | true |
| lr | 3e-4 |
| eps | 1e-5 |
| betas | [0.9, 0.95] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 16 |
| TP size | 4 |
| PP size | 1 |
| seq length | 2048 |
| lr scheduler | cosine |
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 10 |
| global batch size | 1600 |
| bf16 | true |
| lr | 1.5e-4 |
| eps | 1e-5 |
| betas | [0.9, 0.95] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 2 |
| TP size | 8 |
| PP size | 8 |
| seq length | 4096 |
| lr scheduler | cosine |
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 2 |
| global batch size | 512 |
You can see the script we used to pretrain our models through Megatron-LLM here: finetune.sh
We again used the Megatron-LLM distributed training library for supervised finetuning (sinlge-node and multi-node).
We made a file, sft.py, that automatically handles the tokenization and finetuning process through Megatron-LLM. To start a multi-node finetuning process, here is an example:
cd finetuning
python sft.py \
--checkpoint=baseline \
--size=70 \
--run_name=cotmedqa \
--data /pure-mlo-scratch/zechen/meditron/benchmarks/ft_preprocessed/medqa_cot_train.jsonl \
--val /pure-mlo-scratch/zechen/meditron/benchmarks/ft_preprocessed/medqa_cot_validation.jsonl \
--micro_batch=4
--nodes=4 \
--addr=<RANK0_HOST_NAME> \
--save_interval=200 \
--pp=4 \
--seq 4096 \
--rank=<CURRENT_RANK>
Run the above line of code at node rank-0, rank-1, rank-2, and rank3 to start a 4-node finetuning process.
Important!: Make sure to have the proper paths defined in sft.py and finetune_sft.sh.
| bf16 | true |
| lr | 2e-5 |
| eps | 1e-5 |
| betas | [0.9, 0.95] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 16 |
| TP size | 4 |
| PP size | 1 |
| seq length | 2048 or 4096 |
| lr scheduler | cosine |
| min lr | 2e-6 |
| warmup ratio | 0.1 |
| added tokens | [<|im_start|>, <|im_end|>] |
Meditron-70B is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and democratize access to an LLM for healthcare use. Potential use cases may include but are not limited to:
It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. It should not be used directly for production or work that may impact people.
We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.
Meditron-70B and Meditron-7B are both foundation models without finetuning or instruction-tuning. They can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications. There are two ways we have used this model for downstream question-answering tasks.
We encourage and look forward to the adaption of the base model for more diverse applications.
If you want a more interactive way to prompt the model, we recommend using a high-throughput and memory-efficient inference engine with a UI that supports chat and text generation.
You can check out our deployment guide below, where we used FastChat with vLLM. We collected generations for our qualitative analysis through an interactive UI platform, BetterChatGPT. Here is the prompt format we used as an example:
Before you start, please install the necessary packages:
vllm >= 0.2.1
transformers >= 4.34.0
datasets >= 2.14.6
torch >= 2.0.1
For detailed instructions to run inference and evaluation with medical benchmarks, please read the documentation here inference & evaluation instructions.
For detailed instructions to deploy meditron models and have an interactive chat session, please read the documentation here Model Deployment
If you use this software or our paper, please cite them:
@misc{chen2023meditron70b,
title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models},
author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
year={2023},
eprint={2311.16079},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@software{epfmedtrn,
author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models},
month = November,
year = 2023,
url = {https://github.com/epfLLM/meditron}
}
此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容，可点击提交进行申诉，我们将尽快为您处理。
马建仓 AI 助手