Instructions to use PharMolix/BioMedGPT-LM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PharMolix/BioMedGPT-LM-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PharMolix/BioMedGPT-LM-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PharMolix/BioMedGPT-LM-7B")
model = AutoModelForCausalLM.from_pretrained("PharMolix/BioMedGPT-LM-7B", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PharMolix/BioMedGPT-LM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PharMolix/BioMedGPT-LM-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PharMolix/BioMedGPT-LM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PharMolix/BioMedGPT-LM-7B

SGLang

How to use PharMolix/BioMedGPT-LM-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PharMolix/BioMedGPT-LM-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PharMolix/BioMedGPT-LM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "PharMolix/BioMedGPT-LM-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PharMolix/BioMedGPT-LM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PharMolix/BioMedGPT-LM-7B with Docker Model Runner:
```
docker model run hf.co/PharMolix/BioMedGPT-LM-7B
```

BioMedGPT-LM-7B / README.md

youngking0727

Update README.md

3ea1d3b about 3 years ago

3.16 kB

license: apache-2.0
tags:
  - medical
datasets:
  - biomed

BioMedGPT-LM-7B

In this repo, we present a medical language model named BioMedGPT-LM which is the first commercial-friendly GPT model in the biomedical domain and has demonstrated superior performance over existing LLMs of the same parameter size. We are releasing a 7B model BioMedGPT-LM-7B which is LLaMA2-7b-chat finetuned on the PMC abstracts and papers from the S2ORC.

Training Details

The model was trained with the following hyperparameters:

Epochs: 5
Batch size: 192
Cutoff length: 2048
Learning rate: 2e-5

Overview BioMedGPT-LM-7B was finetuned on over 26 billion tokens highly pertinent to the field of biomedicine. The fine-tuning data are extracted from 5.5 million biomedical papers in S2ORC data using PubMed Central (PMC)-ID and PubMed ID as criteria.

Model Developers

PharMolix

How to Use

BioMedGPT-LM-7B is a part of BioMedGPT-10B, an open-source version of BioMedGPT. BioMedGPT is a multimodal generative pre-trained transformer (GPT) for biomedicine, which bridges the natural language modality and diverse biomed- ical data modalities via a single GPT model. BioMedGPT aligns different biological modalities with the text modality via BioMedGPT-LM. The details of BioMedGPT-10B and BioMedGPT-LM-7B can be found in the technical report.

Intended Use Cases

Method	Parameters (B)	Setting	MedMCQA(%)	PubMedQA(%)
Human (pass)*	-	Manual	-	60.0
Human (expert)*	-	Manual	90	78.0
------------------------	----------------	-----------	-------------	--------------
InstructGPT*	175	zero-shot	44.0	73.2
ChatGPT*	-	zero-shot	44.7	63.9
Llama*	7	zero-shot	24.3	5.2
Llama2	7	zero-shot	30.6	3.7
Llama2-Chat	7	zero-shot	35.5	21.9
------------------------	----------------	---------	-------------	--------------
Llama	7	Fine-tuing	48.2	73.4
Llama2-Chat	7	Fine-tuing	48.3	75.5
PMC-Llama	7	Fine-tuing	50.5	69.5
------------------------	----------------	-----------	-------------	--------------
BioMedGPT-LM-7B	7	Fine-tuing	51.4	76.1

Out-of-scope Uses

Technical Report

"BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine"

github

https://github.com/BioFM/OpenBioMed

Limitations

[Highlight any limitations or potential issues of your model.]