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

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	---
	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](https://github.com/BioFM/OpenBioMed), 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]().
	![The architecture of BioMedGPT-10B](BioMedGPT-10B.jpeg)




	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](https://github.com/BioFM/OpenBioMed)


	### Limitations

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