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
```

zhangjh0501 commited on Aug 17, 2023

Commit

fba89a8

1 Parent(s): 5e62b7d

Update README.md

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README.md +10 -9

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@@ -7,10 +7,9 @@ datasets:
 ---
 # 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
@@ -24,19 +23,21 @@ The model was trained with the following hyperparameters:
 * 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**

 ---
 # BioMedGPT-LM-7B
+**BioMedGPT-LM-7B** is the first large generative language model based on Llama2 in the biomedical domain.
+It was fine-tuned from the Llama2-7B-Chat with millions of biomedical papers from the [S2ORC corpus](https://github.com/allenai/s2orc/blob/master/README.md).
+Through further fine-tuning, BioMedGPT-LM-7B outperforms or is on par with human and significantly larger general-purpose foundation models on several biomedical QA benchmarks.
 ### Training Details
 * Cutoff length: 2048
 * Learning rate: 2e-5
+BioMedGPT-LM-7B is 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 the generative language model of **[BioMedGPT-10B](https://github.com/BioFM/OpenBioMed)**, an open-source version of BioMedGPT.
+BioMedGPT is an open multimodal generative pre-trained transformer (GPT) for biomedicine, which bridges the natural language modality and diverse biomedical data modalities via large generative language models.
+More technical details of BioMedGPT-LM-7B, BioMedGPT-10B, and BioMedGPT can be found in the [technical report](https://pan.baidu.com/s/1iAMBkuoZnNAylhopP5OgEg?pwd=7a6b).
+![The architecture of BioMedGPT-10B](BioMedGPT-10B.jpeg)
 **Intended Use Cases**