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

File size: 3,163 Bytes

---
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.]