Instructions to use jiaxianustc/BioMiner-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use jiaxianustc/BioMiner-Instruct with PEFT:
```
Task type is invalid.
```

How to use jiaxianustc/BioMiner-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="jiaxianustc/BioMiner-Instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("jiaxianustc/BioMiner-Instruct")
model = AutoModelForImageTextToText.from_pretrained("jiaxianustc/BioMiner-Instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jiaxianustc/BioMiner-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jiaxianustc/BioMiner-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jiaxianustc/BioMiner-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jiaxianustc/BioMiner-Instruct

SGLang

How to use jiaxianustc/BioMiner-Instruct 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 "jiaxianustc/BioMiner-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jiaxianustc/BioMiner-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jiaxianustc/BioMiner-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jiaxianustc/BioMiner-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jiaxianustc/BioMiner-Instruct with Docker Model Runner:
```
docker model run hf.co/jiaxianustc/BioMiner-Instruct
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

BioMiner-Instruct

This is the official implement of BioMiner-Instruct, an 32B open-source multimodal large language model developed within the BioMiner project

Contact

We welcome all forms of feedback! Please raise an issue for bugs, questions, or suggestions. This helps our team address common problems efficiently and builds a more productive community. If you encounter any issues, please reach out to jiaxianyan@mail.ustc.edu.cn.

Citation

If you find our work helpful, please kindly cite:

@article {Yan2025.04.22.648951,
    author = {Yan, Jiaxian and Zhu, Jintao and Yang, Yuhang and Liu, Qi and Zhang, Kai and Zhang, Zaixi and Liu, Xukai and Zhang, Boyan and Gao, Kaiyuan and Xiao, Jinchuan and Chen, Enhong},
    title = {BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature},
    year = {2025},
    doi = {10.1101/2025.04.22.648951},
    journal = {bioRxiv}
}

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Safetensors

Model size

33B params

Tensor type

BF16

Model tree for jiaxianustc/BioMiner-Instruct

Base model

Qwen/Qwen3-VL-32B-Instruct

Adapter

(9)

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