Instructions to use QizhiPei/BioMatrix-4B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QizhiPei/BioMatrix-4B-Base with Transformers:

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

pipe = pipeline("text-generation", model="QizhiPei/BioMatrix-4B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QizhiPei/BioMatrix-4B-Base")
model = AutoModelForCausalLM.from_pretrained("QizhiPei/BioMatrix-4B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QizhiPei/BioMatrix-4B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QizhiPei/BioMatrix-4B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QizhiPei/BioMatrix-4B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QizhiPei/BioMatrix-4B-Base

SGLang

How to use QizhiPei/BioMatrix-4B-Base 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 "QizhiPei/BioMatrix-4B-Base" \
    --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": "QizhiPei/BioMatrix-4B-Base",
		"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 "QizhiPei/BioMatrix-4B-Base" \
        --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": "QizhiPei/BioMatrix-4B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QizhiPei/BioMatrix-4B-Base with Docker Model Runner:
```
docker model run hf.co/QizhiPei/BioMatrix-4B-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BioMatrix-4B-Base

BioMatrix is a multimodal biological foundation model that natively integrates 1D sequences, 3D structures, and natural language for both molecules and proteins within a single decoder-only architecture.

This is the 4B-parameter Base model, obtained via multimodal continual pretraining of Qwen3-4B-Base on 304.4 billion tokens spanning text, molecular and protein 1D/3D data, and cross-modal corpora. This base checkpoint is intended for further fine-tuning on downstream tasks. For an instruction-tuned model ready for inference, see BioMatrix-4B-SFT.

📄 Paper: BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language
💻 Code: https://github.com/QizhiPei/BioMatrix
🤗 Model & Data Collection: https://huggingface.co/collections/QizhiPei/biomatrix

Model Overview

BioMatrix maps all biological modalities into a shared discrete token space via a unified tokenization scheme:

Molecular 1D sequences (both SMILES and SELFIES notations)
Molecular 3D structures (via MolStrucTok with branch-decoupled decoder)
Protein 1D sequences (residue-level tokens)
Protein 3D structures (via GCP-VQVAE backbone tokenizer)
Natural language (inherited from Qwen3 tokenizer)

All modalities are consumed and produced uniformly under a single next-token prediction objective—without external encoders, projection adapters, or modality-specific output heads.

Model	Molecule 1D	Molecule 3D	Protein 1D	Protein 3D	Natural Language
ESM3	✗	✗	✓	✓	✓
3D-MoLM	✓	✓	✗	✗	✓
AlphaFold3	✓	✓	✓	✓	✗
BioT5/BioT5+	✓	✗	✓	✗	✓
BioMedGPT	✓	✗	✓	✗	✓
BioMatrix	✓	✓	✓	✓	✓

Model Details

Base Architecture: Qwen3-4B-Base
Parameters: 4B
Training Stage: Multimodal Continual Pretraining only (not instruction-tuned)
Training Tokens: 304.4B
Context Length: 8,192 tokens
Tokenizer: Extended Qwen3 vocabulary with:
- 11,294 joint molecular 3D tokens (composed from SELFIES atom × MolStrucTok codes)
- 4,096 protein 3D tokens (GCP-VQVAE codebook)
- 26 protein 1D tokens (amino acids + non-standard/unknown)
- SELFIES atom tokens and modality-specific control tokens

Embedding Initialization

New vocabulary entries are initialized via a description-based scheme: each new token is grounded in the pretrained Qwen3 embedding space by averaging the embeddings of the subword tokens of a short natural-language description (e.g., <A_W> → "Tryptophan"), plus a small isotropic Gaussian perturbation to break symmetry. This provides a more stable starting point than random initialization.

Pretraining Corpus (304.4B tokens)

Category	Tokens	Sources
Text (105.3B)	General: 25.6B	FineWeb-Edu
	Scientific: 79.7B	FineFineWeb (biology/chemistry/medical/health), PubMed Full Articles
Molecule (73.7B)	1D: 36.0B	PubChem, MolTextNet
	3D: 17.6B	PubChem, PCQM4Mv2, PubChemQC
	Other: 24.0B	(text descriptions, properties, IUPAC names)
Protein (77.4B)	1D: 17.1B	UniRef50
	3D: 38.5B	RCSB PDB, AlphaFold DB
	Other: 19.5B	Swiss-Prot, TrEMBL annotations
	Other (additional): 2.9B
Cross-entity (48.0B)	Interleaved Text: 17.1B	PubMed, bioRxiv, S2ORC, USPTO
	3D: 11.4B	CrossDocked, PPIRef
	Other: 19.5B	BindingDB, STITCH, jglaser, AlphaSeq

Training Configuration

Framework: LLaMA-Factory
Hardware: 64 NVIDIA H100 GPUs
Global Batch Size: 1,024
Maximum Sequence Length: 8,192 tokens
Optimizer: AdamW
Peak Learning Rate: 2.0 × 10⁻⁴ (cosine schedule)
Warmup Steps: 2,000
Total Steps: ~36.4K (1 epoch over the full 304.4B-token corpus)

Intended Use

This Base model is not instruction-tuned. It is suitable for:

Further fine-tuning on custom biological tasks
Continued pretraining on domain-specific corpora
Research on representation learning across biomolecular modalities
Embedding extraction for downstream classification/regression tasks

For ready-to-use instruction-following capabilities (e.g., molecule captioning, protein design, property prediction), please use the SFT variant.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "QizhiPei/BioMatrix-4B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

# Example: Continue a SMILES sequence
prompt = "<|mol_smi_start|>CC(=O)"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Modality Wrapping

When constructing inputs, biomolecular content must be wrapped with the corresponding control tokens:

Modality	Wrapping Example
Molecule SMILES	`<\|mol_smi_start\|>CC#CC#N<\|mol_smi_end\|>`
Molecule SELFIES	`<\|mol_sfi_start\|>[C][#C][C][#N]<\|mol_sfi_end\|>`
Molecule 3D	`<\|mol_3d_start\|>[H 3][C 0][#C 6]...<\|mol_3d_end\|>`
Protein 1D	`<\|prot_aa_start\|><A M><A R><A A>...<\|prot_aa_end\|>`
Protein 3D	`<\|prot_3d_start\|><S 4012><S 153><S 2091>...<\|prot_3d_end\|>`

Natural language text is left unwrapped and serves as the default carrier modality.

Limitations

This model is not instruction-tuned and is unlikely to follow natural-language instructions out-of-the-box. Use the SFT variant for instruction-following.
Molecular and protein 3D structures are tokenized in disjoint geometric reference frames, so the model cannot natively represent biomolecular complexes (e.g., docking poses).
Heavy domain specialization may erode some general-purpose language capabilities of the underlying Qwen3 backbone.
Coverage is limited to small molecules and proteins; nucleic acids, carbohydrates, and lipids are not currently supported.

Citation

If you find BioMatrix useful, please cite:

@article{pei2026biomatrix,
  title={BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language},
  author={Pei, Qizhi and Zhou, Zhimeng and Duan, Yi and Zhao, Yiyang and He, Liang and Hsieh, Chang-Yu and He, Conghui and Yan, Rui and Wu, Lijun},
  year={2026}
}