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Model: QizhiPei/BioMatrix-4B-Base Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- biology
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- chemistry
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- molecule
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- protein
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- multimodal
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- foundation-model
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- pretrained
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-4B-Base
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library_name: transformers
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---
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# BioMatrix-4B-Base
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**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.
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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](https://huggingface.co/QizhiPei/BioMatrix-4B-SFT).
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- 📄 **Paper**: [BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language](https://github.com/QizhiPei/BioMatrix/blob/main/biomatrix_tech_report.pdf)
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- 💻 **Code**: [https://github.com/QizhiPei/BioMatrix](https://github.com/QizhiPei/BioMatrix)
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- 🤗 **Model & Data Collection**: [https://huggingface.co/collections/QizhiPei/biomatrix](https://huggingface.co/collections/QizhiPei/biomatrix)
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## Model Overview
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BioMatrix maps **all biological modalities into a shared discrete token space** via a unified tokenization scheme:
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- **Molecular 1D sequences** (both SMILES and SELFIES notations)
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- **Molecular 3D structures** (via MolStrucTok with branch-decoupled decoder)
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- **Protein 1D sequences** (residue-level tokens)
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- **Protein 3D structures** (via GCP-VQVAE backbone tokenizer)
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- **Natural language** (inherited from Qwen3 tokenizer)
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All modalities are consumed and produced uniformly under a **single next-token prediction objective**—without external encoders, projection adapters, or modality-specific output heads.
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| Model | Molecule 1D | Molecule 3D | Protein 1D | Protein 3D | Natural Language |
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|-------|:-----------:|:-----------:|:----------:|:----------:|:----------------:|
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| ESM3 | ✗ | ✗ | ✓ | ✓ | ✓ |
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| 3D-MoLM | ✓ | ✓ | ✗ | ✗ | ✓ |
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| AlphaFold3 | ✓ | ✓ | ✓ | ✓ | ✗ |
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| BioT5/BioT5+ | ✓ | ✗ | ✓ | ✗ | ✓ |
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| BioMedGPT | ✓ | ✗ | ✓ | ✗ | ✓ |
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| **BioMatrix** | **✓** | **✓** | **✓** | **✓** | **✓** |
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## Model Details
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- **Base Architecture**: Qwen3-4B-Base
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- **Parameters**: 4B
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- **Training Stage**: Multimodal Continual Pretraining only (not instruction-tuned)
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- **Training Tokens**: 304.4B
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- **Context Length**: 8,192 tokens
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- **Tokenizer**: Extended Qwen3 vocabulary with:
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- 11,294 joint molecular 3D tokens (composed from SELFIES atom × MolStrucTok codes)
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- 4,096 protein 3D tokens (GCP-VQVAE codebook)
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- 26 protein 1D tokens (amino acids + non-standard/unknown)
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- SELFIES atom tokens and modality-specific control tokens
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### Embedding Initialization
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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.
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## Pretraining Corpus (304.4B tokens)
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| Category | Tokens | Sources |
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|----------|--------|---------|
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| **Text** (105.3B) | General: 25.6B | FineWeb-Edu |
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| | Scientific: 79.7B | FineFineWeb (biology/chemistry/medical/health), PubMed Full Articles |
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| **Molecule** (73.7B) | 1D: 36.0B | PubChem, MolTextNet |
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| | 3D: 17.6B | PubChem, PCQM4Mv2, PubChemQC |
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| | Other: 24.0B | (text descriptions, properties, IUPAC names) |
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| **Protein** (77.4B) | 1D: 17.1B | UniRef50 |
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| | 3D: 38.5B | RCSB PDB, AlphaFold DB |
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| | Other: 19.5B | Swiss-Prot, TrEMBL annotations |
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| | Other (additional): 2.9B | |
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| **Cross-entity** (48.0B) | Interleaved Text: 17.1B | PubMed, bioRxiv, S2ORC, USPTO |
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| | 3D: 11.4B | CrossDocked, PPIRef |
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| | Other: 19.5B | BindingDB, STITCH, jglaser, AlphaSeq |
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### Training Configuration
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- **Framework**: LLaMA-Factory
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- **Hardware**: 64 NVIDIA H100 GPUs
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- **Global Batch Size**: 1,024
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- **Maximum Sequence Length**: 8,192 tokens
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- **Optimizer**: AdamW
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- **Peak Learning Rate**: 2.0 × 10⁻⁴ (cosine schedule)
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- **Warmup Steps**: 2,000
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- **Total Steps**: ~36.4K (1 epoch over the full 304.4B-token corpus)
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## Intended Use
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This **Base model is not instruction-tuned**. It is suitable for:
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- **Further fine-tuning** on custom biological tasks
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- **Continued pretraining** on domain-specific corpora
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- **Research on representation learning** across biomolecular modalities
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- **Embedding extraction** for downstream classification/regression tasks
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For ready-to-use instruction-following capabilities (e.g., molecule captioning, protein design, property prediction), please use the [SFT variant](https://huggingface.co/QizhiPei/BioMatrix-4B-SFT).
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "QizhiPei/BioMatrix-4B-Base"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True
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)
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# Example: Continue a SMILES sequence
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prompt = "<|mol_smi_start|>CC(=O)"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=False))
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```
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## Modality Wrapping
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When constructing inputs, biomolecular content must be wrapped with the corresponding control tokens:
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| Modality | Wrapping Example |
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|----------|------------------|
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| Molecule SMILES | `<\|mol_smi_start\|>CC#CC#N<\|mol_smi_end\|>` |
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| Molecule SELFIES | `<\|mol_sfi_start\|>[C][#C][C][#N]<\|mol_sfi_end\|>` |
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| Molecule 3D | `<\|mol_3d_start\|>[H 3][C 0][#C 6]...<\|mol_3d_end\|>` |
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| Protein 1D | `<\|prot_aa_start\|><A M><A R><A A>...<\|prot_aa_end\|>` |
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| Protein 3D | `<\|prot_3d_start\|><S 4012><S 153><S 2091>...<\|prot_3d_end\|>` |
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Natural language text is left unwrapped and serves as the default carrier modality.
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## Limitations
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- 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.
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- Molecular and protein 3D structures are tokenized in **disjoint geometric reference frames**, so the model cannot natively represent biomolecular complexes (e.g., docking poses).
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- Heavy domain specialization may erode some general-purpose language capabilities of the underlying Qwen3 backbone.
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- Coverage is limited to **small molecules and proteins**; nucleic acids, carbohydrates, and lipids are not currently supported.
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## Citation
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If you find BioMatrix useful, please cite:
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```bibtex
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@article{pei2026biomatrix,
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title={BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language},
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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},
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year={2026}
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}
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```
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## License
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This model is released under the Apache 2.0 license. The base model (Qwen3-4B-Base) is subject to its own license terms.
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