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  1. README.md +102 -3
  2. added_tokens.json +987 -0
  3. config.json +26 -0
  4. pytorch_model.bin +3 -0
  5. special_tokens_map.json +23 -0
  6. tokenizer.model +3 -0
  7. tokenizer_config.json +35 -0
README.md CHANGED
@@ -1,3 +1,102 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: mit
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+ ---
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+
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+
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+ <div align="center">
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+
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+ <h3>InstructBioMol: A Multimodal LLM for Biomolecule Understanding and Design</h3>
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+
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+ <p align="center">
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+ <a href="https://arxiv.org/abs/2410.07919">Paper</a> •
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+ <a href="https://github.com/HICAI-ZJU/InstructBioMol">Project</a> •
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+ <a href="#quickstart">Quickstart</a> •
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+ <a href="#citation">Citation</a>
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+ </p>
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+ </div>
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+
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+ ### Model Description
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+
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+ InstructBioMol is a multimodal large language model that bridges natural language with biomolecules (proteins and small molecules). It achieves any-to-any alignment between natural language, molecules, and proteins through comprehensive instruction tuning.
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+
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+ *For detailed information, please refer to our [paper](https://arxiv.org/abs/2410.07919) and [code repository](https://github.com/HICAI-ZJU/InstructBioMol).*
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+ ### Released Variants
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+
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+ | Model Name | Stage | Multimodal| Description |
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+ |------------|-----------| -------| -------|
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+ | [InstructBioMol-base](https://huggingface.co/hicai-zju/InstructBioMol-base) (*This Model*) | Pretraining | ❎| Continual pretrained model on molecular sequences, protein sequences, and scientific literature. |
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+ | [InstructBioMol-instruct-stage1](https://huggingface.co/hicai-zju/InstructBioMol-instruct-stage1) | Instruction tuning (stage 1) | ✅ | Stage1 instruction-tuned model with biomolecular multimodal processing capabilities. (e.g., 3D molecules/proteins) |
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+ | [InstructBioMol-instruct](https://huggingface.co/hicai-zju/InstructBioMol-instruct) | Instruction tuning (stage 1 and 2) | ✅| Fully instruction-tuned model (stage1 & stage2) with biomolecular multimodal processing capabilities (e.g., 3D molecules/proteins) |
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+ ### Training Details
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+
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+ **Base Architecture**: LLaMA-2-7B
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+
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+ **Training Data**:
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+
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+ ​1. ​Molecular Sequences​​:
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+
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+ - Format: SELFIES
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+ - Source: PubChem
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+ - Size: ​​100 million (100M) entries​​
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+
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+ ​2. ​Protein Sequences​​:
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+ - Format: FASTA-like, prefixed with `<p>` (e.g., `<p>M<p>A<p>L<p>W...`).
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+ - Source: UniRef50
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+ - Size: ​​59 million (59M) entries​​
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+
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+ ​3. ​Natural Language Texts​​:
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+ - Source: Abstracts from ​​PubMed​​, ​​bioRxiv​​, and ​​ChemRxiv​​
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+ - Size: ​​6 million (6M) abstracts​
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+
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+ **Training Objective**: Causal language modeling (self-supervised)
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+
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+ ### Quick Start
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+ ```python
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+ from transformers import LlamaForCausalLM, LlamaTokenizer
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+ import torch
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+
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+ model_name = "hicai-zju/InstructBioMol-base"
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+ tokenizer = LlamaTokenizer.from_pretrained(model_name)
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+ model = LlamaForCausalLM.from_pretrained(model_name, device_map="auto")
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+
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+ prompt = "<p>M" # protein sequence
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+ # prompt = "[C]" # molecule sequence
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+ # prompt = 'Scientific' # natural language
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+
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+ with torch.no_grad():
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+ outputs = model.generate(
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+ **inputs,
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+ max_new_tokens=100,
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+ temperature=0.7,
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+ top_p=0.9,
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+ do_sample=True
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+ )
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+
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+ generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ print(generated_text)
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+ ```
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+
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+ ### Citation
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+
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+ ```bibtex
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+ @article{DBLP:journals/corr/abs-2410-07919,
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+ author = {Xiang Zhuang and
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+ Keyan Ding and
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+ Tianwen Lyu and
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+ Yinuo Jiang and
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+ Xiaotong Li and
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+ Zhuoyi Xiang and
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+ Zeyuan Wang and
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+ Ming Qin and
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+ Kehua Feng and
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+ Jike Wang and
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+ Qiang Zhang and
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+ Huajun Chen},
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+ title = {InstructBioMol: Advancing Biomolecule Understanding and Design Following
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+ Human Instructions},
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+ journal = {CoRR},
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+ volume = {abs/2410.07919},
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+ year = {2024}
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+ }
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+ ```
added_tokens.json ADDED
@@ -0,0 +1,987 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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