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Model: OpenDFM/ChemDFM-v1.5-8B Source: Original Platform
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README.md
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---
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license: agpl-3.0
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pipeline_tag: text-generation
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tags:
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- chemistry
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language:
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- en
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- zh
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---
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# ChemDFM: A Large Language Foundation Model for Chemistry
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ChemDFM is the pioneering open-sourced dialogue foundation model for Chemistry and molecular science, which is built based on LLaMa-13B. ChemDFM outperforms the open-sourced LLMs in all the typical tasks of chemistry, and even reaches comparable or higher performances to GPT-4. For more details, please refer to [our paper](https://arxiv.org/abs/2401.14818).
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## News
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* **2025-04-16**: Our paper is accepted by *Cell Report Physical Science*. The published version can be accessed [HERE](https://www.sciencedirect.com/science/article/pii/S2666386425001225)
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* **2024-11-09**: [ChemDFM-v1.5-8B](https://huggingface.co/OpenDFM/ChemDFM-v1.5-8B) is released! We implemented our domain pre-training and instruction tuning precedure on a stronger base model LLaMA-3-8B.
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* **2024-06-13**: The results on the comprehensive science benchmark [SciKnowEval](https://huggingface.co/datasets/hicai-zju/SciKnowEval) show that "ChemDFM emerged as one of the top open-source models by continuing pre-training and fine-tuning on a vast corpus of scientific literature".
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* **2024-04-17**: The evaluation data (including instructions) we used in our paper is released on [GitHub](https://github.com/OpenDFM/ChemDFM)
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* **2024-03-12**: The parameter of [ChemDFM-v1.0-13B](ihttps://huggingface.co/OpenDFM/ChemDFM-v1.0-13B) is open-sourced!
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* **2024-01-26**: The paper of ChemDFM-13B is released on arXiv: [ChemDFM: Dialogue Foundation Model for Chemistry](https://arxiv.org/abs/2401.14818)
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## Usage Details
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The online demo of ChemDFM will be up soon!
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### local inference
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To load and run ChemDFM locally, here is an example:
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```python
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import torch
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from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
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model_name_or_id = "OpenDFM/ChemDFM-v1.5-8B"
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tokenizer = LlamaTokenizer.from_pretrained(model_name_or_id)
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model = LlamaForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.float16, device_map="auto")
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input_text = "Can you please give detailed descriptions of the molecule below?\nCl.O=C1c2c(O)cccc2-c2nn(CCNCCO)c3ccc(NCCNCCO)c1c23"
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input_text = f"[Round 0]\nHuman: {input_text}\nAssistant:"
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inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
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generation_config = GenerationConfig(
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do_sample=True,
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top_k=20,
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top_p=0.9,
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temperature=0.9,
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max_new_tokens=1024,
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repetition_penalty=1.05,
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eos_token_id=tokenizer.eos_token_id
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)
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outputs = model.generate(**inputs, generation_config=generation_config)
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generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0][len(input_text):]
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print(generated_text.strip())
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```
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### input format
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To get better responses, we recommend to preprocess your input and history with the dialogue templates which are used during instruction tuning of ChemDFM. Specifically, for an input queries
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```python
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{'current_query': current_query, 'history': [(query1, answer1), (query2, answer2), ...]}
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```
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, you can use the following code to preprocess the input and history:
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```python
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def formatting_input(current_query, history):
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input_text = ''
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for idx, (query, answer) in history:
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input_text += f"[Round {idx}]\nHuman: {query}\nAssistant: {answer}\n"
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input_text += f"[Round {len(history)}]\nHuman: {current_query}\nAssistant:"
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return input_text
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```
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### SMILES preprocess
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When there involves SMILES notation in your input, we recommend to preprocess the SMILES with the `rdkit` package to canonicalize the SMILES. Here is an example:
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```python
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from rdkit import Chem
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def canonicalize_smiles(smiles):
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mol = Chem.MolFromSmiles(smiles)
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if mol is None:
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return None
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return Chem.MolToSmiles(mol, isomericSmiles=True, kekuleSmiles=False)
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```
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or directly:
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```python
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from rdkit import Chem
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def canonicalize_smiles(smiles):
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return Chem.CanonSmiles(smiles, useChiral=True)
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```
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## Citation
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```bibtex
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@article{zhao2025developing,
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title={Developing ChemDFM as a large language foundation model for chemistry},
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author={Zhao, Zihan and Ma, Da and Chen, Lu and Sun, Liangtai and Li, Zihao and Xia, Yi and Chen, Bo and Xu, Hongshen and Zhu, Zichen and Zhu, Su and others},
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journal={Cell Reports Physical Science},
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volume={6},
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number={4},
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year={2025},
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publisher={Elsevier}
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}
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```
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## Disclaimer
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Current version of ChemDFM may generate incorrect or misleading information. Please use it with caution and verify the results with domain experts before making any decisions based on the results.
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## Contact
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If you have any questions or further requests, please contact [Zihan Zhao](mailto:zhao_mengxin@sjtu.edu.cn) and [Lu Chen](mailto:chenlusz@sjtu.edu.cn).
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config.json
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{
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"_name_or_path": "/workspace/ckpt/chemdfm2_llama3_pretrain",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dilated_ratio": null,
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"end_token_id": 128001,
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"eos_token_id": 128001,
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"gla_group_size": 1,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"local_window_size": -1,
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"max_position_embeddings": 8192,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128001,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"segment_length": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.0",
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"use_cache": true,
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"vocab_size": 128264
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}
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{"framework":"Pytorch","task":"text-generation"}
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generation_config.json
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"_from_model_config": true,
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"transformers_version": "4.40.0"
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||||||
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||||||
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|
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|
||||||
|
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||||||
|
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|
||||||
|
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||||||
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||||||
|
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||||||
|
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|
||||||
|
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|begin_of_text|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|end_of_text|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "[PAD]",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
410512
tokenizer.json
Normal file
410512
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2071
tokenizer_config.json
Normal file
2071
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user