初始化项目，由ModelHub XC社区提供模型

Model: OpenDFM/ChemDFM-v1.5-8B
Source: Original Platform

README.md

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+---
+license: agpl-3.0
+pipeline_tag: text-generation
+tags:
+- chemistry
+language:
+- en
+- zh
+---
+# ChemDFM: A Large Language Foundation Model for Chemistry
+
+![Main Image](https://github.com/OpenDFM/ChemDFM/raw/main/docs/static/images/main.png)
+
+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).
+
+## News
+
+* **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)
+* **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.
+* **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".
+* **2024-04-17**: The evaluation data (including instructions) we used in our paper is released on [GitHub](https://github.com/OpenDFM/ChemDFM)
+* **2024-03-12**: The parameter of [ChemDFM-v1.0-13B](ihttps://huggingface.co/OpenDFM/ChemDFM-v1.0-13B) is open-sourced!
+* **2024-01-26**: The paper of ChemDFM-13B is released on arXiv: [ChemDFM: Dialogue Foundation Model for Chemistry](https://arxiv.org/abs/2401.14818)
+
+## Usage Details
+
+The online demo of ChemDFM will be up soon!
+
+### local inference
+
+To load and run ChemDFM locally, here is an example:
+
+```python
+import torch
+from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
+
+model_name_or_id = "OpenDFM/ChemDFM-v1.5-8B"
+tokenizer = LlamaTokenizer.from_pretrained(model_name_or_id)
+model = LlamaForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.float16, device_map="auto")
+
+input_text = "Can you please give detailed descriptions of the molecule below?\nCl.O=C1c2c(O)cccc2-c2nn(CCNCCO)c3ccc(NCCNCCO)c1c23"
+input_text = f"[Round 0]\nHuman: {input_text}\nAssistant:"
+
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+generation_config = GenerationConfig(
+    do_sample=True,
+    top_k=20,
+    top_p=0.9,
+    temperature=0.9,
+    max_new_tokens=1024,
+    repetition_penalty=1.05,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+outputs = model.generate(**inputs, generation_config=generation_config)
+generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0][len(input_text):]
+print(generated_text.strip())
+```
+
+### input format
+
+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
+```python
+{'current_query': current_query, 'history': [(query1, answer1), (query2, answer2), ...]}
+```
+, you can use the following code to preprocess the input and history:
+```python
+def formatting_input(current_query, history):
+    input_text = ''
+    for idx, (query, answer) in history:
+        input_text += f"[Round {idx}]\nHuman: {query}\nAssistant: {answer}\n"
+    input_text += f"[Round {len(history)}]\nHuman: {current_query}\nAssistant:"
+    return input_text
+```
+
+### SMILES preprocess
+
+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:
+```python
+from rdkit import Chem
+def canonicalize_smiles(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None:
+        return None
+    return Chem.MolToSmiles(mol, isomericSmiles=True, kekuleSmiles=False)
+```
+or directly:
+```python
+from rdkit import Chem
+def canonicalize_smiles(smiles):
+    return Chem.CanonSmiles(smiles, useChiral=True)
+```
+
+## Citation
+```bibtex
+@article{zhao2025developing,
+  title={Developing ChemDFM as a large language foundation model for chemistry},
+  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},
+  journal={Cell Reports Physical Science},
+  volume={6},
+  number={4},
+  year={2025},
+  publisher={Elsevier}
+}
+```
+
+## Disclaimer
+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.
+
+## Contact
+
+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).