OpenDFM

ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge While large language models (LLMs) have achieved impressive progress, their application in scientific domains such as chemistry remains hindered by shallow domain understanding and limited reasoning capabilities. In this work, we focus on the specific field of chemistry and develop a Chemical Reasoning LLM, ChemDFM-R. We first construct a comprehensive dataset of atomized chemical knowledge, ChemFG, annotating the presence of functional groups in molecules and the changes of functional groups during chemical reactions, to enhance the model’s understanding of the fundamental principles and internal logic of chemistry. Then, we propose a mix-sourced distillation method that integrates expertise in atomized knowledge with general reasoning skills, followed by domain-specific reinforcement learning to enhance chemical reasoning. Experiments on diverse chemical benchmarks demonstrate that ChemDFM-R achieves cutting-edge performance while providing interpretable, rationale-driven outputs. Further case studies illustrate how explicit reasoning chains significantly improve the model's reliability, transparency, and practicality in real-world human-AI collaboration scenarios. For more details, please refer to our paper. 2025-10-26: The parameter of ChemDFM-R-14B is open-sourced! 2025-10-26 : ChemDFM-v2.0-14B is released! The improved domain pre-training and instruction tuning procedure is implemented on Qwen2.5-14B to achieve a more advanced general LLM in Chemistry. More details can be found here. 2025-07-29 : The paper of ChemDFM-R-14B is released on arXiv: ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge. 2024-11-09: ChemDFM-v1.5-8B is released! We implemented our domain pre-training and instruction tuning procedure on a stronger base model LLaMA-3-8B. 2024-03-12: The parameter of 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 To load and run ChemDFM-R locally, here is an example: 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: 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. If you have any questions or further requests, please contact Zihan Zhao, Bo Chen, and Lu Chen.

ChemDFM-v2.0 is the latest non-thinking model of ChemDFM, the pioneering open-sourced dialogue foundation model for Chemistry and molecule science. To achieve better chemical capabilities, both the domain pre-training stage and the instruction tuning stage are upgraded. In the domain pre-training stage, we introduce web-scale molecules and reactions into the corpus along with their functional-group information and properties. In this way, ChemDFM is able to better acquire chemical knowledge at a finer level of granularity. In the instruction tuning stage, we significantly improve the diversity of our instruction tuning dataset by introducing more tasks and increasing the variability in the phrasing and expression of the instruction texts. 2025-10-26: The parameter of ChemDFM-R-14B is open-sourced! 2025-10-26 : ChemDFM-v2.0-14B is released! The improved domain pre-training and instruction tuning procedure is implemented on Qwen2.5-14B to achieve a more advanced general LLM in Chemistry. More details can be found here. 2025-07-29 : The paper of ChemDFM-R-14B is released on arXiv: ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge. 2024-11-09: ChemDFM-v1.5-8B is released! We implemented our domain pre-training and instruction tuning procedure on a stronger base model LLaMA-3-8B. 2024-03-12: The parameter of 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 To load and run ChemDFM-v2.0 locally, here is an example: 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: 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. If you have any questions or further requests, please contact Zihan Zhao, Bo Chen, and Lu Chen.