CrystalReasoner/Qwen2.5-3B-CrysReas-CrystalTextLLM
CrystalReasoner/Qwen2.5-3B-CrysReas-CrystalTextLLM is a 3.1 billion parameter language model developed by CrystalReasoner, built on the Qwen2.5 architecture. This model is specifically fine-tuned for generating crystal structures from natural language instructions. It incorporates supervised fine-tuning, thinking traces for crystallographic and physical priors, and reinforcement learning with verifiable rewards. Its primary strength lies in producing valid, stable, and property-conditioned crystal structures.
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CrystalReasoner/Qwen2.5-3B-CrysReas-CrystalTextLLM Overview
This model, developed by CrystalReasoner, is an end-to-end large language model (LLM) framework designed for the generation of crystal structures based on natural language descriptions. Built upon the Qwen2.5 architecture, it features 3.1 billion parameters and a context length of 32768 tokens.
Key Capabilities
- Crystal Structure Generation: Generates crystal structures directly from natural language instructions, such as chemical formula and bulk modulus.
- Reasoning Integration: Utilizes "thinking traces" to incorporate crystallographic and physical priors before generating atomic coordinates.
- Reinforcement Learning (RL): Employs RL with verifiable rewards to enhance the validity, stability, and property conditioning of the generated structures.
- Pymatgen Compatibility: Provides a script to convert generated text output into
pymatgen.core.Structureformat for further analysis and use.
Good For
- Researchers and engineers in materials science and chemistry who need to generate hypothetical crystal structures.
- Automating the initial design phase of new materials based on desired properties.
- Exploring the space of possible crystal structures under specific constraints.