LLaMat
Collection
Foundational Large Language Models for Materials Research
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6 items
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Updated
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4
Model Card for LLaMat-2-CIF
LLaMat-2-CIF is a specialized large language model designed to generate and extract information from Crystallographic Information Files.
The model is developed after continued pretraining of LLaMat-2 on 7M instruction-output pairs obtained using CIFs from Materials Project, Google GNoME, and AMCSD
Overview
- Model Type: Large Language Model (LLM)
- Base Model: LLaMat-2 (continued pretraining of LLaMat-2 on CIFs)
- Language: English
- License: LLaMA-2 License
- Tags: Material Science, Domain Adaptation, Crystal Structure Generation
Model Details
Key Features
- Instruction Following Abilities: Answers questions based on CIF files.
- Applications: Crystal structure generation
Development and Support
- Developed by: M3RG, IIT Delhi & DAIR, IIT Delhi
- Compute Support:
- Edinburgh International Data Facility (EIDF): Provided access to Cerebras CS2 clusters for pretraining.
- IIT Delhi High-Performance Computing Cluster: Supported fine-tuning and inference stages.
Technical Specifications
Hardware Infrastructure
- Pretraining: 2 Cerebras CS-2 Wafer-Scale Engines (WSE-2)
- Finetuning: 2 Cerebras CS-2 Wafer-Scale Engines (WSE-2)
- Inferencing: 1 NVIDIA A100 80GB GPU
Software Stack
- Frameworks: PyTorch, Hugging Face Transformers
Model Sources
- Repository: LLaMat on GitHub
- Compute Resources: EIDF Cerebras CS Clusters
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Inference Providers
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