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-3-Chat
LLaMat-3-Chat is a specialized large language model designed to serve as a copilot for materials research. Finetuned from LLaMat-3, this model is adapted for tasks such as information extraction from material science text and tabular data.
Overview
- Model Type: Large Language Model (LLM)
- Base Model: LLaMat-3 (continued pretraining of LLaMA-3 on material science data)
- Language: English
- License: LLaMA-3 License
- Tags: Material Science, Domain Adaptation, Table Understanding, Scientific Data Parsing, Materials Copilot
Model Details
Key Features
- Instruction Following Abilities: Optimized for understanding and processing instructions in the material science domain.
- Domain-Specific Expertise: Pretrained on material science tokens, enabling high performance in scientific applications.
- Applications: information extraction, table understanding, and parsing data for research tasks.
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: 8 NVIDIA A100 80GB GPUs
- Inferencing: 1 NVIDIA A100 80GB GPU
Software Stack
- Frameworks: PyTorch, Hugging Face Transformers
Model Sources
- Repository: LLaMat-3 on GitHub
- Compute Resources: EIDF Cerebras CS Clusters
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