Model Card for LLaMat-2-Chat

LLaMat-2-Chat is a specialized large language model designed to serve as a copilot for materials research. Finetuned from LLaMat-2, this model is adapted for tasks such as information extraction from material science text and tabular data.

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Overview

• Model Type: Large Language Model (LLM)
• Base Model: LLaMat-2 (continued pretraining of LLaMA-2 on material science data)
• Language: English
• License: LLaMA-3 License
• Tags: Material Science, Domain Adaptation, Table Understanding, Scientific Data Parsing, Materials Copilot

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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.

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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

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Model Sources

• Repository: LLaMat-2 on GitHub
• Compute Resources: EIDF Cerebras CS Clusters