Snowflake Arctic Instruct
| Property | Value |
|---|---|
| Parameter Count | 479B parameters (17B active) |
| Model Type | Dense-MoE Hybrid Transformer |
| License | Apache 2.0 |
| Release Date | April 24th, 2024 |
| Tensor Type | BF16 |
What is snowflake-arctic-instruct?
Snowflake Arctic Instruct is an innovative large language model that combines a 10B dense transformer with a residual 128x3.66B Mixture of Experts (MoE) architecture. This hybrid approach results in 479B total parameters while maintaining efficiency through top-2 gating that keeps only 17B parameters active during inference. Developed by the Snowflake AI Research Team, it represents a significant advancement in enterprise-focused AI technology.
Implementation Details
The model leverages advanced technologies including DeepSpeed for optimization and supports both FP8 and FP6 quantization. It requires transformers version 4.39.0 or higher and uses custom code implementation through Hugging Face's trust_remote_code feature.
- Hybrid Architecture: Combines dense and sparse transformers
- Efficient Processing: Uses top-2 gating for parameter activation
- Hardware Requirements: Optimized for 8xH100 GPU setups
- Quantization Support: FP8/FP6 through DeepSpeed
Core Capabilities
- Text Generation and Completion
- Code Generation
- Instruction Following
- Enterprise-grade Performance
- Efficient Resource Utilization
Frequently Asked Questions
Q: What makes this model unique?
The model's hybrid architecture combining dense and MoE components makes it uniquely efficient while maintaining high performance. The active parameter count of 17B during inference, despite having 479B total parameters, represents a significant advancement in model efficiency.
Q: What are the recommended use cases?
Arctic Instruct is particularly well-suited for enterprise applications requiring high-quality text and code generation. Its Apache 2.0 license makes it viable for both research and commercial applications, while its efficient architecture makes it practical for production deployment.
