Qwen2-57B-A14B-Instruct

Maintained By
Qwen

Qwen2-57B-A14B-Instruct

PropertyValue
Parameter Count57.4B (14B active)
LicenseApache-2.0
ArchitectureMixture-of-Experts (MoE)
Context Length65,536 tokens
PaperYARN Paper

What is Qwen2-57B-A14B-Instruct?

Qwen2-57B-A14B-Instruct is an advanced Mixture-of-Experts (MoE) language model that represents the latest evolution in the Qwen series. While it contains 57.4B total parameters, it efficiently uses only 14B parameters during actual operation, making it both powerful and computationally efficient. The model features an impressive 65,536 token context length and implements the YARN technique for enhanced length extrapolation.

Implementation Details

The model is built on the Transformer architecture with several key improvements, including SwiGLU activation, attention QKV bias, and group query attention. It utilizes an improved tokenizer specifically designed for handling multiple natural languages and code.

  • BF16 tensor type for optimal performance
  • Supports extensive input processing with YARN scaling
  • Compatible with vLLM for deployment
  • Implements chat template for conversational applications

Core Capabilities

  • Strong performance in MMLU (75.4%) and MMLU-Pro (52.8%)
  • Exceptional coding capabilities with 79.9% on HumanEval
  • Advanced mathematical reasoning with 79.6% on GSM8K
  • Robust multilingual support with 80.5% on C-Eval
  • High-quality conversational abilities with 8.55 on MT-Bench

Frequently Asked Questions

Q: What makes this model unique?

The model's MoE architecture allows it to achieve state-of-the-art performance while only activating 14B parameters, making it more efficient than comparable dense models. Its extensive context length and YARN implementation make it particularly suitable for processing long documents.

Q: What are the recommended use cases?

The model excels in a wide range of applications including coding assistance, mathematical problem-solving, multilingual text processing, and general conversation. It's particularly well-suited for tasks requiring long context understanding and complex reasoning.

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