Qwen2.5-Coder-32B-Instruct-128K-GGUF

Maintained By
unsloth

Qwen2.5-Coder-32B-Instruct-128K-GGUF

PropertyValue
Parameter Count32.5B
Context Length131,072 tokens
LicenseApache 2.0
ArchitectureTransformers with RoPE, SwiGLU, RMSNorm
PaperView Paper

What is Qwen2.5-Coder-32B-Instruct-128K-GGUF?

Qwen2.5-Coder is a state-of-the-art code-specific language model that represents the latest advancement in Alibaba Cloud's Qwen series. This particular version is the instruction-tuned 32B parameter model optimized for GGUF format with extended 128K context window. It has been trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data.

Implementation Details

The model features a sophisticated architecture with 64 layers and employs 40 attention heads for queries and 8 for key-values using Group Query Attention (GQA). It leverages advanced techniques including RoPE for positional encoding, SwiGLU activations, and RMSNorm for normalization.

  • Full 131,072 token context length support
  • 31.0B non-embedding parameters
  • Advanced attention mechanism with GQA
  • Comprehensive instruction tuning

Core Capabilities

  • Superior code generation and completion
  • Advanced code reasoning and problem-solving
  • Efficient code fixing and debugging
  • Strong mathematical reasoning abilities
  • Enhanced performance for Code Agents applications

Frequently Asked Questions

Q: What makes this model unique?

This model stands out for its exceptional code generation capabilities that rival GPT-4, along with its extensive 128K context window and optimized GGUF format for efficient deployment. It's particularly notable for combining strong coding abilities with mathematical reasoning and general competencies.

Q: What are the recommended use cases?

The model excels in software development tasks, including code generation, debugging, and technical problem-solving. It's particularly well-suited for building Code Agents, supporting software development workflows, and handling complex programming challenges that require extended context understanding.

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