unsloth/Qwen2.5-Coder-7B-Instruct
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Sep 23, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

The unsloth/Qwen2.5-Coder-7B-Instruct is a 7.61 billion parameter instruction-tuned causal language model from the Qwen2.5-Coder series, developed by Qwen. This model is specifically designed for advanced code generation, reasoning, and fixing, building upon the strong Qwen2.5 foundation with extensive code-specific training data. It features a 131,072 token context length and maintains strong performance in mathematics and general competencies, making it suitable for complex coding tasks and Code Agent applications.

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Qwen2.5-Coder-7B-Instruct: Code-Optimized LLM

This model is the instruction-tuned 7.61 billion parameter variant of the Qwen2.5-Coder series, developed by Qwen. It represents a significant advancement over its predecessor, CodeQwen1.5, with enhanced capabilities across various coding tasks.

Key Capabilities & Features

  • Advanced Code Performance: Demonstrates significant improvements in code generation, code reasoning, and code fixing.
  • Extensive Training: Trained on 5.5 trillion tokens, including a substantial amount of source code, text-code grounding, and synthetic data.
  • Long-Context Support: Features a full context length of 131,072 tokens, with support for handling even longer texts up to 128K tokens using YaRN scaling.
  • Comprehensive Foundation: Designed to serve as a robust foundation for real-world applications like Code Agents, while also maintaining strong performance in mathematics and general competencies.
  • Architecture: Utilizes transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.

Good For

  • Code Generation: Ideal for generating high-quality code snippets and functions.
  • Code Reasoning: Excels at understanding and solving complex coding problems.
  • Code Fixing: Capable of identifying and correcting errors in code.
  • Code Agent Development: Provides a strong base for building intelligent code-centric agents.
  • Long Code Contexts: Suitable for tasks requiring analysis or generation over very large codebases or extensive documentation due to its 131K context window.
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