Qwen/Qwen2.5-Coder-32B-Instruct
Qwen/Qwen2.5-Coder-32B-Instruct is a 32.8 billion parameter instruction-tuned causal language model developed by Qwen, part of the Qwen2.5-Coder series. This model significantly improves code generation, reasoning, and fixing, building upon the Qwen2.5 architecture and trained on 5.5 trillion tokens including extensive source code and synthetic data. It offers long-context support up to 131,072 tokens, making it highly suitable for complex code-related tasks and real-world applications like Code Agents.
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Qwen2.5-Coder-32B-Instruct: Code-Specific LLM
Qwen2.5-Coder-32B-Instruct is a 32.8 billion parameter instruction-tuned model from the Qwen2.5-Coder series, developed by Qwen. This model represents a significant advancement in code-specific large language models, building on the robust Qwen2.5 foundation and scaling its training data to 5.5 trillion tokens, which includes a substantial amount of source code and text-code grounding data.
Key Capabilities and Features
- Enhanced Code Performance: Demonstrates significant improvements in code generation, code reasoning, and code fixing, aiming to match the coding abilities of models like GPT-4o.
- Comprehensive Foundation: Designed to support real-world applications such as Code Agents, while maintaining strong performance in mathematics and general competencies.
- Extended Context Length: Supports a long context window of up to 131,072 tokens, with specific instructions for deploying YaRN for optimal long-text processing.
- Architecture: Utilizes a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Ideal Use Cases
- Advanced Code Generation: For developers requiring highly accurate and complex code generation.
- Code Reasoning and Debugging: Applications involving understanding and fixing code logic.
- Code Agents: Building intelligent agents that can interact with and manipulate code.
- Long-Context Code Analysis: Tasks requiring processing and understanding large codebases or extensive documentation.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.