unsloth/Qwen2.5-Coder-1.5B-Instruct
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Sep 23, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Warm
The unsloth/Qwen2.5-Coder-1.5B-Instruct is a 1.54 billion parameter instruction-tuned causal language model developed by Qwen, featuring a 131,072 token context length. This model is specifically designed for enhanced code generation, code reasoning, and code fixing, building upon the Qwen2.5 architecture. It excels in programming tasks and maintains strong performance in mathematics and general competencies, making it suitable for applications like Code Agents.
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Qwen2.5-Coder-1.5B-Instruct Overview
This model is an instruction-tuned variant of the Qwen2.5-Coder series, developed by Qwen, focusing on advanced coding capabilities. It is a 1.54 billion parameter causal language model with a substantial context length of 131,072 tokens.
Key Capabilities
- Enhanced Code Performance: Significantly improved in code generation, reasoning, and fixing compared to its predecessor, CodeQwen1.5.
- Comprehensive Foundation: Trained on 5.5 trillion tokens, including extensive source code and text-code grounding, providing a robust base for real-world applications like Code Agents.
- Long-Context Support: Features full 131,072 token context length, with support for YaRN scaling to handle even longer texts, though static YaRN in vLLM may affect performance on shorter inputs.
- General Competencies: Maintains strong performance in mathematics and general language tasks alongside its coding specialization.
Good For
- Code Generation and Debugging: Ideal for tasks requiring the creation, understanding, and correction of code.
- Code Agents: Provides a strong foundation for developing intelligent agents that interact with and manipulate code.
- Long-Context Code Analysis: Suitable for processing and generating code within very large files or complex projects due to its extended context window.