Rakib3005/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-yapping_pudgy_bee

Hugging Face
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Nov 22, 2025Architecture:Transformer Featherless Exclusive Warm

Rakib3005/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-yapping_pudgy_bee is a 0.5 billion parameter instruction-tuned language model. This model is part of the Qwen2.5-Coder family, designed for code-related tasks. With a context length of 32768 tokens, it is optimized for processing and generating code. Its primary strength lies in its instruction-following capabilities within a coding context.

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Model Overview

This model, Rakib3005/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-yapping_pudgy_bee, is a 0.5 billion parameter instruction-tuned language model. It is based on the Qwen2.5-Coder architecture, indicating its specialization in code-centric applications. The model boasts a substantial context length of 32768 tokens, allowing it to handle extensive code snippets and complex programming instructions.

Key Capabilities

  • Instruction Following: Designed to accurately follow instructions, particularly in programming contexts.
  • Code-Oriented Processing: Optimized for understanding and generating code.
  • Extended Context Window: Supports a 32768-token context, beneficial for larger codebases or multi-file projects.

Use Cases

Given its instruction-tuned nature and code-focused design, this model is suitable for:

  • Code Generation: Assisting developers in writing new code based on natural language prompts.
  • Code Completion: Providing intelligent suggestions during coding.
  • Code Refactoring: Helping to improve existing code structures.
  • Educational Tools: Supporting learning environments for programming.

Limitations

As indicated by the model card, specific details regarding its development, training data, and evaluation are currently marked as "More Information Needed." Users should be aware that comprehensive information on bias, risks, and detailed performance metrics is not yet available. Recommendations for use are pending further data.

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