fatepurriyaz/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-foxy_opaque_buffalo

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 26, 2025Architecture:Transformer Featherless Exclusive Warm

The fatepurriyaz/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-foxy_opaque_buffalo is a 0.5 billion parameter instruction-tuned language model with a 32,768 token context length. This model is part of the Qwen2.5-Coder family, indicating a focus on code-related tasks. While specific training details are not provided, its naming suggests an optimization for coding instructions and applications within the Gensyn Swarm ecosystem. It is designed for efficient processing of long code sequences and instruction-following in programming contexts.

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Model Overview

This model, fatepurriyaz/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-foxy_opaque_buffalo, is an instruction-tuned language model with 0.5 billion parameters and a substantial 32,768 token context length. It is based on the Qwen2.5-Coder architecture, which typically implies a strong orientation towards code generation, understanding, and instruction-following.

Key Characteristics

  • Parameter Count: 0.5 billion parameters, making it a relatively compact model suitable for efficient deployment.
  • Context Length: A significant 32,768 tokens, enabling it to process and understand very long code snippets or complex programming instructions.
  • Instruction-Tuned: Designed to follow human instructions effectively, particularly in coding scenarios.
  • Coder-focused: The 'Coder' designation in its name suggests specialized training or fine-tuning for programming tasks.

Potential Use Cases

Given its architecture and instruction-following capabilities, this model is likely well-suited for:

  • Code Generation: Generating code snippets based on natural language descriptions.
  • Code Completion: Assisting developers by completing lines or blocks of code.
  • Code Explanation: Providing explanations for existing code.
  • Debugging Assistance: Identifying potential issues or suggesting fixes in code.
  • Long Context Code Analysis: Handling and processing large codebases or extensive programming documentation due to its long context window.

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