gladiy/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-extinct_tame_scorpion

Hugging Face
TEXT GENERATIONConcurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Nov 12, 2025Architecture:Transformer Featherless Exclusive Warm

gladiy/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-extinct_tame_scorpion is a 0.5 billion parameter instruction-tuned model based on the Qwen2.5 architecture, featuring a 32768 token context length. This model is a variant within the Qwen2.5-Coder series, which typically focuses on code generation and understanding tasks. While specific differentiators for this particular variant are not detailed, the Qwen2.5-Coder family is generally optimized for programming-related applications.

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Model Overview

This model, gladiy/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-extinct_tame_scorpion, is a 0.5 billion parameter instruction-tuned language model. It is built upon the Qwen2.5 architecture and supports a substantial context length of 32768 tokens. As part of the Qwen2.5-Coder series, it is generally designed for tasks involving code generation, comprehension, and related programming challenges.

Key Characteristics

  • Architecture: Qwen2.5 base model.
  • Parameter Count: 0.5 billion parameters, making it a relatively compact model.
  • Context Length: Features a long context window of 32768 tokens, beneficial for handling extensive codebases or complex instructions.
  • Instruction-Tuned: Designed to follow instructions effectively, enhancing its utility for interactive applications.

Intended Use Cases

Given its 'Coder' designation and instruction-tuned nature, this model is likely suitable for:

  • Code Generation: Assisting in writing code snippets or completing functions.
  • Code Explanation: Providing explanations for existing code.
  • Debugging Assistance: Helping identify potential issues in code.
  • Programming Education: Serving as a tool for learning and practicing coding concepts.

Due to the limited information in the provided model card, specific performance benchmarks or unique training details are not available. Users should conduct their own evaluations for specific applications.