XSCP/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shaggy_small_cockroach

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Nov 16, 2025Architecture:Transformer Featherless Exclusive Cold

XSCP/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shaggy_small_cockroach is a 0.5 billion parameter instruction-tuned 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 intended for applications requiring processing of substantial code inputs. Its primary strength lies in its instruction-following capabilities for coding scenarios.

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Model Overview

This model, XSCP/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-shaggy_small_cockroach, is an instruction-tuned variant within the Qwen2.5-Coder series. It features 0.5 billion parameters and supports a substantial context length of 32768 tokens, making it suitable for handling extensive code snippets and complex programming instructions.

Key Capabilities

  • Instruction Following: Designed to accurately interpret and execute instructions, particularly in coding contexts.
  • Code-Oriented: Optimized for tasks related to code generation, completion, and understanding.
  • Extended Context: Benefits from a 32768-token context window, allowing for processing of larger codebases or detailed problem descriptions.

Good For

  • Code Generation: Assisting developers with generating code based on natural language prompts.
  • Code Completion: Providing intelligent suggestions for completing code segments.
  • Instruction-Based Coding Tasks: Any application where the model needs to follow specific programming instructions or constraints.

Limitations

As indicated by the model card, specific details regarding its development, training data, evaluation, biases, risks, and environmental impact are currently marked as "More Information Needed." Users should exercise caution and conduct their own evaluations before deploying this model in critical applications, especially given the lack of detailed performance metrics and ethical considerations.

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