kadrgc/Qwen2.5-Coder-1.5B-Instruct-Gensyn-Swarm-stinging_tough_wallaby

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Nov 16, 2025Architecture:Transformer Featherless Exclusive Cold

The kadrgc/Qwen2.5-Coder-1.5B-Instruct-Gensyn-Swarm-stinging_tough_wallaby is a 1.5 billion parameter instruction-tuned language model with a 32768 token context length. This model is part of the Qwen2.5-Coder family, designed for code-related tasks. Its instruction-tuned nature suggests optimization for following specific programming directives and generating relevant code snippets. The model's architecture and training focus aim to provide efficient performance for developers in coding applications.

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Model Overview

The kadrgc/Qwen2.5-Coder-1.5B-Instruct-Gensyn-Swarm-stinging_tough_wallaby is an instruction-tuned language model featuring 1.5 billion parameters and a substantial context window of 32,768 tokens. While specific development details are not provided in the model card, its naming convention, particularly "Qwen2.5-Coder" and "Instruct," indicates its foundation in the Qwen2.5 series and its specialization in code-related tasks through instruction-following.

Key Characteristics

  • Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: A large 32,768-token context window, beneficial for handling extensive codebases or complex programming problems.
  • Instruction-Tuned: Optimized to understand and execute instructions, making it suitable for tasks requiring precise output based on prompts.

Potential Use Cases

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

  • Code Generation: Generating code snippets, functions, or entire programs based on natural language descriptions.
  • Code Completion: Assisting developers by suggesting code as they type.
  • Code Explanation: Interpreting and explaining existing code segments.
  • Debugging Assistance: Identifying potential issues or suggesting fixes in code.

Further details on its specific training data, performance benchmarks, and intended applications are currently marked as "More Information Needed" in the model card.

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