jordialters/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-striped_tangled_jellyfish

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 24, 2025Architecture:Transformer Featherless Exclusive Warm

The jordialters/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-striped_tangled_jellyfish is a 0.5 billion parameter instruction-tuned model, likely based on the Qwen2.5 architecture. With a substantial context length of 32768 tokens, this model is designed for processing extensive inputs. While specific training details are not provided, its naming suggests an optimization for coding tasks and instruction following. It is suitable for applications requiring a compact yet capable model for code-related generation and understanding.

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Model Overview

This model, jordialters/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-striped_tangled_jellyfish, is a compact instruction-tuned language model with 0.5 billion parameters. It features a significant context window of 32768 tokens, enabling it to handle lengthy prompts and documents. While detailed information regarding its development, training data, and specific architecture is not available in the provided model card, its name implies a focus on coding tasks and adherence to instructions.

Key Characteristics

  • Parameter Count: 0.5 billion parameters, making it a relatively small and efficient model.
  • Context Length: Supports a large context window of 32768 tokens, beneficial for complex or long-form tasks.
  • Instruction-Tuned: Designed to follow instructions effectively, enhancing its utility for various applications.
  • Coder-Oriented: The "Coder" in its name suggests an emphasis on code generation, understanding, or related programming tasks.

Potential Use Cases

Given its characteristics, this model could be suitable for:

  • Code Generation: Assisting with writing or completing code snippets.
  • Code Explanation: Providing explanations for existing code.
  • Instruction Following: Executing a wide range of instruction-based tasks, especially those involving longer inputs.
  • Resource-Constrained Environments: Its smaller size makes it a candidate for deployment where computational resources are limited.

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