AnotherMiner/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-hibernating_agile_marmot

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:0.5BQuant:BF16Ctx Length:32kPublished:Nov 21, 2025Architecture:Transformer Warm

AnotherMiner/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-hibernating_agile_marmot is a 0.5 billion parameter instruction-tuned language model. This model is based on the Qwen2.5 architecture and features an exceptionally large context length of 131,072 tokens. While specific training details are not provided, its name suggests an optimization for coding tasks and potential integration with Gensyn Swarm. It is designed for applications requiring a compact yet capable model with extensive context understanding.

Loading preview...

Model Overview

This model, AnotherMiner/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-hibernating_agile_marmot, is an instruction-tuned language model with 0.5 billion parameters. It is built upon the Qwen2.5 architecture, known for its strong performance in various language understanding and generation tasks. A notable feature of this model is its substantial context window, supporting up to 131,072 tokens, which allows it to process and understand very long sequences of text.

Key Characteristics

  • Parameter Count: 0.5 billion parameters, making it a relatively compact model.
  • Architecture: Based on the Qwen2.5 family, indicating a robust foundation for language tasks.
  • Context Length: Features an impressive 131,072-token context window, enabling deep contextual understanding over extended inputs.
  • Instruction-Tuned: Designed to follow instructions effectively, making it suitable for various prompt-based applications.
  • Potential Specialization: The "Coder" and "Gensyn-Swarm" elements in its name suggest a possible focus on code-related tasks and distributed training or inference environments, though specific details are not provided in the model card.

Intended Use Cases

Given its instruction-tuned nature and large context window, this model is likely suitable for:

  • Applications requiring processing and understanding of extensive documents or codebases.
  • Tasks where a smaller model size is preferred for efficiency, without sacrificing significant context capacity.
  • Instruction-following tasks in environments where the Qwen2.5 architecture is well-supported.