whodisidk/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope

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 23, 2025Architecture:Transformer Featherless Exclusive Warm

The whodisidk/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope is a 0.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. This model is designed for general language tasks, leveraging its compact size and instruction-following capabilities. It offers a balance of performance and efficiency for various natural language processing applications.

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Model Overview

This model, whodisidk/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope, is a compact 0.5 billion parameter instruction-tuned language model built upon the Qwen2.5 architecture. It is designed to follow instructions effectively for a range of natural language processing tasks. The model has a context length of 32768 tokens, allowing it to process substantial amounts of input information.

Key Characteristics

  • Architecture: Based on the Qwen2.5 model family.
  • Parameter Count: 0.5 billion parameters, making it suitable for resource-constrained environments or applications requiring faster inference.
  • Instruction-Tuned: Optimized to understand and execute instructions, enhancing its utility for interactive and task-oriented applications.
  • Context Length: Supports a significant context window of 32768 tokens.

Potential Use Cases

Given its instruction-following capabilities and compact size, this model is well-suited for:

  • General NLP tasks: Text generation, summarization, question answering, and translation where efficiency is key.
  • Edge deployments: Applications on devices with limited computational resources.
  • Rapid prototyping: Quick development and testing of AI features.
  • Fine-tuning: As a base model for further specialization on specific datasets or tasks.