pherztuz/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-rough_galloping_falcon

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 25, 2025Architecture:Transformer Featherless Exclusive Warm

The pherztuz/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-rough_galloping_falcon is a 0.5 billion parameter instruction-tuned model based on the Qwen2.5 architecture. This model is shared on Hugging Face and has a context length of 32768 tokens. Specific details regarding its training, primary differentiators, and intended use cases are not provided in the available model card. It is presented as a general-purpose model with further information needed for specific applications.

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Model Overview

This model, pherztuz/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-rough_galloping_falcon, is a 0.5 billion parameter instruction-tuned model. It is built upon the Qwen2.5 architecture and supports a substantial context length of 32768 tokens, which can be beneficial for processing longer sequences of text or code.

Key Characteristics

  • Parameter Count: 0.5 billion parameters, indicating a relatively compact model size.
  • Context Length: Features a 32768-token context window, allowing for extensive input and output sequences.
  • Architecture: Based on the Qwen2.5 family, suggesting a robust foundation for language understanding and generation.

Further Information Needed

Currently, the model card indicates that more information is needed regarding several critical aspects, including:

  • Developed by: The specific developer or organization behind this model is not detailed.
  • Model Type: The precise model type and its specific optimizations are not specified.
  • Language(s): Information on the languages it supports or is trained on is pending.
  • Training Details: Comprehensive details about its training data, procedure, and hyperparameters are not yet available.
  • Evaluation: No evaluation results or benchmarks are provided to assess its performance.
  • Intended Uses: Specific direct or downstream use cases are not outlined, making it challenging to determine optimal applications.

Users are advised to await further updates to the model card for a complete understanding of its capabilities, limitations, and recommended usage.

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