Rkngktrk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pale_deer

Hugging Face
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 27, 2025Architecture:Transformer Featherless Exclusive Warm

Rkngktrk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pale_deer is a 0.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. This model is shared on the Hugging Face Hub, but specific details regarding its development, training data, and intended use cases are currently marked as 'More Information Needed' in its model card. Due to the lack of detailed information, its primary differentiators and optimal applications are not yet specified.

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Model Overview

This model, Rkngktrk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_pale_deer, is a 0.5 billion parameter instruction-tuned language model built upon the Qwen2.5 architecture. It is hosted on the Hugging Face Hub as a transformers model.

Key Characteristics

  • Parameter Count: 0.5 billion parameters, indicating a relatively compact model size.
  • Context Length: Supports a context window of 32768 tokens.
  • Instruction-Tuned: Designed to follow instructions, suggesting its utility in conversational AI or task-oriented applications.

Current Information Limitations

As per its model card, detailed information regarding its development, funding, specific training data, and evaluation results is currently marked as "More Information Needed." This includes:

  • Developer and Funder: Not explicitly stated.
  • Training Details: Specifics on training data, procedures, and hyperparameters are pending.
  • Evaluation: No benchmark results or testing data details are provided.
  • Intended Uses: Direct and downstream use cases are not yet defined, making it challenging to recommend specific applications.

Recommendations

Users should be aware of the current lack of detailed information regarding this model's biases, risks, and limitations. Further recommendations will be possible once more comprehensive details about its development and evaluation are made available.

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