eekay/Qwen2.5-7B-Instruct-dragon-numbers-ft

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Feb 14, 2026Architecture:Transformer Cold

The eekay/Qwen2.5-7B-Instruct-dragon-numbers-ft is a 7.6 billion parameter instruction-tuned model based on the Qwen2.5 architecture. This model is designed for general-purpose conversational AI tasks, leveraging its instruction-following capabilities. It is suitable for applications requiring robust language understanding and generation across various prompts. The model's 32768 token context length supports processing longer inputs and generating more extensive responses.

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Model Overview

The eekay/Qwen2.5-7B-Instruct-dragon-numbers-ft is an instruction-tuned model built upon the Qwen2.5 architecture, featuring 7.6 billion parameters. This model is designed to understand and follow instructions effectively, making it suitable for a wide range of natural language processing tasks.

Key Characteristics

  • Architecture: Based on the Qwen2.5 model family.
  • Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial context window of 32768 tokens, enabling the processing of longer inputs and maintaining coherence over extended conversations or documents.
  • Instruction-Tuned: Optimized for instruction-following, allowing it to respond accurately and relevantly to diverse user prompts.

Intended Use Cases

This model is well-suited for applications that require a capable instruction-following language model. Potential use cases include:

  • Conversational AI: Building chatbots, virtual assistants, and interactive agents.
  • Content Generation: Creating various forms of text, from summaries to creative writing, based on specific instructions.
  • Question Answering: Providing informative answers to user queries.
  • Text Summarization: Condensing long documents or articles into concise summaries.

Limitations

As indicated in the model card, specific details regarding training data, evaluation metrics, biases, risks, and environmental impact are currently marked as "More Information Needed." Users should exercise caution and conduct their own evaluations when deploying this model in sensitive applications, particularly concerning potential biases or limitations not yet documented.