Zheng-Zong/AronaR1-SFT-stage1-test-f16

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Mar 14, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

AronaR1-SFT-stage1-test-f16 is a 7.6 billion parameter Qwen2.5-based instruction-tuned causal language model developed by Zheng-Zong. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language understanding and generation tasks, leveraging its Qwen2.5 architecture for robust performance.

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Model Overview

The AronaR1-SFT-stage1-test-f16 is a 7.6 billion parameter instruction-tuned language model developed by Zheng-Zong. It is based on the Qwen2.5 architecture and was fine-tuned from unsloth/qwen2.5-7b-instruct-bnb-4bit.

Key Characteristics

  • Architecture: Qwen2.5-based, a powerful causal language model family.
  • Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
  • Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
  • Context Length: Supports a context window of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended outputs.

Potential Use Cases

This model is suitable for a variety of natural language processing tasks, including:

  • Instruction Following: Responding to user prompts and instructions effectively.
  • Text Generation: Creating coherent and contextually relevant text.
  • General Conversational AI: Engaging in dialogue and providing informative responses.
  • Further Fine-tuning: Serving as a strong base model for domain-specific adaptations due to its efficient training methodology.