Zheng-Zong/AronaR1-SFT-stage1-v2-checkpoint250
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Mar 17, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
AronaR1-SFT-stage1-v2-checkpoint250 is a 7.6 billion parameter Qwen2-based causal language model developed by Zheng-Zong. This model was finetuned from Zheng-Zong/AronaR1-SFT-stage1 using Unsloth and Huggingface's TRL library, emphasizing efficient training. With a 32K context length, it is optimized for tasks requiring processing of longer sequences.
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Model Overview
Zheng-Zong/AronaR1-SFT-stage1-v2-checkpoint250 is a 7.6 billion parameter language model based on the Qwen2 architecture. Developed by Zheng-Zong, this model is a finetuned version of Zheng-Zong/AronaR1-SFT-stage1.
Key Characteristics
- Efficient Training: The model was trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Base Model: It is built upon the Qwen2 architecture, known for its strong performance across various language understanding and generation tasks.
- Context Length: The model supports a context length of 32,768 tokens, making it suitable for applications requiring the processing of extensive input sequences.
Potential Use Cases
- Long-form content generation: Its substantial context window allows for coherent and extended text generation.
- Summarization of lengthy documents: Capable of processing and summarizing large texts effectively.
- Applications requiring efficient fine-tuning: Developers looking for models that can be quickly adapted to specific tasks may find its training methodology beneficial.