stefra/qwen-NEAR-full
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:May 11, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The stefra/qwen-NEAR-full model is a 7.6 billion parameter Qwen2-based causal language model developed by stefra. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for efficient deployment and performance, leveraging its Qwen2.5-7B-Instruct foundation.
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Overview
The stefra/qwen-NEAR-full model is a 7.6 billion parameter language model developed by stefra. It is based on the Qwen2 architecture, specifically fine-tuned from unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit. A key characteristic of this model is its training methodology, which utilized Unsloth and Huggingface's TRL library, resulting in a 2x speedup during the fine-tuning process.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth for significantly faster training times compared to standard methods.
- Qwen2.5 Foundation: Built upon the robust Qwen2.5-7B-Instruct base model, inheriting its general language understanding and generation capabilities.
- Optimized Performance: The fine-tuning process aims to deliver an efficient and performant model suitable for various NLP tasks.
Good For
- Developers looking for a Qwen2.5-based model that has undergone an optimized fine-tuning process.
- Applications requiring a 7.6 billion parameter model with a 32768 token context length, benefiting from the efficiency gains of Unsloth.