zypchn/BehChat-qwen-SFT-v2
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:May 29, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
BehChat-qwen-SFT-v2 is a 7.6 billion parameter Qwen2-based causal language model developed by zypchn, fine-tuned from unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit. This model leverages Unsloth for accelerated training, offering a 32768 token context length. It is optimized for chat and instruction-following tasks, benefiting from efficient fine-tuning techniques.
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BehChat-qwen-SFT-v2 Overview
BehChat-qwen-SFT-v2 is a 7.6 billion parameter instruction-tuned language model developed by zypchn. It is based on the Qwen2 architecture and was fine-tuned from the unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit model, utilizing Unsloth for a 2x speedup in training and Huggingface's TRL library.
Key Capabilities
- Efficiently Trained: Leverages Unsloth for faster fine-tuning, making it a resource-efficient option for deployment.
- Qwen2 Architecture: Benefits from the robust capabilities of the Qwen2 model family.
- Instruction Following: Designed for chat and instruction-based interactions due to its supervised fine-tuning (SFT).
- Extended Context: Supports a context length of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended dialogues.
Good For
- Chatbots and Conversational AI: Its instruction-tuned nature makes it suitable for building interactive agents.
- General Text Generation: Capable of generating coherent and contextually relevant text based on prompts.
- Applications requiring efficient deployment: The model's efficient training suggests it might be optimized for performance, making it a good candidate for applications where speed and resource usage are critical.