zypchn/BehChat-llama-SFT-v2
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:May 30, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
zypchn/BehChat-llama-SFT-v2 is an 8 billion parameter Llama-based model developed by zypchn, fine-tuned from unsloth/DeepSeek-R1-Distill-Llama-8B. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general conversational tasks, leveraging its efficient training methodology.
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Model Overview
zypchn/BehChat-llama-SFT-v2 is an 8 billion parameter Llama-based language model, developed by zypchn. It was fine-tuned from the unsloth/DeepSeek-R1-Distill-Llama-8B base model, leveraging the Unsloth library for accelerated training. This approach allowed for a 2x faster training process compared to standard methods, utilizing Huggingface's TRL library.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/DeepSeek-R1-Distill-Llama-8B. - Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Benefits from Unsloth's optimizations, resulting in significantly faster training times.
- License: Distributed under the Apache-2.0 license, allowing for broad use and modification.
Potential Use Cases
- General Conversational AI: Suitable for various dialogue-based applications due to its instruction-tuned nature.
- Efficient Deployment: Its optimized training suggests potential for efficient inference, making it viable for applications where speed is a factor.
- Further Fine-tuning: Can serve as a strong base for additional fine-tuning on specific domain data or tasks.