zypchn/BehChat-llama-SFT-v1
The zypchn/BehChat-llama-SFT-v1 is an 8 billion parameter Llama-based model developed by zypchn, fine-tuned from unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. With a 32768 token context length, it is optimized for efficient performance in conversational AI and instruction-following tasks.
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Model Overview
zypchn/BehChat-llama-SFT-v1 is an 8 billion parameter Llama-based model, developed by zypchn. It is fine-tuned from the unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit base model, leveraging the Unsloth library for accelerated training. This approach allowed for a 2x speedup in the fine-tuning process, utilizing Huggingface's TRL library.
Key Characteristics
- Architecture: Llama-based, fine-tuned from DeepSeek R1 Distill.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Benefits from Unsloth's optimizations, resulting in significantly faster fine-tuning.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for handling longer conversations and complex instructions.
Good For
- Conversational AI: Its Llama foundation and instruction-following fine-tuning make it suitable for chatbots and interactive applications.
- Instruction Following: Designed to accurately respond to user prompts and instructions.
- Resource-Efficient Deployment: The use of Unsloth suggests potential for more efficient deployment, especially in environments where training speed is critical.