koutch/paper_llama_llama3.1-8b_train_sft_train_dual
The koutch/paper_llama_llama3.1-8b_train_sft_train_dual is an 8 billion parameter Llama 3.1-based instruction-tuned language model, fine-tuned by koutch. It was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for general instruction-following tasks, leveraging its Llama 3.1 architecture and efficient training methodology.
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Overview
The koutch/paper_llama_llama3.1-8b_train_sft_train_dual is an 8 billion parameter instruction-tuned language model developed by koutch. It is based on the Llama 3.1 architecture and was fine-tuned from unsloth/meta-llama-3.1-8b-instruct-bnb-4bit.
Key Characteristics
- Architecture: Llama 3.1-8B, providing a robust foundation for general-purpose language tasks.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context window of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended responses.
Intended Use Cases
This model is suitable for a variety of instruction-following applications, benefiting from its Llama 3.1 base and efficient fine-tuning. It can be applied to tasks requiring general language understanding and generation, where the 8B parameter count offers a balance between performance and computational efficiency.