AmirRazer/Llama3-8B-Alpaca-ID-SFT
AmirRazer/Llama3-8B-Alpaca-ID-SFT is an 8 billion parameter Llama 3.1 instruction-tuned causal language model developed by AmirRazer. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging the Llama 3.1 architecture for robust performance.
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Model Overview
AmirRazer/Llama3-8B-Alpaca-ID-SFT is an 8 billion parameter instruction-tuned language model, developed by AmirRazer. It is based on the Llama 3.1 architecture and was fine-tuned from unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Llama 3.1, an advanced causal language model.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitates significantly faster training processes.
- Context Length: Supports an 8192-token context window, suitable for handling moderately long inputs and generating coherent responses.
Intended Use Cases
This model is well-suited for a variety of instruction-following tasks, benefiting from its Llama 3.1 foundation and efficient fine-tuning. Developers can leverage it for applications requiring a capable and relatively fast language model.