CharlesLi/llama_2_alpaca_llama_2
CharlesLi/llama_2_alpaca_llama_2 is a 7 billion parameter language model fine-tuned from Meta's Llama-2-7b-chat-hf. This model was fine-tuned on an unspecified dataset, achieving a validation loss of 0.7577. It is intended for general conversational AI tasks, building upon the Llama 2 architecture with a 4096 token context length.
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Model Overview
CharlesLi/llama_2_alpaca_llama_2 is a 7 billion parameter language model derived from Meta's Llama-2-7b-chat-hf. It has been fine-tuned on an unspecified dataset, demonstrating a final validation loss of 0.7577 after 50 training steps.
Key Training Details
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Parameters: 7 Billion
- Context Length: 4096 tokens
- Learning Rate: 0.0002
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Training Steps: 50
- Frameworks: PEFT 0.12.0, Transformers 4.44.2, Pytorch 2.4.1+cu121
Intended Uses
This model is suitable for general-purpose conversational AI applications, leveraging the robust capabilities of the Llama 2 architecture. Its fine-tuning process suggests an adaptation for instruction-following or chat-based interactions, though specific use cases are not detailed in the original documentation.