CharlesLi/llama_2_sky_safe_o1_llama_3_8B_reflect_4000_100_full
The CharlesLi/llama_2_sky_safe_o1_llama_3_8B_reflect_4000_100_full model is a 7 billion parameter language model fine-tuned from Meta's Llama-2-7b-chat-hf. This model was trained with a focus on reflection, indicated by its name, and achieved a validation loss of 0.7183. It is intended for chat-based applications, leveraging its Llama 2 foundation for conversational tasks.
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Model Overview
This model, llama_2_sky_safe_o1_llama_3_8B_reflect_4000_100_full, is a fine-tuned variant of the Meta Llama-2-7b-chat-hf architecture. It features 7 billion parameters and was trained with a specific emphasis on reflective capabilities, as suggested by its naming convention. During training, the model achieved a validation loss of 0.7183, indicating its performance on the evaluation set.
Training Details
The fine-tuning process utilized a learning rate of 2e-05, with a batch size of 4 across 4 GPUs, resulting in a total effective batch size of 32. The training ran for 1 epoch, employing an Adam optimizer with cosine learning rate scheduling and a warmup ratio of 0.1. Key frameworks used include Transformers 4.44.2 and PyTorch 2.4.1+cu121.
Intended Use Cases
Given its base model, this fine-tuned version is primarily suited for:
- Conversational AI applications: Leveraging the Llama 2 chat foundation.
- Tasks requiring reflection: Potentially enhanced performance in scenarios where the model needs to 'reflect' or process information iteratively, though specific details are not provided in the original documentation.
Further information regarding specific intended uses, limitations, and training data is not detailed in the provided model card.