CharlesLi/llama_2_o1_01_full
The CharlesLi/llama_2_o1_01_full is a 7 billion parameter language model, fine-tuned from Meta's Llama-2-7b-chat-hf architecture. This model was trained for 1 epoch with a learning rate of 2e-05 and a context length of 4096 tokens. While specific differentiators are not detailed, its fine-tuning process suggests potential for specialized conversational or instruction-following tasks.
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Overview
CharlesLi/llama_2_o1_01_full is a 7 billion parameter language model derived from the meta-llama/Llama-2-7b-chat-hf base architecture. It was fine-tuned over a single epoch using a learning rate of 2e-05 and a batch size of 32 across 4 GPUs. The training process utilized Adam optimizer with cosine learning rate scheduler and a warmup ratio of 0.1.
Key Training Details
- Base Model:
meta-llama/Llama-2-7b-chat-hf - Parameters: 7 billion
- Context Length: 4096 tokens
- Learning Rate: 2e-05
- Epochs: 1
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Frameworks: Transformers 4.44.2, Pytorch 2.4.1+cu121, Datasets 3.0.0, Tokenizers 0.19.1
Potential Use Cases
Given its fine-tuned nature from a chat-optimized Llama-2 variant, this model is likely suitable for:
- General conversational AI applications.
- Instruction-following tasks.
- Text generation where the base Llama-2-7b-chat-hf capabilities are desired with further specialization from the fine-tuning dataset (though the dataset details are not provided).
Further evaluation is needed to determine its specific strengths and limitations, as detailed information on the fine-tuning dataset and intended uses is not available in the provided model card.