CharlesLi/llama_2_alpaca_cot_simplest
CharlesLi/llama_2_alpaca_cot_simplest 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 final validation loss of 0.7382. It is intended for general conversational AI tasks, leveraging the Llama 2 architecture with a 4096 token context length.
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Overview
CharlesLi/llama_2_alpaca_cot_simplest is a 7 billion parameter language model derived from Meta's Llama-2-7b-chat-hf. It was fine-tuned using specific hyperparameters, including a learning rate of 0.0002 and a cosine learning rate scheduler, over 50 training steps. The model achieved a validation loss of 0.7382, indicating its performance on the evaluation set.
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
- Final Validation Loss: 0.7382
Intended Uses & Limitations
Due to the limited information provided in the original model card regarding the training dataset and specific intended uses, the model's primary application is inferred to be general conversational tasks, similar to its base model. Developers should be aware that detailed information on specific capabilities, limitations, and appropriate use cases is not extensively documented. Further evaluation and testing are recommended for specific applications.