CharlesLi/llama_2_cot_simplest_alpaca_4_full
The CharlesLi/llama_2_cot_simplest_alpaca_4_full model is a 7 billion parameter language model, fine-tuned from Meta's Llama-2-7b-chat-hf. This model is specifically adapted for tasks related to the 'generator dataset', indicating a specialization in content generation or response formulation. It was trained with a learning rate of 2e-05 over one epoch, achieving a loss of 0.9264 on its evaluation set. Its primary application is likely within conversational AI or text generation systems where its fine-tuning provides targeted performance.
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Model Overview
CharlesLi/llama_2_cot_simplest_alpaca_4_full is a 7 billion parameter language model, fine-tuned from the robust meta-llama/Llama-2-7b-chat-hf base. This model has undergone specialized training on a 'generator dataset', suggesting an optimization for tasks involving text generation or response creation.
Key Characteristics
- Base Model: Fine-tuned from Meta's Llama-2-7b-chat-hf.
- Parameter Count: 7 billion parameters.
- Context Length: Supports a context length of 4096 tokens.
- Training Focus: Specialized fine-tuning on a 'generator dataset'.
- Performance: Achieved a loss of 0.9264 on its evaluation set during training.
Training Details
The model was trained using the following hyperparameters:
- Learning Rate: 2e-05
- Batch Size: A total train batch size of 32 (4 per device across 4 GPUs with 2 gradient accumulation steps).
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08.
- Epochs: Trained for 1 epoch.
Intended Use Cases
Given its fine-tuning on a 'generator dataset', this model is likely best suited for applications requiring:
- Text Generation: Creating coherent and contextually relevant text.
- Conversational AI: Generating responses in dialogue systems.
- Content Creation: Assisting with various forms of written content.