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.