Overview

This model, llama_2_cot_simplest_alpaca_2_full, is a fine-tuned variant of Meta's Llama-2-7b-chat-hf, a 7 billion parameter causal language model. It has been adapted from the original Llama 2 architecture, which is known for its strong performance in various natural language understanding and generation tasks. The fine-tuning process involved training on a specific "generator dataset" over a single epoch, achieving a reported loss of 0.9462 on the evaluation set.

Training Details

The model was trained using the following key hyperparameters:

• Learning Rate: 2e-05
• Batch Size: 4 (train and eval)
• Gradient Accumulation Steps: 2 (resulting in a total train batch size of 32)
• Optimizer: Adam with default betas and epsilon
• LR Scheduler: Cosine with a 0.1 warmup ratio
• Epochs: 1

Intended Use Cases

Given its foundation on Llama-2-7b-chat-hf and the fine-tuning process, this model is primarily intended for conversational AI applications. It can be used for generating human-like text in response to prompts, engaging in dialogue, and other tasks requiring natural language generation. Developers looking for a Llama 2-based model with specific fine-tuning for generative tasks might find this model suitable.