Model Overview

This model, llama_2_cot_simplest_code_math_4_3_epoch_full, is a fine-tuned version of the Meta Llama 2 7B Chat model. It has been specifically adapted from meta-llama/Llama-2-7b-chat-hf to enhance its capabilities, particularly in areas suggested by its 'cot_simplest_code_math' naming convention, implying a focus on chain-of-thought reasoning for code and mathematical problems.

Key Training Details

The model was trained with the following hyperparameters:

• Learning Rate: 2e-05
• Batch Size: 4 (train), 4 (eval)
• Gradient Accumulation: 2 steps, leading to a total train batch size of 32
• Optimizer: Adam with standard betas and epsilon
• Scheduler: Cosine learning rate scheduler with 0.1 warmup ratio
• Epochs: 3

During training, the model achieved a validation loss of 0.5909 at 100 steps in epoch 1.9417, with a final training loss of 0.6812.

Potential Use Cases

Given its fine-tuning, this model is likely well-suited for:

• Mathematical problem-solving: Tasks requiring numerical reasoning or calculations.
• Code-related tasks: Generating or understanding simple code snippets, potentially with a focus on logical flow.
• Chain-of-Thought (CoT) applications: Scenarios where step-by-step reasoning is beneficial for arriving at a solution.