CharlesLi/llama_2_cot_simplest_code_math_4_3_epoch_full
The CharlesLi/llama_2_cot_simplest_code_math_4_3_epoch_full is a 7 billion parameter Llama-2-7b-chat-hf model fine-tuned for improved performance, achieving a validation loss of 0.5909. This model is optimized for tasks requiring reasoning and mathematical capabilities, as indicated by its 'cot_simplest_code_math' designation. It is suitable for applications where a smaller, specialized Llama-2 variant is beneficial for specific computational or logical problem-solving.
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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.