cfierro/llama-3.1-8b-fft-othello-snake-0.5explained-replay-1e-5
The cfierro/llama-3.1-8b-fft-othello-snake-0.5explained-replay-1e-5 is an 8 billion parameter Llama 3.1-based language model. It was fine-tuned with a learning rate of 1e-05 over 2 epochs, achieving a best evaluation loss of 0.2196. This model is specialized through its specific training regimen, making it suitable for tasks aligned with its fine-tuning data and hyperparameters.
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Model Overview
The cfierro/llama-3.1-8b-fft-othello-snake-0.5explained-replay-1e-5 is an 8 billion parameter model built upon the Llama 3.1 architecture. This model has undergone a specific fine-tuning process, indicated by its detailed training hyperparameters and results.
Training Details
The model was trained for 2 epochs with a learning rate of 1e-05, utilizing a per-device batch size of 4 and gradient accumulation steps of 1. Key optimization settings included AdamW_BNB optimizer, bf16 precision, and Deepspeed for efficient training. The training process involved 2066 steps, culminating in a best evaluation loss of 0.2196.
Key Characteristics
- Base Model: Llama 3.1 (8B parameters)
- Fine-tuning: Specific fine-tuning with a low learning rate (1e-05) and 2 epochs.
- Optimization: Leverages
bf16andDeepspeedfor performance. - Performance Metric: Achieved a final evaluation loss of 0.2196.
Potential Use Cases
This model is best suited for applications that align with the specific data and objectives of its fine-tuning. Developers should consider its training parameters and evaluation loss when determining its applicability for tasks requiring specialized knowledge or performance characteristics derived from its unique training regimen.