Initiate a New Training Job

• Navigate to the My Trainings section in the platform.
• Click on Add a Training Job to create a new job.

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Basic Details and Dataset Upload

• Provide a name for your experiment.
• Upload your training dataset.

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Select Training Parameters

Update the training parameters based on your requirements for the training job.

Here is a short explanation of these parameters:

• Specifies a keyword or phrase that activates a specific behaviour in the model during inference. It enables the model to adjust its responses based on the context or task without requiring further training.
• Controls the dimensionality of the LoRA matrices; a higher rank improves adaptation but increases computational cost.
• Defines the number of fine-tuning iterations; more steps improve model performance but increase training time.
• Controls the size of weight updates during fine-tuning. A higher value accelerates training but may risk overshooting, while a lower value offers more precise updates at the cost of longer training time.
• Controls how the model’s parameters are updated during training to minimize loss.
  • AdamW 8-bit: A variant of the Adam optimizer that leverages 8-bit precision to optimize memory usage and increase computational speed. This approach is ideal for large-scale models enabling quicker convergence while preserving model stability and high performance.
  • Prodigy:A custom-built optimizer engineered to boost training speed and model efficiency. It accelerates the convergence process and ensures optimal performance, making it suitable for both fine-tuning and large-scale model training.

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Start and Monitor the Training Job

Once the configuration is updated, start the training job and monitor its progress in the Recent Jobs section of the UI. Keep track of metrics, logs, and any intermediate results to ensure the training meets your requirements.