Llama Factory

The llama-factory skill provides a streamlined interface for configuring and fine-tuning Llama models using the popular LLaMA-Factory framework. It allows users to define training parameters, dataset paths, and hyperparameters directly through prompts to customize model behavior for specific tasks.

When to use it

• You need to fine-tune an open-source Llama model on a custom dataset without writing complex Python scripts.
• You want to experiment with different training strategies like LoRA or full fine-tuning via natural language instructions.
• Your project requires adjusting specific hyperparameters such as learning rate, batch size, or sequence length for optimal performance.
• You are looking to deploy a specialized model variant quickly using pre-configured templates from the community.

Key capabilities

• Direct configuration of LLaMA-Factory training jobs through text prompts.
• Management of dataset inputs and preprocessing requirements.
• Adjustment of core hyperparameters including learning rate, epochs, and batch size.
• Support for various quantization methods to optimize memory usage during training.

Example prompts

• "Configure a LoRA fine-tuning job for Llama-3-8B using the provided legal documents dataset with a learning rate of 1e-4."
• "Set up a full parameter fine-tuning run on a custom customer support corpus, ensuring a sequence length of 2048 tokens."
• "Optimize the training configuration for low-resource hardware by enabling 4-bit quantization and reducing the batch size to 4."

Tips & gotchas

Ensure you have access to sufficient GPU memory before initiating fine-tuning jobs, as full parameter updates require significantly more resources than LoRA. Always validate your dataset format against LLaMA-Factory requirements to prevent training errors.