axolotl

To fine-tune a large language model, you can use a YAML configuration file to define training parameters and execute the process via command line. This approach supports 100+ model configurations and automates the training setup.

Yes, LoRA and QLoRA are supported for parameter-efficient LLM training. These advanced techniques allow you to fine-tune large models by training a smaller number of parameters, reducing computational overhead.

You need axolotl, torch, transformers, datasets, peft, accelerate, and deepspeed installed to set up model training. These dependencies provide the foundational framework for data processing and distributed training.

Yes, this fine-tuning approach supports DPO, KTO, ORPO, and GRPO techniques. These methods provide various optimization strategies for aligning large language models with specific preferences and training objectives.

The best way to configure multimodal functionality is through YAML configuration files tailored for your specific model. This allows you to customize the training process for models handling multiple data types.

Your LLM training script requires deepspeed and accelerate to manage distributed training and memory optimization. These libraries enable the efficient handling of large models across multiple GPUs during fine-tuning.