Automate LLM fine-tuning with Axolotl YAML configurations.
npx skills add https://github.com/heximao/agents --skill axolotl-heximao
Please help me install this Agent Skill. Skill: axolotl Source: https://github.com/heximao/agents/tree/main/skill-archive/docker-hermes/mlops/training/axolotl Command: npx skills add https://github.com/heximao/agents --skill axolotl-heximao
This skill streamlines the process of fine-tuning Large Language Models (LLMs) with Axolotl, covering LoRA, DPO, GRPO, and multimodal support.
To fine-tune a model with Axolotl, run the following command:
axolotl train config.yaml
High-intent search queries and answers about installing and using this skill.
You can fine-tune large language models by defining YAML configurations and executing the training command. This automates the fine-tuning process using Axolotl, supporting model selection and parameter adjustments.
Yes, Axolotl supports LoRA, QLoRA, and various preference optimization methods including DPO, KTO, ORPO, and GRPO. These options are configurable directly within your YAML setup.
You need PyTorch, Transformers, Datasets, PEFT, Accelerate, and DeepSpeed installed. These frameworks provide the foundational environment for Axolotl to execute large language model training.
Yes, Axolotl provides dedicated support for multimodal adjustments. You can configure multimodal parameters within your YAML file to train models handling multiple data types.
The best way to debug Axolotl code is utilizing the provided comprehensive documentation and code examples. These resources offer expert guidance for troubleshooting your YAML configurations and training scripts.
ldzhhxxGuide fine-tuning of LLMs with Axolotl using LoRA, DPO, and GRPO.
pmcdowallConfigure vLLM deployments with OpenAI-compatible APIs and performance tuning.
longkeyyAutomate content discovery across ArXiv, GitHub, and HuggingFace with semantic filtering and publishing.
huggingfaceTrain and fine-tune language models on Hugging Face Jobs using TRL methods.