Configure and troubleshoot Axolotl YAML training workflows for LLM fine-tuning.
npx skills add https://github.com/zerwiz/wayofpi --skill axolotl-zerwiz
Please help me install this Agent Skill. Skill: axolotl Source: https://github.com/zerwiz/wayofpi/tree/main/.hermes/hermes-agent/skills/mlops/training/axolotl Command: npx skills add https://github.com/zerwiz/wayofpi --skill axolotl-zerwiz
Axolotl Skill removes the complexity of configuring, launching, and troubleshooting modern LLM training runs, so you can focus on model quality instead of wiring.
Ask for an Axolotl configuration plan for your target model, dataset format, and training method, and include any constraints such as LoRA, DPO, or multimodal inputs.
High-intent search queries and answers about installing and using this skill.
YAML-driven LLM fine-tuning configures supervised training by defining dataset formats, chat templates, and tokenizer settings in a single file. You can set up LoRA or QLoRA experiments and distributed training options to launch runs without manual wiring.
DPO preference optimization supports custom chat templates by mapping instruction pairs and template-free prompts to the correct format. You can configure dataset compatibility and troubleshoot tokenization or masking issues directly from the YAML file.
Multimodal training for an LLM is best set up by defining multimodal inputs alongside reward modeling or quantization parameters in a YAML config. This approach handles dataset preprocessing and chat-template formatting to streamline the workflow.
Troubleshooting tokenization and masking issues during QLoRA experiments involves validating your dataset format compatibility and verifying chat-template handling within your YAML config. Adjust tokenizer settings to ensure prompt masks align with training objectives.
LoRA fine-tuning supports pre-tokenized data and distributed training by specifying the appropriate dataset format and training settings in YAML. You can configure tokenizer handling and distribute workloads while maintaining quantization parameters.
musical-basicsGuide Axolotl fine-tuning with YAML configs, adapters, and distributed training.
lidge-junTrain and fine-tune language models via Hugging Face Jobs and TRL.
TitoPrauseeTranslate training goals into Axolotl YAML configurations and operational steps.
twjarviszyra-webConfigure Axolotl YAML for LLM fine-tuning and evaluation workflows.
