Configure and validate Axolotl YAML files for model fine-tuning workflows.
npx skills add https://github.com/JKhyro/HERMES-AGENT --skill axolotl-jkhyro
Please help me install this Agent Skill. Skill: axolotl Source: https://github.com/JKhyro/HERMES-AGENT/tree/main/skills/mlops/training/axolotl Command: npx skills add https://github.com/JKhyro/HERMES-AGENT --skill axolotl-jkhyro
This Skill helps you configure, troubleshoot, and operate Axolotl for model training workflows without having to piece together the documentation yourself.
Ask for help creating or debugging an Axolotl config for your model, dataset, and training goal.
High-intent search queries and answers about installing and using this skill.
Fine-tuning large language models with LoRA or QLoRA requires a YAML-based configuration to define training parameters, model setup, and dataset paths. This workflow supports supervised fine-tuning, quantization, and chat-template-aware datasets.
Yes, Axolotl supports preference optimization training including DPO, KTO, ORPO, and GRPO. You can configure these RLHF-style training workflows through YAML configuration files tailored to your specific model and dataset requirements.
Setting up multimodal training and FSDP involves specifying the appropriate parameters within your Axolotl YAML configuration. This enables distributed training across multiple GPUs and supports multimodal model architectures for complex training scenarios.
Yes, Axolotl supports dataset preprocessing and chat-template-aware datasets for supervised fine-tuning. You can select dataset formats and preprocess data directly within your YAML configuration to ensure proper tokenizer setup.
Preparing a fine-tuned model for inference and merging involves post-training steps configured through Axolotl. You can merge LoRA adapters with the base model and set up inference endpoints using the same YAML-based workflow.
Axolotl training config issues during model and tokenizer setup often stem from incorrect YAML parameters, mismatched chat templates, or dataset format errors. Troubleshooting involves validating the configuration against your specific training goal.
ScientiaCapitalAutomate dataset formatting, cleaning, augmentation, and quality assessment for LLM fine-tuning.
ScientiaCapitalExport fine-tuned models to GGUF and deploy with vLLM or Ollama.
zerwizConfigure and troubleshoot Axolotl YAML training workflows for LLM fine-tuning.
twjarviszyra-webConfigure Axolotl YAML for LLM fine-tuning and evaluation workflows.
