Translate training goals into Axolotl YAML configurations and operational steps.
npx skills add https://github.com/TitoPrausee/nexus-toti --skill axolotl-titoprausee
Please help me install this Agent Skill. Skill: axolotl Source: https://github.com/TitoPrausee/nexus-toti/tree/main/data/skills/mlops/training/axolotl Command: npx skills add https://github.com/TitoPrausee/nexus-toti --skill axolotl-titoprausee
This Skill helps you configure, train, evaluate, and deploy large language models with Axolotl without getting lost in complex setup details. It reduces the friction of working with dataset formats, adapter tuning, distributed training, and post-training workflows.
Ask for an Axolotl configuration tailored to your model, dataset format, and training goal so you can launch a correct fine-tuning run immediately.
High-intent search queries and answers about installing and using this skill.
To configure YAML for LoRA or QLoRA fine-tuning, you define valid training parameters, supported model and tokenizer options, and dataset formats within the config. This translates your training goals into correct operational steps for a repeatable pipeline.
Yes, DPO, KTO, ORPO, and GRPO are supported RLHF post-training flows. You can set up these training scenarios by providing appropriate YAML settings and post-training parameters to guide the large language model alignment process.
Preparing pretraining, conversation, instruction, template-free, and pre-tokenized datasets requires correct formatting and preprocessing steps. Valid dataset formatting ensures the YAML config accurately maps training data to the model during fine-tuning.
Yes, multimodal training and quantization are supported operational workflows. You can configure these scenarios using valid YAML settings alongside appropriate training parameters to process and compress large language models.
The best way to merge adapters and run inference is by following the supported post-training operational workflows. Using valid YAML settings, you can execute merging, evaluation, and inference steps to validate and deploy your fine-tuned model.
Axolotl fine-tuning requires valid YAML settings, supported model and tokenizer options, and appropriate training parameters. Ensure your dataset formatting matches the expected preprocessing requirements to complete each workflow successfully.
aayushsoamManage models and datasets on the Hugging Face Hub.
OmidZamaniFine-tune a DSPy program into deployable model weights via BootstrapFinetune.
JKhyroFine-tune language models with TRL for SFT, DPO, PPO, and GRPO training.
gagan114662Fine-tune large language models with LoRA and QLoRA adapter methods.
