Generate and validate YAML configs for LoRA, QLoRA, DPO, and GRPO fine-tuning.
npx skills add https://github.com/iani-kuli/harness_bro --skill axolotl-iani-kuli
Please help me install this Agent Skill. Skill: axolotl Source: https://github.com/iani-kuli/harness_bro/tree/main/.claude/skills/ported/axolotl Command: npx skills add https://github.com/iani-kuli/harness_bro --skill axolotl-iani-kuli
This skill simplifies the complex process of fine-tuning Large Language Models by providing expert guidance on Axolotl configurations, training strategies, and debugging.
Use the axolotl skill to generate a base YAML configuration for fine-tuning a Llama 3 model using LoRA.
High-intent search queries and answers about installing and using this skill.
You can generate a YAML configuration for Llama 3 fine-tuning with LoRA using this skill to structure your config and select prompt strategies. It provides expert guidance for setting up Axolotl workflows.
The best way to configure DPO and GRPO training workflows is by generating and validating specific YAML configs through this skill. It offers expert guidance on training strategy selection and debugging for these complex pipelines.
Axolotl supports FSDP and DeepSpeed optimizations for LLM fine-tuning. This skill provides access to best practices for implementing these complex training pipelines to optimize model performance.
You can use Axolotl for multimodal model training. This skill provides access to best practices and expert guidance for configuring and optimizing training pipelines for multimodal models.
To troubleshoot common training errors when fine-tuning large language models, this skill provides expert debugging guidance for Axolotl configurations. It helps resolve issues across LoRA, QLoRA, DPO, and GRPO workflows.
QLoRA reduces memory usage compared to LoRA by quantizing model weights, allowing fine-tuning on limited hardware. This skill helps validate YAML configs and select training strategies for both QLoRA and LoRA workflows.
eliottbusinessFine-tune large language models with Axolotl using YAML configurations.
eddieluengTrain low-rank LoRA/QLoRA adapters for parameter-efficient fine-tuning of large language models.
TitoPrauseeTranslate training goals into Axolotl YAML configurations and operational steps.
LynxLabVNFine-tune LLMs with Axolotl using YAML configurations.
