Configure YAML-based Axolotl pipelines for LLM fine-tuning with RLHF and LoRA/QLoRA.
npx skills add https://github.com/OpenLAIR/dr-claw --skill axolotl-openlair
Please help me install this Agent Skill. Skill: axolotl Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/fine-tuning/axolotl Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill axolotl-openlair
Axolotl provides a comprehensive framework for end-to-end fine-tuning of large language models, including support for RLHF, LoRA/QLoRA optimization, and PEFT.
Configure and run a full Axolotl fine-tuning workflow to start training a model with your dataset.
High-intent search queries and answers about installing and using this skill.
YAML-based pipelines for LLM fine-tuning are configured by defining training regimes, custom datasets, and adapter settings within a YAML file, enabling reproducible end-to-end model training workflows.
Yes, LoRA and QLoRA adapters are fully supported for efficient LLM fine-tuning, complete with optimization and monkey-patching utilities to accelerate training.
Handling diverse datasets for RLHF training is streamlined through support for both streaming and prepared data pipelines, effectively accommodating pretraining, SFT, and post-training PRM workflows.
Yes, the framework supports end-to-end RLHF workflows, targeting researchers and engineers who need configurable data pipelines and patching utilities for advanced post-training reinforcement learning.
Patching utilities like monkey-patching for LoRA and fast attention are integrated to accelerate large language model training and optimize the fine-tuning process without requiring external modifications.
QLoRA should be used over standard fine-tuning when optimizing memory efficiency for large language models, leveraging PEFT and adapter integrations to maintain reproducible training workflows.
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JeffallanAutomate fine-tuning workflows for large language models using LoRA/QLoRA and PEFT methods.
