Does this skill support advanced alignment techniques?

Yes, it includes detailed guidance for implementing DPO, KTO, ORPO, and GRPO preference alignment methods.

Is multimodal model training supported?

Yes, the skill contains documentation and patterns for fine-tuning multimodal models within the Axolotl ecosystem.

Can I use this for distributed training setups?

Absolutely. The skill provides specific patterns for FSDP, DeepSpeed, and NCCL performance testing to ensure efficient training across multiple nodes and GPUs.

What is the Axolotl skill for Claude Code?

It is a specialized capability that provides Claude with the domain-specific knowledge to help users configure, debug, and optimize LLM fine-tuning workflows using the Axolotl library.

How does it help with dataset preparation?

It offers reference patterns for various dataset formats and provides logic for handling both single-example and batched data processing.

Axolotl LLM Fine-Tuning

Name: Axolotl LLM Fine-Tuning
Author: Orchestra-Research

byOrchestra-Research

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3,983

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데이터 과학 및 ML

Streamlines the fine-tuning of large language models using Axolotl through expert YAML configuration and advanced training pattern guidance.

The Axolotl skill empowers your AI agent with deep domain expertise in fine-tuning over 100 different LLMs using the Axolotl framework. It provides specialized guidance on complex configurations including LoRA/QLoRA, preference alignment algorithms like DPO, KTO, and GRPO, and multimodal model support. By integrating official documentation and proven architectural patterns, this skill assists researchers and engineers in debugging NCCL bottlenecks, optimizing FSDP configurations for distributed training, and managing diverse dataset formats, transforming your environment into a high-performance AI research hub.

주요 기능

01Implementation patterns for LoRA, QLoRA, and preference alignment algorithms

02Seamless integration patterns for cloud platforms like Modal and HuggingFace

03Comprehensive guidance for multimodal model training and dataset formatting

043,983 GitHub stars

05Expert YAML configuration assistance for 100+ model architectures

06Distributed training optimization using FSDP, DeepSpeed, and NCCL testing

사용 사례

01Converting and formatting complex datasets for compatibility with Axolotl training pipelines

02Implementing advanced preference alignment techniques like DPO or GRPO to improve model behavior

03Configuring and debugging large-scale distributed training jobs across multiple GPUs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills axolotl

For use in Claude.ai and ChatGPT

Download Skill