skills.rest
graniet
granietCOMMUNITY
Explore all skills by this author →
Website𝕏@graniet75GitHub

axolotl

Generate Axolotl YAML configurations for fine-tuning large language models.

247|22|Updated Dec 11, 2024
Search Tags:#multimodal#lora#qlora#dpo#grpo#axolotl#llm fine-tuning
One-click install
npx skills add https://github.com/graniet/kheish --skill axolotl-graniet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/graniet/kheish/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/graniet/kheish --skill axolotl-graniet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill eliminates the steep learning curve and configuration overhead of fine-tuning large language models with the Axolotl framework, which requires complex YAML setups for distributed training, LoRA adapters, and custom dataset formatting.

Core Features & Use Cases

  • Multi-Paradigm Fine-tuning Support: Covers supervised fine-tuning (SFT), preference-based post-training (DPO, KTO, ORPO, GRPO), and pre-training workflows for 100+ LLM architectures including Llama, Mistral, and multimodal models.
  • Production-Grade Configuration Guidance: Provides pre-validated YAML templates for common setups like single-GPU LoRA/QLoRA, multi-GPU DeepSpeed/FSDP distributed training, and mixed precision (FP16/BF16/FP8) training.
  • End-to-End Workflow Support: Includes guidance for dataset formatting (conversation, instruction, pre-tokenized), prompt strategy selection, inference, LoRA merging, and troubleshooting common training errors via an extensive FAQ.
  • Use Case: A machine learning engineer can use this skill to quickly configure a QLoRA fine-tuning job for a 7B parameter Llama model on a single consumer GPU, or scale to a 70B model fine-tuning job across 8 GPUs with DeepSpeed ZeRO-3 without manually writing hundreds of lines of error-prone configuration.

Quick Start

Provide your base model name, dataset path, and fine-tuning method (e.g. LoRA, DPO) to generate a ready-to-run Axolotl YAML config and launch your LLM fine-tuning job immediately.

Frequently Asked Questions about axolotl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate an Axolotl YAML config for LoRA or QLoRA fine-tuning?▼

Generate a ready-to-run Axolotl YAML config for LoRA fine-tuning by providing your base model name, dataset path, and fine-tuning method to eliminate manual configuration complexity.

What is the best way to configure DeepSpeed or FSDP for distributed LLM training?▼

The best way to configure distributed LLM training is using pre-validated YAML templates for multi-GPU DeepSpeed and FSDP setups, avoiding error-prone manual configuration for large models.

Does Axolotl support preference-based post-training methods like DPO, KTO, and GRPO?▼

Yes, Axolotl supports preference-based post-training workflows including DPO, KTO, ORPO, and GRPO, alongside supervised fine-tuning and pre-training for over 100 open-source LLM architectures.

Can I fine-tune multimodal models using the Axolotl framework?▼

Yes, you can fine-tune multimodal models using the Axolotl framework, which provides configuration guidance and YAML templates specifically designed for multimodal architectures alongside text-only LLMs.

How do I format custom datasets for supervised fine-tuning in Axolotl?▼

Format custom datasets for supervised fine-tuning by following Axolotl's end-to-end dataset formatting guidance, which supports conversation, instruction, and pre-tokenized data structures for seamless training.

Why does my LLM fine-tuning job fail with common YAML configuration errors?▼

LLM fine-tuning jobs often fail due to YAML configuration errors, which you can resolve using the skill's extensive troubleshooting guidance for common training errors and pre-validated templates.

Related Skills

Explore More Data & Analytics Skills→
projectedanxprojectedanx
1

pytorch-lightning

Standardize PyTorch training workflows with distributed strategies and mixed precision.

Community
Advanced
Lhhiep-maxcodeLhhiep-maxcode

nemotron-ultra

Retrieve NVIDIA Nemotron 3 Ultra architecture, training, evaluation, and release details from source files.

Community
Advanced
KarlinskySKarlinskyS

huggingface-accelerate

Add distributed training to PyTorch scripts with mixed precision and multiple parallelism backends.

Community
Advanced
MarbleSodasMarbleSodas

axolotl

Configure Axolotl YAML for LLM fine-tuning with LoRA/QLoRA and DPO/KTO/ORPO/GRPO preferences.

Community
Intermediate