How does it handle memory optimization during training?

The skill includes patterns for context parallelism, micro-batch size adjustment, and sequence length filtering to ensure models fit within available VRAM.

Can this skill help with distributed training issues?

Yes, it provides specific configuration patterns for FSDP (Fully Sharded Data Parallel), DeepSpeed, and NCCL performance testing to identify and resolve hardware bottlenecks.

What models does this Axolotl skill support?

It supports over 100 different models, including popular architectures like Llama, Mistral, and various multimodal models via HuggingFace integrations.

Does it support alignment techniques like RLHF?

It focuses on state-of-the-art alternatives to RLHF, providing detailed implementation guidance for DPO, KTO, ORPO, and GRPO.

Axolotl LLM Fine-Tuning

Name: Axolotl LLM Fine-Tuning
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

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

About

This skill transforms Claude into an expert AI research assistant specialized in the Axolotl framework for large language model fine-tuning. It provides deep technical guidance on configuring over 100 models, implementing parameter-efficient methods like LoRA and QLoRA, and utilizing advanced alignment techniques such as DPO, KTO, and GRPO. Users can leverage validated patterns for distributed training with FSDP, optimize data transfer via NCCL tests, and manage complex multimodal training workflows, making it an essential tool for high-performance AI engineering and research.

Key Features

Distributed training optimization via FSDP, DeepSpeed, and context parallelism
Debugging tools for NCCL bottlenecks and custom data collator implementation
Automated YAML configuration generation for 100+ LLM architectures
Multimodal training support and compressed model saving patterns
384 GitHub stars
Expert guidance on LoRA, QLoRA, and advanced alignment (DPO/ORPO/GRPO)

Use Cases

Implementing RLHF alternatives like Direct Preference Optimization (DPO) for model alignment
Setting up high-performance fine-tuning pipelines for Llama, Mistral, and other open-source models
Configuring memory-efficient distributed training across multiple GPUs using FSDP

About

Key Features

Distributed training optimization via FSDP, DeepSpeed, and context parallelism
Debugging tools for NCCL bottlenecks and custom data collator implementation
Automated YAML configuration generation for 100+ LLM architectures
Multimodal training support and compressed model saving patterns
384 GitHub stars
Expert guidance on LoRA, QLoRA, and advanced alignment (DPO/ORPO/GRPO)

Use Cases

Implementing RLHF alternatives like Direct Preference Optimization (DPO) for model alignment
Setting up high-performance fine-tuning pipelines for Llama, Mistral, and other open-source models
Configuring memory-efficient distributed training across multiple GPUs using FSDP