Table of Contents

Examples

LoRA Fine-Tuning

(Continuous) Pre-Training

Supervised Fine-Tuning

Multimodal Supervised Fine-Tuning

DPO/ORPO/SimPO Training

Multimodal DPO/ORPO/SimPO Training

Reward Modeling

PPO Training

KTO Training

Preprocess Dataset

Evaluating on MMLU/CMMLU/C-Eval Benchmarks

Supervised Fine-Tuning on Multiple Nodes

Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)

Supervised Fine-Tuning with Ray on 4 GPUs

QLoRA Fine-Tuning

Supervised Fine-Tuning with 4/8-bit Bitsandbytes/HQQ/EETQ Quantization (Recommended)

Supervised Fine-Tuning with 4-bit Bitsandbytes Quantization on Ascend NPU

Supervised Fine-Tuning with 4/8-bit GPTQ Quantization

Supervised Fine-Tuning with 4-bit AWQ Quantization

Supervised Fine-Tuning with 2-bit AQLM Quantization

Full-Parameter Fine-Tuning

Supervised Fine-Tuning on Single Node

Supervised Fine-Tuning on Multiple Nodes

Elastic and Fault-Tolerant Supervised Fine-Tuning on Multiple Nodes

Multimodal Supervised Fine-Tuning

Merging LoRA Adapters and Quantization

Merge LoRA Adapters

Quantizing Model using AutoGPTQ

Save Ollama modelfile

Inferring LoRA Fine-Tuned Models

Evaluation using vLLM's Multi-GPU Inference

Use CLI ChatBox

Use Web UI ChatBox

Launch OpenAI-style API

Extras

Full-Parameter Fine-Tuning using GaLore

Full-Parameter Fine-Tuning using APOLLO

Full-Parameter Fine-Tuning using BAdam

Full-Parameter Fine-Tuning using Adam-mini

Full-Parameter Fine-Tuning using Muon

LoRA+ Fine-Tuning

PiSSA Fine-Tuning

Mixture-of-Depths Fine-Tuning

LLaMA-Pro Fine-Tuning

FSDP+QLoRA Fine-Tuning