Unsloth Integration

Unsloth provides optimized training for LoRA fine-tuning, significantly reducing training time and memory usage.

Requirements

Requirement	Details
Installation	`pip install unsloth`
Supported Trainers	`default`, `sft` only
Supported Models	`llama`, `mistral`, `gemma`, `qwen2`
Platform	Linux recommended

Unsloth only works with SFT training (--trainer sft or --trainer default). DPO, ORPO, PPO, and other trainers are not supported.

Supported Model Architectures

Unsloth is optimized for specific model families:

Architecture	Example Models
`llama`	Llama 2, Llama 3, Llama 3.1, Llama 3.2
`mistral`	Mistral 7B, Mistral Nemo
`gemma`	Gemma, Gemma 2
`qwen2`	Qwen 2, Qwen 2.5

Other model architectures will fall back to standard training with a warning.

Quick Start

aitraining llm --train \
  --model meta-llama/Llama-3.2-1B \
  --data-path ./data \
  --project-name fast-model \
  --trainer sft \
  --unsloth \
  --peft \
  --lora-r 16

Parameters

Parameter	CLI Flag	Default	Description
`unsloth`	`--unsloth`	`False`	Enable Unsloth for faster training
`use_sharegpt_mapping`	`--use-sharegpt-mapping`	`False`	Use Unsloth’s ShareGPT mapping instead of converting

Python API

from autotrain.trainers.clm.params import LLMTrainingParams
from autotrain.project import AutoTrainProject

params = LLMTrainingParams(
    model="meta-llama/Llama-3.2-1B",
    data_path="./data",
    project_name="fast-model",

    trainer="sft",
    unsloth=True,

    peft=True,
    lora_r=16,
    lora_alpha=32,

    epochs=3,
    batch_size=4,
)

project = AutoTrainProject(params=params, backend="local", process=True)
project.create()

With Quantization

Unsloth works with int4 and int8 quantization for reduced memory usage:

aitraining llm --train \
  --model meta-llama/Llama-3.2-3B \
  --data-path ./data \
  --project-name quantized-model \
  --trainer sft \
  --unsloth \
  --peft \
  --quantization int4 \
  --lora-r 16

How It Works

When Unsloth is enabled and requirements are met:

Uses FastLanguageModel from Unsloth library for optimized model loading
Applies optimized gradient checkpointing (use_gradient_checkpointing="unsloth")
Automatically configures LoRA target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Integrates with PEFT for efficient adapter training

Fallback Behavior

If Unsloth cannot be used, training continues with standard transformers/PEFT:

Unsloth not installed: Warning logged, continues without Unsloth
Unsupported model type: Warning logged, continues without Unsloth
Unsupported trainer: Unsloth not applied (only SFT supported)

WARNING: Unsloth not available, continuing without it...

ShareGPT Mapping

Use --use-sharegpt-mapping to preserve ShareGPT format instead of converting:

aitraining llm --train \
  --model meta-llama/Llama-3.2-1B \
  --data-path ./sharegpt_data.json \
  --project-name model \
  --trainer sft \
  --unsloth \
  --use-sharegpt-mapping \
  --peft

Training Techniques

Optimization

Custom Development

Evaluation

Research

Production

Unsloth Integration

Unsloth Integration

Requirements

Supported Model Architectures

Quick Start

Parameters

Python API

With Quantization

How It Works

Fallback Behavior

ShareGPT Mapping

Next Steps

LoRA/PEFT

Quantization

​Unsloth Integration

​Requirements

​Supported Model Architectures

​Quick Start

​Parameters

​Python API

​With Quantization

​How It Works

​Fallback Behavior

​ShareGPT Mapping

​Next Steps

LoRA/PEFT

Quantization

Unsloth Integration

Requirements

Supported Model Architectures

Quick Start

Parameters

Python API

With Quantization

How It Works

Fallback Behavior

ShareGPT Mapping

Next Steps