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Unsloth

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Unsloth

This guide will teach you how to easily train Qwen3 models with Unsloth. Unsloth simplifies local model training, handling everything from loading and quantization to training, evaluation, running, and deployment with inference engines (Ollama, llama.cpp, vLLM). Train Qwen models 2× faster using 70% less VRAM.

GitHub repo: Unsloth

⭐ Key Features

  • Supports full fine-tuning, pretraining, LoRA, QLoRA, 8-bit training & more
  • Single and multi-GPU support (Linux, Windows, Colab, Kaggle; NVIDIA GPUs, soon AMD & Intel)
  • Compatible with all transformer models: TTS, multimodal, STT, BERT, RL
  • RLHF support: GRPO, DPO, DAPO, RM, PPO, KTO, etc.
  • Hand-written Triton kernels and a manual backprop engine ensure no accuracy degradation (0% approximation).

Quickstart

Local Installation (Linux recommended):

bash
pip install unsloth

You can view Unsloth’s full installation instructions here.

Fine-tuning Qwen3 with Unsloth

Unsloth makes Qwen3 fine-tuning 2× faster, uses 70% less VRAM, with 8× longer contexts. Qwen3 (14B) fits in a free 16 GB Colab Tesla T4 GPU.

To retain Qwen3's reasoning capabilities, use a 75% reasoning to 25% non-reasoning dataset ratio (e.g., NVIDIA’s math‑reasoning dataset + Maxime’s FineTome).

For more details, see Unsloth’s full Qwen3 fine-tuning guide.

Colab Notebooks

Update Unsloth locally:

bash
pip install --upgrade --force-reinstall --no-cache-dir unsloth unsloth_zoo

Fine-tuning Qwen3 MoE Models

Supported MoE models include 30B‑A3B and 235B‑A22B. Unsloth fine-tunes the 30B‑A3B model with just 17.5 GB VRAM. Router-layer fine-tuning is disabled by default.

Use FastModel for MoE fine-tuning:

python
from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/Qwen3-30B-A3B",
    max_seq_length=2048,
    load_in_4bit=True,
    load_in_8bit=False,
    full_finetuning=False,
)

Notebook Guide

For an end-to-end walkthrough, see Unsloth’s full end-to-end fine-tuning guide.

  • Open the notebook → click Runtime ▸ Run all.
  • Adjust settings (e.g., model name, context length) directly in the notebook:
    • max_seq_length: Recommended 2048 (Qwen3 supports up to 40960).
    • load_in_4bit=True: reduces memory usage by 4×.
    • Enable full fine-tuning (full_finetuning=True) or 8-bit training (load_in_8bit=True).

If you want to use models directly from ModelScope, use:

bash
pip install modelscope -qqq
python
import os
os.environ["UNSLOTH_USE_MODELSCOPE"] = "1"

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen3-4B-Base",
    max_seq_length=2048,
)

RL & GRPO with Qwen3

You can also train Qwen models with reinforcement learning (RL) using Unsloth. Explore Unsloth’s advanced GRPO notebook, featuring proximity-based reward scoring and Hugging Face's Open‑R1 math dataset: Qwen3 (4B) Advanced GRPO LoRA notebook.

  • Proximity-based rewards for closer answers
  • Custom GRPO formatting and templates
  • Enhanced evaluation accuracy with regex matching

That’s how you can easily train Qwen models with Unsloth. If you need any help, join the discussion on Unsloth’s Discord or GitHub pages.

Links: