# How to Fine-Tune LLMs on Windows with Unsloth (Step-by-Step Guide)
You can now fine-tune models directly on your local Windows device without WSL by using [Unsloth](https://github.com/unslothai/unsloth). For this guide, there are 3 main methods which you can use ([Conda](#method-1-windows-via-conda), [Docker](#method-2-docker) and [WSL](#method-3-wsl)).\
If you already have PyTorch installed on Windows, `pip install unsloth` should work. Otherwise, follow our guides below:
Conda TutorialDocker TutorialWSL Tutorial
### Unsloth Studio
We have launched a new web UI called [Unsloth Studio](https://unsloth.ai/docs/new/studio/install) that works on Windows out of the box:
```bash
irm https://unsloth.ai/install.ps1 | iex
```
Use the same command to update or use `unsloth studio update`.
Then to launch every time:
```bash
unsloth studio -H 0.0.0.0 -p 8888
```
For detailed Unsloth Studio install instructions and requirements, [view our guide](https://unsloth.ai/docs/new/studio/install).
Below are installation instructions for the original **Unsloth Core**:
### Method #1 - Windows via Conda:
{% stepper %}
{% step %}
**Install Miniconda (or Anaconda)**
Download Anaconda [here](https://www.anaconda.com/download). Our suggestion is to use [Miniconda](https://www.anaconda.com/docs/getting-started/miniconda/install#quickstart-install-instructions). To use it, first enter Powershell - search "Windows Powershell" in Start:
Then it'll open up Powershell:
Then copy paste the below: CTRL+C, and paste it in Powershell CTRL+V:
{% code overflow="wrap" %}
```ps
Invoke-WebRequest -Uri "https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe" -OutFile ".\miniconda.exe"
Start-Process -FilePath ".\miniconda.exe" -ArgumentList "/S" -Wait
del .\miniconda.exe
```
{% endcode %}
Accept the warning and press "Paste anyway" and wait.
It's downloading the installer like below:
After installing, open up open **Anaconda Powershell Prompt** to use Miniconda via Start -> Search for it:
Then you'll see:
{% endstep %}
{% step %}
**Make conda environment**
```bash
conda create --name unsloth_env python==3.12 -y
conda activate unsloth_env
```
**You will see:**
{% endstep %}
{% step %}
**Check `nvidia-smi` to confirm you have a GPU, and look for the CUDA version**
After typing `nvidia-smi` in Powershell, you should see something like below. If you don't have `nvidia-smi` or the below fails to pop up, you need to reinstall [NVIDIA drivers](https://www.nvidia.com/en-us/drivers/).
{% endstep %}
{% step %}
**Install PyTorch**
When running `nvidia-smi` you will see at the top right corner: "CUDA Version: 13.0". Install PyTorch in PowerShell via. Change `130` to your CUDA version - ensure the [version exists](https://pytorch.org/) and matches your CUDA driver version.
{% code overflow="wrap" %}
```bash
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
```
{% endcode %}
You will see:
Try running this in Python via `python` after PyTorch is installed:
{% code overflow="wrap" %}
```python
import torch
print(torch.cuda.is_available())
A = torch.ones((10, 10), device = "cuda")
B = torch.ones((10, 10), device = "cuda")
A @ B
```
{% endcode %}
You should see a matrix of 10s. Also verify True for the first.
{% endstep %}
{% step %}
**Install Unsloth (only if PyTorch works!)**
{% hint style="danger" %}
**Confirm PyTorch works fine and runs - if not PyTorch is broken and that means your Windows machine might need a re installation of CUDA drivers unfortunately.**
{% endhint %}
In Powershell (after exiting Python via `exit()` , do and wait:
```bash
pip install unsloth
```
{% endstep %}
{% step %}
**Verify Unsloth works**
Now use any script in [unsloth-notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks "mention") (save to .py file), or use the below basic script:
{% code expandable="true" %}
```python
from unsloth import FastLanguageModel, FastModel
import torch
from trl import SFTTrainer, SFTConfig
from datasets import load_dataset
max_seq_length = 512
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/gemma-3-270m-it",
max_seq_length = max_seq_length, # Choose any for long context!
load_in_4bit = True, # 4-bit quantization. False = 16-bit LoRA.
load_in_8bit = False, # 8-bit quantization
load_in_16bit = False, # 16-bit LoRA
full_finetuning = False, # Use for full fine-tuning.
trust_remote_code = False, # Enable to support new models
# token = "hf_...", # use one if using gated models
)
# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
model,
r = 16,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
max_seq_length = max_seq_length,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
trainer = SFTTrainer(
model = model,
train_dataset = dataset,
tokenizer = tokenizer,
args = SFTConfig(
max_seq_length = max_seq_length,
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
warmup_steps = 10,
max_steps = 60,
logging_steps = 1,
output_dir = "outputs",
optim = "adamw_8bit",
seed = 3407,
dataset_num_proc = 1,
),
)
trainer.train()
```
{% endcode %}
You should see:
```bash
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
🦥 Unsloth Zoo will now patch everything to make training faster!
==((====))== Unsloth 2026.1.4: Fast Gemma3 patching. Transformers: 4.57.6.
\\ /| NVIDIA GeForce RTX 3060. Num GPUs = 1. Max memory: 12.0 GB. Platform: Windows.
O^O/ \_/ \ Torch: 2.10.0+cu130. CUDA: 8.6. CUDA Toolkit: 13.0. Triton: 3.6.0
\ / Bfloat16 = TRUE. FA [Xformers = 0.0.34. FA2 = False]
"-____-" Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Unsloth: Gemma3 does not support SDPA - switching to fast eager.
Unsloth: Making `model.base_model.model.model` require gradients
Unsloth: Tokenizing ["text"] (num_proc=1): 0%| | 0/210289 [00:00
{% endstep %}
{% endstepper %}
### Method #2 - Docker:
Docker might be the easiest way for Windows users to get started with Unsloth as there is no setup needed or dependency issues. [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and 50-series GPUs, use this same image - no separate image needed.
For installation instructions, please follow our [Docker guide](https://unsloth.ai/docs/blog/how-to-fine-tune-llms-with-unsloth-and-docker), otherwise here is a quickstart guide:
{% stepper %}
{% step %}
**Install Docker and NVIDIA Container Toolkit.**
Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other). Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation):
```bash
export NVIDIA_CONTAINER_TOOLKIT_VERSION=1.17.8-1
sudo apt-get update && sudo apt-get install -y \
nvidia-container-toolkit=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \
nvidia-container-toolkit-base=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \
libnvidia-container-tools=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \
libnvidia-container1=${NVIDIA_CONTAINER_TOOLKIT_VERSION}
```
{% endstep %}
{% step %}
**Run the container.**
[**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image.
```bash
docker run -d -e JUPYTER_PASSWORD="mypassword" \
-p 8888:8888 -p 2222:22 \
-v $(pwd)/work:/workspace/work \
--gpus all \
unsloth/unsloth
```
{% endstep %}
{% step %}
**Access Jupyter Lab**
Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. Access the `unsloth-notebooks` tabs to see Unsloth notebooks.
{% endstep %}
{% step %}
**Start training with Unsloth**
If you're new, follow our step-by-step [Fine-tuning Guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide), [RL Guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks).
{% endstep %}
{% step %}
**Docker issues - GPU not discovered?**
Try doing WSL via [#method-2-wsl](#method-2-wsl "mention")
{% endstep %}
{% endstepper %}
### Method #3 - WSL:
{% stepper %}
{% step %}
**Install WSL**
Open up Command Prompt, the Terminal, and install Ubuntu. Set the password if asked.
```bash
wsl.exe --install Ubuntu-24.04
wsl.exe -d Ubuntu-24.04
```
{% endstep %}
{% step %} **If you did NOT do (1), so you already installed WSL****, enter WSL by typing `wsl` and ENTER in the command prompt**
```bash
wsl
```
{% endstep %}
{% step %}
**Install Python**
{% code overflow="wrap" %}
```bash
sudo apt update
sudo apt install python3 python3-full python3-pip python3-venv -y
```
{% endcode %}
{% endstep %}
{% step %}
**Install PyTorch**
{% code overflow="wrap" %}
```bash
pip install torch torchvision --force-reinstall --index-url https://download.pytorch.org/whl/cu130
```
{% endcode %}
If you encounter permission issues, use `–break-system-packages` so `pip install torch torchvision --force-reinstall --index-url https://download.pytorch.org/whl/cu130 –break-system-packages`
{% endstep %}
{% step %}
**Install Unsloth and Jupyter Notebook**
{% code overflow="wrap" %}
```bash
pip install unsloth jupyter
```
{% endcode %}
If you encounter permission issues, use `–-break-system-packages` so `pip install unsloth jupyter –-break-system-packages`
{% endstep %}
{% step %}
**Launch Unsloth via Jupyter Notebook**
{% code overflow="wrap" %}
```bash
jupyter notebook
```
{% endcode %}
Then open up our notebooks within [unsloth-notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks "mention")and load them up! You can also go to Colab notebooks and download > download .ipynb and load them.
{% endstep %}
{% endstepper %}
{% hint style="warning" %}
If you're using GRPO or plan to use vLLM, currently vLLM does not support Windows directly but only via WSL or Linux.
{% endhint %}
### **Troubleshooting /** Advanced
For **advanced installation instructions** or if you see weird errors during installations:
1. Install `torch` and `triton`. Go to to install it. For example `pip install torch torchvision torchaudio triton`
2. Confirm if CUDA is installated correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.
3. If using an Intel GPU, you will need to follow our [Intel Windows guide](https://unsloth.ai/docs/get-started/intel#windows-only-runtime-configurations)
4. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succeeded with `python -m xformers.info` Go to . Another option is to install `flash-attn` for Ampere GPUs.
5. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix) may be useful.
6. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes`
7. If Unsloth is not detecting or using your GPU and if you are using our Docker container on Windows, your CUDA toolkit version `nvcc --version` should match the version of CUDA shown by nvidia-smi on the host GPU support for Docker containers on Windows is not automatic. [You need to follow Docker's guide](https://docs.docker.com/desktop/features/gpu/).
### Uninstall Unsloth Studio
To uninstall Unsloth Studio on Windows, follow these 4 steps:
#### **1. Remove the application**
* Windows (PowerShell): `Remove-Item -Recurse -Force "$HOME\.unsloth\studio\unsloth", "$HOME\.unsloth\studio\studio"`
This removes the application but keeps your model checkpoints, exports, history, cache, and chats intact.
#### **2. Remove shortcuts and symlinks**
**WSL / Windows (PowerShell):**
```bash
Remove-Item -Force "$HOME\Desktop\Unsloth Studio.lnk"
Remove-Item -Force "$env:APPDATA\Microsoft\Windows\Start Menu\Programs\Unsloth Studio.lnk"
```
#### **3. Remove the CLI command**
**WSL:**
```bash
rm -f ~/.local/bin/unsloth
```
**Windows (PowerShell):** The installer added the venv's `Scripts` directory to your User PATH. To remove it, open Settings → System → About → Advanced system settings → Environment Variables, find `Path` under User variables, and remove the entry pointing to `.unsloth\studio\...\Scripts`.
#### **4. Remove everything (optional)**
To also delete history, cache, chats, model checkpoints, and model exports, delete the entire Unsloth folder:
* WSL, Linux: `rm -rf ~/.unsloth`
* Windows (PowerShell): `Remove-Item -Recurse -Force "$HOME\.unsloth"`
Note that downloaded HF model files are stored separately in the Hugging Face cache - none of the steps above will remove them. See **Deleting model files** below if you want to reclaim that disk space.
