Fine-tune with Unsloth Studio

Unsloth Studio is a browser UI for running and training local models. This guide installs Studio on a Thunder Compute base instance, opens it through Thunder Compute port forwarding, then runs a short text fine-tune with unsloth/gemma-4-E2B-it.

This setup path and Gemma 4 E2B QLoRA training profile were verified on a single Thunder Compute A100 80 GB instance with Unsloth Studio 2026.5.2. The sample dataset below is intentionally tiny so you can confirm the workflow before training on a real dataset.

Create the instance

Create a GPU instance with the base template and enough disk for Studio, models, and datasets:

tnr create --gpu a100 --num-gpus 1 --vcpus 12 --template base --disk 250

If A100 is unavailable, use the same shape with an available GPU:

# RTX A6000
tnr create --gpu a6000 --num-gpus 1 --vcpus 6 --template base --disk 250

# H100 80 GB
tnr create --gpu h100 --num-gpus 1 --vcpus 16 --template base --disk 250

Connect to the instance:

tnr status
tnr connect <instance-id>

Check that the GPU is visible:

nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader

Create an A100 instance and verify the GPU

Install Unsloth Studio

Install the system packages that Unsloth Studio needs to build and run its local inference components:

sudo apt-get update
sudo apt-get install -y cmake libcurl4-openssl-dev

Run the official Unsloth Studio installer:

curl -fsSL https://unsloth.ai/install.sh | sh

The installer creates a Python environment under ~/.unsloth/studio and writes an unsloth launcher into ~/.local/bin. For the current SSH session, add the launcher directory to your path:

export PATH="$HOME/.local/bin:$PATH"

Install Unsloth Studio on a Thunder Compute instance

Start Studio

Start Unsloth Studio on port 8888 and listen on all interfaces:

nohup unsloth studio -H 0.0.0.0 -p 8888 > ~/unsloth-studio.log 2>&1 &

Check that Studio is running:

tail -n 40 ~/unsloth-studio.log
curl -s http://127.0.0.1:8888/api/health | python3 -m json.tool

The health endpoint should return a JSON response with "status":"healthy".

Start Unsloth Studio and check the health endpoint

curl -I http://127.0.0.1:8888 may return 405 Method Not Allowed because Studio does not accept HEAD requests on /. Use curl http://127.0.0.1:8888 or /api/health instead.

Open Studio

On first launch, Studio creates a default admin user named unsloth. Read the generated bootstrap password from the instance:

cat ~/.unsloth/studio/auth/.bootstrap_password

From a second terminal on your local machine, forward port 8888:

tnr ports forward <instance-id> --add 8888

Your Studio URL uses this format:

https://<instance-uuid>-8888.thundercompute.net

Open the URL in your browser and sign in with:

Field	Value
Username	`unsloth`
Password	The value from `~/.unsloth/studio/auth/.bootstrap_password`

After the bootstrap login, Studio prompts you to choose a new password for future sessions.

Unsloth Studio first-run account setup screen

Choose a model

Use unsloth/gemma-4-E2B-it for the first run. It is current, small enough for a quick validation run, and available from the Unsloth model presets. If you want to scale the same flow after the first run works, use a larger available GPU and choose a larger supported model such as unsloth/gemma-4-E4B-it. unsloth/Qwen3.5-2B is also available in Studio, but start with the Gemma 4 E2B path below to keep the first fine-tune simple.

Prepare a small dataset

Create a JSONL file in Alpaca format on the computer where your browser is open. Studio’s Upload button reads from your browser’s local filesystem, not from the SSH session on the instance. Each line should contain instruction, input, and output fields. This demo trains the model to write compact plant-care cards in a consistent format:

cat > plant-care-mini-alpaca.jsonl <<'EOF'
{"instruction":"Write a compact plant care card.","input":"Plant: basil. Location: sunny kitchen windowsill.","output":"Care card:\\nLight: Bright sun for most of the day.\\nWater: Keep soil lightly moist, not soggy.\\nWatch: Pinch flowers early so leaves stay flavorful."}
{"instruction":"Write a compact plant care card.","input":"Plant: snake plant. Location: low-light bedroom corner.","output":"Care card:\\nLight: Tolerates low light, grows faster in indirect light.\\nWater: Let soil dry fully between waterings.\\nWatch: Yellow mushy leaves usually mean overwatering."}
{"instruction":"Write a compact plant care card.","input":"Plant: pothos. Location: office shelf with indirect light.","output":"Care card:\\nLight: Medium indirect light is ideal.\\nWater: Water when the top inch of soil is dry.\\nWatch: Long bare vines can be trimmed to encourage fuller growth."}
{"instruction":"Write a compact plant care card.","input":"Plant: mint. Location: patio container with morning sun.","output":"Care card:\\nLight: Morning sun and afternoon shade work well.\\nWater: Keep soil evenly moist.\\nWatch: Grow mint in a pot so it does not take over nearby beds."}
{"instruction":"Write a compact plant care card.","input":"Plant: monstera. Location: bright living room away from direct sun.","output":"Care card:\\nLight: Bright indirect light encourages larger leaves.\\nWater: Water when the top two inches of soil dry out.\\nWatch: Give it a moss pole or support as it climbs."}
{"instruction":"Write a compact plant care card.","input":"Plant: aloe. Location: warm south-facing window.","output":"Care card:\\nLight: Strong bright light is best.\\nWater: Soak deeply, then let the soil dry out.\\nWatch: Soft translucent leaves usually mean too much water."}
{"instruction":"Write a compact plant care card.","input":"Plant: fern. Location: humid bathroom with filtered light.","output":"Care card:\\nLight: Filtered light, never harsh direct sun.\\nWater: Keep soil consistently damp.\\nWatch: Crispy tips often mean the air is too dry."}
{"instruction":"Write a compact plant care card.","input":"Plant: jade plant. Location: sunny desk near a window.","output":"Care card:\\nLight: Several hours of bright light help compact growth.\\nWater: Let the potting mix dry before watering again.\\nWatch: Wrinkled leaves can mean the plant is thirsty."}
EOF

This tiny file is only for setup validation. Use a larger, higher-quality dataset for a real fine-tune.

Prepare and verify an Alpaca JSONL dataset

Upload the dataset

In Unsloth Studio:

Open Train.
In Dataset, click Upload.
Select plant-care-mini-alpaca.jsonl.
Confirm Studio detects the format as alpaca.

If Studio asks for manual mapping, map:

Dataset column	Studio field
`instruction`	`instruction`
`input`	`input`
`output`	`output`

Configure training

In the Model section:

Setting	Value
Hugging Face Model	`unsloth/gemma-4-E2B-it`
Method	`QLoRA (4-bit)`
Hugging Face Token	Leave blank for this public model

In the Parameters section:

Setting	Value
Max Steps	`8` for the demo run
Context Length	`2048`
Learning Rate	`0.0002`
Batch Size	`2`
Gradient Accumulation	`4`
Optimizer	`AdamW 8-bit`
Gradient Checkpointing	`unsloth`

In LoRA Settings:

Setting	Value
LoRA Rank	`8`
LoRA Alpha	`8`
LoRA Dropout	`0`
Target Modules	`all-linear`
Fine-tune Vision Layers	Off for this text-only dataset
Fine-tune Language Layers	On
Fine-tune Attention Modules	On
Fine-tune MLP Modules	On

For a real run, increase Max Steps or switch to epochs after the first validation run succeeds. Keep the first run short so you can catch dataset or setup issues quickly.

Start training

Click Start Training. The first run may spend a few minutes downloading model weights and initializing kernels before the step counter starts moving. When the run completes, Studio shows the output path and enables Compare in Chat and Export Model. Successful runs save the adapter to a path like:

~/.unsloth/studio/outputs/unsloth_gemma-4-E2B-it_<timestamp>

Keep the adapter folder and the base model name together. A LoRA adapter is not a standalone model; it is loaded alongside the base model it was trained from.

Test the adapter

Click Compare in Chat after training finishes. Studio opens the base model and fine-tuned adapter side by side. Try a prompt that matches the dataset pattern but was not copied directly from the training rows:

Write a compact plant care card.

Plant: rosemary. Location: hot balcony with afternoon sun.

For this setup dataset, look for the fine-tuned side to follow the Care card, Light, Water, and Watch structure. Treat this as a workflow check, not a quality benchmark. For a real adapter, expand the dataset and test with prompts that represent the task you actually need.

Clean up

When you are done testing:

pkill -f "unsloth studio"
exit

Then delete the instance from your local terminal:

tnr delete <instance-id>

Billing stops when the instance is deleted.

Getting Started

Operations

Guides

Reference

API Reference

Fine-tune with Unsloth Studio

Create the instance

Install Unsloth Studio

Start Studio

Open Studio

Choose a model

Prepare a small dataset

Upload the dataset

Configure training

Start training

Test the adapter

Clean up

References

​Create the instance

​Install Unsloth Studio

​Start Studio

​Open Studio

​Choose a model

​Prepare a small dataset

​Upload the dataset

​Configure training

​Start training

​Test the adapter

​Clean up

​References

Create the instance

Install Unsloth Studio

Start Studio

Open Studio

Choose a model

Prepare a small dataset

Upload the dataset

Configure training

Start training

Test the adapter

Clean up

References