Unsloth Studio Training

Unsloth Studio Training

Manage LLM fine-tuning jobs through Unsloth Studio's local web UI and API.

Quick Start

# Check if studio is running
curl -s http://localhost:8888/api/health | jq

# Login and get token
RESPONSE=$(curl -s -X POST http://localhost:8888/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{"username": "admin", "password": "your-password"}')
export UNSLOTH_STUDIO_TOKEN=$(echo $RESPONSE | jq -r '.access_token')

# Start training
curl -X POST http://localhost:8888/api/train/start \
  -H "Authorization: Bearer $UNSLOTH_STUDIO_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model_name": "unsloth/llama-3.2-3b-instruct-bnb-4bit", ...}'

Workflow

1. Docker Setup     → [docker.md](references/docker.md)
2. Authenticate     → [authentication.md](references/authentication.md)
3. Prepare Dataset  → [datasets.md](references/datasets.md)
4. Train Model      → [training.md](references/training.md)
5. Export Model     → [export.md](references/export.md)
6. Run Inference    → [inference.md](references/inference.md)

Reference Files

API Endpoints

Training Methods

Environment Variables

UNSLOTH_STUDIO_URL=http://localhost:8888
UNSLOTH_STUDIO_TOKEN=<jwt_token>

Related Docs

• Unsloth Studio Docs
• Getting Started
• Data Recipes