Google Colab Fine-Tuning

Fine-tune LLMs using Google Colab GPUs directly from the openscience CLI. Connect to free or paid Colab runtimes and run Unsloth training workflows remotely.

When to Use Colab Fine-Tuning

Use Colab when:

• You don't have a local GPU but need to fine-tune a model
• You want free GPU access (T4 with 15GB VRAM on Colab Free)
• Training models up to ~14B parameters (4-bit QLoRA)
• Quick experiments and prototyping before scaling to cloud
• Colab Pro/Pro+ for A100 (40-80GB) access

Don't use Colab when:

• You need persistent long-running jobs (>12h) — use Tinker or cloud providers
• Training 70B+ models — use Lambda, RunPod, or multi-GPU cloud
• You need guaranteed uptime — Colab may disconnect idle sessions
• Production training pipelines — use managed services

Colab vs Alternatives:

Quick Start

Step 1: Generate Bridge Notebook

Use colab_notebook tool with workflow="bridge"

This creates a openscience-bridge.ipynb file that establishes a WebSocket tunnel between openscience and the Colab GPU.

Step 2: Open in Colab

1. Go to colab.research.google.com
2. Upload the bridge notebook (File → Upload notebook)
3. Select GPU runtime (Runtime → Change runtime type → T4 GPU)
4. Run all cells
5. Copy the WebSocket URL that appears

Step 3: Connect from openscience

Use colab_connect tool with connection_url="wss://..."

Step 4: Run Training

Use colab_finetune tool with:
  workflow: "sft"
  model: "unsloth/Qwen3-4B-unsloth-bnb-4bit"
  dataset: "mlabonne/FineTome-100k"

Or execute individual cells:

Use colab_execute tool with code="import torch; print(torch.cuda.get_device_name(0))"

GPU Tiers

See references/gpu-tiers.md for detailed VRAM requirements.

Available Tools

Training Workflows

SFT (Supervised Fine-Tuning)

Standard instruction tuning. Best for: chat models, domain adaptation, format learning.

colab_finetune workflow=sft model="unsloth/Qwen3-4B-unsloth-bnb-4bit" dataset="mlabonne/FineTome-100k"

GRPO (Reinforcement Learning)

Train reasoning models with reward functions. Best for: math, coding, structured output.

colab_finetune workflow=grpo model="unsloth/Qwen3-4B-unsloth-bnb-4bit" dataset="your-dataset"

DPO (Direct Preference Optimization)

Align models with human preferences. Requires chosen/rejected pairs.

colab_finetune workflow=dpo model="unsloth/Llama-3.1-8B-unsloth-bnb-4bit" dataset="HuggingFaceH4/ultrafeedback_binarized"

Vision Fine-Tuning

Fine-tune vision-language models (Qwen3-VL, Gemma 3, Llama 3.2 Vision).

colab_finetune workflow=vision model="unsloth/Qwen3-VL-2B-unsloth-bnb-4bit" dataset="your-vision-dataset"

TTS Fine-Tuning

Fine-tune text-to-speech models (Orpheus, Sesame-CSM).

colab_finetune workflow=tts model="unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit" dataset="your-tts-dataset"

Troubleshooting

Connection Issues

• "WebSocket connection failed": Ensure the bridge notebook is still running in Colab. Re-run the tunnel cell if the URL expired.
• "Execution timeout": Colab may have disconnected due to inactivity. Run the keep-alive cell.
• "Connection closed during execution": Colab Free disconnects after idle time. Upgrade to Pro for more stability.

Training Issues

• CUDA OOM: Reduce batch_size, max_seq_length, or lora_rank. Use 4-bit quantization.
• Slow training: Ensure GPU runtime is selected. Check with colab_status detail=gpu.
• Package not found: Unsloth is installed automatically by colab_finetune. For custom packages, use colab_execute with pip install.

Colab Enterprise

• Requires GCP project with Vertex AI API enabled
• Use colab_connect mode=enterprise project_id="your-project"
• GPU quotas apply — check GCP console for availability

See references/troubleshooting.md for more solutions.