colab-finetuning

name	colab-finetuning
description	Fine-tune LLMs on Google Colab GPUs directly from openscience. Connects to Colab runtimes via WebSocket bridge for remote training with Unsloth. Supports SFT, GRPO, DPO, vision, and TTS workflows on free T4 to Pro A100 GPUs.
category	ml-training
version	1.0.0
author	Synthetic Sciences
license	MIT
tags	["Fine-Tuning","Google Colab","GPU","Remote Training","Unsloth","LoRA","GRPO","Cloud GPU"]
dependencies	["unsloth","torch","transformers","trl","datasets"]

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:

Need	Use
Free GPU, quick experiments	Google Colab
Managed cloud training (any size)	Tinker
Persistent multi-GPU training	Lambda / RunPod
Local GPU available	Unsloth directly
Enterprise with SLA	Colab Enterprise (Vertex AI)

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

Tier	GPU	VRAM	Max Model (QLoRA)	Session Limit
Free	T4	15 GB	~14B	12h, may disconnect
Pro ($10/mo)	T4/V100/A100	16-40 GB	~32B	24h, priority
Pro+ ($50/mo)	A100 (80GB)	80 GB	~72B	24h, guaranteed
Enterprise	Configurable	Any	Any	No limit

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

Available Tools

Tool	Purpose
colab_connect	Connect to a Colab runtime (standard bridge or enterprise)
colab_execute	Run arbitrary Python code on the connected GPU
colab_status	Check GPU, memory, disk, connection status
colab_finetune	Run complete Unsloth training workflow (SFT/GRPO/DPO/vision/TTS)
colab_notebook	Generate .ipynb notebooks for Colab

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.