---
id: synthetic-sciences/openscience/colab-finetuning
version: "239abf00"
license: Apache-2.0
install: manual
updated: 2026-07-27
---

# colab-finetuning — Run Unsloth-powered LLM training directly on Google Colab GPUs from openscience, connecting via WebSocket bridge for remote execution. Supports supervised fine-tuning, reinforcement learning, preference optimization, vision, and text-to-speech workflows across free T4 through paid A100 tiers.

Publisher: synthetic-sciences · Stars: 2896 · Updated: 2026-07-27

Install (manual): `git clone https://github.com/synthetic-sciences/openscience`

## SKILL.md

# 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](https://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](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](references/troubleshooting.md) for more solutions.


[View on SkillFed](https://skillfed.io/synthetic-sciences/openscience/colab-finetuning) · [View on GitHub](https://github.com/synthetic-sciences/openscience)