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.
Colab-finetuning lets you fine-tune language models on Google Colab GPUs without owning local hardware.
AI-generated summary based on this skill's SKILL.md
Install
synthetic-sciences/openscience/colab-finetuning · repository language: TypeScript
git clone https://github.com/synthetic-sciences/openscience
cp -r openscience/backend/cli/skills/ml-training/colab-finetuning ~/.claude/skills/colab-finetuningnpx skillfed install synthetic-sciences/openscience/colab-finetuningFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How to fine-tune LLM on Google Colab free GPU?
colab-finetuning enables supervised fine-tuning of language models directly on Colab's free T4 GPUs through Unsloth integration. You connect via WebSocket bridge to run training workflows remotely without local hardware. The skill supports models like Qwen and Llama, with 4-bit quantization to fit larger models within Colab's memory constraints.
What training methods does colab-finetuning support?
colab-finetuning supports multiple training paradigms: supervised fine-tuning (SFT) for standard model adaptation, DPO for preference optimization, and GRPO for reinforcement learning workflows. It also handles vision and text-to-speech model tuning, all executable on Colab's GPU tiers from free T4 through paid A100 instances.
How does colab-finetuning connect via WebSocket?
colab-finetuning uses a WebSocket bridge to establish remote execution between your local environment and Colab notebooks. This persistent connection allows you to submit training jobs, monitor progress, and retrieve results without managing Colab session timeouts manually, streamlining the remote training workflow.
Can colab-finetuning train 14B models on free Colab?
colab-finetuning enables 14B model training on free Colab through 4-bit quantization with Unsloth, which dramatically reduces VRAM requirements. While free T4 GPUs have limited memory, quantization makes larger models feasible. For optimal performance and faster training, upgrading to Colab Pro's A100 GPUs is recommended.
What GPU options are available with colab-finetuning?
colab-finetuning supports free Colab's T4 GPUs for cost-free experimentation and Colab Pro's paid A100 tiers for production workloads. The skill adapts training configurations to each GPU's memory and compute capacity, allowing you to start free and scale up as needed without changing your training code.
How does colab-finetuning compare to alternatives?
colab-finetuning leverages Colab's accessibility and cost structure—free T4 or affordable Pro A100 access—versus paid alternatives like Tinker, Lambda, or RunPod. It integrates Unsloth's optimization for efficient training, making it ideal for rapid experimentation and learning without upfront infrastructure investment.
SKILL.md
rendered from the published skill — quoted content, verbatim
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
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backend/cli/skills/ml-training/colab-finetuning/SKILL.md
backend/cli/skills/ml-training/colab-finetuning/references/bridge-setup.md
backend/cli/skills/ml-training/colab-finetuning/references/gpu-tiers.md
backend/cli/skills/ml-training/colab-finetuning/references/troubleshooting.md