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vivacious-cloud/sample-llm-finetuning-dataset

Vivacious Cloud — Official Starter Fine-Tuning Dataset Fine-tune any open-source model on this dataset in 1 terminal command. Always on the cheapest GPU alive. 🎯 The Developer Flow: Try Demo → Understand → Run on Vivacious Cloud Try the Demo: Test drive the VRAM calculation and 12-cloud spot arbitrage in our Hugging Face Space Simulator. Understand the Savings: See how autonomous multi-cloud routing cuts training spend by up to… See the full description on the dataset page: https://huggingface.co/datasets/vivacious-cloud/sample-llm-finetuning-dataset.

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Dataset Card

Vivacious Cloud — Official Starter Fine-Tuning Dataset

<p align="center"> <a href="https://vivaciouscloud.com"> <img src="https://vivaciouscloud.com/assets/logo.svg" alt="Vivacious Cloud" width="100" height="100" /> </a> </p>

<h3 align="center"> Fine-tune any open-source model on this dataset in 1 terminal command.<br> Always on the cheapest GPU alive. </h3>

<p align="center"> <a href="https://vivaciouscloud.com"><img src="https://img.shields.io/badge/Web%20Platform-vivaciouscloud.com-00DC82?style=for-the-badge&logo=google-chrome&logoColor=white" alt="Vivacious Cloud" /></a> <a href="https://huggingface.co/spaces/vivacious-cloud/vivacious-terminal-simulator"><img src="https://img.shields.io/badge/Live%20Simulator-HF%20Space-00E5FF?style=for-the-badge&logo=huggingface&logoColor=white" alt="Live Simulator" /></a> <a href="https://github.com/Viavcious-cloud/vivacious-cli"><img src="https://img.shields.io/badge/GitHub-vivacious--cli-blue?style=for-the-badge&logo=github&logoColor=white" alt="GitHub" /></a> </p>

🎯 The Developer Flow: Try Demo → Understand → Run on Vivacious Cloud

  1. 1.Try the Demo: Test drive the VRAM calculation and 12-cloud spot arbitrage in our Hugging Face Space Simulator.
  2. 2.Understand the Savings: See how autonomous multi-cloud routing cuts training spend by up to 76% with guaranteed preflight OOM protection.
  3. 3.Run on Vivacious Cloud: Deploy your fine-tuning run with zero PyTorch code, zero CUDA drivers, and zero idle costs at [vivaciouscloud.com](https://vivaciouscloud.com).

⚡ Quickstart: Fine-Tune in 1 Command with Vivacious Cloud

No train.py scripts. No CUDA toolkits. No PyTorch configuration. Zero DevOps.

1. Install the CLI

bash
# Linux & macOS
curl -fsSL https://vivaciouscloud.com/install.sh | sh

# Windows (PowerShell)
iwr https://vivaciouscloud.com/install.ps1 -useb | iex

2. Log in and prepare the dataset locally

bash
vivacious login <your-workspace-slug>
vivacious prepare ./train.jsonl

3. Check preflight permit & deploy to the cheapest GPU alive

bash
# Verify analytical VRAM fit & live 12-cloud spot rates
vivacious permit <your-workspace-slug> starter-run --model unsloth/Llama-3.2-3B-Instruct --method lora

# Deploy training run across wholesale cloud spot markets
vivacious deploy <your-workspace-slug>

4. Download your trained weights (Zero egress fees)

bash
vivacious download <job-id>

💡 Why Train with Vivacious Cloud?

Problem with Traditional CloudsHow Vivacious Cloud Solves It
Expensive Vendor Lock-In: Single-cloud providers charge up to 400% markup.Autonomous 60s Arbitrage: Scans 12+ cloud broker networks (AWS, GCP, RunPod, Vast.ai, Lambda) and routes to the cheapest spot node.
Wasted OOM Spend: PyTorch crashes after 15 minutes of paid compute.Guaranteed Preflight OOM Guard: Analyzes token lengths and VRAM headroom before VM boot at ₹0 cost.
Spot Preemption Data Loss: Spot nodes terminate mid-epoch, losing progress.Autonomous Mid-Job Migration: Automatically checkpoints to Cloudflare R2 and resumes on an alternative provider with zero data loss.
Hidden Idle Bills & Egress: Lingering idle VM fees and high download charges.Zero Idle Cost & Zero Egress: Datasets and checkpoints live on Cloudflare R2. Prepaid credits never expire.

📊 Dataset Structure

This dataset contains instruction-response pairs focusing on machine learning optimization, distributed training, GPU memory architectures, and LoRA/QLoRA mechanics.

json
{
  "instruction": "Explain how Low-Rank Adaptation (LoRA) reduces fine-tuning memory footprint.",
  "response": "LoRA freezes pre-trained foundation model weights and injects small, trainable rank decomposition matrices..."
}

🌐 Official Links & Community