kaggle-finetune
End-to-end workflow for fine-tuning LLMs using Kaggle datasets. Use when downloading datasets from Kaggle for model training, preparing conversation/customer service data for chatbot fine-tuning, or building domain-specific AI assistants. Covers dataset discovery, download, preprocessing into chat format, and integration with PEFT/LoRA training.
npx skills add majiayu000/claude-skill-registry --skill kaggle-finetune --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Kaggle Fine-Tuning Workflow ## Overview Complete pipeline for downloading Kaggle datasets and fine-tuning LLMs. Handles dataset discovery, download via Kaggle CLI, preprocessing into HuggingFace chat format, and training with PEFT/LoRA for memory-efficient fine-tuning. ## Prerequisites ```bash pip install kaggle peft transformers accelerate bitsandbytes datasets trl ``` Set Kaggle API token: ```bash export KAGGLE_API_TOKEN=KGAT_xxxxx ``` ## Instructions ### Step 1: Search and download datasets ```bash # Search for relevant datasets kaggle datasets list -s "customer service conversation" --sort-by votes # Download specific dataset kaggle datasets download -d bitext/bitext-gen-ai-chatbot-customer-support-dataset -p ./data --unzip ``` **Recommended datasets for chatbots:** | Dataset | Use Case | |---------|----------| | `bitext/bitext-gen-ai-chatbot-customer-support-dataset` | Customer support | | `kreeshrajani/3k-conversations-dataset-for-chatbot` | General chat | | `oleksiymaliovanyy/call-center-transcripts-dataset` | Call center | | `narendrageek/mental-health-faq-for-chatbot` | FAQ format | ### Step 2: Preprocess into chat format Convert data to HuggingFace messages format: ```p
- Overview
- Prerequisites
- Instructions
- Step 1: Search and download datasets
- Step 2: Preprocess into chat format
- Step 3: Fine-tune with LoRA
- Step 4: Test and deploy
- Examples
- Example 1: Fine-tune a customer service chatbot from a Kaggle dataset
- Example 2: Build a medical FAQ chatbot from Kaggle mental health data
- Guidelines
pip install kaggle peft transformers accelerate bitsandbytes datasets trl export KAGGLE_API_TOKEN=KGAT_xxxxx Search for relevant datasets kaggle datasets list -s "customer service conversation" --sort-by votes Download specific dataset kaggle datasets download -d bitext/bitext-gen-ai-chatbot-customer-support-dataset -p ./data --unzip
What does the kaggle-finetune skill do?
End-to-end workflow for fine-tuning LLMs using Kaggle datasets. Use when downloading datasets from Kaggle for model training, preparing conversation/customer service data for chatbot fine-tuning, or building domain-specific AI assistants. Covers dataset discovery, download, preprocessing into chat format, and integration with PEFT/LoRA training.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill kaggle-finetune --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.