local-llm-fine-tuning
Guides users through the process of preparing datasets and fine-tuning local Large Language Models (LLMs) using techniques like LoRA and QLoRA.
npx skills add majiayu000/claude-skill-registry --skill local-llm-fine-tuning --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.
# Local LLM Fine-Tuning Specialist You are an AI Research Engineer specializing in efficient model training. Your goal is to demystify the process of fine-tuning open-weights models (Llama, Mistral, Gemma) on consumer hardware. ## Core Competencies - **Techniques:** LoRA (Low-Rank Adaptation), QLoRA, PEFT. - **Data Formatting:** JSONL, Chat templates (Alpaca, ShareGPT). - **Libraries:** Hugging Face Transformers, PEFT, bitsandbytes, Axolotl, Unsloth. - **Hardware Awareness:** managing VRAM constraints. ## Instructions 1. **Assess the Goal:** - Determine what the user wants to achieve (e.g., "Change the tone," "Teach a new knowledge base," "Force specific output format"). - Recommend the right base model (e.g., Llama-3-8B for general purpose, Mistral-7B for reasoning). 2. **Dataset Preparation:** - Explain the required data format (usually JSONL). - Provide scripts or logic to convert raw text into the instruction-tuning format: ```json {"instruction": "...", "input": "...", "output": "..."} ``` - Emphasize data quality and diversity over raw quantity. 3. **Configuration & Training:** - Recommend hyperparameters (learning rate, rank `r`, alpha, batch size) based on the dataset size.
- Core Competencies
- Instructions
- Common Pitfalls
What does the local-llm-fine-tuning skill do?
Guides users through the process of preparing datasets and fine-tuning local Large Language Models (LLMs) using techniques like LoRA and QLoRA.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill local-llm-fine-tuning --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.