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finetune-train

Orchestrate supervised fine-tuning of language models with HuggingFace Jobs and GGUF conversion.

4|1|Updated Dec 28, 2025
Search Tags:#fine-tuning#model evaluation#sft#llm training#gguf conversion#huggingface jobs
One-click install
npx skills add https://github.com/marcgreen/therapy-coach-finetune --skill finetune-train
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: finetune-train
Source: https://github.com/marcgreen/therapy-coach-finetune/tree/main/.claude/skills/finetune-train
Command: npx skills add https://github.com/marcgreen/therapy-coach-finetune --skill finetune-train

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of training a fine-tuned language model and rigorously evaluating its performance improvements against a base model, ensuring a robust and effective outcome.

Core Features & Use Cases

  • Supervised Fine-Tuning (SFT): Guides the model through a supervised training process using prepared datasets.
  • GGUF Conversion & Upload: Converts the trained model into the GGUF format for local deployment and uploads it to HuggingFace Hub.
  • Rigorous Evaluation: Compares the fine-tuned model against the base model using statistical methods to confirm significant improvements.
  • Use Case: After preparing a dataset of therapeutic conversations, use this Skill to fine-tune a base LLM, convert it to a locally runnable GGUF format, and then confirm through evaluation that it outperforms the original model in therapeutic coaching quality.

Quick Start

Use the finetune-train skill to train a model using your prepared training data and evaluate its performance.

Frequently Asked Questions about finetune-train

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune an LLM and evaluate it against a base model?▼

Fine-tuning an LLM involves supervised training with prepared datasets via HuggingFace Jobs, followed by rigorous statistical evaluation comparing the new model against the base model to verify domain-specific improvements.

Can I convert a fine-tuned HuggingFace model to GGUF format for local deployment?▼

Yes, you can convert a fine-tuned HuggingFace model to GGUF format for local deployment. This Skill handles GGUF conversion and uploads the resulting model to the HuggingFace Hub for accessible local runtime environments.

What is supervised fine-tuning and when do I need it for domain-specific applications?▼

Supervised fine-tuning (SFT) trains language models on prepared datasets to achieve verifiable domain-specific performance improvements. It is needed when reproducible model adaptation is required for specialized applications like therapeutic coaching.

What data format is required for LLM training with HuggingFace Jobs?▼

LLM training with HuggingFace Jobs requires adherence to specific data formats for dataset preparation. You must structure your training data appropriately before initiating the supervised fine-tuning orchestration process.

Do I need GPU resources to run supervised fine-tuning on language models?▼

Yes, GPU resources are required to run supervised fine-tuning. This Skill requires specific GPU resource allocation to properly execute LLM training jobs and ensure reproducible model improvement.

Why does my fine-tuned model perform worse than the original base model?▼

If your fine-tuned model performs worse, the evaluation protocols compare both models statistically to identify failures. Proper dataset preparation and strict adherence to training data formats are critical to achieving significant improvements.

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