Skip to content
Courses/Fine-tuning and post-training for LLMs/Export to GGUF and Ollama
Lesson

Export to GGUF and Ollama

Training is only half the job. If your adapted model cannot be used with your local stack, you are not done yet. Exporting properly means preserving behavior, quantizing thoughtfully, and testing in the real app.

  • Understand when to use a separate adapter and when to merge.
  • Prepare GGUF for llama.cpp/Ollama.
  • Create a Modelfile with a system prompt and parameters.

Output workflow

Terminal
adapter LoRA
  -> evaluate
  -> merge with base model if appropriate
  -> export safetensors
  -> convert to GGUF
  -> quantize Q4/Q5/Q8
  -> create Ollama Modelfile
  -> test in the real app

Ollama Modelfile

Terminal
FROM ./soporte-qwen-lora-q5_k_m.gguf

PARAMETER temperature 0.2
PARAMETER num_ctx 4096

SYSTEM """
You are an internal support assistant.
Respond in clear English.
If data is missing, ask for clarification.
Do not invent policies, prices, or personal data.
"""
Terminal
ollama create soporte-pyme -f Modelfile
ollama run soporte-pyme "Classify this sample email"
Evals, Overfitting, and RegressionsProject: adapted model for an SMB
Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

AI for small businesses and freelancersUse AI where it improves real work.Beginner to intermediate · 20 lessonsView course Generative AI: image, voice and videoCreate production-ready media with AI pipelines.Beginner to intermediate · 7 lessonsView course Games, 3D and simulations with AIPrototype interactive worlds with AI-assisted tools.Beginner to intermediate · 6 lessonsView course

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

Aulafy foundationsPython, Git, terminal, and Docker for learning AI smoothly.Beginner · 6 lessonsView course Codex for programmersBuild, test, and ship changes with OpenAI Codex.Beginner to advanced · 10 lessonsView course Claude Code, from zero to proBuild real software by talking to AI in your terminal.Beginner to advanced · 24 lessonsView course Claude Code + Local AIPrivate AI tools that run on your machine.Intermediate · 34 lessonsView course

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

Advanced and secure RAGBuild retrieval systems that answer better and leak less.Intermediate · 14 lessonsView course Agents and automationDesign agents that can work, recover and stay under control.Intermediate to advanced · 20 lessonsView course Production agents with LangGraph and n8nTake agent prototypes into reliable operations.Intermediate to advanced · 9 lessonsView course Self-hosted AI automation for small businessesRun AI workflows on your own infrastructure.Intermediate · 10 lessonsView course AI Router and content systemRoute local and frontier models with quality, cost, and privacy controls.Intermediate to advanced · 6 lessonsView course AI security and evaluationMeasure, test and harden AI systems before trusting them.Intermediate · 7 lessonsView course Local MLOps and model deploymentServe, monitor and operate models from your own stack.Intermediate to advanced · 10 lessonsView course Fine-tuning and post-training for LLMsYou are hereTrain useful adapters without losing control.Intermediate to advanced · 8 lessonsView course

You do not need every course: choose the component your system needs and return as it grows.

View full catalogue