Ollama & Local LLMs: Fine-Tune, Deploy, Build Python AI Apps
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Unclaimed ProfileThis comprehensive course, led by Abdurrahman TEKIN, offers a deep dive into the world of private, offline artificial intelligence. Designed for Python developers and AI enthusiasts, the curriculum guides you through the process of setting up and running local Large Language Models (LLMs) on your own hardware using Ollama. You will learn to build responsive Streamlit user interfaces, work with multimodal models to handle vision and video, and implement document-grounded question-answering systems (RAG) using LangChain and spaCy embeddings. Beyond simple generation, the course covers agentic workflows with CrewAI and local model execution with Hugging Face transformers. A major highlight is the hands-on approach to fine-tuning: you will master QLoRA fine-tuning using Unsloth, evaluate your custom models, merge LoRA adapters, and export them into Ollama using Modelfiles. The course culminates in an advanced capstone project where you build a local AI coding assistant capable of editing, planning, and executing code safely. Perfect for developers prioritizing data privacy and custom performance.
About the creator
Abdurrahman TEKIN
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Abdurrahman Tekin is a dedicated software engineer and AI educator who specializes in the practical implementation of local large language models. With a deep-rooted passion for making cutting-edge artificial intelligence accessible to developers, Abdurrahman focuses his teaching on the transition from cloud-based API reliance to high-performance, private, and local deployments. He believes that the future of software development lies in the ability to fine-tune and run sophisticated AI models on local hardware, reducing latency, ensuring data privacy, and optimizing operational costs. Through his curriculum, he bridges the gap between complex theoretical concepts and hands-on Python-based application building. Students often describe…Show more
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Learning format
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Price
Price may change · updated within 1–2 weeks
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What You'll Learn
- Use Ollama from Python: chat, streaming, generation.
- Build Streamlit UIs on top of local models.
- Add vision: images with multimodal models (e.g. LLaVA-style flows).
- Handle video: frames, Whisper transcription, Q&A over audio.
- Do PDF Q&A and document-grounded prompts.
- Use LangChain: Ollama integration, templates, LLMChain, multi-input prompts.
- Chunk long text and summarize with map-style workflows.
- Use spaCy embeddings: similarity, semantic search, RAG over long text.
- Build note/diary style personal knowledge apps.
- Run CrewAI agents with Ollama (sequential and domain examples).
- Run Hugging Face transformers locally; manage cache and disk use.
- Try TTS and text-to-video scripts where GPU allows.
- Run Unsloth inference (e.g. SmolLM2), inspect tokenizer and model behavior.
- Fine-tune with Unsloth + QLoRA; watch validation for forgetting.
- Test base vs fine-tuned models; merge LoRA adapters.
- Export to Ollama (Modelfile, quantization tradeoffs).
- Capstone: local LLM coding assistant (editor, chat, diffs, run code, shell, optional web research, planning, persistence).
Best For
- Python developers eager to implement local, private AI solutions without relying on cloud APIs.
- AI practitioners looking to move beyond simple inference into QLoRA fine-tuning and model deployment.
- Data privacy advocates wanting to process sensitive information securely on their own hardware.
- Students building advanced AI agents with tool-use capabilities like coding assistants and RAG systems.
Not For
- Beginners who do not have a working knowledge of Python programming basics.
- Individuals without access to local hardware (GPU) suitable for running local LLMs.
- People looking for a high-level theory course without hands-on implementation and coding projects.
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