This guide is for people who want to understand how Kllama is intended to be used after setup.
- The app defaults to
http://localhost:11434and can be pointed at another Ollama host from the UI. - Kllama is a deliberately small codebase meant for learning and experimentation, not a full chat platform.
- The Streamlit app keeps the conversation in session state and sends the current transcript to Ollama on each turn.
These sample prompts are intentionally student-friendly and show the kinds of local GenAI tasks Kllama was built to teach.
Summarize the following article in 5 bullet points for an undergraduate student. Keep the language simple and include one key takeaway.
Teach me the concept of gradient descent like I am new to machine learning. Start with intuition, then give a simple numerical example.
I want to design a student project on local LLMs. Give me 5 project ideas with learning goals, required tools, and expected difficulty.
Explain this Python function step by step. Then tell me what could go wrong at runtime and how to improve readability.
Review this generated answer critically. Point out possible hallucinations, missing evidence, and what I should verify before trusting it.
Kllama stays relevant because it teaches durable GenAI patterns without hiding them behind a heavyweight stack. A learner can inspect a few Python files and understand:
- local model execution,
- streamed token generation,
- prompt conditioning,
- chat state management,
- reproducible testing around pure helper logic.
That is exactly the kind of project that ages well if it is maintained.