This is the Windows app named LLM Course whose latest release can be downloaded as llm-coursesourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
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- 7. Download Wine van de softwarebronnen voor Linux-distributies. Eenmaal geïnstalleerd, kunt u vervolgens dubbelklikken op de app om ze met Wine uit te voeren. Je kunt ook PlayOnLinux proberen, een mooie interface via Wine waarmee je populaire Windows-programma's en -games kunt installeren.
Wine is een manier om Windows-software op Linux uit te voeren, maar zonder dat Windows vereist is. Wine is een open-source Windows-compatibiliteitslaag die Windows-programma's rechtstreeks op elke Linux-desktop kan uitvoeren. In wezen probeert Wine genoeg van Windows opnieuw te implementeren, zodat het al die Windows-applicaties kan draaien zonder Windows echt nodig te hebben.
SCREENSHOTS
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LLM-cursus
PRODUCTBESCHRIJVING
LLM Course is a hands-on, notebook-driven path for learning how large language models work in practice, from data curation to training, fine-tuning, evaluating, and deploying. It emphasizes reproducible experiments: each step is demonstrated with runnable code, clear dependencies, and references to commonly used open-source models and libraries. Learners get exposure to multiple adaptation strategies—LoRA/QLoRA, instruction fine-tuning, and alignment techniques—so they can choose approaches that fit their hardware and budgets. The materials also cover inference optimization and quantization to make serving LLMs feasible on commodity GPUs or even CPUs, which is crucial for side projects and startups. Evaluation is treated as a first-class topic, with examples of automatic and human-in-the-loop methods to catch regressions and verify quality beyond simple loss values. By the end, students have a mental model and a practical toolkit for iterating on datasets, training configs, etc.
Kenmerken
- End-to-end notebooks covering data prep, training, fine-tuning, and serving
- Practical focus on LoRA/QLoRA, instruction tuning, and alignment workflows
- Guidance for resource-constrained hardware plus quantization techniques
- Reproducible setups with pinned dependencies and clear configs
- Evaluation notebooks for automated metrics and human review loops
- Tips for packaging, inference optimization, and lightweight deployment
Programmeertaal
JavaScript
Categorieën
This is an application that can also be fetched from https://sourceforge.net/projects/llm-course.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
