Practical overview
When it fits
Use it when you want hands-on, notebook-based learning or experimentation to accompany the Hands-On Large Language Models book, rather than a ready-to-use hosted AI product.
Who it is for
- People learning large-language-model concepts through executable examples
- Developers exploring notebook-based LLM experimentation
- Researchers and technical readers studying transformers, embeddings, retrieval, and LLM applications
Documented uses
- Work through practical LLM concepts in Jupyter Notebooks
- Experiment with embeddings and retrieval workflows
- Study transformer and LLM application examples
- Use notebook demonstrations as a starting point for building and evaluating LLM applications
Requirements & limits
The repository is publicly accessible and uses the Apache-2.0 license. No account, pricing, or broader service-access details are documented here.
- Its main deliverable is notebook code, so it fits practical learners better than readers seeking only conceptual text.
- Topics span transformers, embeddings, retrieval, and LLM applications rather than a single narrowly defined technique.
- It is an educational repository, not a documented hosted AI service with a built-in user interface.
- The public Apache-2.0 licensing is relevant when evaluating reuse of the repository’s code.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Open the LLM book examples in Google Colab
Set up access to the repository’s chapter notebooks for interactive learning and experimentation.
Before you start
- A Google Colab environment.
- Access to the public GitHub repository.
Steps
Open the repository README and locate the table of contents.
Choose a chapter notebook, such as the notebooks for tokens and embeddings, prompt engineering, or semantic search.
Open the notebook through its provided Open in Colab link.
Run the selected examples in Google Colab; the README recommends Colab as the easiest setup and identifies a T4 GPU with 16GB of VRAM as available there.
Expected outcome
The selected chapter notebook is available in Google Colab for working through its documented examples.
Guide limitations
- The repository states that examples were mainly built and tested using Google Colab; other cloud providers may work but are not documented as the most stable platform here.
- The evidence documents notebook access and setup, not a separate deployment workflow or application-serving procedure.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- HandsOnLLM/Hands-On-Large-Language-Models · Official homepage title · retained in its source language