---
title: "Hands-On-Large-Language-Models vs llm-course"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-mlabonne-llm-course"
tools: ["handsonllm-hands-on-large-language-models", "mlabonne-llm-course"]
---

# Hands-On-Large-Language-Models vs llm-course

Neutral, constraint-first comparison with live GitHub stats.

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Tagline | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks |
| Stars | 27,427 | 80,741 |
| Forks | 6,389 | 9,410 |
| Open issues | 37 | 85 |
| Language | Jupyter Notebook | - |
| Adopt for | The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use, | LLM Course offers a structured learning path into Large Language Models with specific modules targeting fundamental knowledge, advanced LLM development techniques, and practical application deployment. It provides hands- |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | Licensed under Apache-2.0 |
| Categories | LLM Frameworks, Developer Tools | Evaluation & Observability, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Slowing (36%) |
| Days since push | 75d | 152d |
| Open issues (now) | 37 | 85 |
| Owner type | Organization | User |
| Security scan | 96 low (96 low) | No lockfile |
| Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) |

**Typed relationship:** Hands-On-Large-Language-Models _(alternative)_ llm-course

These both provide educational material for learning and applying LLMs including colab notebooks.

## Decision facts: Hands-On-Large-Language-Models

- **Adopt for:** The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use,

## Decision facts: llm-course

- **Adopt for:** LLM Course offers a structured learning path into Large Language Models with specific modules targeting fundamental knowledge, advanced LLM development techniques, and practical application deployment. It provides hands-
- **License detail:** Licensed under Apache-2.0

## Choose when

### Choose Hands-On-Large-Language-Models if…

- These both provide educational material for learning and applying LLMs including colab notebooks.
- Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book.
- Also covers Developer Tools.
- When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity;

### Choose llm-course if…

- These both provide educational material for learning and applying LLMs including colab notebooks.
- Tags unique to llm-course: llm, machine-learning, course, roadmap.
- Also covers Evaluation & Observability, Model Training.
- - When you want to understand the foundational aspects of machine learning alongside more advanced topics on building efficient and high-performing large language models.

## When NOT to use Hands-On-Large-Language-Models

- If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures.
- When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab.
- If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory.
- In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L

## When NOT to use llm-course

- - If you're focused primarily on specialized aspects of AI and machine learning that fall outside the scope of large language models.
- - Not recommended if your immediate need is to dive deep into a narrow topic without the structured progression offered here, preferring instead direct access to advanced use-cases or niche LLM areas.

## Common questions

### What is the difference between Hands-On-Large-Language-Models and llm-course?

Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. See the comparison table for live GitHub stats and shared categories.

### When should I choose Hands-On-Large-Language-Models over llm-course?

Choose Hands-On-Large-Language-Models over llm-course when These both provide educational material for learning and applying LLMs including colab notebooks; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book; Also covers Developer Tools; When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity;.

### When should I choose llm-course over Hands-On-Large-Language-Models?

Choose llm-course over Hands-On-Large-Language-Models when These both provide educational material for learning and applying LLMs including colab notebooks; Tags unique to llm-course: llm, machine-learning, course, roadmap; Also covers Evaluation & Observability, Model Training; - When you want to understand the foundational aspects of machine learning alongside more advanced topics on building efficient and high-performing large language models.

### When should I avoid Hands-On-Large-Language-Models?

If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures. When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab. If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory. In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L

### When should I avoid llm-course?

- If you're focused primarily on specialized aspects of AI and machine learning that fall outside the scope of large language models. - Not recommended if your immediate need is to dive deep into a narrow topic without the structured progression offered here, preferring instead direct access to advanced use-cases or niche LLM areas.

### Is Hands-On-Large-Language-Models or llm-course more popular on GitHub?

llm-course has more GitHub stars (80,741 vs 27,427). Stars measure visibility, not whether either tool fits your constraints.

### Are Hands-On-Large-Language-Models and llm-course open source?

Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, llm-course: Apache-2.0).

### Where can I find alternatives to Hands-On-Large-Language-Models or llm-course?

GraphCanon lists graph-backed alternatives at /tools/handsonllm-hands-on-large-language-models/alternatives and /tools/mlabonne-llm-course/alternatives (/tools/handsonllm-hands-on-large-language-models/alternatives.md, /tools/mlabonne-llm-course/alternatives.md), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at /compare/handsonllm-hands-on-large-language-models-vs-mlabonne-llm-course.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Hands-On-Large-Language-Models or llm-course?

Hands-On-Large-Language-Models: Steady. llm-course: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for Hands-On-Large-Language-Models and llm-course?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Hands-On-Large-Language-Models: /tools/handsonllm-hands-on-large-language-models/trust; llm-course: /tools/mlabonne-llm-course/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models`](/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_