---
title: "LLMs-Finetuning-Safety vs llm-course"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/llm-tuning-safety-llms-finetuning-safety-vs-mlabonne-llm-course"
tools: ["llm-tuning-safety-llms-finetuning-safety", "mlabonne-llm-course"]
---

# LLMs-Finetuning-Safety vs llm-course

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick LLMs-Finetuning-Safety when license: LLMs-Finetuning-Safety is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, LLMs-Finetuning-Safety is MIT.

[LLMs-Finetuning-Safety](https://llm-tuning-safety.github.io/) reports 355 GitHub stars, 38 forks, and 3 open issues, last pushed Feb 23, 2024. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [LLMs-Finetuning-Safety's repository](https://github.com/LLM-Tuning-Safety/LLMs-Finetuning-Safety) and [llm-course's repository](https://github.com/mlabonne/llm-course).

| | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Tagline | We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. |
| Stars | 355 | 80,839 |
| Forks | 38 | 9,421 |
| Open issues | 3 | 84 |
| Language | Python | - |
| Adopt for | - | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| Categories | Evaluation & Observability, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

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

| | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) | [llm-course](/tools/mlabonne-llm-course.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 869d | 155d |
| Open issues (now) | 3 | 84 |
| Full report | [trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) |

## Decision facts: llm-course

- **Requirements:** Course materials are available in Colab notebooks; access requires a Google account
- **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to
- **License detail:** Apache-2.0

## Choose when

### Choose LLMs-Finetuning-Safety if…

- License: LLMs-Finetuning-Safety is MIT, llm-course is Apache-2.0.
- Tags unique to LLMs-Finetuning-Safety: alignment, llm, llm-finetuning, python.
- Leaner open-issue backlog (3).

### Choose llm-course if…

- License: llm-course is Apache-2.0, LLMs-Finetuning-Safety is MIT.
- Requirements: Course materials are available in Colab notebooks; access requires a Google account.
- Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning.
- Also covers Inference & Serving.
- - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge

## When NOT to use LLMs-Finetuning-Safety

- Last GitHub push was 869 days ago (dormant maintenance, Feb 23, 2024). Validate activity before betting a new project on LLMs-Finetuning-Safety.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

## When NOT to use llm-course

- - If you only require a quick introduction to LLMs without deep dive into core components
- - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI

## Common questions

### What is the difference between LLMs-Finetuning-Safety and llm-course?

LLMs-Finetuning-Safety: We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs.. 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 LLMs-Finetuning-Safety over llm-course?

Choose LLMs-Finetuning-Safety over llm-course when License: LLMs-Finetuning-Safety is MIT, llm-course is Apache-2.0; Tags unique to LLMs-Finetuning-Safety: alignment, llm, llm-finetuning, python; Leaner open-issue backlog (3).

### When should I choose llm-course over LLMs-Finetuning-Safety?

Choose llm-course over LLMs-Finetuning-Safety when License: llm-course is Apache-2.0, LLMs-Finetuning-Safety is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning; Also covers Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge.

### When should I avoid LLMs-Finetuning-Safety?

Last GitHub push was 869 days ago (dormant maintenance, Feb 23, 2024). Validate activity before betting a new project on LLMs-Finetuning-Safety. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

### When should I avoid llm-course?

- If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI

### Is LLMs-Finetuning-Safety or llm-course more popular on GitHub?

llm-course has more GitHub stars (80,839 vs 355). Stars measure visibility, not whether either tool fits your constraints.

### Are LLMs-Finetuning-Safety and llm-course open source?

Yes - both are open-source projects on GitHub (LLMs-Finetuning-Safety: MIT, llm-course: Apache-2.0).

### Where can I find alternatives to LLMs-Finetuning-Safety or llm-course?

GraphCanon lists graph-backed alternatives at [LLMs-Finetuning-Safety alternatives](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([LLMs-Finetuning-Safety markdown twin](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives.md), [llm-course markdown twin](/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 [this comparison](/compare/llm-tuning-safety-llms-finetuning-safety-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, LLMs-Finetuning-Safety or llm-course?

LLMs-Finetuning-Safety: Dormant. 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 LLMs-Finetuning-Safety and llm-course?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMs-Finetuning-Safety trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=llm-tuning-safety-llms-finetuning-safety`](/api/graphcanon/graph?tool=llm-tuning-safety-llms-finetuning-safety)
- 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/_