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

# LLMs-Finetuning-Safety vs LLMs-from-scratch

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick LLMs-Finetuning-Safety when lLMs-Finetuning-Safety is primarily Python; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; LLMs-Finetuning-Safety is Python.

[LLMs-Finetuning-Safety](https://llm-tuning-safety.github.io/) reports 355 GitHub stars, 38 forks, and 3 open issues, last pushed Feb 23, 2024. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 99k stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [LLMs-Finetuning-Safety's repository](https://github.com/LLM-Tuning-Safety/LLMs-Finetuning-Safety) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch).

| | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.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. | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step |
| Stars | 355 | 98,899 |
| Forks | 38 | 15,183 |
| Open issues | 3 | 4 |
| Language | Python | Jupyter Notebook |
| Adopt for | - | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Other |
| Categories | Evaluation & Observability, LLM Frameworks, Model Training | 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) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Steady (60%) |
| Days since push | 869d | 38d |
| Open issues (now) | 3 | 4 |
| Full report | [trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) |

## Decision facts: LLMs-from-scratch

- **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions.

## Choose when

### Choose LLMs-Finetuning-Safety if…

- LLMs-Finetuning-Safety is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- License: LLMs-Finetuning-Safety is MIT, LLMs-from-scratch is Other.
- Tags unique to LLMs-Finetuning-Safety: alignment, llm, llm-finetuning, python.
- Also covers Evaluation & Observability.

### Choose LLMs-from-scratch if…

- LLMs-from-scratch is primarily Jupyter Notebook; LLMs-Finetuning-Safety is Python.
- License: LLMs-from-scratch is Other, LLMs-Finetuning-Safety is MIT.
- Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention mechanism, deep-learning.
- - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

## 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 LLMs-from-scratch

- - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work.
- - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰
- a deeper learning experience.

## Common questions

### What is the difference between LLMs-Finetuning-Safety and LLMs-from-scratch?

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.. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories.

### When should I choose LLMs-Finetuning-Safety over LLMs-from-scratch?

Choose LLMs-Finetuning-Safety over LLMs-from-scratch when LLMs-Finetuning-Safety is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: LLMs-Finetuning-Safety is MIT, LLMs-from-scratch is Other; Tags unique to LLMs-Finetuning-Safety: alignment, llm, llm-finetuning, python; Also covers Evaluation & Observability.

### When should I choose LLMs-from-scratch over LLMs-Finetuning-Safety?

Choose LLMs-from-scratch over LLMs-Finetuning-Safety when LLMs-from-scratch is primarily Jupyter Notebook; LLMs-Finetuning-Safety is Python; License: LLMs-from-scratch is Other, LLMs-Finetuning-Safety is MIT; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention mechanism, deep-learning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

### 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 LLMs-from-scratch?

- If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience.

### Is LLMs-Finetuning-Safety or LLMs-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (98,899 vs 355). Stars measure visibility, not whether either tool fits your constraints.

### Are LLMs-Finetuning-Safety and LLMs-from-scratch open source?

Yes - both are open-source projects on GitHub (LLMs-Finetuning-Safety: MIT, LLMs-from-scratch: Other).

### Where can I find alternatives to LLMs-Finetuning-Safety or LLMs-from-scratch?

GraphCanon lists graph-backed alternatives at [LLMs-Finetuning-Safety alternatives](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([LLMs-Finetuning-Safety markdown twin](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives.md), [LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/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-rasbt-llms-from-scratch.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 LLMs-from-scratch?

LLMs-Finetuning-Safety: Dormant. LLMs-from-scratch: Steady. 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 LLMs-from-scratch?

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); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/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/_