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

# awesome-llms-fine-tuning vs LLMs-Finetuning-Safety

*GraphCanon updated Aug 24, 2026*

## Verdict

Pick awesome-llms-fine-tuning if a curated list for LLM fine-tuning resources including tutorials, papers, and tools; pick LLMs-Finetuning-Safety if lLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples.

[awesome-llms-fine-tuning](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) reports 525 GitHub stars, 79 forks, and 10 open issues, last pushed Dec 2, 2024. [LLMs-Finetuning-Safety](https://llm-tuning-safety.github.io/) has 358 stars, 38 forks, and 3 open issues, last pushed Feb 23, 2024. Figures are from public GitHub metadata via [awesome-llms-fine-tuning's repository](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) and [LLMs-Finetuning-Safety's repository](https://github.com/LLM-Tuning-Safety/LLMs-Finetuning-Safety).

| | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) |
| --- | --- | --- |
| Tagline | A comprehensive collection of resources for fine-tuning Large Language Models. | Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples |
| Stars | 525 | 358 |
| Forks | 79 | 38 |
| Open issues | 10 | 3 |
| Language | - | Python |
| Adopt for | A curated list for LLM fine-tuning resources including tutorials, papers, and tools. | LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples. |
| Persona | - | - |
| Runtime | - | - |
| License | (unknown) - (unknown) | MIT |
| Categories | LLM Frameworks, Model Training | Evaluation & Observability, Model Training |

## Trust and health

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

| | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [LLMs-Finetuning-Safety](/tools/llm-tuning-safety-llms-finetuning-safety.md) |
| --- | --- | --- |
| Days since push | 629d | 893d |
| Open issues (now) | 10 | 3 |
| Stars delta | 0 (30d) | Unknown |
| Open issues delta | +1 (30d) | Unknown |
| Owner type | Organization | User |
| Full report | [trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust.md) | [trust report](/tools/llm-tuning-safety-llms-finetuning-safety/trust.md) |

## Decision facts: awesome-llms-fine-tuning

- **Adopt for:** A curated list for LLM fine-tuning resources including tutorials, papers, and tools.
- **License detail:** (unknown) - (unknown)

## Decision facts: LLMs-Finetuning-Safety

- **Pricing:** freemium - Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20.
- **Adopt for:** LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples.
- **Runtime:** unknown

## Choose when

### Choose awesome-llms-fine-tuning if…

- Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning.
- Also covers LLM Frameworks.
- Need extensive guidance on LLM-specific fine-tuning strategies

### Choose LLMs-Finetuning-Safety if…

- Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20..
- Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm, llm-finetuning.
- Also covers Evaluation & Observability.
- When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.

## When NOT to use awesome-llms-fine-tuning

- Looking for real-time interactive support or direct code implementation help
- Favor more specialized tools for immediate performance optimization over broad learning

## When NOT to use LLMs-Finetuning-Safety

- When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo.
- If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities.

## Common questions

### What is the difference between awesome-llms-fine-tuning and LLMs-Finetuning-Safety?

awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. LLMs-Finetuning-Safety: Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-llms-fine-tuning over LLMs-Finetuning-Safety?

Choose awesome-llms-fine-tuning over LLMs-Finetuning-Safety when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning; Also covers LLM Frameworks; Need extensive guidance on LLM-specific fine-tuning strategies.

### When should I choose LLMs-Finetuning-Safety over awesome-llms-fine-tuning?

Choose LLMs-Finetuning-Safety over awesome-llms-fine-tuning when Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20.; Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm, llm-finetuning; Also covers Evaluation & Observability; When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.

### When should I avoid awesome-llms-fine-tuning?

Looking for real-time interactive support or direct code implementation help Favor more specialized tools for immediate performance optimization over broad learning

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

When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo. If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities.

### Is awesome-llms-fine-tuning or LLMs-Finetuning-Safety more popular on GitHub?

awesome-llms-fine-tuning has more GitHub stars (525 vs 358). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llms-fine-tuning and LLMs-Finetuning-Safety open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to awesome-llms-fine-tuning or LLMs-Finetuning-Safety?

GraphCanon lists graph-backed alternatives at [awesome-llms-fine-tuning alternatives](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives) and [LLMs-Finetuning-Safety alternatives](/tools/llm-tuning-safety-llms-finetuning-safety/alternatives) ([awesome-llms-fine-tuning markdown twin](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives.md), [LLMs-Finetuning-Safety markdown twin](/tools/llm-tuning-safety-llms-finetuning-safety/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/curated-awesome-lists-awesome-llms-fine-tuning-vs-llm-tuning-safety-llms-finetuning-safety.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, awesome-llms-fine-tuning or LLMs-Finetuning-Safety?

awesome-llms-fine-tuning: Dormant. LLMs-Finetuning-Safety: Dormant. 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 awesome-llms-fine-tuning and LLMs-Finetuning-Safety?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=curated-awesome-lists-awesome-llms-fine-tuning`](/api/graphcanon/graph?tool=curated-awesome-lists-awesome-llms-fine-tuning)
- 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/_