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

# awesome-llms-fine-tuning vs llm-attacks

*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 llm-attacks if llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.

[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. [llm-attacks](https://llm-attacks.org/) has 4.8k stars, 633 forks, and 69 open issues, last pushed Aug 2, 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 [llm-attacks's repository](https://github.com/llm-attacks/llm-attacks).

| | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [llm-attacks](/tools/llm-attacks-llm-attacks.md) |
| --- | --- | --- |
| Tagline | A comprehensive collection of resources for fine-tuning Large Language Models. | Universal and Transferable Attacks on Aligned Language Models |
| Stars | 525 | 4,756 |
| Forks | 79 | 633 |
| Open issues | 10 | 69 |
| Language | - | Python |
| Adopt for | A curated list for LLM fine-tuning resources including tutorials, papers, and tools. | llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat. |
| Persona | - | - |
| Runtime | - | - |
| License | (unknown) - (unknown) | MIT |
| Categories | LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks |

## 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) | [llm-attacks](/tools/llm-attacks-llm-attacks.md) |
| --- | --- | --- |
| Days since push | 629d | 732d |
| Open issues (now) | 10 | 69 |
| Stars delta | 0 (30d) | Unknown |
| Open issues delta | +1 (30d) | Unknown |
| Full report | [trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust.md) | [trust report](/tools/llm-attacks-llm-attacks/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: llm-attacks

- **Adopt for:** llm-attacks: Universal and Transferable Attacks on Aligned Language Models with dependency on FastChat.

## Choose when

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

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

### Choose llm-attacks if…

- Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models.
- Also covers Evaluation & Observability.
- When you need to test the robustness of aligned language models specifically using attacks designed for these systems,

## 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 llm-attacks

- Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing,
- Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it.

## Common questions

### What is the difference between awesome-llms-fine-tuning and llm-attacks?

awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. llm-attacks: Universal and Transferable Attacks on Aligned Language Models. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-llms-fine-tuning over llm-attacks?

Choose awesome-llms-fine-tuning over llm-attacks when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning; Also covers Model Training; Need extensive guidance on LLM-specific fine-tuning strategies.

### When should I choose llm-attacks over awesome-llms-fine-tuning?

Choose llm-attacks over awesome-llms-fine-tuning when Tags unique to llm-attacks: alignment-testing, attacks, fastchat-dependency, language-models; Also covers Evaluation & Observability; When you need to test the robustness of aligned language models specifically using attacks designed for these systems,.

### 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 llm-attacks?

Do not use if you are evaluating generic or unaligned language models without a need for alignment-specific attack testing, Avoid when FastChat is not used in your project as llm-attacks explicitly depends on it.

### Is awesome-llms-fine-tuning or llm-attacks more popular on GitHub?

llm-attacks has more GitHub stars (4,756 vs 525). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-llms-fine-tuning and llm-attacks open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to awesome-llms-fine-tuning or llm-attacks?

GraphCanon lists graph-backed alternatives at [awesome-llms-fine-tuning alternatives](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives) and [llm-attacks alternatives](/tools/llm-attacks-llm-attacks/alternatives) ([awesome-llms-fine-tuning markdown twin](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives.md), [llm-attacks markdown twin](/tools/llm-attacks-llm-attacks/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-attacks-llm-attacks.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 llm-attacks?

awesome-llms-fine-tuning: Dormant. llm-attacks: 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 llm-attacks?

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); [llm-attacks trust report](/tools/llm-attacks-llm-attacks/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/_