---
title: "LlamaFactory vs trap"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-parameterlab-trap"
tools: ["hiyouga-llamafactory", "parameterlab-trap"]
---

# LlamaFactory vs trap

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick LlamaFactory if llamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization; pick trap if tRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.

[LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [trap](https://github.com/parameterlab/trap) has 14 stars, 0 forks, and 0 open issues, last pushed Nov 20, 2024. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [trap's repository](https://github.com/parameterlab/trap).

| | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [trap](/tools/parameterlab-trap.md) |
| --- | --- | --- |
| Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification |
| Stars | 73,157 | 14 |
| Forks | 8,937 | 0 |
| Open issues | 1,067 | 0 |
| Language | Python | Jupyter Notebook |
| Adopt for | LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. | TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT License ensures permissive use and modification of TRAP under its terms. |
| Categories | LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks |

## Trust and health

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

| | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [trap](/tools/parameterlab-trap.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 0d | 598d |
| Open issues (now) | 1.1k | 0 |
| Owner type | User | Organization |
| Security scan | No lockfile | 242 low (242 low) |
| Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/parameterlab-trap/trust.md) |

## Decision facts: LlamaFactory

- **Adopt for:** LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization.

## Decision facts: trap

- **Requirements:** Requires installation and use of HuggingFace transformers for downloading specific models.; Configuration files need to be adapted with the correct paths for model configurations as specified in `detect_llm/configs`.
- **Adopt for:** TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.
- **License detail:** MIT License ensures permissive use and modification of TRAP under its terms.

## Choose when

### Choose LlamaFactory if…

- LlamaFactory is primarily Python; trap is Jupyter Notebook.
- License: LlamaFactory is Apache-2.0, trap is MIT.
- Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning.
- Also covers Model Training.
- When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.

### Choose trap if…

- trap is primarily Jupyter Notebook; LlamaFactory is Python.
- License: trap is MIT, LlamaFactory is Apache-2.0.
- Requirements: Requires installation and use of HuggingFace transformers for downloading specific models.; Configuration files need to be adapted with the correct paths for model configurations as specified in `detect_llm/configs`..
- Tags unique to trap: acl2024, adversarial-attacks, fingerprinting, research.
- Also covers Evaluation & Observability.
- When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings.

## When NOT to use LlamaFactory

- When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory.
- If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa

## When NOT to use trap

- If your objective is not specifically related to identifying or evaluating LLMs through adversarial attacks, and you require a more generalized framework for LLM evaluation or observability.
- When working with models that cannot be subjected to black-box testing due to their deployment environment or company policies.

## Common questions

### What is the difference between LlamaFactory and trap?

LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. trap: TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification. See the comparison table for live GitHub stats and shared categories.

### When should I choose LlamaFactory over trap?

Choose LlamaFactory over trap when LlamaFactory is primarily Python; trap is Jupyter Notebook; License: LlamaFactory is Apache-2.0, trap is MIT; Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; Also covers Model Training; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.

### When should I choose trap over LlamaFactory?

Choose trap over LlamaFactory when trap is primarily Jupyter Notebook; LlamaFactory is Python; License: trap is MIT, LlamaFactory is Apache-2.0; Requirements: Requires installation and use of HuggingFace transformers for downloading specific models.; Configuration files need to be adapted with the correct paths for model configurations as specified in `detect_llm/configs`.; Tags unique to trap: acl2024, adversarial-attacks, fingerprinting, research; Also covers Evaluation & Observability; When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings.

### When should I avoid LlamaFactory?

When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa

### When should I avoid trap?

If your objective is not specifically related to identifying or evaluating LLMs through adversarial attacks, and you require a more generalized framework for LLM evaluation or observability. When working with models that cannot be subjected to black-box testing due to their deployment environment or company policies.

### Is LlamaFactory or trap more popular on GitHub?

LlamaFactory has more GitHub stars (73,157 vs 14). Stars measure visibility, not whether either tool fits your constraints.

### Are LlamaFactory and trap open source?

Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, trap: MIT).

### Where can I find alternatives to LlamaFactory or trap?

GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [trap alternatives](/tools/parameterlab-trap/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [trap markdown twin](/tools/parameterlab-trap/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/hiyouga-llamafactory-vs-parameterlab-trap.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, LlamaFactory or trap?

LlamaFactory: Very active. trap: 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 LlamaFactory and trap?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [trap trust report](/tools/parameterlab-trap/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=hiyouga-llamafactory`](/api/graphcanon/graph?tool=hiyouga-llamafactory)
- 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/_