--- title: "LlamaFactory vs Open-Prompt-Injection" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-liu00222-open-prompt-injection" tools: ["hiyouga-llamafactory", "liu00222-open-prompt-injection"] --- # LlamaFactory vs Open-Prompt-Injection *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when license: LlamaFactory is Apache-2.0, Open-Prompt-Injection is MIT; pick Open-Prompt-Injection when license: Open-Prompt-Injection is MIT, LlamaFactory is Apache-2.0. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [Open-Prompt-Injection](https://github.com/liu00222/Open-Prompt-Injection) has 464 stars, 74 forks, and 14 open issues, last pushed Oct 29, 2025. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [Open-Prompt-Injection's repository](https://github.com/liu00222/Open-Prompt-Injection). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | This repository provides a benchmark for prompt injection attacks and defenses in LLMs | | Stars | 73,157 | 464 | | Forks | 8,937 | 74 | | Open issues | 1,067 | 14 | | Language | Python | Python | | 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. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [Open-Prompt-Injection](/tools/liu00222-open-prompt-injection.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 255d | | Open issues (now) | 1.1k | 14 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/liu00222-open-prompt-injection/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. ## Choose when ### Choose LlamaFactory if… - License: LlamaFactory is Apache-2.0, Open-Prompt-Injection is MIT. - Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose Open-Prompt-Injection if… - License: Open-Prompt-Injection is MIT, LlamaFactory is Apache-2.0. - Tags unique to Open-Prompt-Injection: llms, prompt-injection, llm, python. - Also covers AI Agents. ## 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 Open-Prompt-Injection - Last GitHub push was 255 days ago (slowing maintenance, Oct 29, 2025). Validate activity before betting a new project on Open-Prompt-Injection. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - 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. ## Common questions ### What is the difference between LlamaFactory and Open-Prompt-Injection? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. Open-Prompt-Injection: This repository provides a benchmark for prompt injection attacks and defenses in LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over Open-Prompt-Injection? Choose LlamaFactory over Open-Prompt-Injection when License: LlamaFactory is Apache-2.0, Open-Prompt-Injection is MIT; Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, ai; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose Open-Prompt-Injection over LlamaFactory? Choose Open-Prompt-Injection over LlamaFactory when License: Open-Prompt-Injection is MIT, LlamaFactory is Apache-2.0; Tags unique to Open-Prompt-Injection: llms, prompt-injection, llm, python; Also covers AI Agents. ### 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 Open-Prompt-Injection? Last GitHub push was 255 days ago (slowing maintenance, Oct 29, 2025). Validate activity before betting a new project on Open-Prompt-Injection. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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. ### Is LlamaFactory or Open-Prompt-Injection more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 464). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and Open-Prompt-Injection open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, Open-Prompt-Injection: MIT). ### Where can I find alternatives to LlamaFactory or Open-Prompt-Injection? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [Open-Prompt-Injection alternatives](/tools/liu00222-open-prompt-injection/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [Open-Prompt-Injection markdown twin](/tools/liu00222-open-prompt-injection/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-liu00222-open-prompt-injection.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or Open-Prompt-Injection? LlamaFactory: Very active. Open-Prompt-Injection: 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 LlamaFactory and Open-Prompt-Injection? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [Open-Prompt-Injection trust report](/tools/liu00222-open-prompt-injection/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/_