--- title: "LlamaFactory vs Failed-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-kennethleungty-failed-ml" tools: ["hiyouga-llamafactory", "kennethleungty-failed-ml"] --- # LlamaFactory vs Failed-ML *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when license: LlamaFactory is Apache-2.0, Failed-ML is MIT; pick Failed-ML when license: Failed-ML 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. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) has 753 stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Compilation of high-profile real-world examples of failed machine learning projects | | Stars | 73,157 | 753 | | Forks | 8,937 | 51 | | Open issues | 1,067 | 0 | | Language | 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 | Computer Vision, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 757d | | Open issues (now) | 1.1k | 0 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/kennethleungty-failed-ml/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, Failed-ML is MIT. - Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose Failed-ML if… - License: Failed-ML is MIT, LlamaFactory is Apache-2.0. - Tags unique to Failed-ML: artificial-intelligence, classification, computer-vision, data-engineering. - Also covers Computer Vision. ## 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 Failed-ML - Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML. - 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 Failed-ML? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over Failed-ML? Choose LlamaFactory over Failed-ML when License: LlamaFactory is Apache-2.0, Failed-ML is MIT; Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose Failed-ML over LlamaFactory? Choose Failed-ML over LlamaFactory when License: Failed-ML is MIT, LlamaFactory is Apache-2.0; Tags unique to Failed-ML: artificial-intelligence, classification, computer-vision, data-engineering; Also covers Computer Vision. ### 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 Failed-ML? Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML. 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 Failed-ML more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 753). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and Failed-ML open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, Failed-ML: MIT). ### Where can I find alternatives to LlamaFactory or Failed-ML? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [Failed-ML markdown twin](/tools/kennethleungty-failed-ml/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-kennethleungty-failed-ml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or Failed-ML? LlamaFactory: Very active. Failed-ML: 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 Failed-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [Failed-ML trust report](/tools/kennethleungty-failed-ml/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/_