--- title: "LlamaFactory vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-windmaple-awesome-automl" tools: ["hiyouga-llamafactory", "windmaple-awesome-automl"] --- # LlamaFactory vs awesome-AutoML *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when license: LlamaFactory is Apache-2.0, awesome-AutoML is GPL-3.0; pick awesome-AutoML when license: awesome-AutoML is GPL-3.0, 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. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 940 stars, 155 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Curating a list of AutoML-related research, tools, projects and other resources | | Stars | 73,157 | 940 | | Forks | 8,937 | 155 | | Open issues | 1,067 | 1 | | 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 | GPL-3.0 | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 109d | | Open issues (now) | 1.1k | 1 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/windmaple-awesome-automl/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, awesome-AutoML is GPL-3.0. - 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 awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, LlamaFactory is Apache-2.0. - Also covers AI Agents. - Leaner open-issue backlog (1). ## 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 awesome-AutoML - Last GitHub push was 110 days ago (slowing maintenance, Mar 24, 2026). Validate activity before betting a new project on awesome-AutoML. - 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. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ## Common questions ### What is the difference between LlamaFactory and awesome-AutoML? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. awesome-AutoML: Curating a list of AutoML-related research, tools, projects and other resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over awesome-AutoML? Choose LlamaFactory over awesome-AutoML when License: LlamaFactory is Apache-2.0, awesome-AutoML is GPL-3.0; 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 awesome-AutoML over LlamaFactory? Choose awesome-AutoML over LlamaFactory when License: awesome-AutoML is GPL-3.0, LlamaFactory is Apache-2.0; Also covers AI Agents; Leaner open-issue backlog (1). ### 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 awesome-AutoML? Last GitHub push was 110 days ago (slowing maintenance, Mar 24, 2026). Validate activity before betting a new project on awesome-AutoML. 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. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ### Is LlamaFactory or awesome-AutoML more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 940). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and awesome-AutoML open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to LlamaFactory or awesome-AutoML? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or awesome-AutoML? LlamaFactory: Very active. awesome-AutoML: 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 awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/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/_