--- title: "aim vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimhubio-aim-vs-hiyouga-llamafactory" tools: ["aimhubio-aim", "hiyouga-llamafactory"] --- # aim vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick aim when tags unique to aim: data-science, data-visualization, experiment-tracking, machine-learning; pick LlamaFactory when tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma. [aim](https://aimstack.io) reports 6.2k GitHub stars, 401 forks, and 465 open issues, last pushed Jul 10, 2026. [LlamaFactory](https://llamafactory.readthedocs.io) has 73k stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [aim's repository](https://github.com/aimhubio/aim) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [aim](/tools/aimhubio-aim.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Aim 💫 — An easy-to-use & supercharged open-source experiment tracker. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 6,188 | 73,157 | | Forks | 401 | 8,937 | | Open issues | 465 | 1,067 | | 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 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aim](/tools/aimhubio-aim.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Open issues (now) | 465 | 1.1k | | Owner type | Organization | User | | Full report | [trust report](/tools/aimhubio-aim/trust.md) | [trust report](/tools/hiyouga-llamafactory/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 aim if… - Tags unique to aim: data-science, data-visualization, experiment-tracking, machine-learning. - More recently updated (last pushed Jul 10, 2026). ### Choose LlamaFactory if… - 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. - More GitHub stars (73k vs 6.2k) - visibility, not fit. ## When NOT to use aim - 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. ## 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 ## Common questions ### What is the difference between aim and LlamaFactory? aim: Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose aim over LlamaFactory? Choose aim over LlamaFactory when Tags unique to aim: data-science, data-visualization, experiment-tracking, machine-learning; More recently updated (last pushed Jul 10, 2026). ### When should I choose LlamaFactory over aim? Choose LlamaFactory over aim when 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; More GitHub stars (73k vs 6.2k) - visibility, not fit. ### When should I avoid aim? 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. ### 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 ### Is aim or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 6,188). Stars measure visibility, not whether either tool fits your constraints. ### Are aim and LlamaFactory open source? Yes - both are open-source projects on GitHub (aim: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to aim or LlamaFactory? GraphCanon lists graph-backed alternatives at [aim alternatives](/tools/aimhubio-aim/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([aim markdown twin](/tools/aimhubio-aim/alternatives.md), [LlamaFactory markdown twin](/tools/hiyouga-llamafactory/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/aimhubio-aim-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aim or LlamaFactory? aim: Very active. LlamaFactory: Very active. 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 aim and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aim trust report](/tools/aimhubio-aim/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aimhubio-aim`](/api/graphcanon/graph?tool=aimhubio-aim) - 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/_