--- title: "LlamaFactory vs aqueduct" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-runllm-aqueduct" tools: ["hiyouga-llamafactory", "runllm-aqueduct"] --- # LlamaFactory vs aqueduct *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when llamaFactory is primarily Python; aqueduct is Go; pick aqueduct when aqueduct is primarily Go; LlamaFactory is Python. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [aqueduct](https://aqueducthq.com) has 517 stars, 20 forks, and 11 open issues, last pushed Jun 7, 2023. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [aqueduct's repository](https://github.com/RunLLM/aqueduct). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [aqueduct](/tools/runllm-aqueduct.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure. | | Stars | 73,157 | 517 | | Forks | 8,937 | 20 | | Open issues | 1,067 | 11 | | Language | Python | Go | | 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 | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [aqueduct](/tools/runllm-aqueduct.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1130d | | Open issues (now) | 1.1k | 11 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/runllm-aqueduct/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… - LlamaFactory is primarily Python; aqueduct is Go. - Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose aqueduct if… - aqueduct is primarily Go; LlamaFactory is Python. - Tags unique to aqueduct: data-science, ml, llms, llm. - 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 aqueduct - Last GitHub push was 1130 days ago (dormant maintenance, Jun 7, 2023). Validate activity before betting a new project on aqueduct. - 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 aqueduct? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. aqueduct: Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over aqueduct? Choose LlamaFactory over aqueduct when LlamaFactory is primarily Python; aqueduct is Go; Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose aqueduct over LlamaFactory? Choose aqueduct over LlamaFactory when aqueduct is primarily Go; LlamaFactory is Python; Tags unique to aqueduct: data-science, ml, llms, llm; 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 aqueduct? Last GitHub push was 1130 days ago (dormant maintenance, Jun 7, 2023). Validate activity before betting a new project on aqueduct. 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 aqueduct more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 517). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and aqueduct open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, aqueduct: Apache-2.0). ### Where can I find alternatives to LlamaFactory or aqueduct? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [aqueduct alternatives](/tools/runllm-aqueduct/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [aqueduct markdown twin](/tools/runllm-aqueduct/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-runllm-aqueduct.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or aqueduct? LlamaFactory: Very active. aqueduct: 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 aqueduct? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [aqueduct trust report](/tools/runllm-aqueduct/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/_