--- title: "LlamaFactory vs trainer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-kubeflow-trainer" tools: ["hiyouga-llamafactory", "kubeflow-trainer"] --- # LlamaFactory vs trainer *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when llamaFactory is primarily Python; trainer is Go; pick trainer when trainer 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. [trainer](https://www.kubeflow.org/docs/components/training) has 2.1k stars, 983 forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [trainer's repository](https://github.com/kubeflow/trainer). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [trainer](/tools/kubeflow-trainer.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Distributed AI Model Training and LLM Fine-Tuning on Kubernetes | | Stars | 73,157 | 2,135 | | Forks | 8,937 | 983 | | Open issues | 1,067 | 144 | | 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 | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [trainer](/tools/kubeflow-trainer.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 1.1k | 144 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/kubeflow-trainer/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; trainer is Go. - Tags unique to LlamaFactory: gemma, deepseek, instruction-tuning, large-language-models. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose trainer if… - trainer is primarily Go; LlamaFactory is Python. - Tags unique to trainer: gpu, distributed, kubeflow, huggingface. - Leaner open-issue backlog (144). ## 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 trainer - 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 trainer? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. trainer: Distributed AI Model Training and LLM Fine-Tuning on Kubernetes. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over trainer? Choose LlamaFactory over trainer when LlamaFactory is primarily Python; trainer is Go; Tags unique to LlamaFactory: gemma, deepseek, instruction-tuning, large-language-models; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose trainer over LlamaFactory? Choose trainer over LlamaFactory when trainer is primarily Go; LlamaFactory is Python; Tags unique to trainer: gpu, distributed, kubeflow, huggingface; Leaner open-issue backlog (144). ### 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 trainer? 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 trainer more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 2,135). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and trainer open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, trainer: Apache-2.0). ### Where can I find alternatives to LlamaFactory or trainer? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [trainer alternatives](/tools/kubeflow-trainer/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [trainer markdown twin](/tools/kubeflow-trainer/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-kubeflow-trainer.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or trainer? LlamaFactory: Very active. trainer: 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 LlamaFactory and trainer? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [trainer trust report](/tools/kubeflow-trainer/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/_