--- title: "LlamaFactory vs optimum-tpu" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-huggingface-optimum-tpu" tools: ["hiyouga-llamafactory", "huggingface-optimum-tpu"] --- # LlamaFactory vs optimum-tpu *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; pick optimum-tpu when tags unique to optimum-tpu: python. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [optimum-tpu](https://huggingface.co/docs/optimum-tpu) has 135 stars, 30 forks, and 4 open issues, last pushed Jan 23, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [optimum-tpu's repository](https://github.com/huggingface/optimum-tpu). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [optimum-tpu](/tools/huggingface-optimum-tpu.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Google TPU optimizations for transformers models | | Stars | 73,157 | 135 | | Forks | 8,937 | 30 | | Open issues | 1,067 | 4 | | 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._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [optimum-tpu](/tools/huggingface-optimum-tpu.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 0d | 169d | | Archived on GitHub | No | Yes | | Open issues (now) | 1.1k | 4 | | Owner type | User | Organization | | Security scan | No lockfile | 1 critical, 3 medium, 17 low (1 critical, 3 medium, 17 low) | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/huggingface-optimum-tpu/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… - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 135) - visibility, not fit. ### Choose optimum-tpu if… - Tags unique to optimum-tpu: python. - Leaner open-issue backlog (4). ## 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 optimum-tpu - optimum-tpu is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - 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 optimum-tpu? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. optimum-tpu: Google TPU optimizations for transformers models. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over optimum-tpu? Choose LlamaFactory over optimum-tpu when Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 135) - visibility, not fit. ### When should I choose optimum-tpu over LlamaFactory? Choose optimum-tpu over LlamaFactory when Tags unique to optimum-tpu: python; Leaner open-issue backlog (4). ### 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 optimum-tpu? optimum-tpu is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. 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 optimum-tpu more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 135). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and optimum-tpu open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, optimum-tpu: Apache-2.0). ### Where can I find alternatives to LlamaFactory or optimum-tpu? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [optimum-tpu alternatives](/tools/huggingface-optimum-tpu/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [optimum-tpu markdown twin](/tools/huggingface-optimum-tpu/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-huggingface-optimum-tpu.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or optimum-tpu? LlamaFactory: Very active. optimum-tpu: Archived. 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 optimum-tpu? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [optimum-tpu trust report](/tools/huggingface-optimum-tpu/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/_