--- title: "LlamaFactory vs dvc" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-treeverse-dvc" tools: ["hiyouga-llamafactory", "treeverse-dvc"] --- # LlamaFactory vs dvc *GraphCanon updated Jul 12, 2026* ## Verdict Pick LlamaFactory when tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning; pick dvc when tags unique to dvc: reproducibility, data-science, unstructured-data. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [dvc](https://dvc.org) has 16k stars, 1.3k forks, and 188 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [dvc's repository](https://github.com/treeverse/dvc). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [dvc](/tools/treeverse-dvc.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Data Versioning and ML Experiments | | Stars | 73,157 | 15,740 | | Forks | 8,937 | 1,312 | | Open issues | 1,067 | 188 | | 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 | Model Training, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [dvc](/tools/treeverse-dvc.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 1.1k | 188 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/treeverse-dvc/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: gemma, fine-tuning, deepseek, instruction-tuning. - Also covers LLM Frameworks. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose dvc if… - Tags unique to dvc: reproducibility, data-science, unstructured-data. - Also covers Developer Tools. - Leaner open-issue backlog (188). ## 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 dvc - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## Common questions ### What is the difference between LlamaFactory and dvc? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. dvc: Data Versioning and ML Experiments. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over dvc? Choose LlamaFactory over dvc when Tags unique to LlamaFactory: gemma, fine-tuning, deepseek, instruction-tuning; Also covers LLM Frameworks; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose dvc over LlamaFactory? Choose dvc over LlamaFactory when Tags unique to dvc: reproducibility, data-science, unstructured-data; Also covers Developer Tools; Leaner open-issue backlog (188). ### 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 dvc? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### Is LlamaFactory or dvc more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 15,740). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and dvc open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, dvc: Apache-2.0). ### Where can I find alternatives to LlamaFactory or dvc? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [dvc alternatives](/tools/treeverse-dvc/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [dvc markdown twin](/tools/treeverse-dvc/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-treeverse-dvc.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or dvc? LlamaFactory: Very active. dvc: 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 dvc? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [dvc trust report](/tools/treeverse-dvc/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/_