Comparison
LlamaFactory vs dvc
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.
Markdown twin · LlamaFactory alternatives · dvc alternatives
GraphCanon updated today
Trust & integrity
| Signal | LlamaFactory | dvc |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Very active (1d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- LlamaFactory
- Unified Efficient Fine-Tuning of 100+ LLMs & VLMs
- dvc
- Data Versioning and ML Experiments
Stars
- LlamaFactory
- 73k
- dvc
- 16k
Forks
- LlamaFactory
- 8.9k
- dvc
- 1.3k
Open issues
- LlamaFactory
- 1.1k
- dvc
- 188
Language
- LlamaFactory
- Python
- dvc
- Python
Adopt for
- LlamaFactory
- 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.
- dvc
- -
Persona
- LlamaFactory
- -
- dvc
- -
Runtime
- LlamaFactory
- -
- dvc
- -
License
- LlamaFactory
- Apache-2.0
- dvc
- Apache-2.0
Last pushed
- LlamaFactory
- Jul 10, 2026
- dvc
- Jul 10, 2026
Categories
- LlamaFactory
- Model Training, LLM Frameworks
- dvc
- Model Training, Developer Tools
Trust and health
Days since push
- LlamaFactory
- 0d
- dvc
- 1d
Open issues (now)
- LlamaFactory
- 1.1k
- dvc
- 188
Owner type
- LlamaFactory
- User
- dvc
- Organization
Full report
- LlamaFactory
- Trust report
- dvc
- Trust report
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.
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hiyouga/LlamaFactory) · observed Jul 11, 2026
- GitHub forks (hiyouga/LlamaFactory) · observed Jul 11, 2026
- Last push (hiyouga/LlamaFactory) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (treeverse/dvc) · observed Jul 11, 2026
- GitHub forks (treeverse/dvc) · observed Jul 11, 2026
- Last push (treeverse/dvc) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LlamaFactory 73k · dvc 16k (synced Jul 11, 2026).
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 and dvc alternatives (LlamaFactory markdown twin, dvc markdown twin), 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 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; dvc trust report.