Comparison
lora vs alpaca-lora
Verdict
Pick lora if lora is designed for quick model customization through low-rank adaptation of pre-existing diffusion models like Stable Diffusion using Jupyter Notebooks in an interactive learning environment; pick alpaca-lora if alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
Markdown twin · lora alternatives · alpaca-lora alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | lora | alpaca-lora |
|---|---|---|
| Maintenance | Dormant (854d since push) As of 4w · github_public_v1 | Dormant (734d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- lora
- Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation.
- alpaca-lora
- Instruct-tune LLaMA on consumer hardware
Stars
- lora
- 7.5k
- alpaca-lora
- 19k
Forks
- lora
- 496
- alpaca-lora
- 2.2k
Open issues
- lora
- 88
- alpaca-lora
- 365
Language
- lora
- Jupyter Notebook
- alpaca-lora
- Jupyter Notebook
Adopt for
- lora
- Lora is designed for quick model customization through low-rank adaptation of pre-existing diffusion models like Stable Diffusion using Jupyter Notebooks in an interactive learning environment.
- alpaca-lora
- alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
Persona
- lora
- -
- alpaca-lora
- developer harness
Runtime
- lora
- -
- alpaca-lora
- -
License
- lora
- The project is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution.
- alpaca-lora
- The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables.
Last pushed
- lora
- Mar 22, 2024
- alpaca-lora
- Jul 29, 2024
Categories
- lora
- Model Training
- alpaca-lora
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- lora
- 854d
- alpaca-lora
- 734d
Open issues (now)
- lora
- 88
- alpaca-lora
- 365
OSV dependency advisories
- lora
- No lockfile (source not queried)
- alpaca-lora
- Published findings
Full report
- lora
- Trust report
- alpaca-lora
- Trust report
Choose lora if…
- Requirements: Suitable for developers and researchers looking to experiment with diffusion models in an interactive Jupyter Notebook environment..
- Tags unique to lora: diffusion, fine-tuning, stable-diffusion.
- Use Lora when you need to fine-tune existing diffusion models quickly with interactive experimentation capabilities available via Jupyter Notebooks.
When NOT to use lora
- Do not use Lora when you require a more thorough or comprehensive fine-tuning approach that goes beyond quick customization through low-rank adaptation.
- Avoid Lora if your development workflow is not compatible with Jupyter Notebooks, as this is the primary environment provided by the repository.
Choose alpaca-lora if…
- Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply..
- Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, llama.
- Also covers Inference & Serving, LLM Frameworks.
- alpaca-lora ships Docker support for self-hosted deployment.
- When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker.
When NOT to use alpaca-lora
- When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface.
- For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (cloneofsimo/lora) · observed Jul 24, 2026
- GitHub forks (cloneofsimo/lora) · observed Jul 24, 2026
- Last push (cloneofsimo/lora) · observed Mar 22, 2024
- License file (Apache-2.0) · observed Jul 24, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tloen/alpaca-lora) · observed Aug 3, 2026
- GitHub forks (tloen/alpaca-lora) · observed Aug 3, 2026
- Last push (tloen/alpaca-lora) · observed Jul 29, 2024
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: lora 7.5k · alpaca-lora 19k (synced Jul 24, 2026).
Common questions
- What is the difference between lora and alpaca-lora?
- lora: Jupyter Notebook repository for fine-tuning diffusion models using Low-Rank Adaptation.. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.
- When should I choose lora over alpaca-lora?
- Choose lora over alpaca-lora when Requirements: Suitable for developers and researchers looking to experiment with diffusion models in an interactive Jupyter Notebook environment.; Tags unique to lora: diffusion, fine-tuning, stable-diffusion; Use Lora when you need to fine-tune existing diffusion models quickly with interactive experimentation capabilities available via Jupyter Notebooks.
- When should I choose alpaca-lora over lora?
- Choose alpaca-lora over lora when Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.; Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, llama; Also covers Inference & Serving, LLM Frameworks; alpaca-lora ships Docker support for self-hosted deployment; When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker.
- When should I avoid lora?
- Do not use Lora when you require a more thorough or comprehensive fine-tuning approach that goes beyond quick customization through low-rank adaptation. Avoid Lora if your development workflow is not compatible with Jupyter Notebooks, as this is the primary environment provided by the repository.
- When should I avoid alpaca-lora?
- When you require more advanced customization beyond what is offered through the
finetune.pyscript parameters or Jupyter Notebook interface. For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware. - Is lora or alpaca-lora more popular on GitHub?
- alpaca-lora has more GitHub stars (18,912 vs 7,545). Stars measure visibility, not whether either tool fits your constraints.
- Are lora and alpaca-lora open source?
- Yes - both are open-source projects on GitHub (lora: Apache-2.0, alpaca-lora: Apache-2.0).
- Where can I find alternatives to lora or alpaca-lora?
- GraphCanon lists graph-backed alternatives at lora alternatives and alpaca-lora alternatives (lora markdown twin, alpaca-lora 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, lora or alpaca-lora?
- lora: Dormant. alpaca-lora: 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 lora and alpaca-lora?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lora trust report; alpaca-lora trust report.