Comparison
peft vs alpaca-lora
Verdict
Pick peft if pEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python; 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 · peft alternatives · alpaca-lora alternatives
GraphCanon updated today
Trust & integrity
| Signal | peft | alpaca-lora |
|---|---|---|
| Maintenance | Very active (1d since push) As of today · github_public_v1 | Dormant (734d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · 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
- peft
- State-of-the-art Parameter-Efficient Fine-Tuning
- alpaca-lora
- Instruct-tune LLaMA on consumer hardware
Stars
- peft
- 22k
- alpaca-lora
- 19k
Forks
- peft
- 2.4k
- alpaca-lora
- 2.2k
Open issues
- peft
- 74
- alpaca-lora
- 365
Language
- peft
- Python
- alpaca-lora
- Jupyter Notebook
Adopt for
- peft
- PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.
- alpaca-lora
- alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
Persona
- peft
- -
- alpaca-lora
- developer harness
Runtime
- peft
- -
- alpaca-lora
- -
License
- peft
- Apache-2.0
- 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
- peft
- Aug 22, 2026
- alpaca-lora
- Jul 29, 2024
Categories
- peft
- LLM Frameworks, Model Training
- alpaca-lora
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- peft
- Very active (96%)
- alpaca-lora
- Dormant (18%)
Days since push
- peft
- 1d
- alpaca-lora
- 734d
Open issues (now)
- peft
- 74
- alpaca-lora
- 365
Stars delta
- peft
- +142 (30d)
- alpaca-lora
- Unknown
Open issues delta
- peft
- +16 (30d)
- alpaca-lora
- Unknown
Owner type
- peft
- Organization
- alpaca-lora
- User
OSV dependency advisories
- peft
- No lockfile (source not queried)
- alpaca-lora
- Published findings
Full report
- peft
- Trust report
- alpaca-lora
- Trust report
Choose peft if…
- peft is primarily Python; alpaca-lora is Jupyter Notebook.
- Tags unique to peft: adapter, diffusion, fine-tuning, llm.
- When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
When NOT to use peft
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only.
- When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
Choose alpaca-lora if…
- alpaca-lora is primarily Jupyter Notebook; peft is Python.
- 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.
- 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 (huggingface/peft) · observed Aug 23, 2026
- GitHub forks (huggingface/peft) · observed Aug 23, 2026
- Last push (huggingface/peft) · observed Aug 22, 2026
- License file (Apache-2.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 11, 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: peft 22k · alpaca-lora 19k (synced Aug 23, 2026).
Common questions
- What is the difference between peft and alpaca-lora?
- peft: State-of-the-art Parameter-Efficient Fine-Tuning. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.
- When should I choose peft over alpaca-lora?
- Choose peft over alpaca-lora when peft is primarily Python; alpaca-lora is Jupyter Notebook; Tags unique to peft: adapter, diffusion, fine-tuning, llm; When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
- When should I choose alpaca-lora over peft?
- Choose alpaca-lora over peft when alpaca-lora is primarily Jupyter Notebook; peft is Python; 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; 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 peft?
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
- 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 peft or alpaca-lora more popular on GitHub?
- peft has more GitHub stars (21,585 vs 18,912). Stars measure visibility, not whether either tool fits your constraints.
- Are peft and alpaca-lora open source?
- Yes - both are open-source projects on GitHub (peft: Apache-2.0, alpaca-lora: Apache-2.0).
- Where can I find alternatives to peft or alpaca-lora?
- GraphCanon lists graph-backed alternatives at peft alternatives and alpaca-lora alternatives (peft 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, peft or alpaca-lora?
- peft: Very active. 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 peft and alpaca-lora?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: peft trust report; alpaca-lora trust report.