Comparison
WizardLM vs alpaca-lora
Verdict
Pick WizardLM if wizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling; 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 · WizardLM alternatives · alpaca-lora alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | WizardLM | alpaca-lora |
|---|---|---|
| Maintenance | Dormant (424d since push) As of 2w · github_public_v1 | Dormant (734d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · 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
- WizardLM
- Empowering Large Pre-Trained Language Models to Follow Complex Instructions
- alpaca-lora
- Instruct-tune LLaMA on consumer hardware
Stars
- WizardLM
- 9.5k
- alpaca-lora
- 19k
Forks
- WizardLM
- 749
- alpaca-lora
- 2.2k
Open issues
- WizardLM
- 169
- alpaca-lora
- 365
Language
- WizardLM
- Python
- alpaca-lora
- Jupyter Notebook
Adopt for
- WizardLM
- WizardLM powers language models like WizardCoder and WizardMath to excel in complex instruction handling.
- alpaca-lora
- alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
Persona
- WizardLM
- -
- alpaca-lora
- developer harness
Runtime
- WizardLM
- -
- alpaca-lora
- -
License
- WizardLM
- (unknown)
- 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
- WizardLM
- Jun 7, 2025
- alpaca-lora
- Jul 29, 2024
Categories
- WizardLM
- LLM Frameworks, Model Training
- alpaca-lora
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- WizardLM
- 424d
- alpaca-lora
- 734d
Open issues (now)
- WizardLM
- 169
- alpaca-lora
- 365
OSV dependency advisories
- WizardLM
- No lockfile (source not queried)
- alpaca-lora
- Published findings
Full report
- WizardLM
- Trust report
- alpaca-lora
- Trust report
Choose WizardLM if…
- WizardLM is primarily Python; alpaca-lora is Jupyter Notebook.
- Tags unique to WizardLM: instruction-following, large language models, wizardcoder, wizardmath.
- When advanced coding tasks need precise solutions, surpassing GPT-3.5-Turbo and Gemini Pro
When NOT to use WizardLM
- If real-time updates are needed beyond Nov 2023, as performance is based on past benchmarks
- When looking for broad language capabilities of GPT-4, which outperformance in some benchmarks
Choose alpaca-lora if…
- alpaca-lora is primarily Jupyter Notebook; WizardLM 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 (nlpxucan/WizardLM) · observed Aug 5, 2026
- GitHub forks (nlpxucan/WizardLM) · observed Aug 5, 2026
- Last push (nlpxucan/WizardLM) · observed Jun 7, 2025
- License file (unknown) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 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: WizardLM 9.5k · alpaca-lora 19k (synced Aug 5, 2026).
Common questions
- What is the difference between WizardLM and alpaca-lora?
- WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.
- When should I choose WizardLM over alpaca-lora?
- Choose WizardLM over alpaca-lora when WizardLM is primarily Python; alpaca-lora is Jupyter Notebook; Tags unique to WizardLM: instruction-following, large language models, wizardcoder, wizardmath; When advanced coding tasks need precise solutions, surpassing GPT-3.5-Turbo and Gemini Pro.
- When should I choose alpaca-lora over WizardLM?
- Choose alpaca-lora over WizardLM when alpaca-lora is primarily Jupyter Notebook; WizardLM 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 WizardLM?
- If real-time updates are needed beyond Nov 2023, as performance is based on past benchmarks When looking for broad language capabilities of GPT-4, which outperformance in some benchmarks
- 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 WizardLM or alpaca-lora more popular on GitHub?
- alpaca-lora has more GitHub stars (18,912 vs 9,484). Stars measure visibility, not whether either tool fits your constraints.
- Are WizardLM and alpaca-lora open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to WizardLM or alpaca-lora?
- GraphCanon lists graph-backed alternatives at WizardLM alternatives and alpaca-lora alternatives (WizardLM 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, WizardLM or alpaca-lora?
- WizardLM: 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 WizardLM and alpaca-lora?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: WizardLM trust report; alpaca-lora trust report.