Comparison
qlora vs alpaca-lora
Verdict
Pick qlora if qLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family; 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 · qlora alternatives · alpaca-lora alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | qlora | alpaca-lora |
|---|---|---|
| Maintenance | Dormant (783d since push) As of 3w · github_public_v1 | Dormant (734d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- qlora
- QLoRA finetuning of quantized LLMs
- alpaca-lora
- Instruct-tune LLaMA on consumer hardware
Stars
- qlora
- 11k
- alpaca-lora
- 19k
Forks
- qlora
- 876
- alpaca-lora
- 2.2k
Open issues
- qlora
- 206
- alpaca-lora
- 365
Language
- qlora
- Jupyter Notebook
- alpaca-lora
- Jupyter Notebook
Adopt for
- qlora
- QLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family.
- alpaca-lora
- alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
Persona
- qlora
- -
- alpaca-lora
- developer harness
Runtime
- qlora
- -
- alpaca-lora
- -
License
- qlora
- MIT License; open-source tool for QLoRA fine-tuning process; LLaMA base models must be obtained legally as per their license terms
- 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
- qlora
- Jun 10, 2024
- alpaca-lora
- Jul 29, 2024
Categories
- qlora
- LLM Frameworks, Model Training
- alpaca-lora
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- qlora
- 783d
- alpaca-lora
- 734d
Open issues (now)
- qlora
- 206
- alpaca-lora
- 365
Full report
- qlora
- Trust report
- alpaca-lora
- Trust report
Choose qlora if…
- License: qlora is MIT, alpaca-lora is Apache-2.0.
- Pricing: Open source under MIT License; requires access to LLaMA base models.
- Requirements: Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes.
- Tags unique to qlora: fine-tuning, guanaco, llama models, quantization.
- Need efficient fine-tuning for quantized LLaMA-based models
When NOT to use qlora
- Require native full-precision model tuning without efficiency constraints
- Focusing on non-LLaMA-based language models where specific adaptations may not apply
Choose alpaca-lora if…
- License: alpaca-lora is Apache-2.0, qlora is MIT.
- 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 (artidoro/qlora) · observed Aug 3, 2026
- GitHub forks (artidoro/qlora) · observed Aug 3, 2026
- Last push (artidoro/qlora) · observed Jun 10, 2024
- License file (MIT) · observed Aug 3, 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: qlora 11k · alpaca-lora 19k (synced Aug 3, 2026).
Common questions
- What is the difference between qlora and alpaca-lora?
- qlora: QLoRA finetuning of quantized LLMs. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.
- When should I choose qlora over alpaca-lora?
- Choose qlora over alpaca-lora when License: qlora is MIT, alpaca-lora is Apache-2.0; Pricing: Open source under MIT License; requires access to LLaMA base models; Requirements: Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes; Tags unique to qlora: fine-tuning, guanaco, llama models, quantization; Need efficient fine-tuning for quantized LLaMA-based models.
- When should I choose alpaca-lora over qlora?
- Choose alpaca-lora over qlora when License: alpaca-lora is Apache-2.0, qlora is MIT; 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 qlora?
- Require native full-precision model tuning without efficiency constraints Focusing on non-LLaMA-based language models where specific adaptations may not apply
- 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 qlora or alpaca-lora more popular on GitHub?
- alpaca-lora has more GitHub stars (18,912 vs 10,979). Stars measure visibility, not whether either tool fits your constraints.
- Are qlora and alpaca-lora open source?
- Yes - both are open-source projects on GitHub (qlora: MIT, alpaca-lora: Apache-2.0).
- Where can I find alternatives to qlora or alpaca-lora?
- GraphCanon lists graph-backed alternatives at qlora alternatives and alpaca-lora alternatives (qlora 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, qlora or alpaca-lora?
- qlora: 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 qlora and alpaca-lora?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: qlora trust report; alpaca-lora trust report.