Comparison
FineTuningLLMs vs alpaca-lora
Verdict
Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; 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 · FineTuningLLMs alternatives · alpaca-lora alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | FineTuningLLMs | alpaca-lora |
|---|---|---|
| Maintenance | Slowing (176d since push) As of 2d · github_public_v1 | Dormant (734d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2d · github_public_v1 | Not a fork · Personal account As of 3w · 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
- FineTuningLLMs
- Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'
- alpaca-lora
- Instruct-tune LLaMA on consumer hardware
Stars
- FineTuningLLMs
- 855
- alpaca-lora
- 19k
Forks
- FineTuningLLMs
- 116
- alpaca-lora
- 2.2k
Open issues
- FineTuningLLMs
- 4
- alpaca-lora
- 365
Language
- FineTuningLLMs
- Jupyter Notebook
- alpaca-lora
- Jupyter Notebook
Adopt for
- FineTuningLLMs
- FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
- alpaca-lora
- alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
Persona
- FineTuningLLMs
- -
- alpaca-lora
- developer harness
Runtime
- FineTuningLLMs
- -
- alpaca-lora
- -
License
- FineTuningLLMs
- MIT
- 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
- FineTuningLLMs
- Feb 28, 2026
- alpaca-lora
- Jul 29, 2024
Categories
- FineTuningLLMs
- LLM Frameworks, Model Training
- alpaca-lora
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- FineTuningLLMs
- Slowing (36%)
- alpaca-lora
- Dormant (18%)
Days since push
- FineTuningLLMs
- 176d
- alpaca-lora
- 734d
Open issues (now)
- FineTuningLLMs
- 4
- alpaca-lora
- 365
Stars delta
- FineTuningLLMs
- +4 (30d)
- alpaca-lora
- Unknown
Open issues delta
- FineTuningLLMs
- 0 (30d)
- alpaca-lora
- Unknown
OSV dependency advisories
- FineTuningLLMs
- No lockfile (source not queried)
- alpaca-lora
- Published findings
Full report
- FineTuningLLMs
- Trust report
- alpaca-lora
- Trust report
Choose FineTuningLLMs if…
- License: FineTuningLLMs is MIT, alpaca-lora is Apache-2.0.
- Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face.
- You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem
When NOT to use FineTuningLLMs
- Not interested in PyTorch; prefer TensorFlow or another framework
- Seek theoretical background over practical applications
Choose alpaca-lora if…
- License: alpaca-lora is Apache-2.0, FineTuningLLMs 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 (dvgodoy/FineTuningLLMs) · observed Aug 24, 2026
- GitHub forks (dvgodoy/FineTuningLLMs) · observed Aug 24, 2026
- Last push (dvgodoy/FineTuningLLMs) · observed Feb 28, 2026
- License file (MIT) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 12, 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: FineTuningLLMs 855 · alpaca-lora 19k (synced Aug 24, 2026).
Common questions
- What is the difference between FineTuningLLMs and alpaca-lora?
- FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.
- When should I choose FineTuningLLMs over alpaca-lora?
- Choose FineTuningLLMs over alpaca-lora when License: FineTuningLLMs is MIT, alpaca-lora is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem.
- When should I choose alpaca-lora over FineTuningLLMs?
- Choose alpaca-lora over FineTuningLLMs when License: alpaca-lora is Apache-2.0, FineTuningLLMs 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 FineTuningLLMs?
- Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications
- 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 FineTuningLLMs or alpaca-lora more popular on GitHub?
- alpaca-lora has more GitHub stars (18,912 vs 855). Stars measure visibility, not whether either tool fits your constraints.
- Are FineTuningLLMs and alpaca-lora open source?
- Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, alpaca-lora: Apache-2.0).
- Where can I find alternatives to FineTuningLLMs or alpaca-lora?
- GraphCanon lists graph-backed alternatives at FineTuningLLMs alternatives and alpaca-lora alternatives (FineTuningLLMs 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, FineTuningLLMs or alpaca-lora?
- FineTuningLLMs: Slowing. 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 FineTuningLLMs and alpaca-lora?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FineTuningLLMs trust report; alpaca-lora trust report.