Comparison
FineTuningLLMs vs P-tuning-v2
Verdict
Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick P-tuning-v2 if p-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning.
Markdown twin · FineTuningLLMs alternatives · P-tuning-v2 alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | FineTuningLLMs | P-tuning-v2 |
|---|---|---|
| Maintenance | Slowing (146d since push) As of 1mo · github_public_v1 | Dormant (990d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1mo · github_public_v1 | Not a fork · Organization 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'
- P-tuning-v2
- Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks
Stars
- FineTuningLLMs
- 851
- P-tuning-v2
- 2.1k
Forks
- FineTuningLLMs
- 114
- P-tuning-v2
- 213
Open issues
- FineTuningLLMs
- 4
- P-tuning-v2
- 35
Language
- FineTuningLLMs
- Jupyter Notebook
- P-tuning-v2
- Python
Adopt for
- FineTuningLLMs
- FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
- P-tuning-v2
- P-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning.
Persona
- FineTuningLLMs
- -
- P-tuning-v2
- -
Runtime
- FineTuningLLMs
- -
- P-tuning-v2
- -
License
- FineTuningLLMs
- MIT
- P-tuning-v2
- P-tuning-v2 is provided under the Apache-2.0 license which permits free use, modification and distribution as long as copyright and license notice are preserved.
Last pushed
- FineTuningLLMs
- Feb 28, 2026
- P-tuning-v2
- Nov 16, 2023
Categories
- FineTuningLLMs
- LLM Frameworks, Model Training
- P-tuning-v2
- Model Training
Trust and health
Maintenance
- FineTuningLLMs
- Slowing (36%)
- P-tuning-v2
- Dormant (18%)
Days since push
- FineTuningLLMs
- 146d
- P-tuning-v2
- 990d
Open issues (now)
- FineTuningLLMs
- 4
- P-tuning-v2
- 35
Owner type
- FineTuningLLMs
- User
- P-tuning-v2
- Organization
OSV dependency advisories
- FineTuningLLMs
- No lockfile (source not queried)
- P-tuning-v2
- Published findings
Full report
- FineTuningLLMs
- Trust report
- P-tuning-v2
- Trust report
Choose FineTuningLLMs if…
- FineTuningLLMs is primarily Jupyter Notebook; P-tuning-v2 is Python.
- License: FineTuningLLMs is MIT, P-tuning-v2 is Apache-2.0.
- Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face.
- Also covers LLM Frameworks.
- 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 P-tuning-v2 if…
- P-tuning-v2 is primarily Python; FineTuningLLMs is Jupyter Notebook.
- License: P-tuning-v2 is Apache-2.0, FineTuningLLMs is MIT.
- Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance..
- Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model.
- For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods.
When NOT to use P-tuning-v2
- Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods.
- If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity.
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 Jul 24, 2026
- GitHub forks (dvgodoy/FineTuningLLMs) · observed Jul 24, 2026
- Last push (dvgodoy/FineTuningLLMs) · observed Feb 28, 2026
- License file (MIT) · observed Jul 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (THUDM/P-tuning-v2) · observed Aug 3, 2026
- GitHub forks (THUDM/P-tuning-v2) · observed Aug 3, 2026
- Last push (THUDM/P-tuning-v2) · observed Nov 16, 2023
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FineTuningLLMs 851 · P-tuning-v2 2.1k (synced Jul 24, 2026).
Common questions
- What is the difference between FineTuningLLMs and P-tuning-v2?
- FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. P-tuning-v2: Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks. See the comparison table for live GitHub stats and shared categories.
- When should I choose FineTuningLLMs over P-tuning-v2?
- Choose FineTuningLLMs over P-tuning-v2 when FineTuningLLMs is primarily Jupyter Notebook; P-tuning-v2 is Python; License: FineTuningLLMs is MIT, P-tuning-v2 is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; Also covers LLM Frameworks; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem.
- When should I choose P-tuning-v2 over FineTuningLLMs?
- Choose P-tuning-v2 over FineTuningLLMs when P-tuning-v2 is primarily Python; FineTuningLLMs is Jupyter Notebook; License: P-tuning-v2 is Apache-2.0, FineTuningLLMs is MIT; Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance.; Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model; For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods.
- When should I avoid FineTuningLLMs?
- Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications
- When should I avoid P-tuning-v2?
- Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods. If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity.
- Is FineTuningLLMs or P-tuning-v2 more popular on GitHub?
- P-tuning-v2 has more GitHub stars (2,077 vs 851). Stars measure visibility, not whether either tool fits your constraints.
- Are FineTuningLLMs and P-tuning-v2 open source?
- Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, P-tuning-v2: Apache-2.0).
- Where can I find alternatives to FineTuningLLMs or P-tuning-v2?
- GraphCanon lists graph-backed alternatives at FineTuningLLMs alternatives and P-tuning-v2 alternatives (FineTuningLLMs markdown twin, P-tuning-v2 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 P-tuning-v2?
- FineTuningLLMs: Slowing. P-tuning-v2: 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 P-tuning-v2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FineTuningLLMs trust report; P-tuning-v2 trust report.