Comparison
awesome-llms-fine-tuning vs P-tuning-v2
Verdict
Pick awesome-llms-fine-tuning if a curated list for LLM fine-tuning resources including tutorials, papers, and tools; 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 · awesome-llms-fine-tuning alternatives · P-tuning-v2 alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | awesome-llms-fine-tuning | P-tuning-v2 |
|---|---|---|
| Maintenance | Dormant (599d since push) As of 1mo · github_public_v1 | Dormant (990d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization 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
- awesome-llms-fine-tuning
- A comprehensive collection of resources for fine-tuning Large Language Models.
- P-tuning-v2
- Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks
Stars
- awesome-llms-fine-tuning
- 525
- P-tuning-v2
- 2.1k
Forks
- awesome-llms-fine-tuning
- 78
- P-tuning-v2
- 213
Open issues
- awesome-llms-fine-tuning
- 9
- P-tuning-v2
- 35
Language
- awesome-llms-fine-tuning
- -
- P-tuning-v2
- Python
Adopt for
- awesome-llms-fine-tuning
- A curated list for LLM fine-tuning resources including tutorials, papers, and tools.
- 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
- awesome-llms-fine-tuning
- -
- P-tuning-v2
- -
Runtime
- awesome-llms-fine-tuning
- -
- P-tuning-v2
- -
License
- awesome-llms-fine-tuning
- (unknown) - (unknown)
- 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
- awesome-llms-fine-tuning
- Dec 2, 2024
- P-tuning-v2
- Nov 16, 2023
Categories
- awesome-llms-fine-tuning
- LLM Frameworks, Model Training
- P-tuning-v2
- Model Training
Trust and health
Days since push
- awesome-llms-fine-tuning
- 599d
- P-tuning-v2
- 990d
Open issues (now)
- awesome-llms-fine-tuning
- 9
- P-tuning-v2
- 35
OSV dependency advisories
- awesome-llms-fine-tuning
- No lockfile (source not queried)
- P-tuning-v2
- Published findings
Full report
- awesome-llms-fine-tuning
- Trust report
- P-tuning-v2
- Trust report
Choose awesome-llms-fine-tuning if…
- Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning.
- Also covers LLM Frameworks.
- Need extensive guidance on LLM-specific fine-tuning strategies
When NOT to use awesome-llms-fine-tuning
- Looking for real-time interactive support or direct code implementation help
- Favor more specialized tools for immediate performance optimization over broad learning
Choose P-tuning-v2 if…
- 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 (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Jul 25, 2026
- GitHub forks (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Jul 25, 2026
- Last push (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Dec 2, 2024
- License file (unknown) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 16, 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: awesome-llms-fine-tuning 525 · P-tuning-v2 2.1k (synced Jul 25, 2026).
Common questions
- What is the difference between awesome-llms-fine-tuning and P-tuning-v2?
- awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. 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 awesome-llms-fine-tuning over P-tuning-v2?
- Choose awesome-llms-fine-tuning over P-tuning-v2 when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning; Also covers LLM Frameworks; Need extensive guidance on LLM-specific fine-tuning strategies.
- When should I choose P-tuning-v2 over awesome-llms-fine-tuning?
- Choose P-tuning-v2 over awesome-llms-fine-tuning when 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 awesome-llms-fine-tuning?
- Looking for real-time interactive support or direct code implementation help Favor more specialized tools for immediate performance optimization over broad learning
- 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 awesome-llms-fine-tuning or P-tuning-v2 more popular on GitHub?
- P-tuning-v2 has more GitHub stars (2,077 vs 525). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llms-fine-tuning and P-tuning-v2 open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-llms-fine-tuning or P-tuning-v2?
- GraphCanon lists graph-backed alternatives at awesome-llms-fine-tuning alternatives and P-tuning-v2 alternatives (awesome-llms-fine-tuning 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, awesome-llms-fine-tuning or P-tuning-v2?
- awesome-llms-fine-tuning: Dormant. 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 awesome-llms-fine-tuning and P-tuning-v2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llms-fine-tuning trust report; P-tuning-v2 trust report.