Comparison
Awesome-Prompt-Engineering vs P-tuning-v2
Verdict
Pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license; 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-Prompt-Engineering alternatives · P-tuning-v2 alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | Awesome-Prompt-Engineering | P-tuning-v2 |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4w · github_public_v1 | Dormant (990d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4w · 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-Prompt-Engineering
- Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers
- P-tuning-v2
- Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks
Stars
- Awesome-Prompt-Engineering
- 6.2k
- P-tuning-v2
- 2.1k
Forks
- Awesome-Prompt-Engineering
- 734
- P-tuning-v2
- 213
Open issues
- Awesome-Prompt-Engineering
- 94
- P-tuning-v2
- 35
Language
- Awesome-Prompt-Engineering
- TypeScript
- P-tuning-v2
- Python
Adopt for
- Awesome-Prompt-Engineering
- Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
- 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-Prompt-Engineering
- -
- P-tuning-v2
- -
Runtime
- Awesome-Prompt-Engineering
- -
- P-tuning-v2
- -
License
- Awesome-Prompt-Engineering
- Apache-2.0
- 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-Prompt-Engineering
- Jul 27, 2026
- P-tuning-v2
- Nov 16, 2023
Categories
- Awesome-Prompt-Engineering
- Developer Tools, Model Training
- P-tuning-v2
- Model Training
Trust and health
Maintenance
- Awesome-Prompt-Engineering
- Very active (96%)
- P-tuning-v2
- Dormant (18%)
Days since push
- Awesome-Prompt-Engineering
- 0d
- P-tuning-v2
- 990d
Open issues (now)
- Awesome-Prompt-Engineering
- 94
- P-tuning-v2
- 35
OSV dependency advisories
- Awesome-Prompt-Engineering
- No lockfile (source not queried)
- P-tuning-v2
- Published findings
Full report
- Awesome-Prompt-Engineering
- Trust report
- P-tuning-v2
- Trust report
Shared compatibility
- Python · Awesome-Prompt-Engineering: Python runtime · P-tuning-v2: Python runtime
Choose Awesome-Prompt-Engineering if…
- Awesome-Prompt-Engineering is primarily TypeScript; P-tuning-v2 is Python.
- Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt.
- Also covers Developer Tools.
- You need focused materials on GPT and related models for prompt engineering
When NOT to use Awesome-Prompt-Engineering
- The project requires languages other than TypeScript
- Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering
Choose P-tuning-v2 if…
- P-tuning-v2 is primarily Python; Awesome-Prompt-Engineering is TypeScript.
- 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 (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- GitHub forks (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- Last push (promptslab/Awesome-Prompt-Engineering) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Jul 28, 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-Prompt-Engineering 6.2k · P-tuning-v2 2.1k (synced Jul 28, 2026).
Common questions
- What is the difference between Awesome-Prompt-Engineering and P-tuning-v2?
- Awesome-Prompt-Engineering: Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers. 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-Prompt-Engineering over P-tuning-v2?
- Choose Awesome-Prompt-Engineering over P-tuning-v2 when Awesome-Prompt-Engineering is primarily TypeScript; P-tuning-v2 is Python; Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt; Also covers Developer Tools; You need focused materials on GPT and related models for prompt engineering.
- When should I choose P-tuning-v2 over Awesome-Prompt-Engineering?
- Choose P-tuning-v2 over Awesome-Prompt-Engineering when P-tuning-v2 is primarily Python; Awesome-Prompt-Engineering is TypeScript; 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-Prompt-Engineering?
- The project requires languages other than TypeScript Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering
- 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-Prompt-Engineering or P-tuning-v2 more popular on GitHub?
- Awesome-Prompt-Engineering has more GitHub stars (6,197 vs 2,077). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Prompt-Engineering and P-tuning-v2 open source?
- Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, P-tuning-v2: Apache-2.0).
- Where can I find alternatives to Awesome-Prompt-Engineering or P-tuning-v2?
- GraphCanon lists graph-backed alternatives at Awesome-Prompt-Engineering alternatives and P-tuning-v2 alternatives (Awesome-Prompt-Engineering 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-Prompt-Engineering or P-tuning-v2?
- Awesome-Prompt-Engineering: Very active. 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-Prompt-Engineering and P-tuning-v2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Prompt-Engineering trust report; P-tuning-v2 trust report.