GraphCanon
Home/Compare/LLM-Adapters vs P-tuning-v2

Comparison

LLM-Adapters vs P-tuning-v2

Verdict

Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; 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 · LLM-Adapters alternatives · P-tuning-v2 alternatives

GraphCanon updated Sep 26, 2026

7views this month

LLM-Adapters logo

LLM-Adapters

AGI-Edgerunners/LLM-Adapters

★ 1.2kpushed Mar 10, 2024
vs
P-tuning-v2 logo

P-tuning-v2

THUDM/P-tuning-v2

★ 2.1kpushed Nov 16, 2023

Trust & integrity

SignalLLM-AdaptersP-tuning-v2
Maintenance
Dormant (923d since push)
As of Sep 19, 2026 · github_public_v1
Dormant (1045d since push)
As of Sep 26, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 19, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 26, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Jul 11, 2026 · 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
LLM-Adapters trust reportP-tuning-v2 trust reportTrust methodology

Tagline

LLM-Adapters
Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs
P-tuning-v2
Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks

Stars

LLM-Adapters
1.2k
P-tuning-v2
2.1k

Forks

LLM-Adapters
116
P-tuning-v2
212

Open issues

LLM-Adapters
55
P-tuning-v2
35

Language

LLM-Adapters
Python
P-tuning-v2
Python

Adopt for

LLM-Adapters
LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing.
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

LLM-Adapters
-
P-tuning-v2
-

Runtime

LLM-Adapters
-
P-tuning-v2
-

License

LLM-Adapters
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

LLM-Adapters
Mar 10, 2024
P-tuning-v2
Nov 16, 2023

Categories

LLM-Adapters
LLM Frameworks, Model Training
P-tuning-v2
Model Training

Trust and health

Days since push

LLM-Adapters
923d
P-tuning-v2
1045d

Open issues (now)

LLM-Adapters
55
P-tuning-v2
35

Stars delta

LLM-Adapters
+1 (30d)
P-tuning-v2
+3 (30d)

OSV dependency advisories

LLM-Adapters
No lockfile (source not queried)
P-tuning-v2
Published findings

Full report

LLM-Adapters
Trust report
P-tuning-v2
Trust report

Shared compatibility

  • Python · LLM-Adapters: Python runtime · P-tuning-v2: Python runtime

Choose LLM-Adapters if…

  • Tags unique to LLM-Adapters: adapters, fine-tuning, large-language-models, parameter-efficient.
  • Also covers LLM Frameworks.
  • Optimizing resource usage when you need to fine-tune large language models without altering their core parameters

When NOT to use LLM-Adapters

  • You require a full retraining approach that modifies all model weights, not just adapters
  • Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023
GitHubDetails

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.
GitHubDetails

Explore

LLM-Adapters trust report →P-tuning-v2 trust report →LLM Frameworks category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: LLM-Adapters 1.2k · P-tuning-v2 2.1k (synced Sep 20, 2026).

Common questions

What is the difference between LLM-Adapters and P-tuning-v2?
LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. 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 LLM-Adapters over P-tuning-v2?
Choose LLM-Adapters over P-tuning-v2 when Tags unique to LLM-Adapters: adapters, fine-tuning, large-language-models, parameter-efficient; Also covers LLM Frameworks; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters.
When should I choose P-tuning-v2 over LLM-Adapters?
Choose P-tuning-v2 over LLM-Adapters 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 LLM-Adapters?
You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023
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 LLM-Adapters or P-tuning-v2 more popular on GitHub?
P-tuning-v2 has more GitHub stars (2,080 vs 1,235). Stars measure visibility, not whether either tool fits your constraints.
Are LLM-Adapters and P-tuning-v2 open source?
Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, P-tuning-v2: Apache-2.0).
Where can I find alternatives to LLM-Adapters or P-tuning-v2?
GraphCanon lists graph-backed alternatives at LLM-Adapters alternatives and P-tuning-v2 alternatives (LLM-Adapters 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, LLM-Adapters or P-tuning-v2?
LLM-Adapters: 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 LLM-Adapters and P-tuning-v2?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLM-Adapters trust report; P-tuning-v2 trust report.

Was this helpful?

Anonymous feedback helps us improve pages and translations.