Comparison
gpt-neox vs P-tuning-v2
Verdict
Pick gpt-neox if gPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an 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.
Markdown twin · gpt-neox alternatives · P-tuning-v2 alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | gpt-neox | P-tuning-v2 |
|---|---|---|
| Maintenance | Steady (56d since push) As of 2w · github_public_v1 | Dormant (990d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · 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
- gpt-neox
- Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries
- P-tuning-v2
- Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks
Stars
- gpt-neox
- 7.5k
- P-tuning-v2
- 2.1k
Forks
- gpt-neox
- 1.1k
- P-tuning-v2
- 213
Open issues
- gpt-neox
- 111
- P-tuning-v2
- 35
Language
- gpt-neox
- Python
- P-tuning-v2
- Python
Adopt for
- gpt-neox
- GPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an 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
- gpt-neox
- -
- P-tuning-v2
- -
Runtime
- gpt-neox
- -
- P-tuning-v2
- -
License
- gpt-neox
- The tool is licensed under Apache-2.0, allowing permissive use but emphasizing that derivative works must preserve copyright headers and licenses as per their origins
- 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
- gpt-neox
- Jun 11, 2026
- P-tuning-v2
- Nov 16, 2023
Categories
- gpt-neox
- LLM Frameworks, Model Training
- P-tuning-v2
- Model Training
Trust and health
Maintenance
- gpt-neox
- Steady (60%)
- P-tuning-v2
- Dormant (18%)
Days since push
- gpt-neox
- 56d
- P-tuning-v2
- 990d
Open issues (now)
- gpt-neox
- 111
- P-tuning-v2
- 35
OSV dependency advisories
- gpt-neox
- No lockfile (source not queried)
- P-tuning-v2
- Published findings
Full report
- gpt-neox
- Trust report
- P-tuning-v2
- Trust report
Choose gpt-neox if…
- Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations..
- Tags unique to gpt-neox: deepspeed-library, gpt-3, language-model, transformers.
- Also covers LLM Frameworks.
- - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters.
When NOT to use gpt-neox
- - In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure.
- - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties.
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 (EleutherAI/gpt-neox) · observed Aug 7, 2026
- GitHub forks (EleutherAI/gpt-neox) · observed Aug 7, 2026
- Last push (EleutherAI/gpt-neox) · observed Jun 11, 2026
- License file (Apache-2.0) · observed Aug 7, 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: gpt-neox 7.5k · P-tuning-v2 2.1k (synced Aug 7, 2026).
Common questions
- What is the difference between gpt-neox and P-tuning-v2?
- gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. 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 gpt-neox over P-tuning-v2?
- Choose gpt-neox over P-tuning-v2 when Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations.; Tags unique to gpt-neox: deepspeed-library, gpt-3, language-model, transformers; Also covers LLM Frameworks; - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters.
- When should I choose P-tuning-v2 over gpt-neox?
- Choose P-tuning-v2 over gpt-neox 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 gpt-neox?
- - In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure. - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties.
- 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 gpt-neox or P-tuning-v2 more popular on GitHub?
- gpt-neox has more GitHub stars (7,452 vs 2,077). Stars measure visibility, not whether either tool fits your constraints.
- Are gpt-neox and P-tuning-v2 open source?
- Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, P-tuning-v2: Apache-2.0).
- Where can I find alternatives to gpt-neox or P-tuning-v2?
- GraphCanon lists graph-backed alternatives at gpt-neox alternatives and P-tuning-v2 alternatives (gpt-neox 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, gpt-neox or P-tuning-v2?
- gpt-neox: Steady. 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 gpt-neox and P-tuning-v2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; P-tuning-v2 trust report.