Comparison
gpt-neox vs peft
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 peft if pEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.
Markdown twin · gpt-neox alternatives · peft alternatives
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Trust & integrity
| Signal | gpt-neox | peft |
|---|---|---|
| Maintenance | Steady (56d since push) As of 2w · github_public_v1 | Very active (1d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- peft
- State-of-the-art Parameter-Efficient Fine-Tuning
Stars
- gpt-neox
- 7.5k
- peft
- 22k
Forks
- gpt-neox
- 1.1k
- peft
- 2.4k
Open issues
- gpt-neox
- 111
- peft
- 74
Language
- gpt-neox
- Python
- peft
- 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.
- peft
- PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.
Persona
- gpt-neox
- -
- peft
- -
Runtime
- gpt-neox
- -
- peft
- -
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
- peft
- Apache-2.0
Last pushed
- gpt-neox
- Jun 11, 2026
- peft
- Aug 22, 2026
Categories
- gpt-neox
- LLM Frameworks, Model Training
- peft
- LLM Frameworks, Model Training
Trust and health
Maintenance
- gpt-neox
- Steady (60%)
- peft
- Very active (96%)
Days since push
- gpt-neox
- 56d
- peft
- 1d
Open issues (now)
- gpt-neox
- 111
- peft
- 74
Stars delta
- gpt-neox
- Unknown
- peft
- +142 (30d)
Open issues delta
- gpt-neox
- Unknown
- peft
- +16 (30d)
Full report
- gpt-neox
- Trust report
- peft
- 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.
- - 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 peft if…
- Tags unique to peft: adapter, diffusion, fine-tuning, llm.
- When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
- More GitHub stars (22k vs 7.5k) - visibility, not fit.
When NOT to use peft
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only.
- When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
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 (huggingface/peft) · observed Aug 23, 2026
- GitHub forks (huggingface/peft) · observed Aug 23, 2026
- Last push (huggingface/peft) · observed Aug 22, 2026
- License file (Apache-2.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpt-neox 7.5k · peft 22k (synced Aug 7, 2026).
Common questions
- What is the difference between gpt-neox and peft?
- gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. peft: State-of-the-art Parameter-Efficient Fine-Tuning. See the comparison table for live GitHub stats and shared categories.
- When should I choose gpt-neox over peft?
- Choose gpt-neox over peft 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; - 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 peft over gpt-neox?
- Choose peft over gpt-neox when Tags unique to peft: adapter, diffusion, fine-tuning, llm; When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting; More GitHub stars (22k vs 7.5k) - visibility, not fit.
- 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 peft?
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
- Is gpt-neox or peft more popular on GitHub?
- peft has more GitHub stars (21,585 vs 7,452). Stars measure visibility, not whether either tool fits your constraints.
- Are gpt-neox and peft open source?
- Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, peft: Apache-2.0).
- Where can I find alternatives to gpt-neox or peft?
- GraphCanon lists graph-backed alternatives at gpt-neox alternatives and peft alternatives (gpt-neox markdown twin, peft 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 peft?
- gpt-neox: Steady. peft: Very active. 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 peft?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; peft trust report.