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Comparison

LLM-Adapters vs gpt-neox

Verdict

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

Markdown twin · LLM-Adapters alternatives · gpt-neox alternatives

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LLM-Adapters logo

LLM-Adapters

AGI-Edgerunners/LLM-Adapters

★ 1.2kpushed Mar 10, 2024
vs
gpt-neox logo

gpt-neox

EleutherAI/gpt-neox

★ 7.5kpushed Jun 11, 2026

Trust & integrity

SignalLLM-Adaptersgpt-neox
Maintenance
Dormant (896d since push)
As of today · github_public_v1
Steady (56d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of 2w · 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
LLM-Adapters trust reportgpt-neox trust reportTrust methodology

Tagline

LLM-Adapters
Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs
gpt-neox
Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries

Stars

LLM-Adapters
1.2k
gpt-neox
7.5k

Forks

LLM-Adapters
115
gpt-neox
1.1k

Open issues

LLM-Adapters
55
gpt-neox
111

Language

LLM-Adapters
Python
gpt-neox
Python

Adopt for

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

Persona

LLM-Adapters
-
gpt-neox
-

Runtime

LLM-Adapters
-
gpt-neox
-

License

LLM-Adapters
Apache-2.0
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

Last pushed

LLM-Adapters
Mar 10, 2024
gpt-neox
Jun 11, 2026

Categories

LLM-Adapters
LLM Frameworks, Model Training
gpt-neox
LLM Frameworks, Model Training

Trust and health

Maintenance

LLM-Adapters
Dormant (18%)
gpt-neox
Steady (60%)

Days since push

LLM-Adapters
896d
gpt-neox
56d

Open issues (now)

LLM-Adapters
55
gpt-neox
111

Stars delta

LLM-Adapters
-1 (30d)
gpt-neox
Unknown

Open issues delta

LLM-Adapters
0 (30d)
gpt-neox
Unknown

Full report

LLM-Adapters
Trust report
gpt-neox
Trust report

Choose LLM-Adapters if…

  • Tags unique to LLM-Adapters: adapters, fine-tuning, large language models, parameter-efficient.
  • Optimizing resource usage when you need to fine-tune large language models without altering their core parameters
  • Leaner open-issue backlog (55).

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 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.
GitHubDetails

Explore

LLM-Adapters trust report →gpt-neox 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 · gpt-neox 7.5k (synced Aug 24, 2026).

Common questions

What is the difference between LLM-Adapters and gpt-neox?
LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. See the comparison table for live GitHub stats and shared categories.
When should I choose LLM-Adapters over gpt-neox?
Choose LLM-Adapters over gpt-neox when Tags unique to LLM-Adapters: adapters, fine-tuning, large language models, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters; Leaner open-issue backlog (55).
When should I choose gpt-neox over LLM-Adapters?
Choose gpt-neox over LLM-Adapters 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 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 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.
Is LLM-Adapters or gpt-neox more popular on GitHub?
gpt-neox has more GitHub stars (7,452 vs 1,233). Stars measure visibility, not whether either tool fits your constraints.
Are LLM-Adapters and gpt-neox open source?
Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, gpt-neox: Apache-2.0).
Where can I find alternatives to LLM-Adapters or gpt-neox?
GraphCanon lists graph-backed alternatives at LLM-Adapters alternatives and gpt-neox alternatives (LLM-Adapters markdown twin, gpt-neox 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 gpt-neox?
LLM-Adapters: Dormant. gpt-neox: Steady. 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 gpt-neox?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLM-Adapters trust report; gpt-neox trust report.

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