--- title: "LLM-Adapters vs gpt-neox" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-eleutherai-gpt-neox" tools: ["agi-edgerunners-llm-adapters", "eleutherai-gpt-neox"] --- # LLM-Adapters vs gpt-neox *GraphCanon updated Aug 24, 2026* ## 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. [LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 2024. [gpt-neox](https://www.eleuther.ai/) has 7.5k stars, 1.1k forks, and 111 open issues, last pushed Jun 11, 2026. Figures are from public GitHub metadata via [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox). | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [gpt-neox](/tools/eleutherai-gpt-neox.md) | | --- | --- | --- | | Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | | Stars | 1,233 | 7,452 | | Forks | 115 | 1,119 | | Open issues | 55 | 111 | | Language | Python | Python | | Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | 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 | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [gpt-neox](/tools/eleutherai-gpt-neox.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 896d | 56d | | Open issues (now) | 55 | 111 | | Stars delta | -1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/eleutherai-gpt-neox/trust.md) | ## Decision facts: LLM-Adapters - **Adopt for:** LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. ## Decision facts: gpt-neox - **Pricing:** freemium - Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations. - **Adopt for:** 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. - **License detail:** 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 ## Choose when ### 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). ### 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 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 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. ## 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](/tools/agi-edgerunners-llm-adapters/alternatives) and [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/alternatives.md), [gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md)), 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](/compare/agi-edgerunners-llm-adapters-vs-eleutherai-gpt-neox.md) 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](/tools/agi-edgerunners-llm-adapters/trust); [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_