--- title: "FineTuningLLMs vs gpt-neox" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dvgodoy-finetuningllms-vs-eleutherai-gpt-neox" tools: ["dvgodoy-finetuningllms", "eleutherai-gpt-neox"] --- # FineTuningLLMs vs gpt-neox *GraphCanon updated Aug 24, 2026* ## Verdict Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; 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. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) reports 855 GitHub stars, 116 forks, and 4 open issues, last pushed Feb 28, 2026. [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 [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs) and [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox). | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [gpt-neox](/tools/eleutherai-gpt-neox.md) | | --- | --- | --- | | Tagline | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | | Stars | 855 | 7,452 | | Forks | 116 | 1,119 | | Open issues | 4 | 111 | | Language | Jupyter Notebook | Python | | Adopt for | FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. | 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 | MIT | 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._ | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [gpt-neox](/tools/eleutherai-gpt-neox.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 176d | 56d | | Open issues (now) | 4 | 111 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | [trust report](/tools/eleutherai-gpt-neox/trust.md) | ## Decision facts: FineTuningLLMs - **Adopt for:** FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. ## 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 FineTuningLLMs if… - FineTuningLLMs is primarily Jupyter Notebook; gpt-neox is Python. - License: FineTuningLLMs is MIT, gpt-neox is Apache-2.0. - Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face. - You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem ### Choose gpt-neox if… - gpt-neox is primarily Python; FineTuningLLMs is Jupyter Notebook. - License: gpt-neox is Apache-2.0, FineTuningLLMs is MIT. - 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 FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## 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 FineTuningLLMs and gpt-neox? FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. 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 FineTuningLLMs over gpt-neox? Choose FineTuningLLMs over gpt-neox when FineTuningLLMs is primarily Jupyter Notebook; gpt-neox is Python; License: FineTuningLLMs is MIT, gpt-neox is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem. ### When should I choose gpt-neox over FineTuningLLMs? Choose gpt-neox over FineTuningLLMs when gpt-neox is primarily Python; FineTuningLLMs is Jupyter Notebook; License: gpt-neox is Apache-2.0, FineTuningLLMs is MIT; 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 FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### 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 FineTuningLLMs or gpt-neox more popular on GitHub? gpt-neox has more GitHub stars (7,452 vs 855). Stars measure visibility, not whether either tool fits your constraints. ### Are FineTuningLLMs and gpt-neox open source? Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, gpt-neox: Apache-2.0). ### Where can I find alternatives to FineTuningLLMs or gpt-neox? GraphCanon lists graph-backed alternatives at [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) and [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) ([FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/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/dvgodoy-finetuningllms-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, FineTuningLLMs or gpt-neox? FineTuningLLMs: Slowing. 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 FineTuningLLMs and gpt-neox? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust); [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dvgodoy-finetuningllms`](/api/graphcanon/graph?tool=dvgodoy-finetuningllms) - 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/_