Comparison
FineTuningLLMs vs gpt-neox
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.
Markdown twin · FineTuningLLMs alternatives · gpt-neox alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FineTuningLLMs | gpt-neox |
|---|---|---|
| Maintenance | Slowing (146d since push) As of 3w · github_public_v1 | Steady (56d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · 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 |
Tagline
- 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
Stars
- FineTuningLLMs
- 851
- gpt-neox
- 7.5k
Forks
- FineTuningLLMs
- 114
- gpt-neox
- 1.1k
Open issues
- FineTuningLLMs
- 4
- gpt-neox
- 111
Language
- FineTuningLLMs
- Jupyter Notebook
- gpt-neox
- Python
Adopt for
- FineTuningLLMs
- FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
- 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
- FineTuningLLMs
- -
- gpt-neox
- -
Runtime
- FineTuningLLMs
- -
- gpt-neox
- -
License
- FineTuningLLMs
- MIT
- 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
- FineTuningLLMs
- Feb 28, 2026
- gpt-neox
- Jun 11, 2026
Categories
- FineTuningLLMs
- LLM Frameworks, Model Training
- gpt-neox
- LLM Frameworks, Model Training
Trust and health
Maintenance
- FineTuningLLMs
- Slowing (36%)
- gpt-neox
- Steady (60%)
Days since push
- FineTuningLLMs
- 146d
- gpt-neox
- 56d
Open issues (now)
- FineTuningLLMs
- 4
- gpt-neox
- 111
Owner type
- FineTuningLLMs
- User
- gpt-neox
- Organization
Full report
- FineTuningLLMs
- Trust report
- gpt-neox
- Trust report
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
When NOT to use FineTuningLLMs
- Not interested in PyTorch; prefer TensorFlow or another framework
- Seek theoretical background over practical applications
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dvgodoy/FineTuningLLMs) · observed Jul 24, 2026
- GitHub forks (dvgodoy/FineTuningLLMs) · observed Jul 24, 2026
- Last push (dvgodoy/FineTuningLLMs) · observed Feb 28, 2026
- License file (MIT) · observed Jul 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: FineTuningLLMs 851 · gpt-neox 7.5k (synced Jul 24, 2026).
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 851). 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 and gpt-neox alternatives (FineTuningLLMs 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, 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; gpt-neox trust report.