Comparison
transformers vs gpt-home
Verdict
Pick transformers when license: transformers is Apache-2.0, gpt-home is GPL-3.0; pick gpt-home when license: gpt-home is GPL-3.0, transformers is Apache-2.0.
Markdown twin · transformers alternatives · gpt-home alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | transformers | gpt-home |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4d · github_public_v1 | Slowing (119d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | No lockfile (source not queried) As of today · 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
- transformers
- Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models
- gpt-home
- ChatGPT at home! A better alternative to commercial smart home assistants, built on the Raspberry Pi using LiteLLM and LangGraph.
Stars
- transformers
- 162k
- gpt-home
- 644
Forks
- transformers
- 34k
- gpt-home
- 66
Open issues
- transformers
- 2.5k
- gpt-home
- 0
Language
- transformers
- Python
- gpt-home
- Python
Adopt for
- transformers
- Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3
- gpt-home
- -
Persona
- transformers
- -
- gpt-home
- -
Runtime
- transformers
- -
- gpt-home
- -
License
- transformers
- Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems.
- gpt-home
- GPL-3.0
Last pushed
- transformers
- Jul 11, 2026
- gpt-home
- Mar 17, 2026
Categories
- transformers
- Computer Vision, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
- gpt-home
- LLM Frameworks, Speech & Audio
Trust and health
Maintenance
- transformers
- Very active (96%)
- gpt-home
- Slowing (36%)
Days since push
- transformers
- 0d
- gpt-home
- 119d
Open issues (now)
- transformers
- 2.5k
- gpt-home
- 0
Owner type
- transformers
- Organization
- gpt-home
- User
Full report
- transformers
- Trust report
- gpt-home
- Trust report
Choose transformers if…
- License: transformers is Apache-2.0, gpt-home is GPL-3.0.
- Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+.
- Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing.
- Also covers Computer Vision, Inference & Serving, Model Training.
- The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.
When NOT to use transformers
- If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable.
- It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.
Choose gpt-home if…
- License: gpt-home is GPL-3.0, transformers is Apache-2.0.
- Tags unique to gpt-home: ai, automation, docker, fastapi.
- Leaner open-issue backlog (0).
When NOT to use gpt-home
- Last GitHub push was 119 days ago (slowing maintenance, Mar 17, 2026). Validate activity before betting a new project on gpt-home.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huggingface/transformers) · observed Jul 11, 2026
- GitHub forks (huggingface/transformers) · observed Jul 11, 2026
- Last push (huggingface/transformers) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (judahpaul16/gpt-home) · observed Jul 15, 2026
- GitHub forks (judahpaul16/gpt-home) · observed Jul 15, 2026
- Last push (judahpaul16/gpt-home) · observed Mar 17, 2026
- License file (GPL-3.0) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: transformers 162k · gpt-home 644 (synced Jul 11, 2026).
Common questions
- What is the difference between transformers and gpt-home?
- transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. gpt-home: ChatGPT at home! A better alternative to commercial smart home assistants, built on the Raspberry Pi using LiteLLM and LangGraph.. See the comparison table for live GitHub stats and shared categories.
- When should I choose transformers over gpt-home?
- Choose transformers over gpt-home when License: transformers is Apache-2.0, gpt-home is GPL-3.0; Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing; Also covers Computer Vision, Inference & Serving, Model Training; The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.
- When should I choose gpt-home over transformers?
- Choose gpt-home over transformers when License: gpt-home is GPL-3.0, transformers is Apache-2.0; Tags unique to gpt-home: ai, automation, docker, fastapi; Leaner open-issue backlog (0).
- When should I avoid transformers?
- If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.
- When should I avoid gpt-home?
- Last GitHub push was 119 days ago (slowing maintenance, Mar 17, 2026). Validate activity before betting a new project on gpt-home. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is transformers or gpt-home more popular on GitHub?
- transformers has more GitHub stars (162,482 vs 644). Stars measure visibility, not whether either tool fits your constraints.
- Are transformers and gpt-home open source?
- Yes - both are open-source projects on GitHub (transformers: Apache-2.0, gpt-home: GPL-3.0).
- Where can I find alternatives to transformers or gpt-home?
- GraphCanon lists graph-backed alternatives at transformers alternatives and gpt-home alternatives (transformers markdown twin, gpt-home 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, transformers or gpt-home?
- transformers: Very active. gpt-home: Slowing. 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 transformers and gpt-home?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: transformers trust report; gpt-home trust report.