Home/Compare/transformers vs sacred

Comparison

transformers vs sacred

Verdict

Pick transformers when license: transformers is Apache-2.0, sacred is MIT; pick sacred when license: sacred is MIT, transformers is Apache-2.0.

Markdown twin · transformers alternatives · sacred alternatives

GraphCanon updated today

transformers logo

transformers

huggingface/transformers

162kpushed Jul 11, 2026
vs
sacred logo

sacred

IDSIA/sacred

4.4kpushed Oct 22, 2025

Trust & integrity

Signaltransformerssacred
Maintenance
Very active (0d since push)
As of 1d · github_public_v1
Slowing (262d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of 1d · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of 1d · none
No criticals
As of today · osv@v1

Tagline

transformers
Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models
sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

Stars

transformers
162k
sacred
4.4k

Forks

transformers
34k
sacred
392

Open issues

transformers
2.5k
sacred
107

Language

transformers
Python
sacred
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
sacred
-

Persona

transformers
-
sacred
-

Runtime

transformers
-
sacred
-

License

transformers
Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems.
sacred
MIT

Last pushed

transformers
Jul 11, 2026
sacred
Oct 22, 2025

Categories

transformers
Computer Vision, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
sacred
LLM Frameworks, Model Training

Trust and health

Maintenance

transformers
Very active (96%)
sacred
Slowing (36%)

Days since push

transformers
0d
sacred
262d

Open issues (now)

transformers
2.5k
sacred
107

Security scan

transformers
No lockfile
sacred
No criticals

Full report

transformers
Trust report

Choose transformers if…

  • License: transformers is Apache-2.0, sacred is MIT.
  • Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+.
  • Tags unique to transformers: audio, deep-learning, natural-language-processing, pretrained models.
  • Also covers Computer Vision, Inference & Serving, Speech & Audio.
  • 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 sacred if…

  • License: sacred is MIT, transformers is Apache-2.0.
  • Tags unique to sacred: infrastructure, mongodb, reproducibility, reproducible-research.
  • Leaner open-issue backlog (107).

When NOT to use sacred

  • Last GitHub push was 263 days ago (slowing maintenance, Oct 22, 2025). Validate activity before betting a new project on sacred.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: transformers 162k · sacred 4.4k (synced Jul 11, 2026).

Common questions

What is the difference between transformers and sacred?
transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. sacred: Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.. See the comparison table for live GitHub stats and shared categories.
When should I choose transformers over sacred?
Choose transformers over sacred when License: transformers is Apache-2.0, sacred is MIT; Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: audio, deep-learning, natural-language-processing, pretrained models; Also covers Computer Vision, Inference & Serving, Speech & Audio; 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 sacred over transformers?
Choose sacred over transformers when License: sacred is MIT, transformers is Apache-2.0; Tags unique to sacred: infrastructure, mongodb, reproducibility, reproducible-research; Leaner open-issue backlog (107).
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 sacred?
Last GitHub push was 263 days ago (slowing maintenance, Oct 22, 2025). Validate activity before betting a new project on sacred. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Is transformers or sacred more popular on GitHub?
transformers has more GitHub stars (162,482 vs 4,367). Stars measure visibility, not whether either tool fits your constraints.
Are transformers and sacred open source?
Yes - both are open-source projects on GitHub (transformers: Apache-2.0, sacred: MIT).
Where can I find alternatives to transformers or sacred?
GraphCanon lists graph-backed alternatives at transformers alternatives and sacred alternatives (transformers markdown twin, sacred 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 sacred?
transformers: Very active. sacred: 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 sacred?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: transformers trust report; sacred trust report.