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
vs
Trust & integrity
| Signal | transformers | sacred |
|---|---|---|
| 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
- sacred
- 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 (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 (IDSIA/sacred) · observed Jul 11, 2026
- GitHub forks (IDSIA/sacred) · observed Jul 11, 2026
- Last push (IDSIA/sacred) · observed Oct 22, 2025
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
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.