Comparison
transformers vs Alpaca
Verdict
Pick transformers when license: transformers is Apache-2.0, Alpaca is GPL-3.0; pick Alpaca when license: Alpaca is GPL-3.0, transformers is Apache-2.0.
Markdown twin · transformers alternatives · Alpaca alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | transformers | Alpaca |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4d · github_public_v1 | Active (19d 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
- Alpaca
- 🦙 Local and online AI hub
Stars
- transformers
- 162k
- Alpaca
- 1.6k
Forks
- transformers
- 34k
- Alpaca
- 144
Open issues
- transformers
- 2.5k
- Alpaca
- 122
Language
- transformers
- Python
- Alpaca
- 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
- Alpaca
- -
Persona
- transformers
- -
- Alpaca
- -
Runtime
- transformers
- -
- Alpaca
- -
License
- transformers
- Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems.
- Alpaca
- GPL-3.0
Last pushed
- transformers
- Jul 11, 2026
- Alpaca
- Jun 26, 2026
Categories
- transformers
- Computer Vision, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
- Alpaca
- Computer Vision, Inference & Serving, Vector Databases
Trust and health
Maintenance
- transformers
- Very active (96%)
- Alpaca
- Active (82%)
Days since push
- transformers
- 0d
- Alpaca
- 19d
Open issues (now)
- transformers
- 2.5k
- Alpaca
- 122
Owner type
- transformers
- Organization
- Alpaca
- User
Full report
- transformers
- Trust report
- Alpaca
- Trust report
Choose transformers if…
- License: transformers is Apache-2.0, Alpaca 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 LLM Frameworks, Model Training, 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 Alpaca if…
- License: Alpaca is GPL-3.0, transformers is Apache-2.0.
- Tags unique to Alpaca: adwaita, flatpak, gnome, gtk4.
- Also covers Vector Databases.
When NOT to use Alpaca
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
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 (Jeffser/Alpaca) · observed Jul 15, 2026
- GitHub forks (Jeffser/Alpaca) · observed Jul 15, 2026
- Last push (Jeffser/Alpaca) · observed Jun 26, 2026
- License file (GPL-3.0) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: transformers 162k · Alpaca 1.6k (synced Jul 11, 2026).
Common questions
- What is the difference between transformers and Alpaca?
- transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. Alpaca: 🦙 Local and online AI hub. See the comparison table for live GitHub stats and shared categories.
- When should I choose transformers over Alpaca?
- Choose transformers over Alpaca when License: transformers is Apache-2.0, Alpaca 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 LLM Frameworks, Model Training, 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 Alpaca over transformers?
- Choose Alpaca over transformers when License: Alpaca is GPL-3.0, transformers is Apache-2.0; Tags unique to Alpaca: adwaita, flatpak, gnome, gtk4; Also covers Vector Databases.
- 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 Alpaca?
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Is transformers or Alpaca more popular on GitHub?
- transformers has more GitHub stars (162,482 vs 1,601). Stars measure visibility, not whether either tool fits your constraints.
- Are transformers and Alpaca open source?
- Yes - both are open-source projects on GitHub (transformers: Apache-2.0, Alpaca: GPL-3.0).
- Where can I find alternatives to transformers or Alpaca?
- GraphCanon lists graph-backed alternatives at transformers alternatives and Alpaca alternatives (transformers markdown twin, Alpaca 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 Alpaca?
- transformers: Very active. Alpaca: Active. 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 Alpaca?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: transformers trust report; Alpaca trust report.