Technology · Library
Transformers
Library · Hugging Face · Catalog tech@1
Hugging Face's library of pretrained models.
- Matched as
- hugging face transformers
- transformers
Momentum
Each signal is ranked as a percentile among the technologies that have it. A signal without enough history is not counted, and never shown as zero. How momentum works
Momentum
Collecting0 of 5 signalsNo score yet. Momentum is published only when at least 2 of the 5 signals have history. Transformers has 0, so there is no single number: each signal is shown on its own below. It is not a zero, and nothing is estimated in its place.
Snapshot OCT 24, 2026 · Scoring tech@1
- Official repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.166,907 stars now
- Member repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.0 new members in 30 days
- Package adoption
- Not published by its registries
- Its packages’ registries publish no adoption figure, so there is nothing to grow.
- Open models
- No official models
- No official model on Hugging Face, so this signal does not apply.
- News
- Collecting
- News counts once there are 3 events across the last 60 days; it has 1.0 in the last 30 days · 1 in the 30 before
Adoption by registry
The growth of Transformers’s own packages, one row per registry in that registry’s own unit. Registries count different things over different windows, so they are never added together.
No registry has two observations of Transformers’s own packages about 30 days apart yet, so there is no growth to show. Nothing is estimated in its place.
News activity
- Events · 7 days
- 0published events that mention it
- Events · 30 days
- 0published events that mention it
- Previous 30 days
- 1events in the 30 days before
Momentum history
Momentum history starts OCT 8, 2026. A chart appears once two daily snapshots carry a score; none does so far.
Repositories
Official 1
- huggingface/transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
166.9K stars
Members 140repositories tagged with one of its aliases, most-starred first
- jla524/fromthetensor
From the Tensor to Stable Diffusion, a rough outline for a 10 week course.
1.1K stars
- OpenBMB/VisCPM
[ICLR'24 spotlight] Chinese and English Multimodal Large Model Series (Chat and Paint) | 基于CPM基础模型的中英双语多模态大模型系列
1.1K stars
- graphdeeplearning/graphtransformer
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
1K stars
- turtlesoupy/this-word-does-not-exist
This Word Does Not Exist
1K stars
- buaacyw/MeshAnythingV2
[ICCV 2025] From anything to mesh like human artists. Official impl. of "MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization"
1K stars
- kakaobrain/kogpt
KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)
1K stars
- TinyLLaVA/TinyLLaVA_Factory
A Framework of Small-scale Large Multimodal Models
1K stars
- nokia-applied-research/AnyJev
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)
1K stars
Packages
“Is” packages are Transformers itself; ecosystem packages depend on them. The two are counted separately and never added together. Adoption is each registry’s own figure, with its percentile within that registry.
Is 11the technology itself, most-adopted first
- divyanx-transformers
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
Not published by PyPI - mcwtransformers
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
Not published by PyPI - my-transformers
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
Not published by PyPI - rlinf-transformer-openpi
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
Not published by PyPI - t-draft-123
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
Not published by PyPI - transformers
Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Not published by PyPI - transformers-kt
Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Not published by PyPI - transformers-mcw
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
Not published by PyPI - transformers-openpi
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
Not published by PyPI - transformers-qwenomni
Transformers: With code for Qwen 2.5 Omni
Not published by PyPI - transformers-usf-om-vl-exp-v0
Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Not published by PyPI
Ecosystem 279packages that depend on it, most-adopted first
- pyabsa
This tool provides the state-of-the-art models for aspect term extraction (ATE), aspect polarity classification (APC), and text classification (TC).
Not published by PyPI - pycorrector
Chinese Text Error Corrector
Not published by PyPI - pygpt-net
Open-source desktop AI assistant for OpenAI, Gemini, Claude, Grok, DeepSeek, Ollama and more, with agents, RAG, Skills, plugins/MCP, Python/OS tools, vision, image/video generation and Computer use.
Not published by PyPI - pyhealth
A Python library for healthcare AI
Not published by PyPI - pyiqa
PyTorch Toolbox for Image Quality Assessment
Not published by PyPI - pymilvus-model
Model components for PyMilvus, the Python SDK for Milvus
Not published by PyPI - pypots
A Python Toolbox for Machine Learning on Partially-Observed Time Series
Not published by PyPI - pyreft
REFT: Representation Finetuning for Language Models
Not published by PyPI - pyrit
The Python Risk Identification Tool for LLMs (PyRIT) is a library used to assess the robustness of LLMs
Not published by PyPI - pyserini
A Python toolkit for reproducible information retrieval research with sparse and dense representations
Not published by PyPI - pyserini-install
A Python toolkit for reproducible information retrieval research with sparse and dense representations
Not published by PyPI - pytdc
Therapeutics Commons
Not published by PyPI
Latest news the 2 latest events that mention it
- 33d agoSEP 22, 2026
- 4mo agoJUL 8, 2026