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
- QwenAudio/Fun-ASR
Fun-ASR speech recognition models, with native Hugging Face Transformers support for Fun-ASR-Nano and separate FunASR, vLLM and llama.cpp deployment paths.
1.6K stars
- Tencent/TurboTransformers
a fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU.
1.6K stars
- combust/mleap
MLeap: Deploy ML Pipelines to Production
1.5K stars
- lucidrains/soundstorm-pytorch
Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch
1.5K stars
- cevek/ttypescript
Over TypeScript tool to use custom transformers in the tsconfig.json
1.5K stars
- code-kern-ai/refinery
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
1.5K stars
- elicit/machine-learning-list
A curriculum for learning about foundation models, from scratch to the frontier
1.5K stars
- edwko/OuteTTS
Interface for OuteTTS models.
1.4K stars
- SKTBrain/KoBERT
Korean BERT pre-trained cased (KoBERT)
1.4K stars
- cdpierse/transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
1.4K stars
- ARahim3/mlx-tune
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
1.4K stars
- lucidrains/self-rewarding-lm-pytorch
Implementation of the training framework proposed in Self-Rewarding Language Model, from MetaAI
1.4K 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
- opencompass
A comprehensive toolkit for large model evaluation
Not published by PyPI - optimum
Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
Not published by PyPI - optimum-intel
Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
Not published by PyPI - optimum-nvidia
Optimum Nvidia is the interface between the Hugging Face Transformers and NVIDIA GPUs. "
Not published by PyPI - optimum-onnx
Optimum ONNX is an interface between the Hugging Face libraries and ONNX / ONNX Runtime
Not published by PyPI - oumi
Oumi - Modeling Platform
Not published by PyPI - outetts
OuteAI Text-to-Speech (TTS)
Not published by PyPI - paddleformers
Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Neural Search, Question Answering, Information Extraction and Sentiment Analysis end-to-end system.
Not published by PyPI - pai-easycv
An all-in-one toolkit for computer vision
Not published by PyPI - peft
Parameter-Efficient Fine-Tuning (PEFT)
Not published by PyPI - petals
Easy way to efficiently run 100B+ language models without high-end GPUs
Not published by PyPI - pix2tex
pix2tex: Using a ViT to convert images of equations into LaTeX code.
Not published by PyPI
Latest news the 2 latest events that mention it
- 33d agoSEP 22, 2026
- 4mo agoJUL 8, 2026