---
title: "Hugging Face"
description: "AI platform and community providing machine learning models, datasets, libraries, and tools for building AI applications."
---

# Hugging Face
> AI platform and community providing machine learning models, datasets, libraries, and tools for building AI applications.

## Articles

### [~Don't~ Repeat Yourself](https://yomu.fyi/post/don-t-repeat-yourself.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Patrick von Platen
- Published: Apr 5, 2022
- Type: Explainer

Hugging Face's Transformers library intentionally departs from the traditional "Don't repeat yourself" (DRY) software design principle by adopting a single model file policy. This architecture places all code required for a model's forward pass into a dedicated file, avoiding centralized shared modules for components like attention mechanisms. Because machine learning research evolves rapidly and published model architectures remain static, isolating code protects individual models from breaking changes and lowers contribution barriers for the open-source community. To maintain consistency without manually syncing duplicated code across successor models, the library uses automated tooling driven by Copied from comments. While this approach complicates unified API enforcement and component-wide research integration, Transformers validates API consistency by running approximately 20,000 automated tests daily.


### [Introducing Decision Transformers on Hugging Face 🤗](https://yomu.fyi/post/introducing-decision-transformers-on-hugging-face.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Edward Beeching, Thomas Simonini
- Published: Mar 28, 2022
- Type: Announcement

Hugging Face has integrated the Decision Transformer architecture into its transformers library and Hub, expanding support for offline reinforcement learning. Unlike standard reinforcement learning methods that maximize returns by learning value functions or policies through active environment interaction, Decision Transformers model trajectories as conditional sequence problems. The model feeds past states, actions, and target returns-to-go over a context window into a GPT-2 autoregressive backbone to generate future actions. Evaluating pre-trained continuous control checkpoints requires standardizing observations with training set statistics and iteratively updating the target return based on received rewards. This sequence modeling paradigm enables practitioners to adjust agent behaviors and skill levels directly by specifying different target returns at runtime.


### [Machine Learning Experts - Margaret Mitchell](https://yomu.fyi/post/machine-learning-experts-margaret-mitchell.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Britney Muller
- Published: Mar 23, 2022
- Type: Explainer

Machine learning models often internalize harmful dataset skews and fail to evaluate real-world contexts accurately. During work on vision-to-language generation, visual recognition systems learned to misinterpret destructive disasters as positive scenes because training images predominantly featured sunsets and fireworks. Addressing these structural flaws requires moving beyond benchmark optimization toward critical data analysis, dataset genealogies, and standardized ethical AI protocols. ML organizations must establish shared vocabularies for power differentials and dismantle competitive cultural norms that marginalize underrepresented contributors. Furthermore, lowering technical barriers—such as enabling non-engineers to inspect data without SQL—allows diverse stakeholders to evaluate models and prevent widening societal power divides.


### [Announcing the 🤗 AI Research Residency Program](https://yomu.fyi/post/announcing-the-ai-research-residency-program.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Douwe Kiela
- Published: Mar 22, 2022
- Type: Announcement

Hugging Face launched a nine-month Research Residency Program designed to train participants into impactful machine learning researchers. Residents collaborate directly with researchers from the Science Team to formulate research questions, develop novel machine learning techniques, and openly publish their findings. The initiative specifically encourages proposals from marginalized groups—including women, LGBTQ+ individuals, people of color, and working-class communities—to address disparities exacerbated by artificial intelligence progress. Applicants must demonstrate mathematical and programming abilities through coursework, open-source projects, and end-to-end proposals targeting positive societal impact. The full-time role is fully remote, provides benefits including medical coverage depending on location, and excludes concurrent student enrollment or employment.


### [Fine-Tune a Semantic Segmentation Model with a Custom Dataset](https://yomu.fyi/post/fine-tune-a-semantic-segmentation-model-with-a-custom-dataset.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tobias Cornille, Niels Rogge
- Published: Mar 17, 2022
- Type: Tutorial

Fine-tuning a semantic segmentation model requires domain-appropriate training data and an efficient pipeline. Existing autonomous driving datasets feature roadway imagery captured by cars, creating a distribution mismatch for sidewalk-based delivery robots. To resolve this discrepancy, a dedicated dataset of sidewalk imagery is loaded from the Hugging Face Hub, split into training and test sets, and augmented on-the-fly using SegformerImageProcessor and torchvision. The smallest SegFormer architecture, B0, is fine-tuned using Hugging Face's Trainer API with mean Intersection over Union evaluation metrics. The final pipeline pushes the fine-tuned model to the Hub and executes inference by upsampling output logits to original image dimensions.


### [Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia](https://yomu.fyi/post/accelerate-bert-inference-with-hugging-face-transformers-and-aws-infer.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Philipp Schmid
- Published: Mar 16, 2022
- Type: Tutorial

Production deployments of BERT and Transformer architectures often face cost and latency challenges because these models are significantly larger and more computationally intensive than traditional algorithms. To optimize text classification workloads, developers can compile vanilla PyTorch models for AWS Inferentia using the AWS Neuron SDK and its tracing utilities. Because the Neuron SDK requires static tensor dimensions, the model is traced with fixed input lengths and packaged alongside a custom inference script configuring one Neuron Core per worker. Deploying the resulting artifacts to an Amazon SageMaker ml.inf1.xlarge endpoint yields an average latency of 5 to 6 milliseconds for a sequence length of 128 across 10,000 synchronous evaluation requests.


### [Image search with 🤗 datasets](https://yomu.fyi/post/image-search-with-datasets.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Daniel van Strien
- Published: Mar 16, 2022
- Type: Tutorial

Hugging Face datasets expanded its capabilities by introducing an Image feature type, enabling image processing and integration with vector indexing tools. The library was applied to a sample of historical book embellishments extracted via OCR from the British Library. Images were loaded using the ImageFolder loader, enriched with filename metadata, and pushed to the Hugging Face Hub. A FAISS index and CLIP embeddings were used to retrieve images matching natural language queries, such as categories, specific objects, and boolean operators. While the retrieval system demonstrated reasonable semantic search results across various prompts, full public deployment was avoided due to CLIP model card restrictions and potential bias in the historical dataset.


### [Guiding Text Generation with Constrained Beam Search in 🤗 Transformers](https://yomu.fyi/post/guiding-text-generation-with-constrained-beam-search-in-transformers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Chan Woo Kim
- Published: Mar 11, 2022
- Type: Explainer

Constrained beam search introduces direct control over generated text in Hugging Face Transformers. Standard beam search operates token-by-token without knowing the optimal step to force specific words or phrases, making it difficult to enforce mandatory vocabulary or choose between alternative expressions. To resolve this, the generate interface accepts constraints through arguments such as force\_words\_ids and a list of Constraint subclasses like PhrasalConstraint. This mechanism enables disjunctive constraints, where generation must include at least one phrase from a provided set, alongside strictly required sequences. Consequently, practitioners can inject prior knowledge or formatting requirements directly at generation time rather than filtering candidate outputs afterward.


### [BERT 101 - State Of The Art NLP Model Explained](https://yomu.fyi/post/bert-101-state-of-the-art-nlp-model-explained.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Britney Muller
- Published: Mar 2, 2022
- Type: Explainer

Developed in 2018 by Google AI Language, Bidirectional Encoder Representations from Transformers addresses the historical challenge of machines lacking contextual understanding of human language. The model relies on an encoder-only Transformer architecture pre-trained on a 3.3-billion-word corpus consisting of Wikipedia and Google BooksCorpus. Training simultaneously combines masked language modeling, which hides 15% of tokenized words to enforce bidirectional context learning, with next sentence prediction across balanced sentence pairs. Pre-trained on Cloud TPUs over four days, BERT unifies solutions for more than eleven common NLP tasks and can be fine-tuned on task-specific annotated data within minutes. Unmasking experiments demonstrate that the model can also inherit distinct societal and gender biases from its underlying training corpora when predicting professions.


### [Fine-Tune ViT for Image Classification with 🤗 Transformers](https://yomu.fyi/post/fine-tune-vit-for-image-classification-with-transformers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Nate Raw
- Published: Feb 11, 2022
- Type: Tutorial

Vision Transformer models bring transformer architectures to computer vision by splitting images into grids of sub-image patches and projecting them into token sequences. To classify healthy and diseased leaves using the beans dataset, the Hugging Face datasets and transformers libraries enable streamlined data ingestion and model fine-tuning. Preprocessing relies on ViTImageProcessor paired with lazy on-the-fly dataset transforms to dynamically generate normalized pixel tensors. Training leverages ViTForImageClassification with the Trainer API, requiring remove\_unused\_columns set to False so raw image data is preserved for batch collation. Over four training epochs, fine-tuning the google/vit-base-patch16-224-in21k checkpoint achieves an evaluation accuracy of 98.5% alongside an evaluation loss of 0.0637.


### [Getting Started with Sentiment Analysis using Python](https://yomu.fyi/post/getting-started-with-sentiment-analysis-using-python.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Federico Pascual
- Published: Feb 2, 2022
- Type: Tutorial

Modern natural language processing tools allow developers to perform sentiment analysis without deep machine learning expertise. Sentiment analysis categorizes text polarity into positive, negative, or neutral labels to extract insights from large volumes of social posts, reviews, and support tickets. Using the Hugging Face Transformers library and the Hub, practitioners can quickly run pre-trained transformer pipelines or target specific multilingual and emotion-detection models with minimal Python code. For custom requirements, developers can fine-tune models like DistilBERT using the IMDB dataset via the Trainer API or train models automatically with AutoNLP. Applying these workflows to social media data enables automated sentiment distribution analysis and visualization using pandas, matplotlib, and word clouds.


### [Making automatic speech recognition work on large files with Wav2Vec2 in 🤗 Transformers](https://yomu.fyi/post/making-automatic-speech-recognition-work-on-large-files-with-wav2vec2.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Nicolas Patry
- Published: Feb 1, 2022
- Type: Problem & solution

Transformer-based automatic speech recognition models like Wav2Vec2 crash on long audio inputs because the O(n²) attention mechanism rapidly exhausts GPU memory. Simple audio chunking avoids out-of-memory errors but causes poor transcription accuracy at chunk boundaries due to a lack of surrounding context. To resolve this, Hugging Face Transformers leverages the Connectionist Temporal Classification architecture to process overlapping audio chunks with configurable strides. The pipeline discards low-quality logits at chunk edges and chains the remaining central predictions together to reconstruct a seamless transcript. This striding technique operates out of the box with language model-augmented pipelines and adapts directly to low-latency live audio streaming.


### [Supercharged Searching on the 🤗 Hub](https://yomu.fyi/post/supercharged-searching-on-the-hub.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Zachary Mueller
- Published: Jan 25, 2022
- Type: Tutorial

Programmatically searching the Hugging Face Hub previously required navigating web browser widgets or enduring trial and error to guess exact query string formats. The huggingface\_hub library solves this issue by introducing helper utilities such as ModelSearchArguments, DatasetSearchArguments, and ModelFilter alongside HfApi. These namespace helpers translate accessible Python attributes into the formatted parameters expected by the backend API, covering datasets, tasks, and libraries. For complex queries across multiple tasks, frameworks, and datasets, developers can combine criteria inside ModelFilter instances and retrieve matching model metadata via api.list\_models. Under the hood, the library uses AttributeDictionary, a data structure inspired by fastcore that enables nested tab-completion while supporting standard dictionary key indexing for special characters.


### [Welcome Stable-baselines3 to the Hugging Face Hub 🤗](https://yomu.fyi/post/welcome-stable-baselines3-to-the-hugging-face-hub.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Thomas Simonini
- Published: Jan 21, 2022
- Type: Announcement

Hugging Face announced an official integration with Stable-Baselines3, a popular PyTorch library for training and testing Deep Reinforcement Learning agents across diverse environments like Gym, Atari, MuJoco, and Procgen. The integration enables researchers and developers to host their saved reinforcement learning checkpoints on the Hugging Face Hub and download pre-trained community models. Interacting with the Hub requires installing the huggingface\_hub and huggingface\_sb3 packages, which supply helper methods for authentication, downloading, and uploading. Practitioners can retrieve checkpoint zip files using the load\_from\_hub function by providing the target repository identifier and filename before loading them into Stable-Baselines3 algorithms. Furthermore, users authenticated via CLI or notebook login can train policies such as PPO and publish their saved zip files to the Hub using push\_to\_hub.


### [Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs](https://yomu.fyi/post/case-study-millisecond-latency-using-hugging-face-infinity-and-modern.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Philipp Schmid, Jeff Boudier, Morgan Funtowicz
- Published: Jan 13, 2022
- Type: Benchmark

Deploying large Transformer models at scale often faces severe prediction latency bottlenecks, driving up infrastructure costs and limiting real-time production use cases. To address this challenge, Hugging Face evaluated Infinity, a containerized hardware-optimized inference solution paired with the Infinity Multiverse model optimization service. Testing covered 192 configurations on Amazon EC2 C6i instances powered by 3rd generation Intel Xeon Scalable processors across varying CPU cores, sequence lengths, and batch sizes. The benchmarks demonstrated that an Ice Lake-optimized DistilBERT container achieved up to 800% higher throughput than vanilla Transformers and delivered 1 to 4 millisecond end-to-end latencies for sequence lengths up to 64 tokens. Although Infinity was later discontinued in favor of Inference Endpoints and Optimum libraries, the results demonstrated substantial efficiency gains on modern CPU hardware.


### [Boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://yomu.fyi/post/boosting-wav2vec2-with-n-grams-in-transformers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Patrick von Platen
- Published: Jan 12, 2022
- Type: Tutorial

Wav2Vec2 models fine-tuned with Connectionist Temporal Classification transcribe speech without external language models, but decoding can still suffer from spelling inaccuracies. Hugging Face Transformers addressed this by integrating Kensho Technologies' pyctcdecode library to support decoding with n-gram language models. Instead of decoding using simple argmax operations over logits, the Wav2Vec2ProcessorWithLM class feeds full probability matrices into beam search guided by KenLM n-gram probabilities. KenLM's build\_binary utility compresses language model ARPA files into binary formats, reducing file size by more than half for faster loading and hub deployment. In Swedish xls-r-300m-sv benchmarks on Common Voice 7, this 5-gram boosted decoding setup achieved an 18.85% word error rate, delivering an approximate 30% relative performance gain.


### [Deploy GPT-J 6B for inference using  Hugging Face Transformers and Amazon SageMaker](https://yomu.fyi/post/deploy-gpt-j-6b-for-inference-using-hugging-face-transformers-and-amaz.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Philipp Schmid
- Published: Jan 11, 2022
- Type: Tutorial

Deploying EleutherAI's 6 billion parameter GPT-J model for production inference presents latency hurdles due to large memory footprints and lengthy startup times. Loading the model via standard methods takes up to several minutes, conflicting with strict real-time response limits such as Amazon SageMaker's 60-second threshold. To overcome this limitation, the model is serialized using PyTorch's native save mechanisms, packaged into a compressed archive with supporting assets, and stored on Amazon S3. This alternative loading workflow reduces GPT-J load times down to 7.7 seconds. An Amazon SageMaker real-time endpoint is then deployed on an NVIDIA T4 GPU instance using the Hugging Face Inference Toolkit.


### [Active Learning with AutoNLP and Prodigy](https://yomu.fyi/post/active-learning-with-autonlp-and-prodigy.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abhishek
- Published: Dec 23, 2021
- Type: Tutorial

Active learning requires iteratively adding labeled data, retraining models, and serving them to end users. Building such pipelines often demands substantial effort in data labeling, model selection, hyperparameter tuning, and training infrastructure. The author demonstrates a low-code active learning pipeline using Explosion's Prodigy for entity annotation and Hugging Face's AutoNLP for automatic training and evaluation. After first training a news categorization model achieving 98.67% accuracy on Kaggle's BBC News dataset, the author manually annotated named entities across iterative batches. An export script converted annotations to JSONL with IOB tags, showing progressive metric improvements from 20 samples to 250 samples, where the token classification model attained 95.9% accuracy, 0.73 precision, and 0.79 recall.


### [Gradio is joining Hugging Face!](https://yomu.fyi/post/gradio-is-joining-hugging-face.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abubakar Abid
- Published: Dec 21, 2021
- Type: Announcement

Hugging Face has acquired Gradio, the open-source machine learning library designed for building and sharing interactive model interfaces. Gradio originated in 2019 when its founder struggled to share a medical computer vision model with a physician collaborator who did not write Python. Co-founded alongside Ali Abdalla, Ali Abid, and Dawood Khan, the project expanded from computer vision into text, speech, and video modalities. Over 300,000 demos have been built using Gradio, allowing interdisciplinary industry teams and researchers to debug models internally and showcase them externally to non-technical users. The acquisition unites Gradio with Hugging Face to broaden browser-based machine learning accessibility and expand hiring efforts across the joint team.


### [Perceiver IO: a scalable, fully-attentional model that works on any modality](https://yomu.fyi/post/perceiver-io-a-scalable-fully-attentional-model-that-works-on-any-moda.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Niels Rogge
- Published: Dec 15, 2021
- Type: Announcement

Standard Transformer architectures scale poorly in compute and memory because pairwise dot-product self-attention depends quadratically on input size. Perceiver IO addresses this constraint by computing self-attention across a small set of latent variables rather than directly on high-dimensional inputs. Inputs and outputs interact with the model via cross-attention operations, decoupling compute and memory costs from input and output dimensions. Integrated into Hugging Face Transformers via the PerceiverModel class, the architecture supports diverse data types using optional preprocessors, decoders, and postprocessors. Experiments demonstrate competitive performance across text, multimodal video classification, 3D point cloud classification on ModelNet40, and StarCraft II reinforcement learning in AlphaStar.


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