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How to host a Unity game in a Space
Did you know you can host a Unity game in a Hugging Face Space? No? Well, you can!
Hugging Face Spaces are an easy way to build, host, and share demos. While they are typically used for Machine Learning demos, they can also host playable Unity games. Here are some examples:
Huggy
Farming Game
Unity API Demo
Here's how you can host your own Unity game in a Space.
Step 1: Create a Space using the Static HTML template
First, navigate to Hugging Face Spaces to create a space.
Select the "Static HTML" template, give your Space a name, and create it.
Step 2: Use Git to Clone the Space
Clone your newly created Space to your local machine using Git. You can do this by running the following command in your terminal or command prompt:
git clone https://huggingface.co/spaces/{your-username}/{your-space-name}
Step 3: Open your Unity Project
Open the Unity project you want to host in your Space.
Step 4: Switch the Build Target to WebGL
Navigate to File > Build Settings and switch the Build Target to WebGL.
Step 5: Open Player Settings
In the Build Settings window, click the "Player Settings" button to open the Player Settings panel.
Step 6: Optionally, Download the Hugging Face Unity WebGL Template
You can enhance your game's appearance in a Space by downloading the Hugging Face Unity WebGL template, available here. Just download the repository and drop it in your project files.
Then, in the Player Settings panel, switch the WebGL template to Hugging Face. To do so, in Player Settings, click "Resolution and Presentation", then select the Hugging Face WebGL template.
Step 7: Change the Compression Format to Disabled
In the Player Settings panel, navigate to the "Publishing Settings" section and change the Compression Format to "Disabled".
Step 8: Build your Project
Return to the Build Settings window and click the "Build" button. Choose a location to save your build files, and Unity will build the project for WebGL.
Step 9: Copy the Contents of the Build Folder
After the build process is finished, navigate to the folder containing your build files. Copy the files in the build folder to the repository you cloned in Step 2.
Step 10: Enable Git-LFS for Large File Storage
Navigate to your repository. Use the following commands to track large build files.
git lfs install
git lfs track Build/*
Step 11: Push your Changes
Finally, use the following Git commands to push your changes:
git add .
git commit -m "Add Unity WebGL build files"
git push
Done!
Did you know you can host a Unity game in a Hugging Face Space? No? Well, you can!
Hugging Face Spaces are an easy way to build, host, and share demos. While they are typically used for Machine Learning demos, they can also host playable Unity games. Here are some examples:
Huggy
Farming Game
Unity API Demo
Here's how you can host your own Unity game in a Space.
Step 1: Create a Space using the Static HTML template
First, navigate to Hugging Face Spaces to create a space.
Select the "Static HTML" template, give your Space a name, and create it.
Step 2: Use Git to Clone the Space
Clone your newly created Space to your local machine using Git. You can do this by running the following command in your terminal or command prompt:
git clone https://huggingface.co/spaces/{your-username}/{your-space-name}
Step 3: Open your Unity Project
Open the Unity project you want to host in your Space.
Step 4: Switch the Build Target to WebGL
Navigate to File > Build Settings and switch the Build Target to WebGL.
Step 5: Open Player Settings
In the Build Settings window, click the "Player Settings" button to open the Player Settings panel.
Step 6: Optionally, Download the Hugging Face Unity WebGL Template
You can enhance your game's appearance in a Space by downloading the Hugging Face Unity WebGL template, available here. Just download the repository and drop it in your project files.
Then, in the Player Settings panel, switch the WebGL template to Hugging Face. To do so, in Player Settings, click "Resolution and Presentation", then select the Hugging Face WebGL template.
Step 7: Change the Compression Format to Disabled
In the Player Settings panel, navigate to the "Publishing Settings" section and change the Compression Format to "Disabled".
Step 8: Build your Project
Return to the Build Settings window and click the "Build" button. Choose a location to save your build files, and Unity will build the project for WebGL.
Step 9: Copy the Contents of the Build Folder
After the build process is finished, navigate to the folder containing your build files. Copy the files in the build folder to the repository you cloned in Step 2.
Step 10: Enable Git-LFS for Large File Storage
Navigate to your repository. Use the following commands to track large build files.
git lfs install
git lfs track Build/*
Step 11: Push your Changes
Finally, use the following Git commands to push your changes:
git add .
git commit -m "Add Unity WebGL build files"
git push
Done!
Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL
Pulling your hair out because LLM fine-tuning is taking forever? In this post, we introduce a lightweight tool developed by the community to make LLM fine-tuning go super fast!
Before diving into Unsloth, it may be helpful to read our QLoRA blog post, or be familiar with LLM fine-tuning using the 🤗 PEFT library.
Unsloth - 2x faster, -40% memory usage, 0% accuracy degradation
Unsloth is a lightweight library for faster LLM fine-tuning which is fully compatible with the Hugging Face ecosystem (Hub, transformers, PEFT, TRL). The library is actively developed by the Unsloth team (Daniel and Michael) and the open source community. The library supports most NVIDIA GPUs –from GTX 1070 all the way up to H100s–, and can be used with the entire trainer suite from the TRL library (SFTTrainer, DPOTrainer, PPOTrainer). At the time of writing, Unsloth supports the Llama (CodeLlama, Yi, etc) and Mistral architectures.
Unsloth works by overwriting some parts of the modeling code with optimized operations. By manually deriving backpropagation steps and rewriting all Pytorch modules into Triton kernels, Unsloth can both reduce memory usage and make fine-tuning faster. Crucially, accuracy degradation is 0% with respect to normal QLoRA, because no approximations are made in the optimized code.
Pulling your hair out because LLM fine-tuning is taking forever? In this post, we introduce a lightweight tool developed by the community to make LLM fine-tuning go super fast!
Before diving into Unsloth, it may be helpful to read our QLoRA blog post, or be familiar with LLM fine-tuning using the 🤗 PEFT library.
Unsloth - 2x faster, -40% memory usage, 0% accuracy degradation
Unsloth is a lightweight library for faster LLM fine-tuning which is fully compatible with the Hugging Face ecosystem (Hub, transformers, PEFT, TRL). The library is actively developed by the Unsloth team (Daniel and Michael) and the open source community. The library supports most NVIDIA GPUs –from GTX 1070 all the way up to H100s–, and can be used with the entire trainer suite from the TRL library (SFTTrainer, DPOTrainer, PPOTrainer). At the time of writing, Unsloth supports the Llama (CodeLlama, Yi, etc) and Mistral architectures.
Unsloth works by overwriting some parts of the modeling code with optimized operations. By manually deriving backpropagation steps and rewriting all Pytorch modules into Triton kernels, Unsloth can both reduce memory usage and make fine-tuning faster. Crucially, accuracy degradation is 0% with respect to normal QLoRA, because no approximations are made in the optimized code.
AI Policy @🤗: Comments on U.S. National AI Research Resource Interim Report
In late June 2022, Hugging Face submitted a response to the White House Office of Science and Technology Policy and National Science Foundation’s Request for Information on a roadmap for implementing the National Artificial Intelligence Research Resource (NAIRR) Task Force’s interim report findings. As a platform working to democratize machine learning by empowering all backgrounds to contribute to AI, we strongly support NAIRR’s efforts.
In late June 2022, Hugging Face submitted a response to the White House Office of Science and Technology Policy and National Science Foundation’s Request for Information on a roadmap for implementing the National Artificial Intelligence Research Resource (NAIRR) Task Force’s interim report findings. As a platform working to democratize machine learning by empowering all backgrounds to contribute to AI, we strongly support NAIRR’s efforts.
Using Machine Learning to Aid Survivors and Race through Time
On February 6, 2023, earthquakes measuring 7.7 and 7.6 hit South Eastern Turkey, affecting 10 cities and resulting in more than 42,000 deaths and 120,000 injured as of February 21.
A few hours after the earthquake, a group of programmers started a Discord server to roll out an application called afetharita, literally meaning, disaster map. This application would serve search & rescue teams and volunteers to find survivors and bring them help. The need for such an app arose when survivors posted screenshots of texts with their addresses and what they needed (including rescue) on social media. Some survivors also tweeted what they needed so their relatives knew they were alive and that they need rescue. Needing to extract information from these tweets, we developed various applications to turn them into structured data and raced against time in developing and deploying these apps.
On February 6, 2023, earthquakes measuring 7.7 and 7.6 hit South Eastern Turkey, affecting 10 cities and resulting in more than 42,000 deaths and 120,000 injured as of February 21.
A few hours after the earthquake, a group of programmers started a Discord server to roll out an application called afetharita, literally meaning, disaster map. This application would serve search & rescue teams and volunteers to find survivors and bring them help. The need for such an app arose when survivors posted screenshots of texts with their addresses and what they needed (including rescue) on social media. Some survivors also tweeted what they needed so their relatives knew they were alive and that they need rescue. Needing to extract information from these tweets, we developed various applications to turn them into structured data and raced against time in developing and deploying these apps.
Deep Dive: Vision Transformers On Hugging Face Optimum Graphcore
This blog post will show how easy it is to fine-tune pre-trained Transformer models for your dataset using the Hugging Face Optimum library on Graphcore Intelligence Processing Units (IPUs). As an example, we will show a step-by-step guide and provide a notebook that takes a large, widely-used chest X-ray dataset and trains a vision transformer (ViT) model.
Introducing vision transformer (ViT) models
In 2017 a group of Google AI researchers published a paper introducing the transformer model architecture. Characterised by a novel self-attention mechanism, transformers were proposed as a new and efficient group of models for language applications. Indeed, in the last five years, transformers have seen explosive popularity and are now accepted as the de facto standard for natural language processing (NLP).
This blog post will show how easy it is to fine-tune pre-trained Transformer models for your dataset using the Hugging Face Optimum library on Graphcore Intelligence Processing Units (IPUs). As an example, we will show a step-by-step guide and provide a notebook that takes a large, widely-used chest X-ray dataset and trains a vision transformer (ViT) model.
Introducing vision transformer (ViT) models
In 2017 a group of Google AI researchers published a paper introducing the transformer model architecture. Characterised by a novel self-attention mechanism, transformers were proposed as a new and efficient group of models for language applications. Indeed, in the last five years, transformers have seen explosive popularity and are now accepted as the de facto standard for natural language processing (NLP).
A Dive into Vision-Language Models
Human learning is inherently multi-modal as jointly leveraging multiple senses helps us understand and analyze new information better. Unsurprisingly, recent advances in multi-modal learning take inspiration from the effectiveness of this process to create models that can process and link information using various modalities such as image, video, text, audio, body gestures, facial expressions, and physiological signals.
Since 2021, we’ve seen an increased interest in models that combine vision and language modalities (also called joint vision-language models), such as OpenAI’s CLIP. Joint vision-language models have shown particularly impressive capabilities in very challenging tasks such as image captioning, text-guided image generation and manipulation, and visual question-answering. This field continues to evolve, and so does its effectiveness in improving zero-shot generalization leading to various practical use cases.
Human learning is inherently multi-modal as jointly leveraging multiple senses helps us understand and analyze new information better. Unsurprisingly, recent advances in multi-modal learning take inspiration from the effectiveness of this process to create models that can process and link information using various modalities such as image, video, text, audio, body gestures, facial expressions, and physiological signals.
Since 2021, we’ve seen an increased interest in models that combine vision and language modalities (also called joint vision-language models), such as OpenAI’s CLIP. Joint vision-language models have shown particularly impressive capabilities in very challenging tasks such as image captioning, text-guided image generation and manipulation, and visual question-answering. This field continues to evolve, and so does its effectiveness in improving zero-shot generalization leading to various practical use cases.
Kakao Brain’s Open Source ViT, ALIGN, and the New COYO Text-Image Dataset
Kakao Brain and Hugging Face are excited to release a new open-source image-text dataset COYO of 700 million pairs and two new visual language models trained on it, ViT and ALIGN. This is the first time ever the ALIGN model is made public for free and open-source use and the first release of ViT and ALIGN models that come with the train dataset.
Kakao Brain’s ViT and ALIGN models follow the same architecture and hyperparameters as provided in the original respective Google models but are trained on the open source COYO dataset. Google’s ViT and ALIGN models, while trained on huge datasets (ViT trained on 300 million images and ALIGN trained on 1.8 billion image-text pairs respectively), cannot be replicated because the datasets are not public. This contribution is particularly valuable to researchers who want to reproduce visual language modeling with access to the data as well.
Kakao Brain and Hugging Face are excited to release a new open-source image-text dataset COYO of 700 million pairs and two new visual language models trained on it, ViT and ALIGN. This is the first time ever the ALIGN model is made public for free and open-source use and the first release of ViT and ALIGN models that come with the train dataset.
Kakao Brain’s ViT and ALIGN models follow the same architecture and hyperparameters as provided in the original respective Google models but are trained on the open source COYO dataset. Google’s ViT and ALIGN models, while trained on huge datasets (ViT trained on 300 million images and ALIGN trained on 1.8 billion image-text pairs respectively), cannot be replicated because the datasets are not public. This contribution is particularly valuable to researchers who want to reproduce visual language modeling with access to the data as well.
What is a Vision Language Model?
Vision language models are broadly defined as multimodal models that can learn from images and text. They are a type of generative models that take image and text inputs, and generate text outputs. Large vision language models have good zero-shot capabilities, generalize well, and can work with many types of images, including documents, web pages, and more. The use cases include chatting about images, image recognition via instructions, visual question answering, document understanding, image captioning, and others. Some vision language models can also capture spatial properties in an image. These models can output bounding boxes or segmentation masks when prompted to detect or segment a particular subject, or they can localize different entities or answer questions about their relative or absolute positions. There’s a lot of diversity within the existing set of large vision language models, the data they were trained on, how they encode images, and, thus, their capabilities.
Vision language models are broadly defined as multimodal models that can learn from images and text. They are a type of generative models that take image and text inputs, and generate text outputs. Large vision language models have good zero-shot capabilities, generalize well, and can work with many types of images, including documents, web pages, and more. The use cases include chatting about images, image recognition via instructions, visual question answering, document understanding, image captioning, and others. Some vision language models can also capture spatial properties in an image. These models can output bounding boxes or segmentation masks when prompted to detect or segment a particular subject, or they can localize different entities or answer questions about their relative or absolute positions. There’s a lot of diversity within the existing set of large vision language models, the data they were trained on, how they encode images, and, thus, their capabilities.
VQ-Diffusion
Vector Quantized Diffusion (VQ-Diffusion) is a conditional latent diffusion model developed by the University of Science and Technology of China and Microsoft. Unlike most commonly studied diffusion models, VQ-Diffusion's noising and denoising processes operate on a quantized latent space, i.e., the latent space is composed of a discrete set of vectors. Discrete diffusion models are less explored than their continuous counterparts and offer an interesting point of comparison with autoregressive (AR) models.
Hugging Face model card
Hugging Face Spaces
Original Implementation
Paper
Demo
🧨 Diffusers lets you run VQ-Diffusion with just a few lines of code.
Vector Quantized Diffusion (VQ-Diffusion) is a conditional latent diffusion model developed by the University of Science and Technology of China and Microsoft. Unlike most commonly studied diffusion models, VQ-Diffusion's noising and denoising processes operate on a quantized latent space, i.e., the latent space is composed of a discrete set of vectors. Discrete diffusion models are less explored than their continuous counterparts and offer an interesting point of comparison with autoregressive (AR) models.
Hugging Face model card
Hugging Face Spaces
Original Implementation
Paper
Demo
🧨 Diffusers lets you run VQ-Diffusion with just a few lines of code.
Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models
Open In Colab
Transformer-based encoder-decoder models were proposed in Vaswani et al. (2017) and have recently experienced a surge of interest, e.g. Lewis et al. (2019), Raffel et al. (2019), Zhang et al. (2020), Zaheer et al. (2020), Yan et al. (2020).
Similar to BERT and GPT2, massive pre-trained encoder-decoder models have shown to significantly boost performance on a variety of sequence-to-sequence tasks Lewis et al. (2019), Raffel et al. (2019). However, due to the enormous computational cost attached to pre-training encoder-decoder models, the development of such models is mainly limited to large companies and institutes.
In Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2020), Sascha Rothe, Shashi Narayan and Aliaksei Severyn initialize encoder-decoder model with pre-trained encoder and/or decoder-only checkpoints (e.g. BERT, GPT2) to skip the costly pre-training. The authors show that such warm-started encoder-decoder models yield competitive results to large pre-trained encoder-decoder models, such as T5, and Pegasus on multiple sequence-to-sequence tasks at a fraction of the training cost.
In this notebook, we will explain in detail how encoder-decoder models can be warm-started, give practical tips based on Rothe et al. (2020), and finally go over a complete code example showing how to warm-start encoder-decoder models with 🤗Transformers.
This notebook is divided into 4 parts:
Introduction - Short summary of pre-trained language models in NLP and the need for warm-starting encoder-decoder models.
Warm-starting encoder-decoder models (Theory) - Illustrative explanation on how encoder-decoder models are warm-started?
Warm-starting encoder-decoder models (Analysis) - Summary of Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2020) - What model combinations are effective to warm-start encoder-decoder models; How does it differ from task to task?
Warm-starting encoder-decoder models with 🤗Transformers (Practice) - Complete code example showcasing in-detail how to use the EncoderDecoderModel framework to warm-start transformer-based encoder-decoder models.
It is highly recommended (probably even necessary) to have read this blog post about transformer-based encoder-decoder models.
Let's start by giving some back-ground on warm-starting encoder-decoder models.
Open In Colab
Transformer-based encoder-decoder models were proposed in Vaswani et al. (2017) and have recently experienced a surge of interest, e.g. Lewis et al. (2019), Raffel et al. (2019), Zhang et al. (2020), Zaheer et al. (2020), Yan et al. (2020).
Similar to BERT and GPT2, massive pre-trained encoder-decoder models have shown to significantly boost performance on a variety of sequence-to-sequence tasks Lewis et al. (2019), Raffel et al. (2019). However, due to the enormous computational cost attached to pre-training encoder-decoder models, the development of such models is mainly limited to large companies and institutes.
In Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2020), Sascha Rothe, Shashi Narayan and Aliaksei Severyn initialize encoder-decoder model with pre-trained encoder and/or decoder-only checkpoints (e.g. BERT, GPT2) to skip the costly pre-training. The authors show that such warm-started encoder-decoder models yield competitive results to large pre-trained encoder-decoder models, such as T5, and Pegasus on multiple sequence-to-sequence tasks at a fraction of the training cost.
In this notebook, we will explain in detail how encoder-decoder models can be warm-started, give practical tips based on Rothe et al. (2020), and finally go over a complete code example showing how to warm-start encoder-decoder models with 🤗Transformers.
This notebook is divided into 4 parts:
Introduction - Short summary of pre-trained language models in NLP and the need for warm-starting encoder-decoder models.
Warm-starting encoder-decoder models (Theory) - Illustrative explanation on how encoder-decoder models are warm-started?
Warm-starting encoder-decoder models (Analysis) - Summary of Leveraging Pre-trained Checkpoints for Sequence Generation Tasks (2020) - What model combinations are effective to warm-start encoder-decoder models; How does it differ from task to task?
Warm-starting encoder-decoder models with 🤗Transformers (Practice) - Complete code example showcasing in-detail how to use the EncoderDecoderModel framework to warm-start transformer-based encoder-decoder models.
It is highly recommended (probably even necessary) to have read this blog post about transformer-based encoder-decoder models.
Let's start by giving some back-ground on warm-starting encoder-decoder models.
AI Watermarking 101: Tools and Techniques
In recent months, we've seen multiple news stories involving ‘deepfakes’, or AI-generated content: from images of Taylor Swift to videos of Tom Hanks and recordings of US President Joe Biden. Whether they are selling products, manipulating images of people without their consent, supporting phishing for private information, or creating misinformation materials intended to mislead voters, deepfakes are increasingly being shared on social media platforms. This enables them to be quickly propagated and have a wider reach and therefore, the potential to cause long-lasting damage.
In this blog post, we will describe approaches to carry out watermarking of AI-generated content, discuss their pros and cons, and present some of the tools available on the Hugging Face Hub for adding/detecting watermarks.
In recent months, we've seen multiple news stories involving ‘deepfakes’, or AI-generated content: from images of Taylor Swift to videos of Tom Hanks and recordings of US President Joe Biden. Whether they are selling products, manipulating images of people without their consent, supporting phishing for private information, or creating misinformation materials intended to mislead voters, deepfakes are increasingly being shared on social media platforms. This enables them to be quickly propagated and have a wider reach and therefore, the potential to cause long-lasting damage.
In this blog post, we will describe approaches to carry out watermarking of AI-generated content, discuss their pros and cons, and present some of the tools available on the Hugging Face Hub for adding/detecting watermarks.
Boosting Wav2Vec2 with n-grams in 🤗 Transformers
Open In Colab
Wav2Vec2 is a popular pre-trained model for speech recognition. Released in September 2020 by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, e.g. G. Ng et al., 2021, Chen et al, 2021, Hsu et al., 2021 and Babu et al., 2021. On the Hugging Face Hub, Wav2Vec2's most popular pre-trained checkpoint currently amounts to over 250,000 monthly downloads.
Using Connectionist Temporal Classification (CTC), pre-trained Wav2Vec2-like checkpoints are extremely easy to fine-tune on downstream speech recognition tasks. In a nutshell, fine-tuning pre-trained Wav2Vec2 checkpoints works as follows:
A single randomly initialized linear layer is stacked on top of the pre-trained checkpoint and trained to classify raw audio input to a sequence of letters. It does so by:
extracting audio representations from the raw audio (using CNN layers),
processing the sequence of audio representations with a stack of transformer layers, and,
classifying the processed audio representations into a sequence of output letters.
Previously audio classification models required an additional language model (LM) and a dictionary to transform the sequence of classified audio frames to a coherent transcription. Wav2Vec2's architecture is based on transformer layers, thus giving each processed audio representation context from all other audio representations. In addition, Wav2Vec2 leverages the CTC algorithm for fine-tuning, which solves the problem of alignment between a varying "input audio length"-to-"output text length" ratio.
Open In Colab
Wav2Vec2 is a popular pre-trained model for speech recognition. Released in September 2020 by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, e.g. G. Ng et al., 2021, Chen et al, 2021, Hsu et al., 2021 and Babu et al., 2021. On the Hugging Face Hub, Wav2Vec2's most popular pre-trained checkpoint currently amounts to over 250,000 monthly downloads.
Using Connectionist Temporal Classification (CTC), pre-trained Wav2Vec2-like checkpoints are extremely easy to fine-tune on downstream speech recognition tasks. In a nutshell, fine-tuning pre-trained Wav2Vec2 checkpoints works as follows:
A single randomly initialized linear layer is stacked on top of the pre-trained checkpoint and trained to classify raw audio input to a sequence of letters. It does so by:
extracting audio representations from the raw audio (using CNN layers),
processing the sequence of audio representations with a stack of transformer layers, and,
classifying the processed audio representations into a sequence of output letters.
Previously audio classification models required an additional language model (LM) and a dictionary to transform the sequence of classified audio frames to a coherent transcription. Wav2Vec2's architecture is based on transformer layers, thus giving each processed audio representation context from all other audio representations. In addition, Wav2Vec2 leverages the CTC algorithm for fine-tuning, which solves the problem of alignment between a varying "input audio length"-to-"output text length" ratio.
From screenshots to HTML code: Introducing the WebSight dataset
In the world of web development, turning designs into functional websites usually involves a lot of coding and careful testing. What if we could simplify this process, making it possible to convert web designs into working websites more easily and quickly? WebSight is a new dataset that aims at building AI systems capable of transforming screenshots to HTML code.
The challenge
Turning a website design or screenshot into HTML code usually needs an experienced developer. But what if this could be more efficient? Motivated by this question, we investigated how vision-language models (VLMs) could be used in web development to create low-code solutions that improve efficiency.
Today, the main challenge towards that goal is the lack of high-quality datasets tailored for this task. WebSight aims to fill that gap.
In the world of web development, turning designs into functional websites usually involves a lot of coding and careful testing. What if we could simplify this process, making it possible to convert web designs into working websites more easily and quickly? WebSight is a new dataset that aims at building AI systems capable of transforming screenshots to HTML code.
The challenge
Turning a website design or screenshot into HTML code usually needs an experienced developer. But what if this could be more efficient? Motivated by this question, we investigated how vision-language models (VLMs) could be used in web development to create low-code solutions that improve efficiency.
Today, the main challenge towards that goal is the lack of high-quality datasets tailored for this task. WebSight aims to fill that gap.
Speculative Decoding for 2x Faster Whisper Inference
Open In Colab
Open AI's Whisper is a general purpose speech transcription model that achieves state-of-the-art results across a range of different benchmarks and audio conditions. The latest large-v3 model tops the OpenASR Leaderboard, ranking as the best open-source speech transcription model for English. The model also demonstrates strong multilingual performance, achieving less than 30% word error rate (WER) on 42 of the 58 languages tested in the Common Voice 15 dataset.
While the transcription accuracy is exceptional, the inference time is very slow. A 1 hour audio clip takes upwards of 6 minutes to transcribe on a 16GB T4 GPU, even after leveraging inference optimisations like flash attention, half-precision, and chunking.
In this blog post, we demonstrate how Speculative Decoding can be employed to reduce the inference time of Whisper by a factor of 2, while mathematically ensuring exactly the same outputs are achieved from the model. As a result, this method provides a perfect drop-in replacement for existing Whisper pipelines, since it provides free 2x speed-up while maintaining the same accuracy. For a more streamlined version of the blog post with fewer explanations but all the code, see the accompanying Google Colab.
Open In Colab
Open AI's Whisper is a general purpose speech transcription model that achieves state-of-the-art results across a range of different benchmarks and audio conditions. The latest large-v3 model tops the OpenASR Leaderboard, ranking as the best open-source speech transcription model for English. The model also demonstrates strong multilingual performance, achieving less than 30% word error rate (WER) on 42 of the 58 languages tested in the Common Voice 15 dataset.
While the transcription accuracy is exceptional, the inference time is very slow. A 1 hour audio clip takes upwards of 6 minutes to transcribe on a 16GB T4 GPU, even after leveraging inference optimisations like flash attention, half-precision, and chunking.
In this blog post, we demonstrate how Speculative Decoding can be employed to reduce the inference time of Whisper by a factor of 2, while mathematically ensuring exactly the same outputs are achieved from the model. As a result, this method provides a perfect drop-in replacement for existing Whisper pipelines, since it provides free 2x speed-up while maintaining the same accuracy. For a more streamlined version of the blog post with fewer explanations but all the code, see the accompanying Google Colab.
How NuminaMath Won the 1st AIMO Progress Prize
This year, Numina and Hugging Face collaborated to compete in the 1st Progress Prize of the AI Math Olympiad (AIMO). This competition involved fine-tuning open LLMs to solve difficult math problems that high school students use to train for the International Math Olympiad. We’re excited to share that our model — NuminaMath 7B TIR — was the winner and managed to solve 29 out of 50 problems on the private test set 🥳!
This year, Numina and Hugging Face collaborated to compete in the 1st Progress Prize of the AI Math Olympiad (AIMO). This competition involved fine-tuning open LLMs to solve difficult math problems that high school students use to train for the International Math Olympiad. We’re excited to share that our model — NuminaMath 7B TIR — was the winner and managed to solve 29 out of 50 problems on the private test set 🥳!
Leveraging Hugging Face for complex generative AI use casess
In this conversation, Jeff Boudier asks Waseem Alshikh, Co-founder and CTO of Writer, about their journey from a Hugging Face user, to a customer and now an open source model contributor.
why was Writer started?
what are the biggest misconceptions in Generative AI today?
why is Writer now contributing open source models?
what has been the value of the Hugging Face Expert Acceleration Program service for Writer?
how it Writer approaching production on CPU and GPU to serve LLMs at scale?
how important is efficiency and using CPUs for production?
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If you’re interested in Hugging Face Expert Acceleration Program for your company, please contact us here - our team will contact you to discuss your requirements!
In this conversation, Jeff Boudier asks Waseem Alshikh, Co-founder and CTO of Writer, about their journey from a Hugging Face user, to a customer and now an open source model contributor.
why was Writer started?
what are the biggest misconceptions in Generative AI today?
why is Writer now contributing open source models?
what has been the value of the Hugging Face Expert Acceleration Program service for Writer?
how it Writer approaching production on CPU and GPU to serve LLMs at scale?
how important is efficiency and using CPUs for production?
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If you’re interested in Hugging Face Expert Acceleration Program for your company, please contact us here - our team will contact you to discuss your requirements!
How to use Würstchen?
You can either try it using the Demo here:
<script type="module" src="https://gradio.s3-us-west-2.amazonaws.com/3.44.2/gradio.js"> </script>
Otherwise, the model is available through the Diffusers Library, so you can use the interface you are already familiar with. For example, this is how to run inference using the AutoPipeline:
You can either try it using the Demo here:
<script type="module" src="https://gradio.s3-us-west-2.amazonaws.com/3.44.2/gradio.js"> </script>
Otherwise, the model is available through the Diffusers Library, so you can use the interface you are already familiar with. For example, this is how to run inference using the AutoPipeline:
Why another text-to-image model?
Well, this one is pretty fast and efficient. Würstchen’s biggest benefits come from the fact that it can generate images much faster than models like Stable Diffusion XL, while using a lot less memory! So for all of us who don’t have A100s lying around, this will come in handy. Here is a comparison with SDXL over different batch sizes: #text-to-image
Well, this one is pretty fast and efficient. Würstchen’s biggest benefits come from the fact that it can generate images much faster than models like Stable Diffusion XL, while using a lot less memory! So for all of us who don’t have A100s lying around, this will come in handy. Here is a comparison with SDXL over different batch sizes: #text-to-image
Introducing Würstchen: Fast Diffusion for Image Generation
What is Würstchen?
Würstchen is a diffusion model, whose text-conditional component works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by orders of magnitude. Training on 1024×1024 images is way more expensive than training on 32×32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, it achieves a 42x spatial compression! This had never been seen before, because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the paper).
What is Würstchen?
Würstchen is a diffusion model, whose text-conditional component works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by orders of magnitude. Training on 1024×1024 images is way more expensive than training on 32×32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, it achieves a 42x spatial compression! This had never been seen before, because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the paper).
XetHub is joining Hugging Face!
We are super excited to officially announce that Hugging Face acquired XetHub 🔥
XetHub is a Seattle-based company founded by Yucheng Low, Ajit Banerjee, Rajat Arya who previously worked at Apple where they built and scaled Apple’s internal ML infrastructure. XetHub’s mission is to enable software engineering best practices for AI development. XetHub has developed technologies to enable Git to scale to TB repositories and enable teams to explore, understand and work together on large evolving datasets and models. They were soon joined by a talented team of 12 team members. You should give them a follow at their new org page: hf.co/xet-team
Our common goal at HF
The XetHub team will help us unlock the next 5 years of growth of HF datasets and models by switching to our own, better version of LFS as storage backend for the Hub's repos.
– Julien Chaumond, HF CTO
Back in 2020 when we built the first version of the HF Hub, we decided to build it on top of Git LFS because it was decently well-known and it was a reasonable choice to bootstrap the Hub’s usage.
We knew back then, however, that we would want to switch to our own, more optimized storage and versioning backend at some point. Git LFS – even though it stands for Large File storage – was just never meant for the type of large files we handle in AI, which are not just large, but very very large 😃#XetHub
We are super excited to officially announce that Hugging Face acquired XetHub 🔥
XetHub is a Seattle-based company founded by Yucheng Low, Ajit Banerjee, Rajat Arya who previously worked at Apple where they built and scaled Apple’s internal ML infrastructure. XetHub’s mission is to enable software engineering best practices for AI development. XetHub has developed technologies to enable Git to scale to TB repositories and enable teams to explore, understand and work together on large evolving datasets and models. They were soon joined by a talented team of 12 team members. You should give them a follow at their new org page: hf.co/xet-team
Our common goal at HF
The XetHub team will help us unlock the next 5 years of growth of HF datasets and models by switching to our own, better version of LFS as storage backend for the Hub's repos.
– Julien Chaumond, HF CTO
Back in 2020 when we built the first version of the HF Hub, we decided to build it on top of Git LFS because it was decently well-known and it was a reasonable choice to bootstrap the Hub’s usage.
We knew back then, however, that we would want to switch to our own, more optimized storage and versioning backend at some point. Git LFS – even though it stands for Large File storage – was just never meant for the type of large files we handle in AI, which are not just large, but very very large 😃#XetHub
huggingface.co
xet-team (Xet Team)
Org profile for Xet Team on Hugging Face, the AI community building the future.