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https://huggingface.co/docs/transformers.js/guides/node-audio-processing | Server-side Audio Processing in Node.js
A major benefit of writing code for the web is that you can access the multitude of APIs that are available in modern browsers. Unfortunately, when writing server-side code, we are not afforded such luxury, so we have to find another way. In this tutorial, we will design a simple... |
https://huggingface.co/docs/transformers.js/pipelines | The pipeline API
Just like the transformers Python library, Transformers.js provides users with a simple way to leverage the power of transformers. The pipeline() function is the easiest and fastest way to use a pretrained model for inference.
For the full list of available tasks/pipelines, check out this table.
The ba... |
https://huggingface.co/docs/transformers.js/tutorials/next | Building a Next.js application
In this tutorial, we’ll build a simple Next.js application that performs sentiment analysis using Transformers.js! Since Transformers.js can run in the browser or in Node.js, you can choose whether you want to perform inference client-side or server-side (we’ll show you how to do both). I... |
https://huggingface.co/docs/tokenizers/api/tokenizer | class tokenizers.Tokenizer
( model )
Parameters
model (Model) — The core algorithm that this Tokenizer should be using.
A Tokenizer works as a pipeline. It processes some raw text as input and outputs an Encoding.
The optional Decoder in use by the Tokenizer
The Model in use by the Tokenizer
property padding
A dict ... |
https://huggingface.co/docs/transformers.js/tutorials/vanilla-js | Building a Vanilla JavaScript Application
In this tutorial, you’ll build a simple web application that detects objects in images using Transformers.js! To follow along, all you need is a code editor, a browser, and a simple server (e.g., VS Code Live Server).
Here’s how it works: the user clicks “Upload image” and sele... |
https://huggingface.co/docs/transformers.js/custom_usage | Use custom models
By default, Transformers.js uses hosted pretrained models and precompiled WASM binaries, which should work out-of-the-box. You can customize this as follows:
Settings
import { env } from '@xenova/transformers';
// Specify a custom location for models (defaults to '/models/').
env.localModelPath = '/p... |
https://huggingface.co/tasks/zero-shot-image-classification | Tasks
Zero-Shot Image Classification
Zero shot image classification is the task of classifying previously unseen classes during training of a model.
Inputs
Zero-Shot Image Classification Model
About Zero-Shot Image Classification
About the Task
Zero-shot image classification is a computer vision task to classify ima... |
https://huggingface.co/docs/transformers.js/tutorials/node | Server-side Inference in Node.js
Although Transformers.js was originally designed to be used in the browser, it’s also able to run inference on the server. In this tutorial, we will design a simple Node.js API that uses Transformers.js for sentiment analysis.
We’ll also show you how to use the library in both CommonJS ... |
https://huggingface.co/docs/transformers.js/tutorials/react | Building a React application
In this tutorial, we’ll be building a simple React application that performs multilingual translation using Transformers.js! The final product will look something like this:
Useful links:
Demo site
Source code
Prerequisites
Node.js version 18+
npm version 9+
Step 1: Initialise the project
F... |
https://huggingface.co/docs/transformers.js/api/pipelines | pipelines
Pipelines provide a high-level, easy to use, API for running machine learning models.
Example: Instantiate pipeline using the pipeline function.
import { pipeline } from '@xenova/transformers';
let classifier = await pipeline('sentiment-analysis');
let output = await classifier('I love transformers!');
pipel... |
https://huggingface.co/docs/transformers.js/api/transformers | Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes |
https://huggingface.co/docs/transformers.js/guides/private | Accessing Private/Gated Models
Due to the possibility of leaking access tokens to users of your website or web application, we only support accessing private/gated models from server-side environments (e.g., Node.js) that have access to the process’ environment variables.
Step 1: Generating a User Access Token
User Acc... |
https://huggingface.co/docs/transformers.js/api/configs | configs
Helper module for using model configs. For more information, see the corresponding Python documentation.
Example: Load an AutoConfig.
import { AutoConfig } from '@xenova/transformers';
let config = await AutoConfig.from_pretrained('bert-base-uncased');
console.log(config);
configs
static
.Pretrained... |
https://huggingface.co/docs/transformers.js/api/env | env
Module used to configure Transformers.js.
Example: Disable remote models.
import { env } from '@xenova/transformers';
env.allowRemoteModels = false;
Example: Set local model path.
import { env } from '@xenova/transformers';
env.localModelPath = '/path/to/local/models/';
Example: Set cache directory.
import { env } ... |
https://huggingface.co/models?library=transformers.js | Active filters: transformers.js |
https://huggingface.co/models?pipeline_tag=question-answering&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/models?pipeline_tag=fill-mask&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/docs/transformers.js/api/processors | processors
Processors are used to prepare non-textual inputs (e.g., image or audio) for a model.
Example: Using a WhisperProcessor to prepare an audio input for a model.
import { AutoProcessor, read_audio } from '@xenova/transformers';
let processor = await AutoProcessor.from_pretrained('openai/whisper-tiny.en');
let ... |
https://huggingface.co/tasks/conversational | Tasks
Conversational
Conversational response modelling is the task of generating conversational text that is relevant, coherent and knowledgable given a prompt. These models have applications in chatbots, and as a part of voice assistants
Inputs
Input
Hey my name is Julien! How are you?
Output
Answer
Hi Julien! My nam... |
https://huggingface.co/docs/transformers.js/api/models | models
Definitions of all models available in Transformers.js.
Example: Load and run an AutoModel.
import { AutoModel, AutoTokenizer } from '@xenova/transformers';
let tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
let model = await AutoModel.from_pretrained('Xenova/bert-base-uncased');
... |
https://huggingface.co/docs/transformers.js/api/tokenizers | tokenizers
Tokenizers are used to prepare textual inputs for a model.
Example: Create an AutoTokenizer and use it to tokenize a sentence. This will automatically detect the tokenizer type based on the tokenizer class defined in tokenizer.json.
import { AutoTokenizer } from '@xenova/transformers';
let tokenizer = await... |
https://huggingface.co/tasks/fill-mask | Tasks
Fill-Mask
Masked language modeling is the task of masking some of the words in a sentence and predicting which words should replace those masks. These models are useful when we want to get a statistical understanding of the language in which the model is trained in.
Inputs
Input
The <mask> barked at me
About Fil... |
https://huggingface.co/tasks/sentence-similarity | Tasks
Sentence Similarity
Sentence Similarity is the task of determining how similar two texts are. Sentence similarity models convert input texts into vectors (embeddings) that capture semantic information and calculate how close (similar) they are between them. This task is particularly useful for information retrie... |
https://huggingface.co/models?pipeline_tag=text-classification&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/tasks/question-answering | Tasks
Question Answering
Question Answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document. Some question answering models can generate answers without context!
Inputs
Question
Which name is also used to describe the Amazon rainforest in Englis... |
https://huggingface.co/tasks/text-generation | Tasks
Text Generation
Generating text is the task of producing new text. These models can, for example, fill in incomplete text or paraphrase.
Output
Output
Once upon a time, we knew that our ancestors were on the verge of extinction. The great explorers and poets of the Old World, from Alexander the Great to Chaucer,... |
https://huggingface.co/models?pipeline_tag=text-generation&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/tasks/translation | Tasks
Translation
Translation is the task of converting text from one language to another.
Inputs
Input
My name is Omar and I live in Zürich.
Output
Output
Mein Name ist Omar und ich wohne in Zürich.
About Translation
Use Cases
You can find over a thousand Translation models on the Hub, but sometimes you might not fi... |
https://huggingface.co/tasks/summarization | Tasks
Summarization
Summarization is the task of producing a shorter version of a document while preserving its important information. Some models can extract text from the original input, while other models can generate entirely new text.
Inputs
Input
The tower is 324 metres (1,063 ft) tall, about the same height as ... |
https://huggingface.co/tasks/zero-shot-classification | Tasks
Zero-Shot Classification
Zero-shot text classification is a task in natural language processing where a model is trained on a set of labeled examples but is then able to classify new examples from previously unseen classes.
Inputs
Text Input
Dune is the best movie ever.
Candidate Labels
CINEMA, ART, MUSIC
Zero-S... |
https://huggingface.co/tasks/text-classification | Tasks
Text Classification
Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness.
Inputs
Input
I love Hugging Face!
Text Classification Model
About Text Classification
Use Cases
Sentiment ... |
https://huggingface.co/models?pipeline_tag=image-classification&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/tasks/token-classification | Tasks
Token Classification
Token classification is a natural language understanding task in which a label is assigned to some tokens in a text. Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models could be trained to identify specific entities in a ... |
https://huggingface.co/tasks/image-segmentation | Tasks
Image Segmentation
Image Segmentation divides an image into segments where each pixel in the image is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation.
Inputs
Output
About Image Segmentation
Use Cases
Autonomous Driving
Segmenta... |
https://huggingface.co/models?pipeline_tag=translation&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/models?pipeline_tag=summarization&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/models?pipeline_tag=token-classification&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/models?pipeline_tag=text2text-generation&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/tasks/image-classification | Tasks
Image Classification
Image classification is the task of assigning a label or class to an entire image. Images are expected to have only one class for each image. Image classification models take an image as input and return a prediction about which class the image belongs to.
Inputs
Image Classification Model
... |
https://huggingface.co/models?pipeline_tag=zero-shot-classification&library=transformers.js | Active filters: zero-shot-classification, transformers.js |
https://huggingface.co/models?pipeline_tag=image-segmentation&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/tasks/mask-generation | 404
Sorry, we can't find the page you are looking for. |
https://huggingface.co/tasks/depth-estimation | Tasks
Depth Estimation
Depth estimation is the task of predicting depth of the objects present in an image.
Inputs
Output
About Depth Estimation
Use Cases
Depth estimation models can be used to estimate the depth of different objects present in an image.
Estimation of Volumetric Information
Depth estimation models a... |
https://huggingface.co/tasks/table-question-answering | Tasks
Table Question Answering
Table Question Answering (Table QA) is the answering a question about an information on a given table.
Inputs
Question
What is the number of reigns for Harley Race?
Table Question Answering Model
About Table Question Answering
Use Cases
SQL execution
You can use the Table Question Ans... |
https://huggingface.co/tasks/image-to-image | Tasks
Image-to-Image
Image-to-image is the task of transforming a source image to match the characteristics of a target image or a target image domain. Any image manipulation and enhancement is possible with image to image models.
Inputs
Output
About Image-to-Image
Use Cases
Style transfer
One of the most popular us... |
https://huggingface.co/tasks/object-detection | Tasks
Object Detection
Object Detection models allow users to identify objects of certain defined classes. Object detection models receive an image as input and output the images with bounding boxes and labels on detected objects.
Inputs
Output
About Object Detection
Use Cases
Autonomous Driving
Object Detection is ... |
https://huggingface.co/docs/timm/reference/schedulers | Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in timm.
Schedulers
Factory functions
timm.scheduler.create_scheduler
< source >
( args optimizer: Optimizer updates_per_epoch: int = 0 )
timm.scheduler.create_scheduler_v2
< source >
( optimizer: Optimize... |
https://huggingface.co/docs/timm/training_script | Scripts
A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release.
The training and validation scripts evolved from early versions of the PyTorch Imagenet Examples. I have added significant functionality over time, including ... |
https://huggingface.co/docs/peft/quicktour | Quicktour
🤗 PEFT contains parameter-efficient finetuning methods for training large pretrained models. The traditional paradigm is to finetune all of a model’s parameters for each downstream task, but this is becoming exceedingly costly and impractical because of the enormous number of parameters in models today. Inst... |
https://huggingface.co/docs/timm/installation | Installation
Before you start, you’ll need to setup your environment and install the appropriate packages. timm is tested on Python 3+.
Virtual Environment
You should install timm in a virtual environment to keep things tidy and avoid dependency conflicts.
Create and navigate to your project directory:
mkdir ~/my-proje... |
https://huggingface.co/docs/timm/reference/models | Models
timm.create_model
< source >
( model_name: str pretrained: bool = False pretrained_cfg: typing.Union[str, typing.Dict[str, typing.Any], timm.models._pretrained.PretrainedCfg, NoneType] = None pretrained_cfg_overlay: typing.Union[typing.Dict[str, typing.Any], NoneType] = None checkpoint_path: str = '' scriptable:... |
https://huggingface.co/docs/timm/hf_hub | Sharing and Loading Models From the Hugging Face Hub
The timm library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub.
In this short guide, we’ll see how to:
Share a timm model on the Hub
How to load that model back from the Hub
Authenticating
First, you’ll ... |
https://huggingface.co/docs/peft/task_guides/clm-prompt-tuning | Prompt tuning for causal language modeling
Prompting helps guide language model behavior by adding some input text specific to a task. Prompt tuning is an additive method for only training and updating the newly added prompt tokens to a pretrained model. This way, you can use one pretrained model whose weights are froz... |
https://huggingface.co/docs/timm/reference/data | Data
timm.data.create_dataset
< source >
( name root split = 'validation' search_split = True class_map = None load_bytes = False is_training = False download = False batch_size = None seed = 42 repeats = 0 **kwargs )
Dataset factory method
In parenthesis after each arg are the type of dataset supported for each arg, ... |
https://huggingface.co/docs/timm/feature_extraction | Feature Extraction
All of the models in timm have consistent mechanisms for obtaining various types of features from the model for tasks besides classification.
Penultimate Layer Features (Pre-Classifier Features)
The features from the penultimate model layer can be obtained in several ways without requiring model surg... |
https://huggingface.co/models?pipeline_tag=object-detection&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/docs/peft/task_guides/token-classification-lora | LoRA for token classification
Low-Rank Adaptation (LoRA) is a reparametrization method that aims to reduce the number of trainable parameters with low-rank representations. The weight matrix is broken down into low-rank matrices that are trained and updated. All the pretrained model parameters remain frozen. After trai... |
https://huggingface.co/docs/peft/task_guides/int8-asr | int8 training for automatic speech recognition
Quantization reduces the precision of floating point data types, decreasing the memory required to store model weights. However, quantization degrades inference performance because you lose information when you reduce the precision. 8-bit or int8 quantization uses only a q... |
https://huggingface.co/docs/peft/install | Installation
Before you start, you will need to setup your environment, install the appropriate packages, and configure 🤗 PEFT. 🤗 PEFT is tested on Python 3.8+.
🤗 PEFT is available on PyPI, as well as GitHub:
PyPI
To install 🤗 PEFT from PyPI:
Source
New features that haven’t been released yet are added every day, w... |
https://huggingface.co/docs/peft/task_guides/semantic_segmentation_lora | Semantic segmentation using LoRA
This guide demonstrates how to use LoRA, a low-rank approximation technique, to finetune a SegFormer model variant for semantic segmentation. By using LoRA from 🤗 PEFT, we can reduce the number of trainable parameters in the SegFormer model to only 14% of the original trainable paramet... |
https://huggingface.co/docs/peft/task_guides/image_classification_lora | Image classification using LoRA
This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune an image classification model. By using LoRA from 🤗 PEFT, we can reduce the number of trainable parameters in the model to only 0.77% of the original.
LoRA achieves this reduction by adding low-ran... |
https://huggingface.co/docs/peft/task_guides/seq2seq-prefix-tuning | Prefix tuning for conditional generation
Prefix tuning is an additive method where only a sequence of continuous task-specific vectors is attached to the beginning of the input, or prefix. Only the prefix parameters are optimized and added to the hidden states in every layer of the model. The tokens of the input sequen... |
https://huggingface.co/docs/peft/task_guides/ptuning-seq-classification | P-tuning for sequence classification
It is challenging to finetune large language models for downstream tasks because they have so many parameters. To work around this, you can use prompts to steer the model toward a particular downstream task without fully finetuning a model. Typically, these prompts are handcrafted, ... |
https://huggingface.co/docs/timm/reference/optimizers | Optimization
This page contains the API reference documentation for learning rate optimizers included in timm.
Optimizers
Factory functions
timm.optim.create_optimizer
< source >
( args model filter_bias_and_bn = True )
Legacy optimizer factory for backwards compatibility. NOTE: Use create_optimizer_v2 for new code.
t... |
https://huggingface.co/docs/peft/task_guides/dreambooth_lora | DreamBooth fine-tuning with LoRA
This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune DreamBooth with the CompVis/stable-diffusion-v1-4 model.
Although LoRA was initially designed as a technique for reducing the number of trainable parameters in large-language models, the technique ... |
https://huggingface.co/docs/timm/quickstart | Quickstart
This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate timm into their model training workflow.
First, you’ll need to install timm. For more information on installation, see Installation.
Load a Pretrained Model
Pretrained models can be loaded us... |
https://huggingface.co/docs/peft/task_guides/semantic-similarity-lora | LoRA for semantic similarity tasks
Low-Rank Adaptation (LoRA) is a reparametrization method that aims to reduce the number of trainable parameters with low-rank representations. The weight matrix is broken down into low-rank matrices that are trained and updated. All the pretrained model parameters remain frozen. After... |
https://huggingface.co/docs/peft/developer_guides/low_level_api | PEFT as a utility library
Let’s cover in this section how you can leverage PEFT’s low level API to inject trainable adapters into any torch module. The development of this API has been motivated by the need for super users to not rely on modeling classes that are exposed in PEFT library and still be able to use adapter... |
https://huggingface.co/docs/peft/developer_guides/contributing | Contributing to PEFT
We are happy to accept contributions to PEFT. If you plan to contribute, please read this document to make the process as smooth as possible.
Installation
The installation instructions can be found here. If you want to provide code contributions to PEFT, you should choose the “source” installation ... |
https://huggingface.co/docs/peft/developer_guides/custom_models | Working with custom models
Some fine-tuning techniques, such as prompt tuning, are specific to language models. That means in 🤗 PEFT, it is assumed a 🤗 Transformers model is being used. However, other fine-tuning techniques - like LoRA - are not restricted to specific model types.
In this guide, we will see how LoRA ... |
https://huggingface.co/docs/peft/package_reference/config | Configuration
The configuration classes stores the configuration of a PeftModel, PEFT adapter models, and the configurations of PrefixTuning, PromptTuning, and PromptEncoder. They contain methods for saving and loading model configurations from the Hub, specifying the PEFT method to use, type of task to perform, and mo... |
https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads&search=vit | Active filters: image-classification |
https://huggingface.co/task_guides/image_classification_lora | 404
Sorry, we can't find the page you are looking for. |
https://huggingface.co/docs/peft/conceptual_guides/ia3 | IA3
This conceptual guide gives a brief overview of IA3, a parameter-efficient fine tuning technique that is intended to improve over LoRA.
To make fine-tuning more efficient, IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations) rescales inner activations with learned vectors. These learned vectors are ... |
https://huggingface.co/docs/peft/conceptual_guides/lora | LoRA
This conceptual guide gives a brief overview of LoRA, a technique that accelerates the fine-tuning of large models while consuming less memory.
To make fine-tuning more efficient, LoRA’s approach is to represent the weight updates with two smaller matrices (called update matrices) through low-rank decomposition. T... |
https://huggingface.co/docs/peft/developer_guides/troubleshooting | Troubleshooting
If you encounter any issue when using PEFT, please check the following list of common issues and their solutions.
Examples don't work
Examples often rely on the most recent package versions, so please ensure they’re up-to-date. In particular, check the version of the following packages:
peft
transformer... |
https://huggingface.co/docs/peft/accelerate/fsdp | Fully Sharded Data Parallel
Fully sharded data parallel (FSDP) is developed for distributed training of large pretrained models up to 1T parameters. FSDP achieves this by sharding the model parameters, gradients, and optimizer states across data parallel processes and it can also offload sharded model parameters to a C... |
https://huggingface.co/docs/peft/conceptual_guides/prompting | Prompting
Training large pretrained language models is very time-consuming and compute-intensive. As they continue to grow in size, there is increasing interest in more efficient training methods such as prompting. Prompting primes a frozen pretrained model for a specific downstream task by including a text prompt that... |
https://huggingface.co/docs/accelerate/usage_guides/big_modeling | Handling big models for inference
One of the biggest advancements 🤗 Accelerate provides is the concept of large model inference wherein you can perform inference on models that cannot fully fit on your graphics card.
This tutorial will be broken down into two parts showcasing how to use both 🤗 Accelerate and 🤗 Tra... |
https://huggingface.co/VictorSanh | Victor Sanh PRO
VictorSanh
Research interests
None yet
Organizations
Papers 9
models 2
datasets 8 |
https://huggingface.co/docs/peft/accelerate/deepspeed-zero3-offload | DeepSpeed
DeepSpeed is a library designed for speed and scale for distributed training of large models with billions of parameters. At its core is the Zero Redundancy Optimizer (ZeRO) that shards optimizer states (ZeRO-1), gradients (ZeRO-2), and parameters (ZeRO-3) across data parallel processes. This drastically redu... |
https://huggingface.co/tasks/text-to-video | Tasks
Text-to-Video
Text-to-video models can be used in any application that requires generating consistent sequence of images from text.
Inputs
Input
Darth Vader is surfing on the waves.
Output
About Text-to-Video
Use Cases
Script-based Video Generation
Text-to-video models can be used to create short-form video c... |
https://huggingface.co/pierric | 25 136
Pierric Cistac PRO
pierric
pierrci
pierrci
Research interests
None yet
Organizations
Papers 2
arxiv:1910.03771
arxiv:2109.02846
models 5
pierric/tetsetestset
Updated Mar 1
pierric/settings
Updated Dec 22, 2022
pierric/test-EsperBERTo-small
Fill-Mask • Updated Oct 19, 2022
pierric/ny-cr-fr
Image Classifi... |
https://huggingface.co/srush | 5
Sasha Rush
srush
http://rush-nlp.com/
srush_nlp
srush
Research interests
None yet
Organizations
Papers 7
arxiv:1910.03771
arxiv:2211.05100
arxiv:2109.04838
arxiv:2109.02846
spaces 27
pinned
Runtime error
2
⛓️
Minichain
pinned
Runtime error
⛓️
None
pinned
Runtime error
1
⛓️
MiniChain-Table
pinned
Runtime e... |
https://huggingface.co/models?pipeline_tag=image-segmentation&sort=downloads | Active filters: image-segmentation |
https://huggingface.co/docs/peft/package_reference/tuners | Tuners
Each tuner (or PEFT method) has a configuration and model.
LoRA
For finetuning a model with LoRA.
class peft.LoraConfig
< source >
( peft_type: typing.Union[str, peft.utils.peft_types.PeftType] = None auto_mapping: typing.Optional[dict] = None base_model_name_or_path: str = None revision: str = None task_type: t... |
https://huggingface.co/anthony | 1 3
Anthony Moi
anthony
https://github.com/n1t0
moi_anthony
n1t0
Research interests
None yet
Organizations
Papers 1
arxiv:1910.03771
models 1
anthony/tokenizers-test
Updated Aug 24, 2021 • 1
datasets
None public yet |
https://huggingface.co/julien-c | 1299 1071 240
Julien Chaumond PRO
julien-c
https://huggingface.co
julien-c
Research interests
<3 ML/AI for everyone, building products to propel communities fwd
Organizations
Papers 5
arxiv:1910.01108
arxiv:1910.03771
arxiv:2210.01970
arxiv:2303.12582
spaces 18
📓
Livebook
4
😈
nvidia-smi
30
💚
NLLB Translation ... |
https://huggingface.co/clem | 🤗
clem
Research interests
machine learning
Organizations
Collections 1
Papers 2
spaces 11
models 7
datasets 2 |
https://huggingface.co/docs/peft/package_reference/peft_model | Models
PeftModel is the base model class for specifying the base Transformer model and configuration to apply a PEFT method to. The base PeftModel contains methods for loading and saving models from the Hub, and supports the PromptEncoder for prompt learning.
PeftModel
class peft.PeftModel
< source >
( model: PreTraine... |
https://huggingface.co/mfuntowicz | Morgan Funtowicz
mfuntowicz
Research interests
Model inference low-level optimization, hardware affinity and large-scale distributed training.
Organizations
Collections 1
Papers 1
spaces 1
models 4 |
https://huggingface.co/yjernite | Yacine Jernite
yjernite
Research interests
Technical, community, and regulatory tools of AI governance @HuggingFace
Organizations
Papers 20
models 2
datasets 40 |
https://huggingface.co/lhoestq | Quentin Lhoest
lhoestq
Research interests
Maintainer of 🤗Datasets: CV, NLP, Audio data processing and sharing
Organizations
Papers 6
spaces 1
models 1
datasets 18 |
https://huggingface.co/victor | Victor Mustar PRO
victor
Research interests
None yet
Organizations
Collections 2
Papers 2
spaces 60
models 18
datasets 14 |
https://huggingface.co/Narsil | Nicolas Patry PRO
Narsil
Research interests
None yet
Organizations
Papers 1
spaces 12
models 44
datasets 6 |
https://huggingface.co/valhalla | Suraj Patil
valhalla
Research interests
None yet
Organizations
Papers 1
spaces 3
models 142
datasets 6 |
https://huggingface.co/abhishek | Abhishek Thakur
abhishek
Research interests
None yet
Organizations
Papers 3
spaces 6
models 58
datasets 16 |
https://huggingface.co/annatrdj | 1
Anna Tordjmann
annatrdj
annatrdj
annatrdj
Research interests
None yet
Organizations
models
None public yet
datasets
None public yet |
https://huggingface.co/severo | Sylvain Lesage
severo
Research interests
Data visualization
Organizations
spaces 2
models 5
datasets 19 |
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