text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def deprecate(*args, take_from: Optional[Union[Dict, Any]] = None, standard_warn=True, stacklevel=2):
from .. import __version__
deprecated_kwargs = take_from
values = ()
if not isinstance(args... | diffusers/src/diffusers/utils/deprecation_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/deprecation_utils.py",
"repo_id": "diffusers",
"token_count": 793
} | 127 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | diffusers/src/diffusers/utils/hub_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/hub_utils.py",
"repo_id": "diffusers",
"token_count": 8141
} | 128 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/lora/test_lora_layers_old_backend.py/0 | {
"file_path": "diffusers/tests/lora/test_lora_layers_old_backend.py",
"repo_id": "diffusers",
"token_count": 44656
} | 129 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/models/unets/test_models_unet_1d.py/0 | {
"file_path": "diffusers/tests/models/unets/test_models_unet_1d.py",
"repo_id": "diffusers",
"token_count": 3986
} | 130 |
import pickle as pkl
import unittest
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from diffusers.utils.outputs import BaseOutput
from diffusers.utils.testing_utils import require_torch
@dataclass
class CustomOutput(BaseOutput):
images: Union[List[PIL.Image... | diffusers/tests/others/test_outputs.py/0 | {
"file_path": "diffusers/tests/others/test_outputs.py",
"repo_id": "diffusers",
"token_count": 1506
} | 131 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/blipdiffusion/test_blipdiffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/blipdiffusion/test_blipdiffusion.py",
"repo_id": "diffusers",
"token_count": 3066
} | 132 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/kandinsky/test_kandinsky_combined.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky/test_kandinsky_combined.py",
"repo_id": "diffusers",
"token_count": 5549
} | 133 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/pndm/test_pndm.py/0 | {
"file_path": "diffusers/tests/pipelines/pndm/test_pndm.py",
"repo_id": "diffusers",
"token_count": 1318
} | 134 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 12007
} | 135 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py",
"repo_id": "diffusers",
"token_count": 15654
} | 136 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video.py/0 | {
"file_path": "diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video.py",
"repo_id": "diffusers",
"token_count": 3407
} | 137 |
import torch
from diffusers import DDIMInverseScheduler
from .test_schedulers import SchedulerCommonTest
class DDIMInverseSchedulerTest(SchedulerCommonTest):
scheduler_classes = (DDIMInverseScheduler,)
forward_default_kwargs = (("num_inference_steps", 50),)
def get_scheduler_config(self, **kwargs):
... | diffusers/tests/schedulers/test_scheduler_ddim_inverse.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_ddim_inverse.py",
"repo_id": "diffusers",
"token_count": 2258
} | 138 |
import tempfile
from typing import Dict, List, Tuple
import torch
from diffusers import LCMScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class LCMSchedulerTest(SchedulerCommonTest):
scheduler_classes = (LCMScheduler,)
forward_default_kwarg... | diffusers/tests/schedulers/test_scheduler_lcm.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_lcm.py",
"repo_id": "diffusers",
"token_count": 5668
} | 139 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | diffusers/utils/custom_init_isort.py/0 | {
"file_path": "diffusers/utils/custom_init_isort.py",
"repo_id": "diffusers",
"token_count": 5346
} | 140 |
# Stable Diffusion Deep Dive
<CourseFloatingBanner unit={3}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Stable Diffusion Deep Dive", value: "https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/units/en/unit3/stable_diffusion_deep_dive.ipynb"},
{label: "S... | diffusion-models-class/units/en/unit3/3.mdx/0 | {
"file_path": "diffusion-models-class/units/en/unit3/3.mdx",
"repo_id": "diffusion-models-class",
"token_count": 20868
} | 141 |
# Sprint ControlNet en JAX/Diffusers
Bienvenue au sprint communautaire en JAX/Diffusers ! L'objectif de ce sprint est de travailler sur des modèles de diffusion amusants et créatifs en utilisant JAX et Diffusers.
Lors de cet événement, nous créerons diverses applications avec des modèles de diffusion en JAX/Flax et D... | diffusion-models-class/units/fr/events/4.mdx/0 | {
"file_path": "diffusion-models-class/units/fr/events/4.mdx",
"repo_id": "diffusion-models-class",
"token_count": 15277
} | 142 |
<jupyter_start><jupyter_text>Traduction (TensorFlow) Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!apt install git-lfs<jupyter_output>Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab... | notebooks/course/fr/chapter7/section4_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section4_tf.ipynb",
"repo_id": "notebooks",
"token_count": 3062
} | 143 |
<jupyter_start><jupyter_text>Integrations avec le *Hub* d'Hugging Face Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install gradio
import gradio as gr
title = "GPT-J-6B (Boris)"
description = "Démo Gradio pour ... | notebooks/course/fr/chapter9/section5.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter9/section5.ipynb",
"repo_id": "notebooks",
"token_count": 653
} | 144 |
<jupyter_start><jupyter_text>**The Stable Diffusion Guide** 🎨 *...using `🧨 diffusers`* **Intro**Stable Diffusion is a [Latent Diffusion model](https://github.com/CompVis/latent-diffusion) developed by researchers from the Machine Vision and Learning group at LMU Munich, *a.k.a* CompVis.Model checkpoints were publicly... | notebooks/diffusers/sd_101_guide.ipynb/0 | {
"file_path": "notebooks/diffusers/sd_101_guide.ipynb",
"repo_id": "notebooks",
"token_count": 5420
} | 145 |
"""
On one node, launch with `deepspeed --num_gpus N idefics_zero3_finetuning.py`
by replacing N with the number of your GPUs
For several nodes, using Slurm, a template script is provided at
`examples/idefics/idefics_zero3_finetuning/slurm_script_idefics_zero3_finetuning_multinode.slurm`
For more information, follow ... | notebooks/examples/idefics/idefics_zero3_finetuning/idefics_zero3_finetuning.py/0 | {
"file_path": "notebooks/examples/idefics/idefics_zero3_finetuning/idefics_zero3_finetuning.py",
"repo_id": "notebooks",
"token_count": 1980
} | 146 |
<jupyter_start><jupyter_text>PatchTSMixer in HuggingFace - Getting Started `PatchTSMixer` is a lightweight time-series modeling approach based on the MLP-Mixer architecture. It is proposed in [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://huggingface.co/papers/2306.09364) by IBM... | notebooks/examples/patch_tsmixer.ipynb/0 | {
"file_path": "notebooks/examples/patch_tsmixer.ipynb",
"repo_id": "notebooks",
"token_count": 7682
} | 147 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.<jupyter_code>#! pip install datasets transformers<jupyter_output><empty_output><jupyter_text>If you're opening this notebook locally, make su... | notebooks/examples/text_classification.ipynb/0 | {
"file_path": "notebooks/examples/text_classification.ipynb",
"repo_id": "notebooks",
"token_count": 7676
} | 148 |
<jupyter_start><jupyter_text>Explain *Anything* Like I'm Five: A Model for Open Domain Long Form Question Answering--- Table of Contents 1. [**Introduction**](intro) a. [Preliminaries](prelims) b. [Note on Data and Biases](reddit_biases)2. [**Task and Data Description**](task_description) 3. [**Sparse Ret... | notebooks/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb/0 | {
"file_path": "notebooks/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb",
"repo_id": "notebooks",
"token_count": 13060
} | 149 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Distributed Training Demo Distributed Summarization with `transformers` scripts + `Trainer` and `samsum` dataset 1. [Tutorial](Tutorial) 2. [Set up a development environment and install sagemaker](Set-up-a-development-environment-and-install-sagemaker) 1. [In... | notebooks/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 5673
} | 150 |
import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
from datasets import load_from_disk, load_metric
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from transformers.trainer_utils import get_last_checkpoint
if __name... | notebooks/sagemaker/14_train_and_push_to_hub/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/14_train_and_push_to_hub/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1998
} | 151 |
<jupyter_start><jupyter_text>Serverless Inference with Hugging Face's Transformers & Amazon SageMaker Welcome to this getting started guide. We will use the Hugging Face Inference DLCs and Amazon SageMaker Python SDK to create a [Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints... | notebooks/sagemaker/19_serverless_inference/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/19_serverless_inference/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 1569
} | 152 |
import base64
import torch
from io import BytesIO
from diffusers import StableDiffusionPipeline
def model_fn(model_dir):
# Load stable diffusion and move it to the GPU
pipe = StableDiffusionPipeline.from_pretrained(model_dir, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
return pipe
def predict_fn(data,... | notebooks/sagemaker/23_stable_diffusion_inference/code/inference.py/0 | {
"file_path": "notebooks/sagemaker/23_stable_diffusion_inference/code/inference.py",
"repo_id": "notebooks",
"token_count": 400
} | 153 |
import os
import argparse
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
set_seed,
default_data_collator,
)
from datasets import load_from_disk
import torch
from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer, DonutProcessor, VisionEncoderDecoderModel,VisionEncoderDecoderC... | notebooks/sagemaker/26_document_ai_donut/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/26_document_ai_donut/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1878
} | 154 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/conceptual_guides/ia3.md/0 | {
"file_path": "peft/docs/source/conceptual_guides/ia3.md",
"repo_id": "peft",
"token_count": 1030
} | 155 |
<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# int8 training for automatic speech recognition
Quantization reduces the precision of floating point data types, decreasing the memory required to ... | peft/docs/source/task_guides/int8-asr.md/0 | {
"file_path": "peft/docs/source/task_guides/int8-asr.md",
"repo_id": "peft",
"token_count": 6115
} | 156 |
import os
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import AdaLoraConfig, PeftConfig, PeftModel, TaskType, get_peft_model
... | peft/examples/conditional_generation/peft_adalora_seq2seq.py/0 | {
"file_path": "peft/examples/conditional_generation/peft_adalora_seq2seq.py",
"repo_id": "peft",
"token_count": 2250
} | 157 |
<jupyter_start><jupyter_text>Using PEFT with timm `peft` allows us to train any model with LoRA as long as the layer type is supported. Since `Conv2D` is one of the supported layer types, it makes sense to test it on image models.In this short notebook, we will demonstrate this with an image classification task using [... | peft/examples/image_classification/image_classification_timm_peft_lora.ipynb/0 | {
"file_path": "peft/examples/image_classification/image_classification_timm_peft_lora.ipynb",
"repo_id": "peft",
"token_count": 3067
} | 158 |
<jupyter_start><jupyter_code>!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q git+https://github.com/huggingface/peft.git
!pip install -q git+https://github.com/huggingface/accelerate.git@main
!pip install huggingface_hub
!pip install bitsandbytes
!pip install SentencePiece
import os
... | peft/examples/multi_adapter_examples/PEFT_Multi_LoRA_Inference.ipynb/0 | {
"file_path": "peft/examples/multi_adapter_examples/PEFT_Multi_LoRA_Inference.ipynb",
"repo_id": "peft",
"token_count": 1328
} | 159 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/peft_model.py/0 | {
"file_path": "peft/src/peft/peft_model.py",
"repo_id": "peft",
"token_count": 40197
} | 160 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/ia3/config.py/0 | {
"file_path": "peft/src/peft/tuners/ia3/config.py",
"repo_id": "peft",
"token_count": 1850
} | 161 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/lora/model.py/0 | {
"file_path": "peft/src/peft/tuners/lora/model.py",
"repo_id": "peft",
"token_count": 12522
} | 162 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/poly/config.py/0 | {
"file_path": "peft/src/peft/tuners/poly/config.py",
"repo_id": "peft",
"token_count": 1415
} | 163 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/utils/save_and_load.py/0 | {
"file_path": "peft/src/peft/utils/save_and_load.py",
"repo_id": "peft",
"token_count": 6533
} | 164 |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | peft/tests/test_low_level_api.py/0 | {
"file_path": "peft/tests/test_low_level_api.py",
"repo_id": "peft",
"token_count": 1311
} | 165 |
#!/usr/bin/env python3
""" Model Benchmark Script
An inference and train step benchmark script for timm models.
Hacked together by Ross Wightman (https://github.com/rwightman)
"""
import argparse
import csv
import json
import logging
import time
from collections import OrderedDict
from contextlib import suppress
from... | pytorch-image-models/benchmark.py/0 | {
"file_path": "pytorch-image-models/benchmark.py",
"repo_id": "pytorch-image-models",
"token_count": 13272
} | 166 |
# AdvProp (EfficientNet)
**AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.
The w... | pytorch-image-models/docs/models/.templates/models/advprop.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/advprop.md",
"repo_id": "pytorch-image-models",
"token_count": 5211
} | 167 |
# (Gluon) ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformatio... | pytorch-image-models/docs/models/.templates/models/gloun-resnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-resnext.md",
"repo_id": "pytorch-image-models",
"token_count": 1879
} | 168 |
# NASNet
**NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-mo... | pytorch-image-models/docs/models/.templates/models/nasnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/nasnet.md",
"repo_id": "pytorch-image-models",
"token_count": 730
} | 169 |
# SK-ResNeXt
**SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK ... | pytorch-image-models/docs/models/.templates/models/skresnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/skresnext.md",
"repo_id": "pytorch-image-models",
"token_count": 822
} | 170 |
# CSP-ResNeXt
**CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use o... | pytorch-image-models/hfdocs/source/models/csp-resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/csp-resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1725
} | 171 |
# HRNet
**HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradual... | pytorch-image-models/hfdocs/source/models/hrnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/hrnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 5056
} | 172 |
# RegNetY
**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of mode... | pytorch-image-models/hfdocs/source/models/regnety.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/regnety.mdx",
"repo_id": "pytorch-image-models",
"token_count": 6770
} | 173 |
# SWSL ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations)... | pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3472
} | 174 |
# 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](https://github.com/pytorch/examples). I have added sign... | pytorch-image-models/hfdocs/source/training_script.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/training_script.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2320
} | 175 |
DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 *... | pytorch-image-models/timm/data/constants.py/0 | {
"file_path": "pytorch-image-models/timm/data/constants.py",
"repo_id": "pytorch-image-models",
"token_count": 236
} | 176 |
""" A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | pytorch-image-models/timm/data/readers/reader_image_folder.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_folder.py",
"repo_id": "pytorch-image-models",
"token_count": 1510
} | 177 |
""" Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... | pytorch-image-models/timm/layers/attention_pool2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool2d.py",
"repo_id": "pytorch-image-models",
"token_count": 2301
} | 178 |
""" EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato ... | pytorch-image-models/timm/layers/evo_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/evo_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 6684
} | 179 |
from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identi... | pytorch-image-models/timm/layers/ml_decoder.py/0 | {
"file_path": "pytorch-image-models/timm/layers/ml_decoder.py",
"repo_id": "pytorch-image-models",
"token_count": 3177
} | 180 |
""" Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
Thi... | pytorch-image-models/timm/layers/split_batchnorm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/split_batchnorm.py",
"repo_id": "pytorch-image-models",
"token_count": 1394
} | 181 |
import os
from typing import Any, Dict, Optional, Union
from urllib.parse import urlsplit
from timm.layers import set_layer_config
from ._helpers import load_checkpoint
from ._hub import load_model_config_from_hf
from ._pretrained import PretrainedCfg
from ._registry import is_model, model_entrypoint, split_model_name... | pytorch-image-models/timm/models/_factory.py/0 | {
"file_path": "pytorch-image-models/timm/models/_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 1944
} | 182 |
""" Bring-Your-Own-Blocks Network
A flexible network w/ dataclass based config for stacking those NN blocks.
This model is currently used to implement the following networks:
GPU Efficient (ResNets) - gernet_l/m/s (original versions called genet, but this was already used (by SENet author)).
Paper: `Neural Architect... | pytorch-image-models/timm/models/byobnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/byobnet.py",
"repo_id": "pytorch-image-models",
"token_count": 42793
} | 183 |
""" The EfficientNet Family in PyTorch
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* EfficientNet-V2
- `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/A... | pytorch-image-models/timm/models/efficientnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientnet.py",
"repo_id": "pytorch-image-models",
"token_count": 47473
} | 184 |
""" Pytorch Inception-Resnet-V2 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functiona... | pytorch-image-models/timm/models/inception_resnet_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_resnet_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 6015
} | 185 |
""" Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | pytorch-image-models/timm/models/pvt_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/pvt_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 9047
} | 186 |
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... | pytorch-image-models/timm/models/swin_transformer_v2_cr.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2_cr.py",
"repo_id": "pytorch-image-models",
"token_count": 18939
} | 187 |
from .adabelief import AdaBelief
from .adafactor import Adafactor
from .adahessian import Adahessian
from .adamp import AdamP
from .adamw import AdamW
from .adan import Adan
from .lamb import Lamb
from .lars import Lars
from .lookahead import Lookahead
from .madgrad import MADGRAD
from .nadam import Nadam
from .nvnovog... | pytorch-image-models/timm/optim/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/optim/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 170
} | 188 |
"""RAdam Optimizer.
Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
def __init__(self, params, ... | pytorch-image-models/timm/optim/radam.py/0 | {
"file_path": "pytorch-image-models/timm/optim/radam.py",
"repo_id": "pytorch-image-models",
"token_count": 1967
} | 189 |
import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
""" Dispatch to gradient clipping method
Args:
parameters (Iterable): model parameters to clip
value (float): clipping value/factor/norm, m... | pytorch-image-models/timm/utils/clip_grad.py/0 | {
"file_path": "pytorch-image-models/timm/utils/clip_grad.py",
"repo_id": "pytorch-image-models",
"token_count": 306
} | 190 |
aml
target
server/transformers
server/flash-attention
| text-generation-inference/.dockerignore/0 | {
"file_path": "text-generation-inference/.dockerignore",
"repo_id": "text-generation-inference",
"token_count": 16
} | 191 |
import pytest
from text_generation import __version__
from huggingface_hub.utils import build_hf_headers
@pytest.fixture
def flan_t5_xxl():
return "google/flan-t5-xxl"
@pytest.fixture
def fake_model():
return "fake/model"
@pytest.fixture
def unsupported_model():
return "gpt2"
@pytest.fixture
def ba... | text-generation-inference/clients/python/tests/conftest.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 390
} | 192 |
# Non-core Model Serving
TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced b... | text-generation-inference/docs/source/basic_tutorials/non_core_models.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/non_core_models.md",
"repo_id": "text-generation-inference",
"token_count": 475
} | 193 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 15,
"logprob": null,
"text": ","
},
{
"id": 1669,
"logprob": -5.4414062,
"text": " il"
},
{
"id": 1158... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1199
} | 194 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.6015625,
"text": "Test"
},
{
"id": 20... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1024
} | 195 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
"text": " print... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder_load.json",
"repo_id": "text-generation-inference",
"token_count": 5188
} | 196 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|prompter|>"
},
{
"id": 1276,
"logprob": -8.0234375,
"text": "What"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox.json",
"repo_id": "text-generation-inference",
"token_count": 1966
} | 197 |
import pytest
@pytest.fixture(scope="module")
def flash_santacoder_handle(launcher):
with launcher("bigcode/santacoder") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_santacoder(flash_santacoder_handle):
await flash_santacoder_handle.health(300)
return flash_santacoder_... | text-generation-inference/integration-tests/models/test_flash_santacoder.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_santacoder.py",
"repo_id": "text-generation-inference",
"token_count": 387
} | 198 |
use clap::{Parser, ValueEnum};
use nix::sys::signal::{self, Signal};
use nix::unistd::Pid;
use serde::Deserialize;
use std::env;
use std::ffi::OsString;
use std::io::{BufRead, BufReader, Lines};
use std::os::unix::process::{CommandExt, ExitStatusExt};
use std::path::Path;
use std::process::{Child, Command, ExitStatus, ... | text-generation-inference/launcher/src/main.rs/0 | {
"file_path": "text-generation-inference/launcher/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 19492
} | 199 |
//! A crate to extract and inject a OpenTelemetry context from and to a gRPC request.
//! Inspired by: https://github.com/open-telemetry/opentelemetry-rust gRPC examples
use opentelemetry::global;
use opentelemetry::propagation::{Extractor, Injector};
use tracing_opentelemetry::OpenTelemetrySpanExt;
/// Extract conte... | text-generation-inference/router/grpc-metadata/src/lib.rs/0 | {
"file_path": "text-generation-inference/router/grpc-metadata/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 889
} | 200 |
vllm-cuda:
# Clone vllm
pip install -U ninja packaging --no-cache-dir
git clone https://github.com/vllm-project/vllm.git vllm
build-vllm-cuda: vllm-cuda
cd vllm && git fetch && git checkout f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
cd vllm && python setup.py build
install-vllm-cuda: build-vllm-cuda
pip uninst... | text-generation-inference/server/Makefile-vllm/0 | {
"file_path": "text-generation-inference/server/Makefile-vllm",
"repo_id": "text-generation-inference",
"token_count": 332
} | 201 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _matrix_cuh
#define _matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
class MatrixView_half
{
public:
const half* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_half(const half*... | text-generation-inference/server/exllama_kernels/exllama_kernels/matrix.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/matrix.cuh",
"repo_id": "text-generation-inference",
"token_count": 5380
} | 202 |
#ifndef _qdq_4_cuh
#define _qdq_4_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_4BIT == 1
// Permutation:
//
// 77775555 33331111 66664444 22220000
__forceinline__ __device__ void shuffle_4bit_8
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unroll... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_4.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_4.cuh",
"repo_id": "text-generation-inference",
"token_count": 3278
} | 203 |
import pytest
import torch
from transformers import AutoTokenizer
from text_generation_server.models import Model
def get_test_model():
class TestModel(Model):
def batch_type(self):
raise NotImplementedError
def generate_token(self, batch):
raise NotImplementedError
... | text-generation-inference/server/tests/models/test_model.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_model.py",
"repo_id": "text-generation-inference",
"token_count": 829
} | 204 |
# coding=utf-8
# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | text-generation-inference/server/text_generation_server/models/custom_modeling/neox_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/neox_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 14270
} | 205 |
import inspect
import torch
from abc import ABC, abstractmethod
from typing import List, Tuple, Optional, TypeVar, Type
from transformers import PreTrainedTokenizerBase, PretrainedConfig
from text_generation_server.models.types import Batch, Generation
from text_generation_server.utils.speculate import get_speculate
... | text-generation-inference/server/text_generation_server/models/model.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/model.py",
"repo_id": "text-generation-inference",
"token_count": 1674
} | 206 |
import os
import torch
from loguru import logger
from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
raise ImportError("`USE_FLASH_ATTENTION` is false.")
if not torch.cuda.is_available():
raise ImportError("CUDA is ... | text-generation-inference/server/text_generation_server/utils/flash_attn.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/flash_attn.py",
"repo_id": "text-generation-inference",
"token_count": 2911
} | 207 |
# coding=utf-8
# Copyright 2023 Authors of "A Watermark for Large Language Models"
# available at https://arxiv.org/abs/2301.10226
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... | text-generation-inference/server/text_generation_server/utils/watermark.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/watermark.py",
"repo_id": "text-generation-inference",
"token_count": 1489
} | 208 |
extern crate napi_build;
fn main() {
napi_build::setup();
}
| tokenizers/bindings/node/build.rs/0 | {
"file_path": "tokenizers/bindings/node/build.rs",
"repo_id": "tokenizers",
"token_count": 26
} | 209 |
// import { promisify } from 'util'
import { BPE, Tokenizer, mergeEncodings, slice } from '../../'
describe('slice', () => {
const text = 'My name is John 👋'
const sliceText = slice.bind({}, text)
it('returns the full text when no params', () => {
const sliced = sliceText()
expect(sliced).toEqual(text... | tokenizers/bindings/node/lib/bindings/utils.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/utils.test.ts",
"repo_id": "tokenizers",
"token_count": 1866
} | 210 |
{
"name": "tokenizers-linux-arm64-musl",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"arm64"
],
"main": "tokenizers.linux-arm64-musl.node",
"files": [
"tokenizers.linux-arm64-musl.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
... | tokenizers/bindings/node/npm/linux-arm64-musl/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-musl/package.json",
"repo_id": "tokenizers",
"token_count": 291
} | 211 |
#![deny(clippy::all)]
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
mod arc_rwlock_serde;
pub mod decoders;
pub mod encoding;
pub mod models;
pub mod normalizers;
pub mod pre_tokenizers;
pub mod processors;
pub mod tasks;
pub mod tokenizer;
pub mod trainers;
pub mod utils;
| tokenizers/bindings/node/src/lib.rs/0 | {
"file_path": "tokenizers/bindings/node/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 102
} | 212 |
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.13.2]
- [#1096] Python 3.11 support
## [0.13.1]
- [#1072]... | tokenizers/bindings/python/CHANGELOG.md/0 | {
"file_path": "tokenizers/bindings/python/CHANGELOG.md",
"repo_id": "tokenizers",
"token_count": 7408
} | 213 |
from .base_tokenizer import BaseTokenizer
from .bert_wordpiece import BertWordPieceTokenizer
from .byte_level_bpe import ByteLevelBPETokenizer
from .char_level_bpe import CharBPETokenizer
from .sentencepiece_bpe import SentencePieceBPETokenizer
from .sentencepiece_unigram import SentencePieceUnigramTokenizer
| tokenizers/bindings/python/py_src/tokenizers/implementations/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/__init__.py",
"repo_id": "tokenizers",
"token_count": 94
} | 214 |
.tokenized-text {
width:100%;
padding:2rem;
max-height: 400px;
overflow-y: auto;
box-sizing:border-box;
line-height:4rem; /* Lots of space between lines */
font-family: "Roboto Light", "Ubuntu Light", "Ubuntu", monospace;
box-shadow: 2px 2px 2px rgba(0,0,0,0.2);
background-color: rgb... | tokenizers/bindings/python/py_src/tokenizers/tools/visualizer-styles.css/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/tools/visualizer-styles.css",
"repo_id": "tokenizers",
"token_count": 1806
} | 215 |
use std::sync::{Arc, RwLock};
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::ser::SerializeStruct;
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use tk::normalizer::SplitDelimiterBehavior;
use tk::pre_tokenizers::bert::BertPreTokenizer;
use tk::pre_tokenizers::byte_level::... | tokenizers/bindings/python/src/pre_tokenizers.rs/0 | {
"file_path": "tokenizers/bindings/python/src/pre_tokenizers.rs",
"repo_id": "tokenizers",
"token_count": 13027
} | 216 |
import pickle
import pytest
from tokenizers.models import BPE, Model, WordLevel, WordPiece
from ..utils import bert_files, data_dir, roberta_files
class TestBPE:
def test_instantiate(self, roberta_files):
assert isinstance(BPE(), Model)
assert isinstance(BPE(), BPE)
vocab = {"a": 0, "b... | tokenizers/bindings/python/tests/bindings/test_models.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_models.py",
"repo_id": "tokenizers",
"token_count": 2254
} | 217 |
import json
import os
import unittest
import tqdm
from huggingface_hub import HfApi, cached_download, hf_hub_url
from tokenizers import Tokenizer
from .utils import albert_base, data_dir
class TestSerialization:
def test_full_serialization_albert(self, albert_base):
# Check we can read this file.
... | tokenizers/bindings/python/tests/test_serialization.py/0 | {
"file_path": "tokenizers/bindings/python/tests/test_serialization.py",
"repo_id": "tokenizers",
"token_count": 1251
} | 218 |
# Visualizer
<tokenizerslangcontent>
<python>
## Annotation
[[autodoc]] tokenizers.tools.Annotation
## EncodingVisualizer
[[autodoc]] tokenizers.tools.EncodingVisualizer
- __call__
</python>
<rust>
The Rust API Reference is available directly on the [Docs.rs](https://docs.rs/tokenizers/latest/tokenizers/) webs... | tokenizers/docs/source-doc-builder/api/visualizer.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/visualizer.mdx",
"repo_id": "tokenizers",
"token_count": 134
} | 219 |
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.13.2]
- Python only changes
## [0.13.1]
- [#1072] Fixing ... | tokenizers/tokenizers/CHANGELOG.md/0 | {
"file_path": "tokenizers/tokenizers/CHANGELOG.md",
"repo_id": "tokenizers",
"token_count": 3388
} | 220 |
pub fn set_panic_hook() {
// When the `console_error_panic_hook` feature is enabled, we can call the
// `set_panic_hook` function at least once during initialization, and then
// we will get better error messages if our code ever panics.
//
// For more details see
// https://github.com/rustwasm/... | tokenizers/tokenizers/examples/unstable_wasm/src/utils.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/src/utils.rs",
"repo_id": "tokenizers",
"token_count": 150
} | 221 |
use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize)]
/// Allows decoding Original BPE by joining all the tokens and then replacing
/// the suffix used to identify end-of-words by whitespaces
#[serde(tag = "type")]
#[non_exhaustive]
pub struct BP... | tokenizers/tokenizers/src/decoders/bpe.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/bpe.rs",
"repo_id": "tokenizers",
"token_count": 419
} | 222 |
//! [Unigram](https://arxiv.org/abs/1804.10959) model.
mod lattice;
mod model;
mod serialization;
mod trainer;
mod trie;
pub use lattice::*;
pub use model::*;
pub use trainer::*;
| tokenizers/tokenizers/src/models/unigram/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/mod.rs",
"repo_id": "tokenizers",
"token_count": 72
} | 223 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
use unicode_normalization_alignments::char::is_combining_mark;
#[derive(Copy, Clone, Debug, Deserialize, Serialize)]
#[serde(tag = "type")]
#[non_exhaustive]
pub struct Strip {
... | tokenizers/tokenizers/src/normalizers/strip.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/strip.rs",
"repo_id": "tokenizers",
"token_count": 2512
} | 224 |
use crate::tokenizer::{Encoding, PostProcessor, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::iter::FromIterator;
#[derive(Serialize, Deserialize, Clone, Debug, PartialEq, Eq)]
#[serde(tag = "type")]
pub struct BertProcessing {
sep: (String, u32),
cls: (String, u32),
}
... | tokenizers/tokenizers/src/processors/bert.rs/0 | {
"file_path": "tokenizers/tokenizers/src/processors/bert.rs",
"repo_id": "tokenizers",
"token_count": 7375
} | 225 |
pub(crate) mod cache;
#[cfg(feature = "http")]
pub(crate) mod from_pretrained;
#[cfg(feature = "unstable_wasm")]
mod fancy;
#[cfg(feature = "unstable_wasm")]
pub use fancy::SysRegex;
#[cfg(not(feature = "unstable_wasm"))]
mod onig;
#[cfg(not(feature = "unstable_wasm"))]
pub use crate::utils::onig::SysRegex;
pub mod i... | tokenizers/tokenizers/src/utils/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/mod.rs",
"repo_id": "tokenizers",
"token_count": 3092
} | 226 |
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