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# Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
# William Peebles and Saining Xie
#
# Copyright (c) 2021 OpenAI
# MIT License
#
# 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 wi... | diffusers/src/diffusers/pipelines/dit/pipeline_dit.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/dit/pipeline_dit.py",
"repo_id": "diffusers",
"token_count": 4180
} | 118 |
from dataclasses import dataclass
from typing import TYPE_CHECKING, List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_... | diffusers/src/diffusers/pipelines/paint_by_example/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/paint_by_example/__init__.py",
"repo_id": "diffusers",
"token_count": 599
} | 119 |
# Copyright 2023 Open AI and 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 ... | diffusers/src/diffusers/pipelines/shap_e/renderer.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/shap_e/renderer.py",
"repo_id": "diffusers",
"token_count": 18167
} | 120 |
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput, is_flax_available
@dataclass
class StableDiffusionXLPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Im... | diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 401
} | 121 |
import copy
import inspect
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from torch.nn.functional import grid_sample
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
... | diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py",
"repo_id": "diffusers",
"token_count": 28769
} | 122 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py",
"repo_id": "diffusers",
"token_count": 10628
} | 123 |
# Copyright 2023 Stanford University Team and 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
#
#... | diffusers/src/diffusers/schedulers/scheduling_lcm.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_lcm.py",
"repo_id": "diffusers",
"token_count": 13496
} | 124 |
# 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 applicabl... | diffusers/src/diffusers/utils/accelerate_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/accelerate_utils.py",
"repo_id": "diffusers",
"token_count": 559
} | 125 |
# 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/dynamic_modules_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dynamic_modules_utils.py",
"repo_id": "diffusers",
"token_count": 7560
} | 126 |
# 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/transformers/test_models_prior.py/0 | {
"file_path": "diffusers/tests/models/transformers/test_models_prior.py",
"repo_id": "diffusers",
"token_count": 2767
} | 127 |
# 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/audioldm2/test_audioldm2.py/0 | {
"file_path": "diffusers/tests/pipelines/audioldm2/test_audioldm2.py",
"repo_id": "diffusers",
"token_count": 10092
} | 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/pipelines/kandinsky3/test_kandinsky3.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky3/test_kandinsky3.py",
"repo_id": "diffusers",
"token_count": 3517
} | 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/pipelines/pixart/test_pixart.py/0 | {
"file_path": "diffusers/tests/pipelines/pixart/test_pixart.py",
"repo_id": "diffusers",
"token_count": 7103
} | 130 |
# 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/test_stable_diffusion_instruction_pix2pix.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py",
"repo_id": "diffusers",
"token_count": 7736
} | 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/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py",
"repo_id": "diffusers",
"token_count": 14125
} | 132 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class OnnxPipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,
equivalence of ... | diffusers/tests/pipelines/test_pipelines_onnx_common.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipelines_onnx_common.py",
"repo_id": "diffusers",
"token_count": 118
} | 133 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class CMStochasticIterativeSchedulerTest(SchedulerCommonTest):
scheduler_classes = (CMStochasticIterativeScheduler,)
num_inference_steps = 10
def get_scheduler_config(self, **kwargs):
... | diffusers/tests/schedulers/test_scheduler_consistency_model.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_consistency_model.py",
"repo_id": "diffusers",
"token_count": 3029
} | 134 |
import torch
from diffusers import KDPM2AncestralDiscreteScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class KDPM2AncestralDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (KDPM2AncestralDiscreteScheduler,)
num_inference_step... | diffusers/tests/schedulers/test_scheduler_kdpm2_ancestral.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_kdpm2_ancestral.py",
"repo_id": "diffusers",
"token_count": 2516
} | 135 |
# 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/check_repo.py/0 | {
"file_path": "diffusers/utils/check_repo.py",
"repo_id": "diffusers",
"token_count": 12371
} | 136 |
<jupyter_start><jupyter_text>DreamBooth Hackathon 🏆 Welcome to the DreamBooth Hackathon! In this competition, you'll **personalise a Stable Diffusion model by fine-tuning it on a handful of your own images.** To do so, we'll use a technique called [_DreamBooth_](https://arxiv.org/abs/2208.12242), which allows one to i... | diffusion-models-class/units/en/events/dreambooth.ipynb/0 | {
"file_path": "diffusion-models-class/units/en/events/dreambooth.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 9183
} | 137 |
<jupyter_start><jupyter_text>*FineTuning* et guidageDans ce *notebook*, nous allons couvrir deux approches principales pour adapter les modèles de diffusion existants :* Avec le *finetuning*, nous entraînons de nouveau les modèles existants sur de nouvelles données dans le but de modifier le résultat qu'ils produisent... | diffusion-models-class/units/fr/unit2/finetuning_and_guidance.ipynb/0 | {
"file_path": "diffusion-models-class/units/fr/unit2/finetuning_and_guidance.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 15878
} | 138 |
<jupyter_start><jupyter_text>Recherche sémantique avec FAISS (PyTorch) Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*.<jupyter_code>!pip install datasets evaluate transformers[sentencepiece]
!pip install faiss-gpu
from huggingface_hub import hf_hub_url
data_files = hf_hub_url(
... | notebooks/course/fr/chapter5/section6_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter5/section6_pt.ipynb",
"repo_id": "notebooks",
"token_count": 1233
} | 139 |
<jupyter_start><jupyter_text>Finetuner un modèle de language masqué (TensorFlow) Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!apt install git-lfs<jupyter_output><empty_output><jupyter_text>Vous aurez besoin de... | notebooks/course/fr/chapter7/section3_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section3_tf.ipynb",
"repo_id": "notebooks",
"token_count": 2949
} | 140 |
<jupyter_start><jupyter_text>Comprendre la classe Interface Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install gradio
import numpy as np
import gradio as gr
def reverse_audio(audio):
sr, data = audio
... | notebooks/course/fr/chapter9/section3.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter9/section3.ipynb",
"repo_id": "notebooks",
"token_count": 759
} | 141 |
<jupyter_start><jupyter_text>Image super-resolution using Latent Diffusion This colab notebook shows how to use the Latent Diffusion image super-resolution model using 🧨 [diffusers](https://github.com/huggingface/diffusers) libray.The model was originally released in [Latent Diffusion repo](https://github.com/CompVis/... | notebooks/diffusers/latent_diffusion_upscaler.ipynb/0 | {
"file_path": "notebooks/diffusers/latent_diffusion_upscaler.ipynb",
"repo_id": "notebooks",
"token_count": 656
} | 142 |
# adapted from https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/pytorch/image_captioning.ipynb
# This example demonstrates normal finetuning (w/o peft) - for the sake of keeping the memory
# requirements small it freezes the original pre-trained text and image layers to keep the memory
# requirem... | notebooks/examples/idefics/finetune_image_captioning.py/0 | {
"file_path": "notebooks/examples/idefics/finetune_image_captioning.py",
"repo_id": "notebooks",
"token_count": 1670
} | 143 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers as well as some other libraries. Uncomment the following cell and run it.<jupyter_code># Install
!pip install -q biopython transformers datasets huggingface_hub accelerate peft<jupyter_output>[2K ... | notebooks/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb/0 | {
"file_path": "notebooks/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb",
"repo_id": "notebooks",
"token_count": 8292
} | 144 |
<jupyter_start><jupyter_text>How to fine-tune a T5 model with ONNX RuntimeThis notebook is largely inspired by the summarization [notebook of Transformers](https://github.com/huggingface/notebooks/blob/main/examples/summarization.ipynb) which takes PyTorch as backend for fine tuning.Here you will use the `ORTSeq2SeqTra... | notebooks/examples/summarization_ort.ipynb/0 | {
"file_path": "notebooks/examples/summarization_ort.ipynb",
"repo_id": "notebooks",
"token_count": 6048
} | 145 |
<jupyter_start><jupyter_text>Getting started with Owl-ViTIn this notebook, we are going to run the [OWL-ViT](https://arxiv.org/abs/2205.06230) model (an open-vocabulary object detection model) by Google Research on scikit-image samples images. OWL-ViT: A Quick IntroOWL-ViT is an open-vocabulary object detector. Given ... | notebooks/examples/zeroshot_object_detection_with_owlvit.ipynb/0 | {
"file_path": "notebooks/examples/zeroshot_object_detection_with_owlvit.ipynb",
"repo_id": "notebooks",
"token_count": 4929
} | 146 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Distributed Training Demo for `TensorFlow` Distributed Data Parallelism with `transformers` and `tensorflow` 1. [Introduction](Introduction) 2. [Development Environment and Permissions](Development-Environment-and-Permissions) 1. [Installation](Installation) ... | notebooks/sagemaker/07_tensorflow_distributed_training_data_parallelism/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/07_tensorflow_distributed_training_data_parallelism/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3614
} | 147 |
<jupyter_start><jupyter_text>Accelerate BERT Inference with Hugging Face Transformers and AWS inferentia In this end-to-end tutorial, you will learn how to speed up BERT inference for text classification with Hugging Face Transformers, Amazon SageMaker, and AWS Inferentia. You will learn how to: 1. Convert your Hugging... | notebooks/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3902
} | 148 |
<jupyter_start><jupyter_text>Document AI: Fine-tuning Donut for document-parsing using Hugging Face Transformers on Amazon SageMakerIn this tutorial, you will learn how to fine-tune and deploy [Donut-base](https://huggingface.co/naver-clova-ix/donut-base) for document-understand/document-parsing using Hugging Face Tran... | notebooks/sagemaker/26_document_ai_donut/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/26_document_ai_donut/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 7780
} | 149 |
<!--⚠️ 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.
-->
# Fully Sharded Data Parallel
[Fully sharded data parallel](https://pytorch.org/docs/stable/fsdp.html) (FSDP) is developed for distributed training ... | peft/docs/source/accelerate/fsdp.md/0 | {
"file_path": "peft/docs/source/accelerate/fsdp.md",
"repo_id": "peft",
"token_count": 2180
} | 150 |
<!--⚠️ 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.
-->
# Configuration
[`PeftConfigMixin`] is the base configuration class for storing the adapter configuration of a [`PeftModel`], and [`PromptLearningCo... | peft/docs/source/package_reference/config.md/0 | {
"file_path": "peft/docs/source/package_reference/config.md",
"repo_id": "peft",
"token_count": 224
} | 151 |
<!--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/task_guides/dreambooth_lora.md/0 | {
"file_path": "peft/docs/source/task_guides/dreambooth_lora.md",
"repo_id": "peft",
"token_count": 3693
} | 152 |
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 1
gradient_clipping: 1.0
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: true
zero3_save_16bit_model: true
zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_co... | peft/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml/0 | {
"file_path": "peft/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml",
"repo_id": "peft",
"token_count": 198
} | 153 |
# Fine-tuning for image classification using LoRA and 🤗 PEFT
## Vision Transformer model from transformers
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_peft_lora.ipyn... | peft/examples/image_classification/README.md/0 | {
"file_path": "peft/examples/image_classification/README.md",
"repo_id": "peft",
"token_count": 457
} | 154 |
# 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/mapping.py/0 | {
"file_path": "peft/src/peft/mapping.py",
"repo_id": "peft",
"token_count": 2278
} | 155 |
# 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/gptq.py/0 | {
"file_path": "peft/src/peft/tuners/lora/gptq.py",
"repo_id": "peft",
"token_count": 1516
} | 156 |
# 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/p_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/p_tuning/model.py",
"repo_id": "peft",
"token_count": 2483
} | 157 |
# 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/other.py/0 | {
"file_path": "peft/src/peft/utils/other.py",
"repo_id": "peft",
"token_count": 8600
} | 158 |
# 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/tests/test_initialization.py/0 | {
"file_path": "peft/tests/test_initialization.py",
"repo_id": "peft",
"token_count": 5568
} | 159 |
# PyTorch Image Models
- [What's New](#whats-new)
- [Introduction](#introduction)
- [Models](#models)
- [Features](#features)
- [Results](#results)
- [Getting Started (Documentation)](#getting-started-documentation)
- [Train, Validation, Inference Scripts](#train-validation-inference-scripts)
- [Awesome PyTorch Resourc... | pytorch-image-models/README.md/0 | {
"file_path": "pytorch-image-models/README.md",
"repo_id": "pytorch-image-models",
"token_count": 19602
} | 160 |
"""
Run this script to generate the model-index files in `models` from the templates in `.templates/models`.
"""
import argparse
from pathlib import Path
from jinja2 import Environment, FileSystemLoader
import modelindex
def generate_readmes(templates_path: Path, dest_path: Path):
"""Add the code snippet templ... | pytorch-image-models/docs/models/.templates/generate_readmes.py/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/generate_readmes.py",
"repo_id": "pytorch-image-models",
"token_count": 725
} | 161 |
# (Gluon) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswit... | pytorch-image-models/docs/models/.templates/models/gloun-inception-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-inception-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1073
} | 162 |
# MobileNet v2
**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expa... | pytorch-image-models/docs/models/.templates/models/mobilenet-v2.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/mobilenet-v2.md",
"repo_id": "pytorch-image-models",
"token_count": 2583
} | 163 |
# SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
{% include 'code_snippets.md'... | pytorch-image-models/docs/models/.templates/models/seresnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/seresnext.md",
"repo_id": "pytorch-image-models",
"token_count": 1929
} | 164 |
# Wide ResNet
**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).
{% include 'code_snippet... | pytorch-image-models/docs/models/.templates/models/wide-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/wide-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1220
} | 165 |
# CSP-DarkNet
**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The u... | pytorch-image-models/hfdocs/source/models/csp-darknet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/csp-darknet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1756
} | 166 |
# (Gluon) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this... | pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2538
} | 167 |
# PNASNet
**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to comple... | pytorch-image-models/hfdocs/source/models/pnasnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/pnasnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1622
} | 168 |
# SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual b... | pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2425
} | 169 |
# Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in `timm`.
## Schedulers
### Factory functions
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2
### Scheduler Classes
[[... | pytorch-image-models/hfdocs/source/reference/schedulers.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/reference/schedulers.mdx",
"repo_id": "pytorch-image-models",
"token_count": 242
} | 170 |
""" AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/au... | pytorch-image-models/timm/data/auto_augment.py/0 | {
"file_path": "pytorch-image-models/timm/data/auto_augment.py",
"repo_id": "pytorch-image-models",
"token_count": 15929
} | 171 |
""" Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | pytorch-image-models/timm/data/readers/reader_hfds.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_hfds.py",
"repo_id": "pytorch-image-models",
"token_count": 1150
} | 172 |
""" PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | pytorch-image-models/timm/layers/adaptive_avgmax_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/adaptive_avgmax_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 2903
} | 173 |
""" DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl ins... | pytorch-image-models/timm/layers/drop.py/0 | {
"file_path": "pytorch-image-models/timm/layers/drop.py",
"repo_id": "pytorch-image-models",
"token_count": 3016
} | 174 |
""" Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kern... | pytorch-image-models/timm/layers/median_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/median_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 883
} | 175 |
import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
bs: torch.jit.Final[int]
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, s... | pytorch-image-models/timm/layers/space_to_depth.py/0 | {
"file_path": "pytorch-image-models/timm/layers/space_to_depth.py",
"repo_id": "pytorch-image-models",
"token_count": 938
} | 176 |
""" EfficientNet, MobileNetV3, etc Blocks
Hacked together by / Copyright 2019, Ross Wightman
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
from timm.layers import create_conv2d, DropPath, make_divisible, create_act_layer, get_norm_act_layer
__all__ = [
'SqueezeExcite', 'ConvBnAct',... | pytorch-image-models/timm/models/_efficientnet_blocks.py/0 | {
"file_path": "pytorch-image-models/timm/models/_efficientnet_blocks.py",
"repo_id": "pytorch-image-models",
"token_count": 5589
} | 177 |
""" BEiT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
Model from official source: https://github.com/microsoft/unilm/tree/master/beit
@inproceedings{beit,
title={{BEiT}: {BERT} Pre-Training of Image Transformers},
author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
booktitle=... | pytorch-image-models/timm/models/beit.py/0 | {
"file_path": "pytorch-image-models/timm/models/beit.py",
"repo_id": "pytorch-image-models",
"token_count": 12467
} | 178 |
""" EfficientFormer
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov,
Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={20... | pytorch-image-models/timm/models/efficientformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientformer.py",
"repo_id": "pytorch-image-models",
"token_count": 9481
} | 179 |
""" Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | pytorch-image-models/timm/models/pit.py/0 | {
"file_path": "pytorch-image-models/timm/models/pit.py",
"repo_id": "pytorch-image-models",
"token_count": 7347
} | 180 |
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | pytorch-image-models/timm/models/swin_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 16908
} | 181 |
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partia... | pytorch-image-models/timm/models/xception_aligned.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception_aligned.py",
"repo_id": "pytorch-image-models",
"token_count": 7719
} | 182 |
""" Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... | pytorch-image-models/timm/optim/nvnovograd.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nvnovograd.py",
"repo_id": "pytorch-image-models",
"token_count": 2415
} | 183 |
""" Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021... | pytorch-image-models/timm/utils/agc.py/0 | {
"file_path": "pytorch-image-models/timm/utils/agc.py",
"repo_id": "pytorch-image-models",
"token_count": 661
} | 184 |
#!/usr/bin/env python3
""" ImageNet Training Script
This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
training results with some of the latest networks and training techniques. It favours canonical PyTorch
and standard Python style over trying to be able to 'do it al... | pytorch-image-models/train.py/0 | {
"file_path": "pytorch-image-models/train.py",
"repo_id": "pytorch-image-models",
"token_count": 24011
} | 185 |
install-server:
cd server && make install
install-custom-kernels:
if [ "$$BUILD_EXTENSIONS" = "True" ]; then cd server/custom_kernels && python setup.py install; else echo "Custom kernels are disabled, you need to set the BUILD_EXTENSIONS environment variable to 'True' in order to build them. (Please read the docs, ... | text-generation-inference/Makefile/0 | {
"file_path": "text-generation-inference/Makefile",
"repo_id": "text-generation-inference",
"token_count": 498
} | 186 |
# Serving Private & Gated Models
If the model you wish to serve is behind gated access or the model repository on Hugging Face Hub is private, and you have access to the model, you can provide your Hugging Face Hub access token. You can generate and copy a read token from [Hugging Face Hub tokens page](https://hugging... | text-generation-inference/docs/source/basic_tutorials/gated_model_access.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/gated_model_access.md",
"repo_id": "text-generation-inference",
"token_count": 320
} | 187 |
import sys
import subprocess
import contextlib
import pytest
import asyncio
import os
import docker
import json
import math
import time
import random
from docker.errors import NotFound
from typing import Optional, List, Dict
from syrupy.extensions.json import JSONSnapshotExtension
from aiohttp import ClientConnectorEr... | text-generation-inference/integration-tests/conftest.py/0 | {
"file_path": "text-generation-inference/integration-tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 6015
} | 188 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json",
"repo_id": "text-generation-inference",
"token_count": 4901
} | 189 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 14402,
"logprob": null,
"text": "Test"
},
{
"id": 2581,
"logprob": -11.6171875,
"text": " ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi_load.json",
"repo_id": "text-generation-inference",
"token_count": 4672
} | 190 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|USER|>"
},
{
"id": 1276,
"logprob": -4.5546875,
"text": "What"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox.json",
"repo_id": "text-generation-inference",
"token_count": 1351
} | 191 |
import pytest
@pytest.fixture(scope="module")
def flash_neox_sharded_handle(launcher):
with launcher("OpenAssistant/oasst-sft-1-pythia-12b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_neox_sharded(flash_neox_sharded_handle):
await flash_neox_sharded_handle.h... | text-generation-inference/integration-tests/models/test_flash_neox_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_neox_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 491
} | 192 |
use std::error::Error;
use vergen::EmitBuilder;
fn main() -> Result<(), Box<dyn Error>> {
// Emit cargo and rustc compile time values
EmitBuilder::builder().all_cargo().all_rustc().emit()?;
// Try to get the git sha from the local git repository
if EmitBuilder::builder()
.fail_on_error()
... | text-generation-inference/launcher/build.rs/0 | {
"file_path": "text-generation-inference/launcher/build.rs",
"repo_id": "text-generation-inference",
"token_count": 363
} | 193 |
use crate::client::{DecodeTimings, PrefillTimings};
/// Multi shard Client
use crate::{Batch, CachedBatch, Client, Generation, HealthResponse, ShardInfo};
use crate::{ClientError, Result};
use futures::future::join_all;
use tonic::transport::Uri;
use tracing::instrument;
#[derive(Debug, Clone)]
/// Text Generation Inf... | text-generation-inference/router/client/src/sharded_client.rs/0 | {
"file_path": "text-generation-inference/router/client/src/sharded_client.rs",
"repo_id": "text-generation-inference",
"token_count": 2837
} | 194 |
flash_att_commit := 3a9bfd076f98746c73362328958dbc68d145fbec
flash-attention:
# Clone flash attention
pip install -U packaging ninja --no-cache-dir
git clone https://github.com/HazyResearch/flash-attention.git
build-flash-attention: flash-attention
cd flash-attention && git fetch && git checkout $(flash_att_c... | text-generation-inference/server/Makefile-flash-att/0 | {
"file_path": "text-generation-inference/server/Makefile-flash-att",
"repo_id": "text-generation-inference",
"token_count": 242
} | 195 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include <torch/extension.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include "util.cuh"
#include "tuning.h"
#include "cuda_buffers.cu... | text-generation-inference/server/exllama_kernels/exllama_kernels/exllama_ext.cpp/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/exllama_ext.cpp",
"repo_id": "text-generation-inference",
"token_count": 3215
} | 196 |
#ifndef _qdq_2_cuh
#define _qdq_2_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_2BIT == 1
// Permutation:
//
// ffddbb99 77553311 eeccaa88 66442200
__forceinline__ __device__ void shuffle_2bit_16
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unrol... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh",
"repo_id": "text-generation-inference",
"token_count": 1588
} | 197 |
import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.utils import weight_hub_files, download_weights
from text_generation_server.models.bl... | text-generation-inference/server/tests/models/test_bloom.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_bloom.py",
"repo_id": "text-generation-inference",
"token_count": 5296
} | 198 |
import math
import torch
from typing import Optional, List, Tuple
BLOCK_SIZE: int = 16
# Will be set in warmup
CACHE_MANAGER: Optional["CacheManager"] = None
class CacheManager:
def __init__(
self,
num_blocks: int,
num_layers: int,
num_heads: int,
head_size: int,
... | text-generation-inference/server/text_generation_server/models/cache_manager.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/cache_manager.py",
"repo_id": "text-generation-inference",
"token_count": 2033
} | 199 |
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and 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/L... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_vision.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_vision.py",
"repo_id": "text-generation-inference",
"token_count": 9661
} | 200 |
import torch
import torch.distributed
from typing import List, Optional, Tuple
from transformers import (
AutoTokenizer,
AutoConfig,
AutoProcessor,
)
from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
from text_generation_server.models.custom_modeling.idefics_processin... | text-generation-inference/server/text_generation_server/models/idefics.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/idefics.py",
"repo_id": "text-generation-inference",
"token_count": 1306
} | 201 |
import datetime
import torch
import os
from loguru import logger
from pathlib import Path
from safetensors.torch import save_file, load_file, _find_shared_tensors, _is_complete
from typing import List, Dict
from collections import defaultdict
def _remove_duplicate_names(
state_dict: Dict[str, torch.Tensor],
... | text-generation-inference/server/text_generation_server/utils/convert.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/convert.py",
"repo_id": "text-generation-inference",
"token_count": 1769
} | 202 |
SPECULATE = None
def get_speculate() -> int:
global SPECULATE
return SPECULATE
def set_speculate(speculate: int):
global SPECULATE
SPECULATE = speculate
| text-generation-inference/server/text_generation_server/utils/speculate.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/speculate.py",
"repo_id": "text-generation-inference",
"token_count": 66
} | 203 |
.PHONY: style check-style test
DATA_DIR = data
dir_guard=@mkdir -p $(@D)
# Format source code automatically
style:
npm run lint
# Check the source code is formatted correctly
check-style:
npm run lint-check
TESTS_RESOURCES = $(DATA_DIR)/small.txt $(DATA_DIR)/roberta.json $(DATA_DIR)/tokenizer-wiki.json $(DATA_DI... | tokenizers/bindings/node/Makefile/0 | {
"file_path": "tokenizers/bindings/node/Makefile",
"repo_id": "tokenizers",
"token_count": 406
} | 204 |
import {
byteLevelPreTokenizer,
metaspacePreTokenizer,
punctuationPreTokenizer,
sequencePreTokenizer,
splitPreTokenizer,
whitespaceSplitPreTokenizer,
} from '../../'
describe('byteLevelPreTokenizer', () => {
it('instantiates correctly', () => {
const processor = byteLevelPreTokenizer()
expect(pro... | tokenizers/bindings/node/lib/bindings/pre-tokenizers.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/pre-tokenizers.test.ts",
"repo_id": "tokenizers",
"token_count": 728
} | 205 |
{
"name": "tokenizers-linux-arm64-gnu",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"arm64"
],
"main": "tokenizers.linux-arm64-gnu.node",
"files": [
"tokenizers.linux-arm64-gnu.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"N... | tokenizers/bindings/node/npm/linux-arm64-gnu/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-gnu/package.json",
"repo_id": "tokenizers",
"token_count": 289
} | 206 |
use crate::arc_rwlock_serde;
use serde::{Deserialize, Serialize};
extern crate tokenizers as tk;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use std::sync::{Arc, RwLock};
use tk::decoders::DecoderWrapper;
/// Decoder
#[derive(Clone, Serialize, Deserialize)]
#[napi]
pub struct Decoder {
#[serde(flatten, wi... | tokenizers/bindings/node/src/decoders.rs/0 | {
"file_path": "tokenizers/bindings/node/src/decoders.rs",
"repo_id": "tokenizers",
"token_count": 1821
} | 207 |
[target.x86_64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
"-C", "link-arg=-mmacosx-version-min=10.11",
]
[target.aarch64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
"-C", "link-arg=-mmacosx-version-min=10.11",
]
| tokenizers/bindings/python/.cargo/config.toml/0 | {
"file_path": "tokenizers/bindings/python/.cargo/config.toml",
"repo_id": "tokenizers",
"token_count": 146
} | 208 |
from .. import decoders
Decoder = decoders.Decoder
ByteLevel = decoders.ByteLevel
Replace = decoders.Replace
WordPiece = decoders.WordPiece
ByteFallback = decoders.ByteFallback
Fuse = decoders.Fuse
Strip = decoders.Strip
Metaspace = decoders.Metaspace
BPEDecoder = decoders.BPEDecoder
CTC = decoders.CTC
Sequence = dec... | tokenizers/bindings/python/py_src/tokenizers/decoders/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/decoders/__init__.py",
"repo_id": "tokenizers",
"token_count": 128
} | 209 |
# Generated content DO NOT EDIT
class PostProcessor:
"""
Base class for all post-processors
This class is not supposed to be instantiated directly. Instead, any implementation of
a PostProcessor will return an instance of this class when instantiated.
"""
def num_special_tokens_to_add(self, is... | tokenizers/bindings/python/py_src/tokenizers/processors/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/processors/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 4779
} | 210 |
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::sync::{Arc, RwLock};
use crate::token::PyToken;
use crate::trainers::PyTrainer;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::{Deserialize, Serialize};
use tk::models::bpe::{BpeBuilder, Merges, Vocab, BPE};
use tk::mod... | tokenizers/bindings/python/src/models.rs/0 | {
"file_path": "tokenizers/bindings/python/src/models.rs",
"repo_id": "tokenizers",
"token_count": 14445
} | 211 |
import json
import pickle
import pytest
from tokenizers.decoders import (
CTC,
BPEDecoder,
ByteLevel,
Decoder,
Metaspace,
Sequence,
WordPiece,
ByteFallback,
Replace,
Strip,
Fuse,
)
class TestByteLevel:
def test_instantiate(self):
assert ByteLevel() is not None... | tokenizers/bindings/python/tests/bindings/test_decoders.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_decoders.py",
"repo_id": "tokenizers",
"token_count": 3521
} | 212 |
import pytest
from tokenizers import CharBPETokenizer
from ..utils import data_dir, multiprocessing_with_parallelism, openai_files
class TestCharBPETokenizer:
def test_basic_encode(self, openai_files):
tokenizer = CharBPETokenizer.from_file(openai_files["vocab"], openai_files["merges"])
output ... | tokenizers/bindings/python/tests/implementations/test_char_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_char_bpe.py",
"repo_id": "tokenizers",
"token_count": 1099
} | 213 |
# Tokenizer
<tokenizerslangcontent>
<python>
## Tokenizer
[[autodoc]] tokenizers.Tokenizer
- all
- decoder
- model
- normalizer
- padding
- post_processor
- pre_tokenizer
- truncation
</python>
<rust>
The Rust API Reference is available directly on the [Docs.rs](https://docs.rs/tokeniz... | tokenizers/docs/source-doc-builder/api/tokenizer.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/tokenizer.mdx",
"repo_id": "tokenizers",
"token_count": 156
} | 214 |
.highlight .c1, .highlight .sd{
color: #999
}
.highlight .nn, .highlight .k, .highlight .s1, .highlight .nb, .highlight .bp, .highlight .kc, .highlight .kt {
color: #FB8D68;
}
.highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow, .highlight .kd, .highlight .kr, .highlight .s {
color: #6670FF;
}... | tokenizers/docs/source/_static/css/code-snippets.css/0 | {
"file_path": "tokenizers/docs/source/_static/css/code-snippets.css",
"repo_id": "tokenizers",
"token_count": 166
} | 215 |
Quicktour
====================================================================================================
Let's have a quick look at the 🤗 Tokenizers library features. The library provides an
implementation of today's most used tokenizers that is both easy to use and blazing fast.
.. only:: python
It can b... | tokenizers/docs/source/quicktour.rst/0 | {
"file_path": "tokenizers/docs/source/quicktour.rst",
"repo_id": "tokenizers",
"token_count": 8904
} | 216 |
<div align="center">
<h1><code>wasm-pack-template</code></h1>
<strong>A template for kick starting a Rust and WebAssembly project using <a href="https://github.com/rustwasm/wasm-pack">wasm-pack</a>.</strong>
<p>
<a href="https://travis-ci.org/rustwasm/wasm-pack-template"><img src="https://img.shields.io/tr... | tokenizers/tokenizers/examples/unstable_wasm/README.md/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/README.md",
"repo_id": "tokenizers",
"token_count": 811
} | 217 |
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