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# coding=utf-8
# Copyright 2024 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_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py",
"repo_id": "diffusers",
"token_count": 4788
} | 140 |
# coding=utf-8
# Copyright 2024 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/unclip/test_unclip_image_variation.py/0 | {
"file_path": "diffusers/tests/pipelines/unclip/test_unclip_image_variation.py",
"repo_id": "diffusers",
"token_count": 8161
} | 141 |
import tempfile
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVeScheduler
class ScoreSdeVeSchedulerTest(unittest.TestCase):
# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
scheduler_classes = (ScoreSdeVeScheduler,)
forward_default_kwargs = ... | diffusers/tests/schedulers/test_scheduler_score_sde_ve.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_score_sde_ve.py",
"repo_id": "diffusers",
"token_count": 3215
} | 142 |
# 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
} | 143 |
# 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
} | 144 |
<jupyter_start><jupyter_text>Traduction (PyTorch) Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install accelerate
# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :
# !pip i... | notebooks/course/fr/chapter7/section4_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section4_pt.ipynb",
"repo_id": "notebooks",
"token_count": 3791
} | 145 |
<jupyter_start><jupyter_text>Partager ses démos avec d'autres 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 = "Poser une question (en anglais) à Rick"
description = """
L... | notebooks/course/fr/chapter9/section4.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter9/section4.ipynb",
"repo_id": "notebooks",
"token_count": 1441
} | 146 |
<jupyter_start><jupyter_text>IntroductionThis notebook is designed to run inference on the [Diffuser](https://arxiv.org/abs/2205.09991) planning model for model-based RL. The notebook is modified from the authors' [original](https://colab.research.google.com/drive/1YajKhu-CUIGBJeQPehjVPJcK_b38a8Nc?usp=sharingscrollTo=5... | notebooks/diffusers/reinforcement_learning_with_diffusers.ipynb/0 | {
"file_path": "notebooks/diffusers/reinforcement_learning_with_diffusers.ipynb",
"repo_id": "notebooks",
"token_count": 8060
} | 147 |
<jupyter_start><jupyter_text>IDEFICS: A Flamingo-based model, trained at scale for the community Finetuning Demo Notebook: Credit: [Flamingo blog](https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model)This google colab notebook shows how to run predictions with the 4-bit quantized... | notebooks/examples/idefics/finetune_image_captioning_peft.ipynb/0 | {
"file_path": "notebooks/examples/idefics/finetune_image_captioning_peft.ipynb",
"repo_id": "notebooks",
"token_count": 3875
} | 148 |
<jupyter_start><jupyter_text>How to export 🤗 Transformers Models to ONNX ? [ONNX](http://onnx.ai/) is open format for machine learning models. It allows to save your neural network's computation graph in a framework agnostic way, which might be particulary helpful when deploying deep learning models.Indeed, businesses... | notebooks/examples/onnx-export.ipynb/0 | {
"file_path": "notebooks/examples/onnx-export.ipynb",
"repo_id": "notebooks",
"token_count": 6241
} | 149 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install the most recent versions of 🤗 Transformers and 🤗 Datasets. We will also need `scipy` and `scikit-learn` for some of the metrics. Uncomment the following cell and run it.<jupyter_code>#! pip install transformers
#!... | notebooks/examples/text_classification-tf.ipynb/0 | {
"file_path": "notebooks/examples/text_classification-tf.ipynb",
"repo_id": "notebooks",
"token_count": 8177
} | 150 |
<jupyter_start><jupyter_text><jupyter_code>!pip install transformers
!sudo apt-get install git-lfs
!git config --global user.email "julien@huggingface.co"
!git config --global user.name "Julien Chaumond"
!transformers-cli login
!pwd
!transformers-cli repo create policy-distilbert-7d
!git clone https://julien-c:...token... | notebooks/huggingface_hub/upload_hf_model.ipynb/0 | {
"file_path": "notebooks/huggingface_hub/upload_hf_model.ipynb",
"repo_id": "notebooks",
"token_count": 478
} | 151 |
<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
} | 152 |
<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
} | 153 |
<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
} | 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/developer_guides/troubleshooting.md/0 | {
"file_path": "peft/docs/source/developer_guides/troubleshooting.md",
"repo_id": "peft",
"token_count": 1890
} | 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.
-->
# Models
[`PeftModel`] is the base model class for specifying the base Transformer model and configuration to apply a PEFT method to. The base `Peft... | peft/docs/source/package_reference/peft_model.md/0 | {
"file_path": "peft/docs/source/package_reference/peft_model.md",
"repo_id": "peft",
"token_count": 540
} | 156 |
<jupyter_start><jupyter_code>from transformers import AutoModelForCausalLM
from peft import get_peft_config, get_peft_model, PrefixTuningConfig, TaskType, PeftType
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers im... | peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb",
"repo_id": "peft",
"token_count": 4714
} | 157 |
<jupyter_start><jupyter_code>import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
from tqdm import tqdm
import datasets
from datasets import load_dataset, DatasetDict
import evaluate
import torch
from torch import nn
from torch.utils.data import DataLoader
import tr... | peft/examples/feature_extraction/peft_lora_embedding_semantic_similarity_inference.ipynb/0 | {
"file_path": "peft/examples/feature_extraction/peft_lora_embedding_semantic_similarity_inference.ipynb",
"repo_id": "peft",
"token_count": 2675
} | 158 |
<jupyter_start><jupyter_code>!git clone https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth
%cd "peft-lora-sd-dreambooth"
!pip install -r requirements.txt
!python colab.py<jupyter_output><empty_output> | peft/examples/lora_dreambooth/colab_notebook.ipynb/0 | {
"file_path": "peft/examples/lora_dreambooth/colab_notebook.ipynb",
"repo_id": "peft",
"token_count": 91
} | 159 |
<jupyter_start><jupyter_code>import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
import peft
import evaluate
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | peft/examples/sequence_classification/IA3.ipynb/0 | {
"file_path": "peft/examples/sequence_classification/IA3.ipynb",
"repo_id": "peft",
"token_count": 1903
} | 160 |
# 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... | peft/setup.py/0 | {
"file_path": "peft/setup.py",
"repo_id": "peft",
"token_count": 1546
} | 161 |
# 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 applicable law or... | peft/src/peft/tuners/adalora/model.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/model.py",
"repo_id": "peft",
"token_count": 7189
} | 162 |
# 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 applicable law or... | peft/src/peft/tuners/multitask_prompt_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/multitask_prompt_tuning/config.py",
"repo_id": "peft",
"token_count": 883
} | 163 |
# 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 applicable law or... | peft/src/peft/tuners/prefix_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/prefix_tuning/model.py",
"repo_id": "peft",
"token_count": 1228
} | 164 |
# 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 applicable law or... | peft/tests/test_adaption_prompt.py/0 | {
"file_path": "peft/tests/test_adaption_prompt.py",
"repo_id": "peft",
"token_count": 16295
} | 165 |
#!/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_poly.py/0 | {
"file_path": "peft/tests/test_poly.py",
"repo_id": "peft",
"token_count": 1541
} | 166 |
#!/usr/bin/env python3
""" Checkpoint Cleaning Script
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
calculation for model zoo compatibility.
Hacked together by / Copyright 2020 Ross Wightman (https:... | pytorch-image-models/clean_checkpoint.py/0 | {
"file_path": "pytorch-image-models/clean_checkpoint.py",
"repo_id": "pytorch-image-models",
"token_count": 1771
} | 167 |
# 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/docs/models/.templates/models/csp-darknet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/csp-darknet.md",
"repo_id": "pytorch-image-models",
"token_count": 947
} | 168 |
# (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/docs/models/.templates/models/gloun-seresnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-seresnext.md",
"repo_id": "pytorch-image-models",
"token_count": 1705
} | 169 |
# 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/docs/models/.templates/models/pnasnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/pnasnet.md",
"repo_id": "pytorch-image-models",
"token_count": 813
} | 170 |
# 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/docs/models/.templates/models/ssl-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ssl-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1616
} | 171 |
# 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 signi... | pytorch-image-models/docs/scripts.md/0 | {
"file_path": "pytorch-image-models/docs/scripts.md",
"repo_id": "pytorch-image-models",
"token_count": 511
} | 172 |
#!/usr/bin/env python3
"""PyTorch Inference Script
An example inference script that outputs top-k class ids for images in a folder into a csv.
Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
"""
import argparse
import json
import logging
import os
import time
from contextlib import su... | pytorch-image-models/inference.py/0 | {
"file_path": "pytorch-image-models/inference.py",
"repo_id": "pytorch-image-models",
"token_count": 6803
} | 173 |
[dist_conda]
conda_name_differences = 'torch:pytorch'
channels = pytorch
noarch = True
[metadata]
url = "https://github.com/huggingface/pytorch-image-models" | pytorch-image-models/setup.cfg/0 | {
"file_path": "pytorch-image-models/setup.cfg",
"repo_id": "pytorch-image-models",
"token_count": 65
} | 174 |
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractmethod
def label_descriptions(sel... | pytorch-image-models/timm/data/dataset_info.py/0 | {
"file_path": "pytorch-image-models/timm/data/dataset_info.py",
"repo_id": "pytorch-image-models",
"token_count": 941
} | 175 |
""" Dataset reader that wraps TFDS datasets
Wraps many (most?) TFDS image-classification datasets
from https://github.com/tensorflow/datasets
https://www.tensorflow.org/datasets/catalog/overview#image_classification
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
import os
import sys
from typing imp... | pytorch-image-models/timm/data/readers/reader_tfds.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_tfds.py",
"repo_id": "pytorch-image-models",
"token_count": 7089
} | 176 |
""" CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | pytorch-image-models/timm/layers/cbam.py/0 | {
"file_path": "pytorch-image-models/timm/layers/cbam.py",
"repo_id": "pytorch-image-models",
"token_count": 2016
} | 177 |
from enum import Enum
from typing import Union
import torch
class Format(str, Enum):
NCHW = 'NCHW'
NHWC = 'NHWC'
NCL = 'NCL'
NLC = 'NLC'
FormatT = Union[str, Format]
def get_spatial_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NLC:
dim = (1,)
elif fmt is Format.NCL:
... | pytorch-image-models/timm/layers/format.py/0 | {
"file_path": "pytorch-image-models/timm/layers/format.py",
"repo_id": "pytorch-image-models",
"token_count": 572
} | 178 |
""" Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm im... | pytorch-image-models/timm/layers/norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/norm.py",
"repo_id": "pytorch-image-models",
"token_count": 2520
} | 179 |
""" Test Time Pooling (Average-Max Pool)
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from torch import nn
import torch.nn.functional as F
from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
_logger = logging.getLogger(__name__)
class TestTimePoolHead(nn.Module):
def __init__(sel... | pytorch-image-models/timm/layers/test_time_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/test_time_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 881
} | 180 |
""" Model creation / weight loading / state_dict helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from collections import OrderedDict
from typing import Any, Callable, Dict, Optional, Union
import torch
try:
import safetensors.torch
_has_safetensors = True
except ImportEr... | pytorch-image-models/timm/models/_helpers.py/0 | {
"file_path": "pytorch-image-models/timm/models/_helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 2546
} | 181 |
""" ConViT Model
@article{d2021convit,
title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
journal={arXiv preprint arXiv:2103.10697},
year={2021}
}
P... | pytorch-image-models/timm/models/convit.py/0 | {
"file_path": "pytorch-image-models/timm/models/convit.py",
"repo_id": "pytorch-image-models",
"token_count": 7716
} | 182 |
""" EVA
EVA from https://github.com/baaivision/EVA , paper: https://arxiv.org/abs/2211.07636
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
Tiejun and ... | pytorch-image-models/timm/models/eva.py/0 | {
"file_path": "pytorch-image-models/timm/models/eva.py",
"repo_id": "pytorch-image-models",
"token_count": 21637
} | 183 |
""" Pytorch Inception-V4 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
from timm.data import IMAGENET... | pytorch-image-models/timm/models/inception_v4.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_v4.py",
"repo_id": "pytorch-image-models",
"token_count": 5528
} | 184 |
""" TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | pytorch-image-models/timm/models/tiny_vit.py/0 | {
"file_path": "pytorch-image-models/timm/models/tiny_vit.py",
"repo_id": "pytorch-image-models",
"token_count": 12415
} | 185 |
import math
import torch
from torch.optim.optimizer import Optimizer
class AdaBelief(Optimizer):
r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optiona... | pytorch-image-models/timm/optim/adabelief.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adabelief.py",
"repo_id": "pytorch-image-models",
"token_count": 5074
} | 186 |
""" RMSProp modified to behave like Tensorflow impl
Originally cut & paste from PyTorch RMSProp
https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py
Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE
Modifications Copyright 2021... | pytorch-image-models/timm/optim/rmsprop_tf.py/0 | {
"file_path": "pytorch-image-models/timm/optim/rmsprop_tf.py",
"repo_id": "pytorch-image-models",
"token_count": 2901
} | 187 |
""" CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call__(
sel... | pytorch-image-models/timm/utils/cuda.py/0 | {
"file_path": "pytorch-image-models/timm/utils/cuda.py",
"repo_id": "pytorch-image-models",
"token_count": 980
} | 188 |
<div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a>
# Text Generation Inference
<a href="https://github.com/huggingface/text-generation-infer... | text-generation-inference/README.md/0 | {
"file_path": "text-generation-inference/README.md",
"repo_id": "text-generation-inference",
"token_count": 3371
} | 189 |
[tool.poetry]
name = "text-generation"
version = "0.6.1"
description = "Hugging Face Text Generation Python Client"
license = "Apache-2.0"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
maintainers = ["Olivier Dehaene <olivier@huggingface.co>"]
readme = "README.md"
homepage = "https://github.com/huggingface/tex... | text-generation-inference/clients/python/pyproject.toml/0 | {
"file_path": "text-generation-inference/clients/python/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 336
} | 190 |
# Text-generation-launcher arguments
<!-- WRAP CODE BLOCKS -->
```shell
Text Generation Launcher
Usage: text-generation-launcher [OPTIONS]
Options:
```
## MODEL_ID
```shell
--model-id <MODEL_ID>
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `Open... | text-generation-inference/docs/source/basic_tutorials/launcher.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/launcher.md",
"repo_id": "text-generation-inference",
"token_count": 6114
} | 191 |
# Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
## Supported Models
The following models are optimized and can be served with TGI, which uses custom CUDA k... | text-generation-inference/docs/source/supported_models.md/0 | {
"file_path": "text-generation-inference/docs/source/supported_models.md",
"repo_id": "text-generation-inference",
"token_count": 1169
} | 192 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -10.0,
"text": "Test"
},
{
"id": 3853,... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1031
} | 193 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral.json",
"repo_id": "text-generation-inference",
"token_count": 1050
} | 194 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 60,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6328125,
"text": " print"
},
{
"id"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_default_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_default_params.json",
"repo_id": "text-generation-inference",
"token_count": 4734
} | 195 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 5,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": 0,
"tokens": [
{
"id": 926,
"logprob": -4.3554688,
"special... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base.json",
"repo_id": "text-generation-inference",
"token_count": 532
} | 196 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle(launcher):
with launcher(
"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
num_shard=1,
quantize="awq",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_awq(... | text-generation-inference/integration-tests/models/test_flash_awq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_awq.py",
"repo_id": "text-generation-inference",
"token_count": 842
} | 197 |
import pytest
@pytest.fixture(scope="module")
def flash_starcoder_gptq_handle(launcher):
with launcher("Narsil/starcoder-gptq", num_shard=2, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder_gptq(flash_starcoder_gptq_handle):
await flash_starcoder_gpt... | text-generation-inference/integration-tests/models/test_flash_starcoder_gptq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_starcoder_gptq.py",
"repo_id": "text-generation-inference",
"token_count": 710
} | 198 |
use std::fmt;
use std::process::Command;
pub(crate) struct Env {
cargo_target: &'static str,
cargo_version: &'static str,
git_sha: &'static str,
docker_label: &'static str,
nvidia_env: String,
}
impl Env {
pub fn new() -> Self {
let nvidia_env = nvidia_smi();
Self {
... | text-generation-inference/launcher/src/env_runtime.rs/0 | {
"file_path": "text-generation-inference/launcher/src/env_runtime.rs",
"repo_id": "text-generation-inference",
"token_count": 650
} | 199 |
[package]
name = "grpc-metadata"
version = "0.1.0"
edition = "2021"
[dependencies]
opentelemetry = "^0.20"
tonic = "^0.10"
tracing = "^0.1"
tracing-opentelemetry = "^0.21"
| text-generation-inference/router/grpc-metadata/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/grpc-metadata/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 83
} | 200 |
flash_att_v2_commit_cuda := 02ac572f3ffc4f402e4183aaa6824b45859d3ed3
flash_att_v2_commit_rocm := 8736558c287ff2ef28b24878e42828c595ac3e69
flash-attention-v2-cuda:
# Clone flash attention
pip install -U packaging ninja --no-cache-dir
git clone https://github.com/HazyResearch/flash-attention.git flash-attention-v2... | text-generation-inference/server/Makefile-flash-att-v2/0 | {
"file_path": "text-generation-inference/server/Makefile-flash-att-v2",
"repo_id": "text-generation-inference",
"token_count": 496
} | 201 |
// 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": 3279
} | 202 |
#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": 1589
} | 203 |
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
} | 204 |
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
} | 205 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 28490
} | 206 |
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_phi_modeling import (
FlashPhiForCausalLM,
PhiCo... | text-generation-inference/server/text_generation_server/models/flash_phi.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_phi.py",
"repo_id": "text-generation-inference",
"token_count": 1738
} | 207 |
import torch
import torch.distributed
from typing import Optional, List
from transformers import AutoTokenizer, AutoModelForCausalLM
from text_generation_server.models import CausalLM
FIM_PREFIX = "<fim-prefix>"
FIM_MIDDLE = "<fim-middle>"
FIM_SUFFIX = "<fim-suffix>"
FIM_PAD = "<fim-pad>"
EOD = "<|endoftext|>"
cla... | text-generation-inference/server/text_generation_server/models/santacoder.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/santacoder.py",
"repo_id": "text-generation-inference",
"token_count": 1196
} | 208 |
import math
import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd
try:
import triton
import triton.language as tl
from . import custom_autotune
# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
configs=[
... | text-generation-inference/server/text_generation_server/utils/gptq/quant_linear.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/gptq/quant_linear.py",
"repo_id": "text-generation-inference",
"token_count": 7008
} | 209 |
# EditorConfig helps developers define and maintain consistent
# coding styles between different editors or IDEs
# http://editorconfig.org
root = true
[*]
indent_style = space
indent_size = 2
end_of_line = lf
charset = utf-8
trim_trailing_whitespace = true
insert_final_newline = true
[*.md]
trim_trailing_whitespace =... | tokenizers/bindings/node/.editorconfig/0 | {
"file_path": "tokenizers/bindings/node/.editorconfig",
"repo_id": "tokenizers",
"token_count": 108
} | 210 |
/* tslint:disable */
/* eslint-disable */
/* prettier-ignore */
/* auto-generated by NAPI-RS */
const { existsSync, readFileSync } = require('fs')
const { join } = require('path')
const { platform, arch } = process
let nativeBinding = null
let localFileExisted = false
let loadError = null
function isMusl() {
// ... | tokenizers/bindings/node/index.js/0 | {
"file_path": "tokenizers/bindings/node/index.js",
"repo_id": "tokenizers",
"token_count": 4683
} | 211 |
{
"name": "tokenizers-android-arm64",
"version": "0.13.4-rc1",
"os": [
"android"
],
"cpu": [
"arm64"
],
"main": "tokenizers.android-arm64.node",
"files": [
"tokenizers.android-arm64.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI"... | tokenizers/bindings/node/npm/android-arm64/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/android-arm64/package.json",
"repo_id": "tokenizers",
"token_count": 264
} | 212 |
{
"name": "tokenizers-linux-x64-musl",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"x64"
],
"main": "tokenizers.linux-x64-musl.node",
"files": [
"tokenizers.linux-x64-musl.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",... | tokenizers/bindings/node/npm/linux-x64-musl/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-x64-musl/package.json",
"repo_id": "tokenizers",
"token_count": 291
} | 213 |
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::processors::PostProcessorWrapper;
use tk::Encoding;
#[derive(Clone, Serialize, Deserialize)]
#[napi]
pub struct Processor {
#[se... | tokenizers/bindings/node/src/processors.rs/0 | {
"file_path": "tokenizers/bindings/node/src/processors.rs",
"repo_id": "tokenizers",
"token_count": 1336
} | 214 |
<p align="center">
<br>
<img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/>
<br>
<p>
<p align="center">
<a href="https://badge.fury.io/py/tokenizers">
<img alt="Build" src="https://badge.fury.io/py/tokenizers.svg">
</a>
<a href="https://github.c... | tokenizers/bindings/python/README.md/0 | {
"file_path": "tokenizers/bindings/python/README.md",
"repo_id": "tokenizers",
"token_count": 1621
} | 215 |
from typing import Dict, Iterator, List, Optional, Tuple, Union
from .. import AddedToken, Tokenizer, decoders, pre_tokenizers, trainers
from ..models import BPE
from ..normalizers import BertNormalizer, Lowercase, Sequence, unicode_normalizer_from_str
from .base_tokenizer import BaseTokenizer
class CharBPETokenizer... | tokenizers/bindings/python/py_src/tokenizers/implementations/char_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/char_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 2509
} | 216 |
[project]
name = 'tokenizers'
requires-python = '>=3.7'
authors = [
{name = 'Nicolas Patry', email = 'patry.nicolas@protonmail.com'},
{name = 'Anthony Moi', email = 'anthony@huggingface.co'}
]
classifiers = [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Intended Audie... | tokenizers/bindings/python/pyproject.toml/0 | {
"file_path": "tokenizers/bindings/python/pyproject.toml",
"repo_id": "tokenizers",
"token_count": 711
} | 217 |
use std::sync::{Arc, RwLock};
use crate::models::PyModel;
use crate::tokenizer::PyAddedToken;
use crate::utils::PyChar;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::{Deserialize, Serialize};
use tk::models::TrainerWrapper;
use tk::Trainer;
use tokenizers as tk;
/// Base class for all tra... | tokenizers/bindings/python/src/trainers.rs/0 | {
"file_path": "tokenizers/bindings/python/src/trainers.rs",
"repo_id": "tokenizers",
"token_count": 17617
} | 218 |
import pickle
import numpy as np
import pytest
from tokenizers import AddedToken, Encoding, Tokenizer
from tokenizers.implementations import BertWordPieceTokenizer
from tokenizers.models import BPE, Model, Unigram
from tokenizers.pre_tokenizers import ByteLevel
from tokenizers.processors import RobertaProcessing
fro... | tokenizers/bindings/python/tests/bindings/test_tokenizer.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_tokenizer.py",
"repo_id": "tokenizers",
"token_count": 8966
} | 219 |
- sections:
- local: index
title: 🤗 Tokenizers
- local: quicktour
title: Quicktour
- local: installation
title: Installation
- local: pipeline
title: The tokenization pipeline
- local: components
title: Components
- local: training_from_memory
title: Training from memory
title: G... | tokenizers/docs/source-doc-builder/_toctree.yml/0 | {
"file_path": "tokenizers/docs/source-doc-builder/_toctree.yml",
"repo_id": "tokenizers",
"token_count": 338
} | 220 |
# The tokenization pipeline
When calling `Tokenizer.encode` or
`Tokenizer.encode_batch`, the input
text(s) go through the following pipeline:
- `normalization`
- `pre-tokenization`
- `model`
- `post-processing`
We'll see in details what happens during each of those steps in detail,
as well as when you want t... | tokenizers/docs/source-doc-builder/pipeline.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/pipeline.mdx",
"repo_id": "tokenizers",
"token_count": 5903
} | 221 |
Documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The Rust API Reference is available directly on the `Docs.rs <https://docs.rs/tokenizers>`__
website.
| tokenizers/docs/source/api/rust.inc/0 | {
"file_path": "tokenizers/docs/source/api/rust.inc",
"repo_id": "tokenizers",
"token_count": 43
} | 222 |
language: node_js
node_js: "10"
script:
- ./node_modules/.bin/webpack
| tokenizers/tokenizers/examples/unstable_wasm/www/.travis.yml/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/.travis.yml",
"repo_id": "tokenizers",
"token_count": 30
} | 223 |
use crate::decoders::DecoderWrapper;
use crate::tokenizer::{Decoder, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Sequence {
decoders: Vec<DecoderWrapper>,
}
impl Sequence {
pub fn new(decod... | tokenizers/tokenizers/src/decoders/sequence.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/sequence.rs",
"repo_id": "tokenizers",
"token_count": 600
} | 224 |
use super::OrderedVocabIter;
use crate::tokenizer::{Model, Result, Token};
use serde_json::Value;
use std::collections::HashMap;
use std::fs::File;
use std::io::{BufReader, Read, Write};
use std::path::{Path, PathBuf};
mod serialization;
mod trainer;
// Re-export
pub use trainer::*;
type Vocab = HashMap<String, u32>... | tokenizers/tokenizers/src/models/wordlevel/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordlevel/mod.rs",
"repo_id": "tokenizers",
"token_count": 3383
} | 225 |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Copy, Clone, Debug, PartialEq, Eq)]
#[non_exhaustive]
#[macro_rules_attribute(impl_serde_type!)]
pub struct CharDelimiterSplit {
pub deli... | tokenizers/tokenizers/src/pre_tokenizers/delimiter.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/delimiter.rs",
"repo_id": "tokenizers",
"token_count": 296
} | 226 |
use super::{
normalizer::Range, Model, NormalizedString, Normalizer, Offsets, PreTokenizedString, Token,
};
use aho_corasick::{AhoCorasick, AhoCorasickBuilder, MatchKind};
use regex::Regex;
use serde::{ser::SerializeSeq, Deserialize, Serialize, Serializer};
use std::collections::{HashMap, HashSet};
/// Represent a... | tokenizers/tokenizers/src/tokenizer/added_vocabulary.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/added_vocabulary.rs",
"repo_id": "tokenizers",
"token_count": 16897
} | 227 |
use crate::tokenizer::{Encoding, Result};
use serde::{Deserialize, Serialize};
use std::cmp;
use std::mem;
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize, Eq, Default)]
pub enum TruncationDirection {
Left,
#[default]
Right,
}
impl std::convert::AsRef<str> for TruncationDirection {
fn a... | tokenizers/tokenizers/src/utils/truncation.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/truncation.rs",
"repo_id": "tokenizers",
"token_count": 5473
} | 228 |
#!/bin/bash
source ~/.bashrc
echo "running docker-entrypoint.sh"
conda activate container
echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS
echo "printed TPU info"
export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}"
exec "$@"#!/bin/bash
| transformers/docker/transformers-pytorch-tpu/docker-entrypoint.sh/0 | {
"file_path": "transformers/docker/transformers-pytorch-tpu/docker-entrypoint.sh",
"repo_id": "transformers",
"token_count": 112
} | 229 |
<!--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... | transformers/docs/source/de/llm_tutorial.md/0 | {
"file_path": "transformers/docs/source/de/llm_tutorial.md",
"repo_id": "transformers",
"token_count": 4767
} | 230 |
<!--Copyright 2020 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... | transformers/docs/source/en/add_new_pipeline.md/0 | {
"file_path": "transformers/docs/source/en/add_new_pipeline.md",
"repo_id": "transformers",
"token_count": 3395
} | 231 |
<!--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... | transformers/docs/source/en/fsdp.md/0 | {
"file_path": "transformers/docs/source/en/fsdp.md",
"repo_id": "transformers",
"token_count": 2239
} | 232 |
<!--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... | transformers/docs/source/en/llm_tutorial.md/0 | {
"file_path": "transformers/docs/source/en/llm_tutorial.md",
"repo_id": "transformers",
"token_count": 4361
} | 233 |
<!--Copyright 2020 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... | transformers/docs/source/en/main_classes/pipelines.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/pipelines.md",
"repo_id": "transformers",
"token_count": 4571
} | 234 |
<!--Copyright 2021 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... | transformers/docs/source/en/model_doc/beit.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/beit.md",
"repo_id": "transformers",
"token_count": 2186
} | 235 |
<!--Copyright 2020 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... | transformers/docs/source/en/model_doc/cpm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/cpm.md",
"repo_id": "transformers",
"token_count": 735
} | 236 |
<!--Copyright 2022 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... | transformers/docs/source/en/model_doc/flan-t5.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/flan-t5.md",
"repo_id": "transformers",
"token_count": 781
} | 237 |
<!--Copyright 2022 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... | transformers/docs/source/en/model_doc/gpt_neox.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/gpt_neox.md",
"repo_id": "transformers",
"token_count": 1662
} | 238 |
<!--Copyright 2021 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... | transformers/docs/source/en/model_doc/layoutlmv2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/layoutlmv2.md",
"repo_id": "transformers",
"token_count": 5361
} | 239 |
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