text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
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# 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/controlnet/test_controlnet_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/controlnet/test_controlnet_img2img.py",
"repo_id": "diffusers",
"token_count": 8247
} | 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_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": 4790
} | 132 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/unclip/test_unclip_image_variation.py/0 | {
"file_path": "diffusers/tests/pipelines/unclip/test_unclip_image_variation.py",
"repo_id": "diffusers",
"token_count": 8162
} | 133 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UniPCMultistepSchedulerTest(SchedulerCommonTest):
scheduler_classes = (UniPCM... | diffusers/tests/schedulers/test_scheduler_unipc.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_unipc.py",
"repo_id": "diffusers",
"token_count": 6988
} | 134 |
# Copyright 2023 The HuggingFace Team, the AllenNLP library authors. 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/utils/stale.py/0 | {
"file_path": "diffusers/utils/stale.py",
"repo_id": "diffusers",
"token_count": 1222
} | 135 |
<jupyter_start><jupyter_text>Implémentation à partir de 0Il est parfois utile de considérer la version la plus simple possible d'une chose pour mieux en comprendre le fonctionnement. C'est ce que nous allons essayer de faire dans ce *notebook*, en commençant par un modèle de diffusion "jouet" pour voir comment les diff... | diffusion-models-class/units/fr/unit1/diffusion_models_from_scratch.ipynb/0 | {
"file_path": "diffusion-models-class/units/fr/unit1/diffusion_models_from_scratch.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 10500
} | 136 |
<jupyter_start><jupyter_text>Stable Diffusion : plongée en profondeurStable Diffusion est un puissant modèle de texte à image. Il existe plusieurs sites web et outils pour rendre son utilisation aussi simple que possible. Il est également intégré à la bibliothèque de Diffusers d'Huggingface, ce qui permet de générer de... | diffusion-models-class/units/fr/unit3/stable_diffusion_deep_dive.ipynb/0 | {
"file_path": "diffusion-models-class/units/fr/unit3/stable_diffusion_deep_dive.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 19297
} | 137 |
<jupyter_start><jupyter_text>Derrière le pipeline (TensorFlow) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="tblard/tf-allocine")
classifier(
["J'ai ... | notebooks/course/fr/chapter2/section2_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section2_tf.ipynb",
"repo_id": "notebooks",
"token_count": 473
} | 138 |
<jupyter_start><jupyter_text>Utilisation de modèles pré-entraînés (PyTorch) Installez la bibliothèque 🤗 Transformers pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert-base")
results... | notebooks/course/fr/chapter4/section2_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter4/section2_pt.ipynb",
"repo_id": "notebooks",
"token_count": 259
} | 139 |
<jupyter_start><jupyter_text>Normalisation et prétokenization. Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("camembert-base")
p... | notebooks/course/fr/chapter6/section4.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter6/section4.ipynb",
"repo_id": "notebooks",
"token_count": 362
} | 140 |
<jupyter_start><jupyter_text>Réponses aux questions (TensorFlow) Installez les bibliothèques Transformers et Datasets 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 configurer git, adaptez v... | notebooks/course/fr/chapter7/section7_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section7_tf.ipynb",
"repo_id": "notebooks",
"token_count": 6501
} | 141 |
<jupyter_start><jupyter_text>ver since Stable Diffusion took the world by storm, people have been looking for ways to have more control over the results of the generation process. ControlNet provides a minimal interface allowing users to customize the generation process up to a great extent. With [ControlNet](https://h... | notebooks/diffusers/controlnet.ipynb/0 | {
"file_path": "notebooks/diffusers/controlnet.ipynb",
"repo_id": "notebooks",
"token_count": 4606
} | 142 |
<jupyter_start><jupyter_text>🧨 Fast Stable Diffusion in free Colab with JAX / Flax on TPU!🤗 Hugging Face [Diffusers](https://github.com/huggingface/diffusers) supports Flax since version `0.5.1`! This allows for snappy inference on Google TPUs, such as those available in Colab, Kaggle or through Google Cloud Platform... | notebooks/diffusers/stable_diffusion_fast_jax.ipynb/0 | {
"file_path": "notebooks/diffusers/stable_diffusion_fast_jax.ipynb",
"repo_id": "notebooks",
"token_count": 1808
} | 143 |
<jupyter_start><jupyter_text>Before we can browse the rest of the notebook, we need to install the dependencies: this example uses `datasets` and `transformers`. To use TPUs on colab, we need to install `torch_xla` and the last line install `accelerate` from source since we the features we are using are very recent and... | notebooks/examples/accelerate_examples/simple_nlp_example.ipynb/0 | {
"file_path": "notebooks/examples/accelerate_examples/simple_nlp_example.ipynb",
"repo_id": "notebooks",
"token_count": 6454
} | 144 |
<jupyter_start><jupyter_text>Fine-tune Pix2Struct using Hugging Face `transformers` and `datasets` 🤗This tutorial is largely based from the [GiT tutorial](https://colab.research.google.com/drive/1HLxgrG7xZJ9FvXckNG61J72FkyrbqKAA?usp=sharing) on how to fine-tune GiT on a custom image captioning dataset. Here we will us... | notebooks/examples/image_captioning_pix2struct.ipynb/0 | {
"file_path": "notebooks/examples/image_captioning_pix2struct.ipynb",
"repo_id": "notebooks",
"token_count": 3848
} | 145 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.<jupyter_code>#! pip install transformers datasets huggingface_hub<jupyter_output><empty_output><jupyter_text>If you're opening this notebook ... | notebooks/examples/language_modeling_from_scratch-tf.ipynb/0 | {
"file_path": "notebooks/examples/language_modeling_from_scratch-tf.ipynb",
"repo_id": "notebooks",
"token_count": 7396
} | 146 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.<jupyter_code>#! pip install datasets transformers<jupyter_output><empty_output><jupyter_text>If you're opening this notebook locally, make su... | notebooks/examples/question_answering.ipynb/0 | {
"file_path": "notebooks/examples/question_answering.ipynb",
"repo_id": "notebooks",
"token_count": 15331
} | 147 |
<jupyter_start><jupyter_text>Time Series DatasetsThis notebook shows how to create a time series dataset from some csv file in order to then share it on the [🤗 hub](https://huggingface.co/docs/datasets/index). We will use the GluonTS library to read the csv into the appropriate format. We start by installing the libra... | notebooks/examples/time_series_datasets.ipynb/0 | {
"file_path": "notebooks/examples/time_series_datasets.ipynb",
"repo_id": "notebooks",
"token_count": 975
} | 148 |
import argparse
import logging
import os
import sys
import tensorflow as tf
from datasets import load_dataset
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, DataCollatorWithPadding, create_optimizer
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Hyperparamete... | notebooks/sagemaker/02_getting_started_tensorflow/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/02_getting_started_tensorflow/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1677
} | 149 |
base_job_name: accelerate-sagemaker-1
compute_environment: AMAZON_SAGEMAKER
distributed_type: DATA_PARALLEL
ec2_instance_type: ml.p3.16xlarge
iam_role_name: xxxxx
image_uri: null
mixed_precision: fp16
num_machines: 1
profile: xxxxx
py_version: py38
pytorch_version: 1.10.2
region: us-east-1
transformers_version: 4.17.0
... | notebooks/sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/accelerate_config.yaml/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/accelerate_config.yaml",
"repo_id": "notebooks",
"token_count": 138
} | 150 |
<jupyter_start><jupyter_text>Efficient Large Language Model training with LoRA and Hugging FaceIn this sagemaker example, we are going to learn how to apply [Low-Rank Adaptation of Large Language Models (LoRA)](https://arxiv.org/abs/2106.09685) to fine-tune BLOOMZ (7 billion parameter version instruction tuned version ... | notebooks/sagemaker/24_train_bloom_peft_lora/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/24_train_bloom_peft_lora/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 4824
} | 151 |
<jupyter_start><jupyter_text>Deploy Zephyr 7B on AWS Inferentia2 using Amazon SageMakerThis tutorial will show how easy it is to deploy Zephyr 7B on AWS Infernetia2 using Amazon SageMaker. Zephyr is a 7B parameter LLM fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) th... | notebooks/sagemaker/29_deploy_llms_on_inferentia2/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/29_deploy_llms_on_inferentia2/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3797
} | 152 |
<!--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/mixed_models.md/0 | {
"file_path": "peft/docs/source/developer_guides/mixed_models.md",
"repo_id": "peft",
"token_count": 697
} | 153 |
<!--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/package_reference/p_tuning.md/0 | {
"file_path": "peft/docs/source/package_reference/p_tuning.md",
"repo_id": "peft",
"token_count": 540
} | 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/tutorial/peft_model_config.md/0 | {
"file_path": "peft/docs/source/tutorial/peft_model_config.md",
"repo_id": "peft",
"token_count": 2415
} | 155 |
<jupyter_start><jupyter_code>from transformers import AutoModelForSeq2SeqLM
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["CUDA_VISIBLE_DEVICES"... | peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb",
"repo_id": "peft",
"token_count": 2479
} | 156 |
accelerate launch --config_file config.yaml peft_adalora_whisper_large_training.py \
--model_name_or_path "openai/whisper-large-v2" \
--language "Marathi" \
--language_abbr "mr" \
--task "transcribe" \
--dataset_name "mozilla-foundation/common_voice_11_0" \
--push_to_hub \
--preprocessing_nu... | peft/examples/int8_training/run_adalora_whisper_int8.sh/0 | {
"file_path": "peft/examples/int8_training/run_adalora_whisper_int8.sh",
"repo_id": "peft",
"token_count": 509
} | 157 |
import json, os
import argparse
from pathlib import Path
from datetime import date
from tabulate import tabulate
MAX_LEN_MESSAGE = 2900 # slack endpoint has a limit of 3001 characters
parser = argparse.ArgumentParser()
parser.add_argument(
"--slack_channel_name",
default="peft-ci-daily"
)
def main(slack_c... | peft/scripts/log_reports.py/0 | {
"file_path": "peft/scripts/log_reports.py",
"repo_id": "peft",
"token_count": 2483
} | 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/src/peft/tuners/adalora/gptq.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/gptq.py",
"repo_id": "peft",
"token_count": 1180
} | 159 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/multitask_prompt_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/multitask_prompt_tuning/config.py",
"repo_id": "peft",
"token_count": 890
} | 160 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/prefix_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/prefix_tuning/model.py",
"repo_id": "peft",
"token_count": 1235
} | 161 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/tests/test_common_gpu.py/0 | {
"file_path": "peft/tests/test_common_gpu.py",
"repo_id": "peft",
"token_count": 14065
} | 162 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/tests/testing_common.py/0 | {
"file_path": "peft/tests/testing_common.py",
"repo_id": "peft",
"token_count": 22859
} | 163 |
# Archived Changes
### Nov 22, 2021
* A number of updated weights anew new model defs
* `eca_halonext26ts` - 79.5 @ 256
* `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288
* `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, ... | pytorch-image-models/docs/archived_changes.md/0 | {
"file_path": "pytorch-image-models/docs/archived_changes.md",
"repo_id": "pytorch-image-models",
"token_count": 9335
} | 164 |
# Deep Layer Aggregation
Extending “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through ... | pytorch-image-models/docs/models/.templates/models/dla.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/dla.md",
"repo_id": "pytorch-image-models",
"token_count": 5955
} | 165 |
# Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
{% include ... | pytorch-image-models/docs/models/.templates/models/inception-resnet-v2.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/inception-resnet-v2.md",
"repo_id": "pytorch-image-models",
"token_count": 864
} | 166 |
# Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-li... | pytorch-image-models/docs/models/.templates/models/res2next.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/res2next.md",
"repo_id": "pytorch-image-models",
"token_count": 905
} | 167 |
# (Tensorflow) EfficientNet CondConv
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method unifo... | pytorch-image-models/docs/models/.templates/models/tf-efficientnet-condconv.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/tf-efficientnet-condconv.md",
"repo_id": "pytorch-image-models",
"token_count": 2457
} | 168 |
""" ONNX-runtime validation script
This script was created to verify accuracy and performance of exported ONNX
models running with the onnxruntime. It utilizes the PyTorch dataloader/processing
pipeline for a fair comparison against the originals.
Copyright 2020 Ross Wightman
"""
import argparse
import numpy as np
im... | pytorch-image-models/onnx_validate.py/0 | {
"file_path": "pytorch-image-models/onnx_validate.py",
"repo_id": "pytorch-image-models",
"token_count": 1960
} | 169 |
"""Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a ... | pytorch-image-models/tests/test_models.py/0 | {
"file_path": "pytorch-image-models/tests/test_models.py",
"repo_id": "pytorch-image-models",
"token_count": 9191
} | 170 |
""" Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from... | pytorch-image-models/timm/data/loader.py/0 | {
"file_path": "pytorch-image-models/timm/data/loader.py",
"repo_id": "pytorch-image-models",
"token_count": 6793
} | 171 |
""" Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabels... | pytorch-image-models/timm/data/real_labels.py/0 | {
"file_path": "pytorch-image-models/timm/data/real_labels.py",
"repo_id": "pytorch-image-models",
"token_count": 854
} | 172 |
""" Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to... | pytorch-image-models/timm/layers/config.py/0 | {
"file_path": "pytorch-image-models/timm/layers/config.py",
"repo_id": "pytorch-image-models",
"token_count": 1787
} | 173 |
from typing import Tuple
import torch
def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
"""generate N-D grid in dimension order.
The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
That is, the statement
[X1,X2,X3] = ndgrid(x1,x2,x3)
produc... | pytorch-image-models/timm/layers/grid.py/0 | {
"file_path": "pytorch-image-models/timm/layers/grid.py",
"repo_id": "pytorch-image-models",
"token_count": 565
} | 174 |
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
num_prefix_tokens: int = 1,
... | pytorch-image-models/timm/layers/patch_dropout.py/0 | {
"file_path": "pytorch-image-models/timm/layers/patch_dropout.py",
"repo_id": "pytorch-image-models",
"token_count": 842
} | 175 |
import torch
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_no... | pytorch-image-models/timm/layers/weight_init.py/0 | {
"file_path": "pytorch-image-models/timm/layers/weight_init.py",
"repo_id": "pytorch-image-models",
"token_count": 1838
} | 176 |
import copy
from collections import deque, defaultdict
from dataclasses import dataclass, field, replace, asdict
from typing import Any, Deque, Dict, Tuple, Optional, Union
__all__ = ['PretrainedCfg', 'filter_pretrained_cfg', 'DefaultCfg']
@dataclass
class PretrainedCfg:
"""
"""
# weight source location... | pytorch-image-models/timm/models/_pretrained.py/0 | {
"file_path": "pytorch-image-models/timm/models/_pretrained.py",
"repo_id": "pytorch-image-models",
"token_count": 1341
} | 177 |
""" CrossViT Model
@inproceedings{
chen2021crossvit,
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
booktitle={International Conference on Computer Vision (ICCV)},
year={2021}
}
Paper l... | pytorch-image-models/timm/models/crossvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/crossvit.py",
"repo_id": "pytorch-image-models",
"token_count": 12463
} | 178 |
"""
Poolformer from MetaFormer is Actually What You Need for Vision https://arxiv.org/abs/2111.11418
IdentityFormer, RandFormer, PoolFormerV2, ConvFormer, and CAFormer
from MetaFormer Baselines for Vision https://arxiv.org/abs/2210.13452
All implemented models support feature extraction and variable input resolution.... | pytorch-image-models/timm/models/metaformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/metaformer.py",
"repo_id": "pytorch-image-models",
"token_count": 17521
} | 179 |
""" ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
from torch import nn
from timm.data... | pytorch-image-models/timm/models/resnest.py/0 | {
"file_path": "pytorch-image-models/timm/models/resnest.py",
"repo_id": "pytorch-image-models",
"token_count": 4439
} | 180 |
""" Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... | pytorch-image-models/timm/models/visformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/visformer.py",
"repo_id": "pytorch-image-models",
"token_count": 10132
} | 181 |
""" Adan Optimizer
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Implementation adapted from https://github.com/sail-sg/Adan
"""
import math
import torch
from torch.optim import Optimizer
class Adan(Opt... | pytorch-image-models/timm/optim/adan.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adan.py",
"repo_id": "pytorch-image-models",
"token_count": 2501
} | 182 |
""" MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List
class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
... | pytorch-image-models/timm/scheduler/multistep_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/multistep_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1029
} | 183 |
""" Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | pytorch-image-models/timm/utils/metrics.py/0 | {
"file_path": "pytorch-image-models/timm/utils/metrics.py",
"repo_id": "pytorch-image-models",
"token_count": 374
} | 184 |
use std::time::{Duration, Instant};
use text_generation_client::{
Batch, CachedBatch, ClientError, NextTokenChooserParameters, Request, ShardedClient,
StoppingCriteriaParameters,
};
use tokenizers::{Tokenizer, TruncationDirection};
use tokio::sync::{broadcast, mpsc};
const LOREM_IPSUM: &str = "Lorem ipsum dolo... | text-generation-inference/benchmark/src/generation.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/generation.rs",
"repo_id": "text-generation-inference",
"token_count": 3201
} | 185 |
import json
import requests
from aiohttp import ClientSession, ClientTimeout
from pydantic import ValidationError
from typing import Dict, Optional, List, AsyncIterator, Iterator
from text_generation.types import (
StreamResponse,
Response,
Request,
Parameters,
)
from text_generation.errors import par... | text-generation-inference/clients/python/text_generation/client.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/client.py",
"repo_id": "text-generation-inference",
"token_count": 9331
} | 186 |
# Safetensors
Safetensors is a model serialization format for deep learning models. It is [faster](https://huggingface.co/docs/safetensors/speed) and safer compared to other serialization formats like pickle (which is used under the hood in many deep learning libraries).
TGI depends on safetensors format mainly to e... | text-generation-inference/docs/source/conceptual/safetensors.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/safetensors.md",
"repo_id": "text-generation-inference",
"token_count": 185
} | 187 |
{
"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_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1041
} | 188 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 3226,
"logprob": -9.0234375,
"text": " ge"
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq_load.json",
"repo_id": "text-generation-inference",
"token_count": 7433
} | 189 |
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": 866
} | 190 |
import pytest
@pytest.fixture(scope="module")
def neox_handle(launcher):
with launcher(
"stabilityai/stablelm-tuned-alpha-3b", num_shard=1, use_flash_attention=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def neox(neox_handle):
await neox_handle.health(300)
r... | text-generation-inference/integration-tests/models/test_neox.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_neox.py",
"repo_id": "text-generation-inference",
"token_count": 499
} | 191 |
[package]
name = "text-generation-router"
description = "Text Generation Webserver"
build = "build.rs"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[lib]
path = "src/lib.rs"
[[bin]]
name = "text-generation-router"
path = "src/main.rs"
[dependencies]
async-strea... | text-generation-inference/router/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 739
} | 192 |
/// HTTP Server logic
use crate::health::Health;
use crate::infer::{InferError, InferResponse, InferStreamResponse};
use crate::validation::ValidationError;
use crate::{
BestOfSequence, ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionDelta,
ChatRequest, CompatGenerateRequest, Details, E... | text-generation-inference/router/src/server.rs/0 | {
"file_path": "text-generation-inference/router/src/server.rs",
"repo_id": "text-generation-inference",
"token_count": 19443
} | 193 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#define _cuda_buffers_cu
#include "cuda_buffers.cuh"
CudaBuffers* g_buffers[CUDA_MAX_DEVICES] = {NULL};
// __constant__ half2 q4_table[16][256];
// half2 q4_table_host[16][256];
// bool q4_table_init = false;
CudaBuffers::CudaBuffers
(
int _... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_buffers.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_buffers.cu",
"repo_id": "text-generation-inference",
"token_count": 680
} | 194 |
#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 "config.h"
#include "cuda/q_matrix.cuh"
#include "cuda/q_gemm.cuh"
#include "cpp/util.h"
// Some decluttering macros
#define... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/ext.cpp/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/ext.cpp",
"repo_id": "text-generation-inference",
"token_count": 2184
} | 195 |
import torch
from text_generation_server.utils.tokens import (
StopSequenceCriteria,
StoppingCriteria,
FinishReason,
batch_top_tokens,
)
def test_stop_sequence_criteria():
criteria = StopSequenceCriteria("/test;")
assert not criteria("/")
assert not criteria("/test")
assert criteria("... | text-generation-inference/server/tests/utils/test_tokens.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_tokens.py",
"repo_id": "text-generation-inference",
"token_count": 1428
} | 196 |
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers im... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 6560
} | 197 |
import math
import torch
import torch.distributed
import numpy as np
from dataclasses import dataclass
from opentelemetry import trace
from transformers import PreTrainedTokenizerBase
from transformers.models.llama import LlamaTokenizerFast
from typing import Optional, Tuple, Type, List
from text_generation_server.p... | text-generation-inference/server/text_generation_server/models/flash_mistral.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_mistral.py",
"repo_id": "text-generation-inference",
"token_count": 7970
} | 198 |
import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_s... | text-generation-inference/server/text_generation_server/models/seq2seq_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/seq2seq_lm.py",
"repo_id": "text-generation-inference",
"token_count": 16012
} | 199 |
import time
import os
from datetime import timedelta
from loguru import logger
from pathlib import Path
from typing import Optional, List
from huggingface_hub import file_download, hf_api, HfApi, hf_hub_download
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub.utils import (
LocalE... | text-generation-inference/server/text_generation_server/utils/hub.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/hub.py",
"repo_id": "text-generation-inference",
"token_count": 3435
} | 200 |
{
"name": "tokenizers-darwin-arm64",
"version": "0.13.4-rc1",
"os": [
"darwin"
],
"cpu": [
"arm64"
],
"main": "tokenizers.darwin-arm64.node",
"files": [
"tokenizers.darwin-arm64.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",
... | tokenizers/bindings/node/npm/darwin-arm64/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/darwin-arm64/package.json",
"repo_id": "tokenizers",
"token_count": 268
} | 201 |
{
"name": "tokenizers-win32-arm64-msvc",
"version": "0.13.4-rc1",
"os": [
"win32"
],
"cpu": [
"arm64"
],
"main": "tokenizers.win32-arm64-msvc.node",
"files": [
"tokenizers.win32-arm64-msvc.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
... | tokenizers/bindings/node/npm/win32-arm64-msvc/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-arm64-msvc/package.json",
"repo_id": "tokenizers",
"token_count": 277
} | 202 |
extern crate tokenizers as tk;
use crate::models::Model;
use napi::bindgen_prelude::*;
use std::sync::{Arc, RwLock};
use tokenizers::models::bpe::{BpeBuilder, BPE};
use tokenizers::models::wordlevel::{WordLevel, WordLevelBuilder};
use tokenizers::models::wordpiece::{WordPiece, WordPieceBuilder};
pub struct BPEFromFil... | tokenizers/bindings/node/src/tasks/models.rs/0 | {
"file_path": "tokenizers/bindings/node/src/tasks/models.rs",
"repo_id": "tokenizers",
"token_count": 800
} | 203 |
from typing import List
import jieba
from tokenizers import NormalizedString, PreTokenizedString, Regex, Tokenizer
from tokenizers.decoders import Decoder
from tokenizers.models import BPE
from tokenizers.normalizers import Normalizer
from tokenizers.pre_tokenizers import PreTokenizer
class JiebaPreTokenizer:
de... | tokenizers/bindings/python/examples/custom_components.py/0 | {
"file_path": "tokenizers/bindings/python/examples/custom_components.py",
"repo_id": "tokenizers",
"token_count": 1293
} | 204 |
import json
import os
from typing import Iterator, List, Optional, Union, Tuple
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.models import Unigram
from .base_tokenizer import BaseTokenizer
class SentencePieceUnigramTokenizer(BaseTokenizer):
... | tokenizers/bindings/python/py_src/tokenizers/implementations/sentencepiece_unigram.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/sentencepiece_unigram.py",
"repo_id": "tokenizers",
"token_count": 3351
} | 205 |
import transformers
from tokenizers.implementations import SentencePieceUnigramTokenizer, BaseTokenizer
from tokenizers.processors import TemplateProcessing
from tokenizers.models import Unigram, BPE
from tokenizers import decoders
from tokenizers import Tokenizer, Regex
from tokenizers.normalizers import (
StripAc... | tokenizers/bindings/python/scripts/convert.py/0 | {
"file_path": "tokenizers/bindings/python/scripts/convert.py",
"repo_id": "tokenizers",
"token_count": 6438
} | 206 |
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use std::marker::PhantomData;
use std::sync::{Arc, Mutex};
mod iterators;
mod normalization;
mod pretokenization;
mod regex;
pub use iterators::*;
pub use normalization::*;
pub use pretokenization::*;
pub use regex::*;
// PyChar
// This type is a tempor... | tokenizers/bindings/python/src/utils/mod.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/mod.rs",
"repo_id": "tokenizers",
"token_count": 1057
} | 207 |
# Training from memory
In the [Quicktour](quicktour), we saw how to build and train a
tokenizer using text files, but we can actually use any Python Iterator.
In this section we'll see a few different ways of training our
tokenizer.
For all the examples listed below, we'll use the same [`~tokenizers.Tokenizer`] and
[... | tokenizers/docs/source-doc-builder/training_from_memory.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/training_from_memory.mdx",
"repo_id": "tokenizers",
"token_count": 1199
} | 208 |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | tokenizers/docs/source/conf.py/0 | {
"file_path": "tokenizers/docs/source/conf.py",
"repo_id": "tokenizers",
"token_count": 781
} | 209 |
#[macro_use]
extern crate criterion;
mod common;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
use criterion::Criterion;
use tokenizers::models::wordpiece::{WordPiece, WordPieceTrainerBuilder};
use tokenizers::normalizers::{BertNormalizer, NormalizerWrapper};
use tokenizers::pre_tokenize... | tokenizers/tokenizers/benches/bert_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/bert_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 1642
} | 210 |
use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize, Default)]
/// Strip is a simple trick which converts tokens looking like `<0x61>`
/// to pure bytes, and attempts to make them into a string. If the tokens
/// cannot be decoded you will get � ... | tokenizers/tokenizers/src/decoders/strip.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/strip.rs",
"repo_id": "tokenizers",
"token_count": 1217
} | 211 |
use super::{super::OrderedVocabIter, WordLevel, WordLevelBuilder};
use serde::{
de::{MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashSet;
impl Serialize for WordLevel {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Er... | tokenizers/tokenizers/src/models/wordlevel/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordlevel/serialization.rs",
"repo_id": "tokenizers",
"token_count": 2084
} | 212 |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
/// Pre tokenizes the numbers into single tokens. If individual_digits is set
/// to true, then all digits are ... | tokenizers/tokenizers/src/pre_tokenizers/digits.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/digits.rs",
"repo_id": "tokenizers",
"token_count": 1667
} | 213 |
use crate::parallelism::*;
use crate::tokenizer::{Offsets, Token};
use crate::utils::padding::PaddingDirection;
use crate::utils::truncation::TruncationDirection;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::ops::Range;
/// Represents the output of a `Tokenizer`.
#[derive(Default, Parti... | tokenizers/tokenizers/src/tokenizer/encoding.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/encoding.rs",
"repo_id": "tokenizers",
"token_count": 17197
} | 214 |
mod common;
use common::*;
use tokenizers::tokenizer::AddedToken;
#[test]
fn add_tokens() {
let mut tokenizer = get_empty();
assert_eq!(
tokenizer.add_special_tokens(&[
AddedToken::from("<cls>", true),
AddedToken::from("<sep>", true)
]),
2
);
assert_eq!... | tokenizers/tokenizers/tests/added_tokens.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/added_tokens.rs",
"repo_id": "tokenizers",
"token_count": 1770
} | 215 |
FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands)
SHELL ["sh", "-lc"]
# The following `ARG` are mainly used to specify the versions explicit... | transformers/docker/transformers-all-latest-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-all-latest-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 1267
} | 216 |
<!--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/de/quicktour.md/0 | {
"file_path": "transformers/docs/source/de/quicktour.md",
"repo_id": "transformers",
"token_count": 7324
} | 217 |
<!--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/big_models.md/0 | {
"file_path": "transformers/docs/source/en/big_models.md",
"repo_id": "transformers",
"token_count": 1718
} | 218 |
<!---
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
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