text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
"""
This script modified from
https://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py
Convert original Zero1to3 checkpoint to diffusers checkpoint.
# run the convert script
$ python convert_zero123_to_diffusers.py \
--checkpoi... | diffusers/scripts/convert_zero123_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_zero123_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 15252
} | 116 |
from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
... | diffusers/src/diffusers/loaders/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/__init__.py",
"repo_id": "diffusers",
"token_count": 1557
} | 117 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/attention.py/0 | {
"file_path": "diffusers/src/diffusers/models/attention.py",
"repo_id": "diffusers",
"token_count": 12530
} | 118 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/lora.py/0 | {
"file_path": "diffusers/src/diffusers/models/lora.py",
"repo_id": "diffusers",
"token_count": 7437
} | 119 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/transformers/t5_film_transformer.py/0 | {
"file_path": "diffusers/src/diffusers/models/transformers/t5_film_transformer.py",
"repo_id": "diffusers",
"token_count": 7148
} | 120 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/unets/unet_3d_blocks.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_3d_blocks.py",
"repo_id": "diffusers",
"token_count": 48442
} | 121 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py",
"repo_id": "diffusers",
"token_count": 8314
} | 122 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
_LazyModule,
)
_import_structure = {
"pipeline_consistency_models": ["ConsistencyModelPipeline"],
}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_consistency_models import ConsistencyModelPipeline
else:
i... | diffusers/src/diffusers/pipelines/consistency_models/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/consistency_models/__init__.py",
"repo_id": "diffusers",
"token_count": 209
} | 123 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
_LazyModule,
)
_import_structure = {"pipeline_ddpm": ["DDPMPipeline"]}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_ddpm import DDPMPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
... | diffusers/src/diffusers/pipelines/ddpm/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/ddpm/__init__.py",
"repo_id": "diffusers",
"token_count": 193
} | 124 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class TransformationModelOutput(ModelOutput):
"""
Base class for text... | diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py",
"repo_id": "diffusers",
"token_count": 2332
} | 125 |
# Copyright 2022 The Music Spectrogram Diffusion Authors.
# 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... | diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py",
"repo_id": "diffusers",
"token_count": 1330
} | 126 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py",
"repo_id": "diffusers",
"token_count": 11561
} | 127 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is... | diffusers/src/diffusers/pipelines/kandinsky2_2/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky2_2/__init__.py",
"repo_id": "diffusers",
"token_count": 1190
} | 128 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput
@dataclass
class SemanticStableDiffusionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image... | diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 306
} | 129 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py",
"repo_id": "diffusers",
"token_count": 12531
} | 130 |
import inspect
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict
from ...image_processor... | diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py",
"repo_id": "diffusers",
"token_count": 17345
} | 131 |
# Copyright 2023 TencentARC and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py",
"repo_id": "diffusers",
"token_count": 29367
} | 132 |
# Copyright (c) 2022 Dominic Rampas MIT License
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py",
"repo_id": "diffusers",
"token_count": 3047
} | 133 |
# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | diffusers/src/diffusers/schedulers/scheduling_ddim.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_ddim.py",
"repo_id": "diffusers",
"token_count": 10493
} | 134 |
# Copyright 2023 Katherine Crowson and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py",
"repo_id": "diffusers",
"token_count": 4574
} | 135 |
# Copyright 2023 TSAIL Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py",
"repo_id": "diffusers",
"token_count": 17327
} | 136 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class LMSDiscreteScheduler(metaclass=DummyObject):
_backends = ["torch", "scipy"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "scipy"])
@class... | diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py",
"repo_id": "diffusers",
"token_count": 220
} | 137 |
import functools
import importlib
import inspect
import io
import logging
import multiprocessing
import os
import random
import re
import struct
import sys
import tempfile
import time
import unittest
import urllib.parse
from contextlib import contextmanager
from distutils.util import strtobool
from io import BytesIO, S... | diffusers/src/diffusers/utils/testing_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/testing_utils.py",
"repo_id": "diffusers",
"token_count": 14068
} | 138 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class ActivationsTests(unittest.TestCase):
def test_swish(self):
act = get_activation("swish")
self.assertIsInstance(act, nn.SiLU)
self.assertEqual(act(torch.tensor(-100, dtype=tor... | diffusers/tests/models/test_activations.py/0 | {
"file_path": "diffusers/tests/models/test_activations.py",
"repo_id": "diffusers",
"token_count": 845
} | 139 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/models/unets/test_unet_blocks_common.py/0 | {
"file_path": "diffusers/tests/models/unets/test_unet_blocks_common.py",
"repo_id": "diffusers",
"token_count": 1806
} | 140 |
# coding=utf-8
# Copyright 2023 Harutatsu Akiyama, Jinbin Bai, and 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
#
# Unle... | diffusers/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py/0 | {
"file_path": "diffusers/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py",
"repo_id": "diffusers",
"token_count": 5256
} | 141 |
# 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/deepfloyd_if/test_if_superresolution.py/0 | {
"file_path": "diffusers/tests/pipelines/deepfloyd_if/test_if_superresolution.py",
"repo_id": "diffusers",
"token_count": 1190
} | 142 |
# 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/kandinsky2_2/test_kandinsky_controlnet_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet_img2img.py",
"repo_id": "diffusers",
"token_count": 4657
} | 143 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py",
"repo_id": "diffusers",
"token_count": 4268
} | 144 |
# 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_upscale.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py",
"repo_id": "diffusers",
"token_count": 9037
} | 145 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class IsSafetensorsCompatibleTests(unittest.TestCase):
def test_all_is_compatible(self):
filenames = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/... | diffusers/tests/pipelines/test_pipeline_utils.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipeline_utils.py",
"repo_id": "diffusers",
"token_count": 2746
} | 146 |
import gc
import random
import traceback
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionModelWithProjection,
GPT2Tokenizer,
)
from diffusers import (
AutoencoderKL,
DPMSolverMultis... | diffusers/tests/pipelines/unidiffuser/test_unidiffuser.py/0 | {
"file_path": "diffusers/tests/pipelines/unidiffuser/test_unidiffuser.py",
"repo_id": "diffusers",
"token_count": 14379
} | 147 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class DPMSolverSinglestepSchedulerTest(SchedulerCommonTest):
scheduler_classes = (D... | diffusers/tests/schedulers/test_scheduler_dpm_single.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_dpm_single.py",
"repo_id": "diffusers",
"token_count": 6088
} | 148 |
# 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/schedulers/test_schedulers.py/0 | {
"file_path": "diffusers/tests/schedulers/test_schedulers.py",
"repo_id": "diffusers",
"token_count": 15806
} | 149 |
# DDIM Inversion
<CourseFloatingBanner unit={4}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "DDIM Inversion", value: "https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/units/en/unit4/ddim_inversion.ipynb"},
{label: "DDIM Inversion", value: "https://stud... | diffusion-models-class/units/en/unit4/2.mdx/0 | {
"file_path": "diffusion-models-class/units/en/unit4/2.mdx",
"repo_id": "diffusion-models-class",
"token_count": 5413
} | 150 |
<jupyter_start><jupyter_text>Modèles (TensorFlow) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
from transformers import CamembertConfig, TFCamembertModel
# Construire la configuration
config = CamembertConfig()
# Construire le modèle à ... | notebooks/course/fr/chapter2/section3_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section3_tf.ipynb",
"repo_id": "notebooks",
"token_count": 351
} | 151 |
<jupyter_start><jupyter_text>Partage de modèles pré-entraînés (PyTorch) Installez la bibliothèque 🤗 Transformers pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!apt install git-lfs<jupyter_output><empty_output><jupyter_text>Vous aurez besoin de configurer git, adaptez votre... | notebooks/course/fr/chapter4/section3_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter4/section3_pt.ipynb",
"repo_id": "notebooks",
"token_count": 1050
} | 152 |
<jupyter_start><jupyter_text>WordPiece tokenizationAucun modèle en français utilise WordPiece. Nous utilisons ici CamemBERT utilise SentencePiece. Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
corpus = [
"C'... | notebooks/course/fr/chapter6/section6.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter6/section6.ipynb",
"repo_id": "notebooks",
"token_count": 1867
} | 153 |
<jupyter_start><jupyter_text>*Introducing Hugging Face's new library for diffusion models*Diffusion models proved themselves very effective in artificial synthesis, even beating GANs for images. Because of that, they gained traction in the machine learning community and play an important role for systems like [DALL-E 2... | notebooks/diffusers/diffusers_intro.ipynb/0 | {
"file_path": "notebooks/diffusers/diffusers_intro.ipynb",
"repo_id": "notebooks",
"token_count": 6228
} | 154 |
<jupyter_start><jupyter_text>Stable Diffusion Textual Inversion - Concept Library navigation and usageNavigate through the [public library of concepts](https://huggingface.co/sd-concepts-library) and use Stable Diffusion with custom concepts. 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffuse... | notebooks/diffusers/stable_diffusion_textual_inversion_library_navigator.ipynb/0 | {
"file_path": "notebooks/diffusers/stable_diffusion_textual_inversion_library_navigator.ipynb",
"repo_id": "notebooks",
"token_count": 5285
} | 155 |
<jupyter_start><jupyter_text>**Fine-tuning for Audio Classification with 🤗 Transformers** This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition. This notebook is built to run on the **Keyword Spotting** subset of the [SUPERB dataset](https://huggingface.co/dataset... | notebooks/examples/audio_classification.ipynb/0 | {
"file_path": "notebooks/examples/audio_classification.ipynb",
"repo_id": "notebooks",
"token_count": 4362
} | 156 |
<jupyter_start><jupyter_text>**Fine-tuning for Image Classification with 🤗 Transformers**This notebook shows how to fine-tune any pretrained Vision model for Image Classification on a custom dataset. The idea is to add a randomly initialized classification head on top of a pre-trained encoder, and fine-tune the model ... | notebooks/examples/image_classification.ipynb/0 | {
"file_path": "notebooks/examples/image_classification.ipynb",
"repo_id": "notebooks",
"token_count": 7435
} | 157 |
<jupyter_start><jupyter_text>Pre-Training a 🤗 Transformers model on TPU with **Flax/JAX**In this notebook, we will see how to pretrain one of the [🤗 Transformers](https://github.com/huggingface/transformers) models on TPU using [**Flax**](https://flax.readthedocs.io/en/latest/index.html). The popular masked language ... | notebooks/examples/masked_language_modeling_flax.ipynb/0 | {
"file_path": "notebooks/examples/masked_language_modeling_flax.ipynb",
"repo_id": "notebooks",
"token_count": 10194
} | 158 |
<jupyter_start><jupyter_text>Segment Anything Model using `transformers` 🤗 library| | | ||---------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------... | notebooks/examples/segment_anything.ipynb/0 | {
"file_path": "notebooks/examples/segment_anything.ipynb",
"repo_id": "notebooks",
"token_count": 5421
} | 159 |
<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 seqeval<jupyter_output><empty_output><jupyter_text>If you're opening this notebook locally,... | notebooks/examples/token_classification.ipynb/0 | {
"file_path": "notebooks/examples/token_classification.ipynb",
"repo_id": "notebooks",
"token_count": 7204
} | 160 |
import functools
import math
import os # noqa: F401
from random import choice, randint
from time import time
import numpy as np
import torch
import torch.utils.checkpoint as checkpoint
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from tqdm import tqdm
import faiss # noqa: F401
... | notebooks/longform-qa/lfqa_utils.py/0 | {
"file_path": "notebooks/longform-qa/lfqa_utils.py",
"repo_id": "notebooks",
"token_count": 12846
} | 161 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Distributed Training Demo Model Parallelism using `SageMakerTrainer` 1. [Introduction](Introduction) 2. [Development Environment and Permissions](Development-Environment-and-Permissions) 1. [Installation](Installation) 2. [Development environment](Devel... | notebooks/sagemaker/04_distributed_training_model_parallelism/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/04_distributed_training_model_parallelism/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3719
} | 162 |
<jupyter_start><jupyter_text>Hugging Face x Amazon SageMaker - Asynchronous Inference with Hugging Face's Transformers Welcome to this getting started guide. We will use the Hugging Face Inference DLCs and Amazon SageMaker Python SDK to run an [Asynchronous Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/asy... | notebooks/sagemaker/16_async_inference_hf_hub/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/16_async_inference_hf_hub/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 4049
} | 163 |
# Builds GPU docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.8
# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/acc... | peft/docker/peft-gpu/Dockerfile/0 | {
"file_path": "peft/docker/peft-gpu/Dockerfile",
"repo_id": "peft",
"token_count": 798
} | 164 |
<!--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": 1893
} | 165 |
<jupyter_start><jupyter_code>from transformers import AutoModelForCausalLM
from peft import PeftModel, PeftConfig
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_wi... | peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb",
"repo_id": "peft",
"token_count": 2945
} | 166 |
<jupyter_start><jupyter_code>import os
import torch
from transformers import (
AutoTokenizer,
default_data_collator,
AutoModelForSeq2SeqLM,
Seq2SeqTrainingArguments,
Seq2SeqTrainer,
GenerationConfig,
)
from peft import get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType
from datasets... | peft/examples/conditional_generation/peft_prompt_tuning_seq2seq_with_generate.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_prompt_tuning_seq2seq_with_generate.ipynb",
"repo_id": "peft",
"token_count": 2021
} | 167 |
# 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/examples/loftq_finetuning/quantize_save_load.py/0 | {
"file_path": "peft/examples/loftq_finetuning/quantize_save_load.py",
"repo_id": "peft",
"token_count": 2842
} | 168 |
<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
} | 169 |
# 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": 1486
} | 170 |
# 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/model.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/model.py",
"repo_id": "peft",
"token_count": 7190
} | 171 |
# 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/prompt_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/prompt_tuning/config.py",
"repo_id": "peft",
"token_count": 1097
} | 172 |
#!/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_custom_models.py/0 | {
"file_path": "peft/tests/test_custom_models.py",
"repo_id": "peft",
"token_count": 36045
} | 173 |
# Feature Extraction
All of the models in `timm` have consistent mechanisms for obtaining various types of features from the model for tasks besides classification.
## Penultimate Layer Features (Pre-Classifier Features)
The features from the penultimate model layer can be obtained in several ways without requiring ... | pytorch-image-models/docs/feature_extraction.md/0 | {
"file_path": "pytorch-image-models/docs/feature_extraction.md",
"repo_id": "pytorch-image-models",
"token_count": 1981
} | 174 |
# ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https:/... | pytorch-image-models/docs/models/.templates/models/ecaresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ecaresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 2832
} | 175 |
# Inception v4
**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
{% include 'code_snippets.md' %}
## How do I t... | pytorch-image-models/docs/models/.templates/models/inception-v4.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/inception-v4.md",
"repo_id": "pytorch-image-models",
"token_count": 816
} | 176 |
# ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the [1×1 convolution](https://paperswithcode.co... | pytorch-image-models/docs/models/.templates/models/resnet-d.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/resnet-d.md",
"repo_id": "pytorch-image-models",
"token_count": 3126
} | 177 |
# (Tensorflow) EfficientNet
**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 uniformly scal... | pytorch-image-models/docs/models/.templates/models/tf-efficientnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/tf-efficientnet.md",
"repo_id": "pytorch-image-models",
"token_count": 7172
} | 178 |
# Dual Path Network (DPN)
A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while DenseNet enables new feature exploration, and both are importa... | pytorch-image-models/docs/models/dpn.md/0 | {
"file_path": "pytorch-image-models/docs/models/dpn.md",
"repo_id": "pytorch-image-models",
"token_count": 3689
} | 179 |
# Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.co... | pytorch-image-models/docs/models/inception-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/inception-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1888
} | 180 |
# ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V... | pytorch-image-models/docs/models/resnest.md/0 | {
"file_path": "pytorch-image-models/docs/models/resnest.md",
"repo_id": "pytorch-image-models",
"token_count": 5449
} | 181 |
# (Tensorflow) EfficientNet Lite
**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 uniformly... | pytorch-image-models/docs/models/tf-efficientnet-lite.md/0 | {
"file_path": "pytorch-image-models/docs/models/tf-efficientnet-lite.md",
"repo_id": "pytorch-image-models",
"token_count": 3355
} | 182 |
# timm
<img class="float-left !m-0 !border-0 !dark:border-0 !shadow-none !max-w-lg w-[150px]" src="https://huggingface.co/front/thumbnails/docs/timm.png"/>
`timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation script... | pytorch-image-models/hfdocs/source/index.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/index.mdx",
"repo_id": "pytorch-image-models",
"token_count": 560
} | 183 |
# ESE-VoVNet
**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel.
Read about [one-shot aggregatio... | pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1951
} | 184 |
# MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
## How do I use this model on an image?
To load a pretrained mo... | pytorch-image-models/hfdocs/source/models/mixnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/mixnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2684
} | 185 |
# SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) 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.
## How do I use this model on an ... | pytorch-image-models/hfdocs/source/models/se-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/se-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2180
} | 186 |
# Wide ResNet
**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).
## How do I use this mod... | pytorch-image-models/hfdocs/source/models/wide-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/wide-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2035
} | 187 |
import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv',
],
'results-imagenet-r-clean.csv... | pytorch-image-models/results/generate_csv_results.py/0 | {
"file_path": "pytorch-image-models/results/generate_csv_results.py",
"repo_id": "pytorch-image-models",
"token_count": 1346
} | 188 |
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops.misc import FrozenBatchNorm2d
import timm
from timm.utils.model import freeze, unfreeze
def test_freeze_unfreeze():
model = timm.create_model('resnet18')
# Freeze all
freeze(model)
# Check top level module
assert model.fc.we... | pytorch-image-models/tests/test_utils.py/0 | {
"file_path": "pytorch-image-models/tests/test_utils.py",
"repo_id": "pytorch-image-models",
"token_count": 776
} | 189 |
""" Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | pytorch-image-models/timm/data/random_erasing.py/0 | {
"file_path": "pytorch-image-models/timm/data/random_erasing.py",
"repo_id": "pytorch-image-models",
"token_count": 2258
} | 190 |
import math
import numbers
import random
import warnings
from typing import List, Sequence, Tuple, Union
import torch
import torchvision.transforms.functional as F
try:
from torchvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
except ImportError:
has_interpolation_mode =... | pytorch-image-models/timm/data/transforms.py/0 | {
"file_path": "pytorch-image-models/timm/data/transforms.py",
"repo_id": "pytorch-image-models",
"token_count": 8734
} | 191 |
""" Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | pytorch-image-models/timm/layers/conv_bn_act.py/0 | {
"file_path": "pytorch-image-models/timm/layers/conv_bn_act.py",
"repo_id": "pytorch-image-models",
"token_count": 1885
} | 192 |
""" Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon Shlens},
Title = {Scaling Local Self... | pytorch-image-models/timm/layers/halo_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/halo_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 4601
} | 193 |
""" AvgPool2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
from .helpers import to_2tuple
from .padding import pad_same, get_padding_value
def avg_pool2d_same(x, kernel_size: List[int... | pytorch-image-models/timm/layers/pool2d_same.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pool2d_same.py",
"repo_id": "pytorch-image-models",
"token_count": 1294
} | 194 |
import torch
import torch.nn as nn
class AsymmetricLossMultiLabel(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
super(AsymmetricLossMultiLabel, self).__init__()
self.gamma_neg = gamma_neg
self.gamma_pos = gamma_pos
... | pytorch-image-models/timm/loss/asymmetric_loss.py/0 | {
"file_path": "pytorch-image-models/timm/loss/asymmetric_loss.py",
"repo_id": "pytorch-image-models",
"token_count": 1620
} | 195 |
""" DaViT: Dual Attention Vision Transformers
As described in https://arxiv.org/abs/2204.03645
Input size invariant transformer architecture that combines channel and spacial
attention in each block. The attention mechanisms used are linear in complexity.
DaViT model defs and weights adapted from https://github.com/... | pytorch-image-models/timm/models/davit.py/0 | {
"file_path": "pytorch-image-models/timm/models/davit.py",
"repo_id": "pytorch-image-models",
"token_count": 11671
} | 196 |
from ._features_fx import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| pytorch-image-models/timm/models/fx_features.py/0 | {
"file_path": "pytorch-image-models/timm/models/fx_features.py",
"repo_id": "pytorch-image-models",
"token_count": 44
} | 197 |
""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import Callable, List, Optional, Tuple
import torch
imp... | pytorch-image-models/timm/models/mobilenetv3.py/0 | {
"file_path": "pytorch-image-models/timm/models/mobilenetv3.py",
"repo_id": "pytorch-image-models",
"token_count": 17103
} | 198 |
"""Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... | pytorch-image-models/timm/models/resnetv2.py/0 | {
"file_path": "pytorch-image-models/timm/models/resnetv2.py",
"repo_id": "pytorch-image-models",
"token_count": 14679
} | 199 |
""" Hybrid Vision Transformer (ViT) in PyTorch
A PyTorch implement of the Hybrid Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transfor... | pytorch-image-models/timm/models/vision_transformer_hybrid.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer_hybrid.py",
"repo_id": "pytorch-image-models",
"token_count": 8049
} | 200 |
""" PyTorch LARS / LARC Optimizer
An implementation of LARS (SGD) + LARC in PyTorch
Based on:
* PyTorch SGD: https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
* NVIDIA APEX LARC: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
Additional cleanup and modifications to properly su... | pytorch-image-models/timm/optim/lars.py/0 | {
"file_path": "pytorch-image-models/timm/optim/lars.py",
"repo_id": "pytorch-image-models",
"token_count": 2571
} | 201 |
""" Polynomial Scheduler
Polynomial LR schedule with warmup, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class PolyLRScheduler(Scheduler):
""" Polynomial LR Scheduler w/ warmup, no... | pytorch-image-models/timm/scheduler/poly_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/poly_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1967
} | 202 |
""" Model / state_dict utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
from copy import deepcopy
import torch
from torchvision.ops.misc import FrozenBatchNorm2d
from timm.layers import BatchNormAct2d, SyncBatchNormAct, FrozenBatchNormAct2d,\
freeze_batch_norm_2d, unfreeze_batch_norm_2d... | pytorch-image-models/timm/utils/model.py/0 | {
"file_path": "pytorch-image-models/timm/utils/model.py",
"repo_id": "pytorch-image-models",
"token_count": 4233
} | 203 |
/// Text Generation Inference benchmarking tool
///
/// Inspired by the great Oha app: https://github.com/hatoo/oha
/// and: https://github.com/orhun/rust-tui-template
use clap::Parser;
use std::path::Path;
use text_generation_client::ShardedClient;
use tokenizers::{FromPretrainedParameters, Tokenizer};
use tracing_sub... | text-generation-inference/benchmark/src/main.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 3014
} | 204 |
import os
import requests
from typing import Dict, Optional, List
from huggingface_hub.utils import build_hf_headers
from text_generation import Client, AsyncClient, __version__
from text_generation.types import DeployedModel
from text_generation.errors import NotSupportedError, parse_error
INFERENCE_ENDPOINT = os.e... | text-generation-inference/clients/python/text_generation/inference_api.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/inference_api.py",
"repo_id": "text-generation-inference",
"token_count": 2183
} | 205 |
# Tensor Parallelism
Tensor parallelism is a technique used to fit a large model in multiple GPUs. For example, when multiplying the input tensors with the first weight tensor, the matrix multiplication is equivalent to splitting the weight tensor column-wise, multiplying each column with the input separately, and the... | text-generation-inference/docs/source/conceptual/tensor_parallelism.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/tensor_parallelism.md",
"repo_id": "text-generation-inference",
"token_count": 273
} | 206 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 1724,
"logprob": -7.6914062,
"text": "What"
},
{
"id": 33... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq_sharded/test_flash_llama_awq_sharded.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq_sharded/test_flash_llama_awq_sharded.json",
"repo_id": "text-generation-inference",
"token_count": 1238
} | 207 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|USER|>"
},
{
"id": 1276,
"logprob": -4.5546875,
"text": "What"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox.json",
"repo_id": "text-generation-inference",
"token_count": 1353
} | 208 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4911,
"logprob": -6.9804688,
"text": "User"
... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics_load.json",
"repo_id": "text-generation-inference",
"token_count": 9573
} | 209 |
import pytest
@pytest.fixture(scope="module")
def flash_falcon_handle(launcher):
with launcher("tiiuae/falcon-7b", trust_remote_code=True) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_falcon(flash_falcon_handle):
await flash_falcon_handle.health(300)
return flash_falco... | text-generation-inference/integration-tests/models/test_flash_falcon.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_falcon.py",
"repo_id": "text-generation-inference",
"token_count": 884
} | 210 |
import pytest
@pytest.fixture(scope="module")
def t5_sharded_handle(launcher):
with launcher("google/flan-t5-xxl", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def t5_sharded(t5_sharded_handle):
await t5_sharded_handle.health(300)
return t5_sharded_handle.client
@... | text-generation-inference/integration-tests/models/test_t5_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_t5_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 427
} | 211 |
use std::error::Error;
use vergen::EmitBuilder;
fn main() -> Result<(), Box<dyn Error>> {
// Try to get the git sha from the local git repository
if EmitBuilder::builder()
.fail_on_error()
.git_sha(false)
.emit()
.is_err()
{
// Unable to get the git sha
if le... | text-generation-inference/router/build.rs/0 | {
"file_path": "text-generation-inference/router/build.rs",
"repo_id": "text-generation-inference",
"token_count": 324
} | 212 |
[toolchain]
# Released on: 28 December, 2023
# Branched from master on: 10 November, 2023
# https://releases.rs/docs/1.75.0/
channel = "1.75.0"
components = ["rustfmt", "clippy"] | text-generation-inference/rust-toolchain.toml/0 | {
"file_path": "text-generation-inference/rust-toolchain.toml",
"repo_id": "text-generation-inference",
"token_count": 68
} | 213 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "column_remap.cuh"
#include "../util.cuh"
const int SHUF_BLOCKSIZE_X = 256;
const int SHUF_BLOCKSIZE_Y = 16;
__global__ void column_remap_kernel
(
const half* __restrict__ x,
half* __restrict__ x_new,
const int x_width,
... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu",
"repo_id": "text-generation-inference",
"token_count": 696
} | 214 |
#include "q_gemm.cuh"
#include "util.cuh"
#include "matrix_view.cuh"
#include "../config.h"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define GPTQ_BLOCK_KN_SIZE 128
#define GPTQ_BLOCK_M_SIZE_MAX 8
#... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu",
"repo_id": "text-generation-inference",
"token_count": 3532
} | 215 |
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