text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
import types
from typing import List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput
from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDi... | diffusers/examples/community/stable_unclip.py/0 | {
"file_path": "diffusers/examples/community/stable_unclip.py",
"repo_id": "diffusers",
"token_count": 5489
} | 112 |
# ControlNet training example
[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
This example is based on the [training example in the original ControlNet repository](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md). I... | diffusers/examples/controlnet/README.md/0 | {
"file_path": "diffusers/examples/controlnet/README.md",
"repo_id": "diffusers",
"token_count": 6041
} | 113 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | diffusers/examples/instruct_pix2pix/train_instruct_pix2pix.py/0 | {
"file_path": "diffusers/examples/instruct_pix2pix/train_instruct_pix2pix.py",
"repo_id": "diffusers",
"token_count": 19465
} | 114 |
import argparse
import itertools
import math
import os
import random
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set... | diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py/0 | {
"file_path": "diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py",
"repo_id": "diffusers",
"token_count": 14370
} | 115 |
# Multi Subject DreamBooth training
[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
This `train_multi_subject_dreambooth.py` script shows how to implement the training procedure for one or more subjects and ada... | diffusers/examples/research_projects/multi_subject_dreambooth/README.md/0 | {
"file_path": "diffusers/examples/research_projects/multi_subject_dreambooth/README.md",
"repo_id": "diffusers",
"token_count": 4807
} | 116 |
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion... | diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md/0 | {
"file_path": "diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md",
"repo_id": "diffusers",
"token_count": 1117
} | 117 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | diffusers/examples/text_to_image/train_text_to_image_lora_sdxl.py/0 | {
"file_path": "diffusers/examples/text_to_image/train_text_to_image_lora_sdxl.py",
"repo_id": "diffusers",
"token_count": 24263
} | 118 |
#!/usr/bin/env python3
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnet1D
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
MODELS_MAP = {
"gwf-440k": {
... | diffusers/scripts/convert_dance_diffusion_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_dance_diffusion_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 4561
} | 119 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to... | diffusers/scripts/convert_unclip_txt2img_to_image_variation.py/0 | {
"file_path": "diffusers/scripts/convert_unclip_txt2img_to_image_variation.py",
"repo_id": "diffusers",
"token_count": 554
} | 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/dependency_versions_check.py/0 | {
"file_path": "diffusers/src/diffusers/dependency_versions_check.py",
"repo_id": "diffusers",
"token_count": 382
} | 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/loaders/textual_inversion.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/textual_inversion.py",
"repo_id": "diffusers",
"token_count": 11257
} | 122 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/autoencoders/vae.py/0 | {
"file_path": "diffusers/src/diffusers/models/autoencoders/vae.py",
"repo_id": "diffusers",
"token_count": 18215
} | 123 |
# 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/resnet_flax.py/0 | {
"file_path": "diffusers/src/diffusers/models/resnet_flax.py",
"repo_id": "diffusers",
"token_count": 1885
} | 124 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/unets/unet_1d.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_1d.py",
"repo_id": "diffusers",
"token_count": 4859
} | 125 |
# 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/vq_model.py/0 | {
"file_path": "diffusers/src/diffusers/models/vq_model.py",
"repo_id": "diffusers",
"token_count": 3140
} | 126 |
# Copyright 2023 CVSSP, ByteDance 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/pipelines/audioldm2/pipeline_audioldm2.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py",
"repo_id": "diffusers",
"token_count": 21703
} | 127 |
# 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/controlnet/pipeline_controlnet_sd_xl.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py",
"repo_id": "diffusers",
"token_count": 33443
} | 128 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput
@dataclass
class IFPipelineOutput(BaseOutput):
"""
Args:
Output class for Stable Diffusion pipelines.
images (`List[PIL.Image.Image]` or `np.ndarray`)
... | diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 409
} | 129 |
from typing import TYPE_CHECKING
from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
_import_structure = {"pipeline_pndm": ["PNDMPipeline"]}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_pndm import PNDMPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
... | diffusers/src/diffusers/pipelines/deprecated/pndm/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/pndm/__init__.py",
"repo_id": "diffusers",
"token_count": 182
} | 130 |
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/kandinsky/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky/__init__.py",
"repo_id": "diffusers",
"token_count": 951
} | 131 |
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/pia/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/pia/__init__.py",
"repo_id": "diffusers",
"token_count": 515
} | 132 |
# 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_stable_diffusion_latent_upscale.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py",
"repo_id": "diffusers",
"token_count": 10812
} | 133 |
# 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_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py",
"repo_id": "diffusers",
"token_count": 22378
} | 134 |
# Copyright 2023 Kakao Brain 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 requi... | diffusers/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py",
"repo_id": "diffusers",
"token_count": 8370
} | 135 |
# 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/schedulers/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/__init__.py",
"repo_id": "diffusers",
"token_count": 3837
} | 136 |
# Copyright 2023 Zhejiang 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_pndm.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_pndm.py",
"repo_id": "diffusers",
"token_count": 9520
} | 137 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/utils/doc_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/doc_utils.py",
"repo_id": "diffusers",
"token_count": 506
} | 138 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/utils/import_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/import_utils.py",
"repo_id": "diffusers",
"token_count": 9368
} | 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/lora/test_lora_layers_peft.py/0 | {
"file_path": "diffusers/tests/lora/test_lora_layers_peft.py",
"repo_id": "diffusers",
"token_count": 46068
} | 140 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/models/unets/test_models_unet_2d.py/0 | {
"file_path": "diffusers/tests/models/unets/test_models_unet_2d.py",
"repo_id": "diffusers",
"token_count": 5101
} | 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/others/test_training.py/0 | {
"file_path": "diffusers/tests/others/test_training.py",
"repo_id": "diffusers",
"token_count": 1482
} | 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/ddpm/test_ddpm.py/0 | {
"file_path": "diffusers/tests/pipelines/ddpm/test_ddpm.py",
"repo_id": "diffusers",
"token_count": 1777
} | 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/kandinsky/test_kandinsky_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky/test_kandinsky_img2img.py",
"repo_id": "diffusers",
"token_count": 6298
} | 144 |
import gc
import inspect
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
LatentConsistencyModelPipeline,
LCMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
en... | diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models.py/0 | {
"file_path": "diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models.py",
"repo_id": "diffusers",
"token_count": 4610
} | 145 |
# 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_attend_and_excite.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py",
"repo_id": "diffusers",
"token_count": 3674
} | 146 |
# 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_image_variation/test_stable_diffusion_image_variation.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_image_variation/test_stable_diffusion_image_variation.py",
"repo_id": "diffusers",
"token_count": 5832
} | 147 |
# coding=utf-8
# Copyright 2023 Harutatsu Akiyama 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
#
# Unless required b... | diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_instruction_pix2pix.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_instruction_pix2pix.py",
"repo_id": "diffusers",
"token_count": 3115
} | 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/pipelines/text_to_video_synthesis/test_text_to_video_zero.py/0 | {
"file_path": "diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero.py",
"repo_id": "diffusers",
"token_count": 560
} | 149 |
# Copyright 2023 ParaDiGMS authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | diffusers/tests/schedulers/test_scheduler_ddim_parallel.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_ddim_parallel.py",
"repo_id": "diffusers",
"token_count": 3802
} | 150 |
import torch
from diffusers import LMSDiscreteScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class LMSDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (LMSDiscreteScheduler,)
num_inference_steps = 10
def get_scheduler_con... | diffusers/tests/schedulers/test_scheduler_lms.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_lms.py",
"repo_id": "diffusers",
"token_count": 2696
} | 151 |
import json
import logging
import os
from collections import defaultdict
from pathlib import Path
from huggingface_hub import HfApi, ModelFilter
import diffusers
PATH_TO_REPO = Path(__file__).parent.parent.resolve()
ALWAYS_TEST_PIPELINE_MODULES = [
"controlnet",
"stable_diffusion",
"stable_diffusion_2",... | diffusers/utils/fetch_torch_cuda_pipeline_test_matrix.py/0 | {
"file_path": "diffusers/utils/fetch_torch_cuda_pipeline_test_matrix.py",
"repo_id": "diffusers",
"token_count": 1082
} | 152 |
# Introduction to 🤗 Diffusers
<CourseFloatingBanner unit={1}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Introduction to Diffusers", value: "https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/units/en/unit1/introduction_to_diffusers.ipynb"},
{label: "I... | diffusion-models-class/units/en/unit1/2.mdx/0 | {
"file_path": "diffusion-models-class/units/en/unit1/2.mdx",
"repo_id": "diffusion-models-class",
"token_count": 8144
} | 153 |
<jupyter_start><jupyter_text>Hackathon DreamBooth 🏆 Bienvenue au Hackathon DreamBooth ! Dans cette compétition, vous allez **personnaliser un modèle de Stable Diffusion en le *finetunant* sur une poignée de vos propres images**. Pour cela, nous allons utiliser une technique appelée [_DreamBooth_](https://arxiv.org/abs... | diffusion-models-class/units/fr/events/dreambooth.ipynb/0 | {
"file_path": "diffusion-models-class/units/fr/events/dreambooth.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 11430
} | 154 |
# Introduction à Stable Diffusion
<CourseFloatingBanner unit={3}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Introduction à Stable Diffusion", value: "https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/units/fr/unit3/stable_diffusion_introduction.ipynb"},
... | diffusion-models-class/units/fr/unit3/2.mdx/0 | {
"file_path": "diffusion-models-class/units/fr/unit3/2.mdx",
"repo_id": "diffusion-models-class",
"token_count": 11154
} | 155 |
<jupyter_start><jupyter_text>Préparer des données (PyTorch) Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
import torch
from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification
# Comme ... | notebooks/course/fr/chapter3/section2_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter3/section2_pt.ipynb",
"repo_id": "notebooks",
"token_count": 784
} | 156 |
<jupyter_start><jupyter_text>Entraîner un nouveau *tokenizer* à partir d'un ancien 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 ... | notebooks/course/fr/chapter6/section2.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter6/section2.ipynb",
"repo_id": "notebooks",
"token_count": 1147
} | 157 |
<jupyter_start><jupyter_text>Résumé (PyTorch) Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install accelerate
# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :
# !pip insta... | notebooks/course/fr/chapter7/section5_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section5_pt.ipynb",
"repo_id": "notebooks",
"token_count": 4623
} | 158 |
<jupyter_start><jupyter_text>Fonctions avancées d'Interface Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install gradio
import random
import gradio as gr
def chat(message, history):
history = history or [... | notebooks/course/fr/chapter9/section6.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter9/section6.ipynb",
"repo_id": "notebooks",
"token_count": 647
} | 159 |
<jupyter_start><jupyter_text>CLIP Guided Stable Diffusion using [d🧨ffusers](https://github.com/huggingface/diffusers) This notebook shows how to do CLIP guidance with Stable diffusion using diffusers libray. This allows you to use newly released [CLIP models by LAION AI.](https://huggingface.co/laion).This notebook is... | notebooks/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb/0 | {
"file_path": "notebooks/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb",
"repo_id": "notebooks",
"token_count": 9092
} | 160 |
<jupyter_start><jupyter_text>Run Dreambooth fine-tuned models for Stable Diffusion using d🧨ffusers This notebook allows you to run Stable Diffusion concepts trained via Dreambooth using 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers). Train your own using [here]() and navigate the [pub... | notebooks/diffusers/sd_dreambooth_inference.ipynb/0 | {
"file_path": "notebooks/diffusers/sd_dreambooth_inference.ipynb",
"repo_id": "notebooks",
"token_count": 1238
} | 161 |
<jupyter_start><jupyter_text>Patch Time Series Transformer in HuggingFace - Getting StartedIn this blog, we provide examples of how to get started with PatchTST. We first demonstrate the forecasting capability of `PatchTST` on the Electricity data. We will then demonstrate the transfer learning capability of `PatchTST`... | notebooks/examples/patch_tst.ipynb/0 | {
"file_path": "notebooks/examples/patch_tst.ipynb",
"repo_id": "notebooks",
"token_count": 7448
} | 162 |
<jupyter_start><jupyter_text>Fine-tuning a 🤗 Transformers model on TPU with **Flax/JAX** In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) models on TPU using [**Flax**](https://flax.readthedocs.io/en/latest/index.html). As can be seen on [this ben... | notebooks/examples/text_classification_flax.ipynb/0 | {
"file_path": "notebooks/examples/text_classification_flax.ipynb",
"repo_id": "notebooks",
"token_count": 9221
} | 163 |
<jupyter_start><jupyter_text>Speed Comparison `Safetensors` is really fast. Let's compare it against `PyTorch` by loading [gpt2](https://huggingface.co/gpt2) weights. To run the [GPU benchmark](gpu-benchmark), make sure your machine has GPU or you have selected `GPU runtime` if you are using Google Colab.Before you beg... | notebooks/safetensors_doc/en/speed.ipynb/0 | {
"file_path": "notebooks/safetensors_doc/en/speed.ipynb",
"repo_id": "notebooks",
"token_count": 893
} | 164 |
<jupyter_start><jupyter_text>Huggingface Sagemaker - Vision Transformer Image Classification with the `google/vit` on `cifar10` 1. [Introduction](Introduction) 2. [Development Environment and Permissions](Development-Environment-and-Permissions) 1. [Installation](Installation) 3. [Permissions](Permissions)3. ... | notebooks/sagemaker/09_image_classification_vision_transformer/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/09_image_classification_vision_transformer/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 2887
} | 165 |
# 🤗 Transformers doc notebooks
These notebooks are automatically generated from the [🤗 Transformers documentation](https://huggingface.co/transformers/)
so you should not make any direct modification here. If there is a typo to fix or a sentence to add, open a pull
request in the [🤗 Transformers repo](https://githu... | notebooks/transformers_doc/README.md/0 | {
"file_path": "notebooks/transformers_doc/README.md",
"repo_id": "notebooks",
"token_count": 169
} | 166 |
<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Soft prompts
Training large pretrained language models is very time-consuming and compute-intensive. As they continue to grow in size, there is in... | peft/docs/source/conceptual_guides/prompting.md/0 | {
"file_path": "peft/docs/source/conceptual_guides/prompting.md",
"repo_id": "peft",
"token_count": 1830
} | 167 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/task_guides/prompt_based_methods.md/0 | {
"file_path": "peft/docs/source/task_guides/prompt_based_methods.md",
"repo_id": "peft",
"token_count": 4608
} | 168 |
<jupyter_start><jupyter_code>from transformers import AutoModelForSeq2SeqLM
import peft
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, IA3Config, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from transformers import AutoT... | peft/examples/conditional_generation/peft_ia3_seq2seq.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_ia3_seq2seq.ipynb",
"repo_id": "peft",
"token_count": 2685
} | 169 |
<jupyter_start><jupyter_text>Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets... | peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb/0 | {
"file_path": "peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb",
"repo_id": "peft",
"token_count": 4274
} | 170 |
<jupyter_start><jupyter_text>IntroductionIn this notebook, we are going to fine-tune the LayoutLM model by Microsoft Research on the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset, which is a collection of annotated form documents. The goal of our model is to learn the annotations of a number of labels ("ques... | peft/examples/token_classification/peft_lora_token_cls.ipynb/0 | {
"file_path": "peft/examples/token_classification/peft_lora_token_cls.ipynb",
"repo_id": "peft",
"token_count": 11949
} | 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/ia3/layer.py/0 | {
"file_path": "peft/src/peft/tuners/ia3/layer.py",
"repo_id": "peft",
"token_count": 6417
} | 172 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/lora/tp_layer.py/0 | {
"file_path": "peft/src/peft/tuners/lora/tp_layer.py",
"repo_id": "peft",
"token_count": 3961
} | 173 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/poly/layer.py/0 | {
"file_path": "peft/src/peft/tuners/poly/layer.py",
"repo_id": "peft",
"token_count": 3329
} | 174 |
import os
if os.environ.get("PEFT_DEBUG_WITH_TORCH_COMPILE") == "1":
# This is a hack purely for debugging purposes. If the environment variable PEFT_DEBUG_WITH_TORCH_COMPILE is set to
# 1, get_peft_model() will return a compiled model. This way, all unit tests that use peft.get_peft_model() will
# use a ... | peft/tests/__init__.py/0 | {
"file_path": "peft/tests/__init__.py",
"repo_id": "peft",
"token_count": 302
} | 175 |
# 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_mixed.py/0 | {
"file_path": "peft/tests/test_mixed.py",
"repo_id": "peft",
"token_count": 18062
} | 176 |
#!/usr/bin/env python3
""" Bulk Model Script Runner
Run validation or benchmark script in separate process for each model
Benchmark all 'vit*' models:
python bulk_runner.py --model-list 'vit*' --results-file vit_bench.csv benchmark.py --amp -b 512
Validate all models:
python bulk_runner.py --model-list all --resul... | pytorch-image-models/bulk_runner.py/0 | {
"file_path": "pytorch-image-models/bulk_runner.py",
"repo_id": "pytorch-image-models",
"token_count": 3409
} | 177 |
# Big Transfer (BiT)
**Big Transfer (BiT)** is a type of pretraining recipe that pre-trains on a large supervised source dataset, and fine-tunes the weights on the target task. Models are trained on the JFT-300M dataset. The finetuned models contained in this collection are finetuned on ImageNet.
{% include 'code_sn... | pytorch-image-models/docs/models/.templates/models/big-transfer.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/big-transfer.md",
"repo_id": "pytorch-image-models",
"token_count": 3274
} | 178 |
# (Gluon) SENet
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this model were ported from [Gluon](http... | pytorch-image-models/docs/models/.templates/models/gloun-senet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-senet.md",
"repo_id": "pytorch-image-models",
"token_count": 747
} | 179 |
# Noisy Student (EfficientNet)
**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training
and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps:
1. train a teacher model on labeled images
2.... | pytorch-image-models/docs/models/.templates/models/noisy-student.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/noisy-student.md",
"repo_id": "pytorch-image-models",
"token_count": 5862
} | 180 |
# SPNASNet
**Single-Path NAS** is a novel differentiable NAS method for designing hardware-efficient ConvNets in less than 4 hours.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new mo... | pytorch-image-models/docs/models/.templates/models/spnasnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/spnasnet.md",
"repo_id": "pytorch-image-models",
"token_count": 699
} | 181 |
# DenseNet
**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each... | pytorch-image-models/hfdocs/source/models/densenet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/densenet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4188
} | 182 |
# Instagram ResNeXt WSL
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transfo... | pytorch-image-models/hfdocs/source/models/ig-resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ig-resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3231
} | 183 |
# Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). 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... | pytorch-image-models/hfdocs/source/models/res2net.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/res2net.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3950
} | 184 |
# (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/hfdocs/source/models/tf-efficientnet-condconv.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3303
} | 185 |
dependencies = ['torch']
import timm
globals().update(timm.models._registry._model_entrypoints)
| pytorch-image-models/hubconf.py/0 | {
"file_path": "pytorch-image-models/hubconf.py",
"repo_id": "pytorch-image-models",
"token_count": 32
} | 186 |
""" Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | pytorch-image-models/timm/data/dataset.py/0 | {
"file_path": "pytorch-image-models/timm/data/dataset.py",
"repo_id": "pytorch-image-models",
"token_count": 2918
} | 187 |
""" A dataset reader that reads tarfile based datasets
This reader can extract image samples from:
* a single tar of image files
* a folder of multiple tarfiles containing imagefiles
* a tar of tars containing image files
Labels are based on the combined folder and/or tar name structure.
Hacked together by / Copyrig... | pytorch-image-models/timm/data/readers/reader_image_in_tar.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_in_tar.py",
"repo_id": "pytorch-image-models",
"token_count": 4050
} | 188 |
"""
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .padding import get_padding
class ... | pytorch-image-models/timm/layers/blur_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/blur_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 625
} | 189 |
""" 'Fast' Normalization Functions
For GroupNorm and LayerNorm these functions bypass typical AMP upcast to float32.
Additionally, for LayerNorm, the APEX fused LN is used if available (which also does not upcast)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import List, Optional
import torch
f... | pytorch-image-models/timm/layers/fast_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/fast_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 1639
} | 190 |
""" MLP module w/ dropout and configurable activation layer
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
from torch import nn as nn
from .grn import GlobalResponseNorm
from .helpers import to_2tuple
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer an... | pytorch-image-models/timm/layers/mlp.py/0 | {
"file_path": "pytorch-image-models/timm/layers/mlp.py",
"repo_id": "pytorch-image-models",
"token_count": 4251
} | 191 |
""" Squeeze-and-Excitation Channel Attention
An SE implementation originally based on PyTorch SE-Net impl.
Has since evolved with additional functionality / configuration.
Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
Also included is Effective Squeeze-Excitation (ESE).
Paper: `CenterMa... | pytorch-image-models/timm/layers/squeeze_excite.py/0 | {
"file_path": "pytorch-image-models/timm/layers/squeeze_excite.py",
"repo_id": "pytorch-image-models",
"token_count": 1859
} | 192 |
""" PyTorch Feature Extraction Helpers
A collection of classes, functions, modules to help extract features from models
and provide a common interface for describing them.
The return_layers, module re-writing idea inspired by torchvision IntermediateLayerGetter
https://github.com/pytorch/vision/blob/d88d8961ae51507d0... | pytorch-image-models/timm/models/_features.py/0 | {
"file_path": "pytorch-image-models/timm/models/_features.py",
"repo_id": "pytorch-image-models",
"token_count": 6555
} | 193 |
""" Class-Attention in Image Transformers (CaiT)
Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239
Original code and weights from https://github.com/facebookresearch/deit, copyright below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# Copy... | pytorch-image-models/timm/models/cait.py/0 | {
"file_path": "pytorch-image-models/timm/models/cait.py",
"repo_id": "pytorch-image-models",
"token_count": 9133
} | 194 |
""" EfficientViT (by MIT Song Han's Lab)
Paper: `Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition`
- https://arxiv.org/abs/2205.14756
Adapted from official impl at https://github.com/mit-han-lab/efficientvit
"""
__all__ = ['EfficientVit']
from typing import Optional
... | pytorch-image-models/timm/models/efficientvit_mit.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientvit_mit.py",
"repo_id": "pytorch-image-models",
"token_count": 18668
} | 195 |
""" Inception-V3
Originally from torchvision Inception3 model
Licensed BSD-Clause 3 https://github.com/pytorch/vision/blob/master/LICENSE
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_IN... | pytorch-image-models/timm/models/inception_v3.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_v3.py",
"repo_id": "pytorch-image-models",
"token_count": 8581
} | 196 |
""" TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | pytorch-image-models/timm/models/tiny_vit.py/0 | {
"file_path": "pytorch-image-models/timm/models/tiny_vit.py",
"repo_id": "pytorch-image-models",
"token_count": 12415
} | 197 |
import math
import torch
from torch.optim.optimizer import Optimizer
class AdaBelief(Optimizer):
r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optiona... | pytorch-image-models/timm/optim/adabelief.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adabelief.py",
"repo_id": "pytorch-image-models",
"token_count": 5074
} | 198 |
""" RMSProp modified to behave like Tensorflow impl
Originally cut & paste from PyTorch RMSProp
https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py
Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE
Modifications Copyright 2021... | pytorch-image-models/timm/optim/rmsprop_tf.py/0 | {
"file_path": "pytorch-image-models/timm/optim/rmsprop_tf.py",
"repo_id": "pytorch-image-models",
"token_count": 2901
} | 199 |
""" CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call__(
sel... | pytorch-image-models/timm/utils/cuda.py/0 | {
"file_path": "pytorch-image-models/timm/utils/cuda.py",
"repo_id": "pytorch-image-models",
"token_count": 980
} | 200 |
import pytest
from text_generation import Client, AsyncClient
from text_generation.errors import NotFoundError, ValidationError
from text_generation.types import FinishReason, InputToken
def test_generate(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
response = client.generate("t... | text-generation-inference/clients/python/tests/test_client.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_client.py",
"repo_id": "text-generation-inference",
"token_count": 2116
} | 201 |
# Preparing the Model
Text Generation Inference improves the model in several aspects.
## Quantization
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference wi... | text-generation-inference/docs/source/basic_tutorials/preparing_model.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/preparing_model.md",
"repo_id": "text-generation-inference",
"token_count": 551
} | 202 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
},
{
"id": 49833,
"logprob": -10.5625,
"text": " dé... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_load.json",
"repo_id": "text-generation-inference",
"token_count": 7244
} | 203 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.6015625,
"text": "Test"
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_load.json",
"repo_id": "text-generation-inference",
"token_count": 4856
} | 204 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6289062,
"text": " print"
},
{
"id"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder.json",
"repo_id": "text-generation-inference",
"token_count": 1111
} | 205 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|prompter|>"
},
{
"id": 1276,
"logprob": -8.0234375,
"te... | text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox_load.json",
"repo_id": "text-generation-inference",
"token_count": 9164
} | 206 |
import pytest
@pytest.fixture(scope="module")
def flash_starcoder_handle(launcher):
with launcher("bigcode/starcoder", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder(flash_starcoder_handle):
await flash_starcoder_handle.health(300)
return flash_sta... | text-generation-inference/integration-tests/models/test_flash_starcoder.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_starcoder.py",
"repo_id": "text-generation-inference",
"token_count": 578
} | 207 |
import { check, randomSeed } from 'k6';
import http from 'k6/http';
import { Trend, Counter } from 'k6/metrics';
import { randomItem } from 'https://jslib.k6.io/k6-utils/1.2.0/index.js';
const seed = 0;
const host = __ENV.HOST || '127.0.0.1:8000';
const timePerToken = new Trend('time_per_token', true);
const tokens =... | text-generation-inference/load_tests/common.js/0 | {
"file_path": "text-generation-inference/load_tests/common.js",
"repo_id": "text-generation-inference",
"token_count": 1026
} | 208 |
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;
use text_generation_client::{
Batch, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
};
// Note: Request ids and batch ids cannot collide.
const LIVENESS_ID: u64 = u64::MAX;
const BATCH_ID: u64 = u64::MAX;
#[derive(... | text-generation-inference/router/src/health.rs/0 | {
"file_path": "text-generation-inference/router/src/health.rs",
"repo_id": "text-generation-inference",
"token_count": 1198
} | 209 |
# Text Generation Inference Python gRPC Server
A Python gRPC server for Text Generation Inference
## Install
```shell
make install
```
## Run
```shell
make run-dev
``` | text-generation-inference/server/README.md/0 | {
"file_path": "text-generation-inference/server/README.md",
"repo_id": "text-generation-inference",
"token_count": 55
} | 210 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _tuning_h
#define _tuning_h
struct ExLlamaTuning
{
int matmul_recons_thd;
bool matmul_fused_remap;
bool matmul_no_half2;
};
#endif
| text-generation-inference/server/exllama_kernels/exllama_kernels/tuning.h/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/tuning.h",
"repo_id": "text-generation-inference",
"token_count": 106
} | 211 |
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