text stringlengths 7 328k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 459 |
|---|---|---|---|
<!--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... | diffusers/docs/source/ko/using-diffusers/other-formats.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/other-formats.md",
"repo_id": "diffusers",
"token_count": 6827
} | 109 |
import inspect
from typing import List, Optional, Union
import numpy as np
import PIL.Image
import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | diffusers/examples/community/clip_guided_stable_diffusion_img2img.py/0 | {
"file_path": "diffusers/examples/community/clip_guided_stable_diffusion_img2img.py",
"repo_id": "diffusers",
"token_count": 9396
} | 110 |
import inspect
import re
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.configuration_utils imp... | diffusers/examples/community/lpw_stable_diffusion.py/0 | {
"file_path": "diffusers/examples/community/lpw_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 30900
} | 111 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2D... | diffusers/examples/community/stable_diffusion_comparison.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_comparison.py",
"repo_id": "diffusers",
"token_count": 7381
} | 112 |
import inspect
from typing import List, Optional, Union
import PIL.Image
import torch
from torch.nn import functional as F
from transformers import (
CLIPImageProcessor,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
Imag... | diffusers/examples/community/unclip_image_interpolation.py/0 | {
"file_path": "diffusers/examples/community/unclip_image_interpolation.py",
"repo_id": "diffusers",
"token_count": 9334
} | 113 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/examples/dreambooth/test_dreambooth.py/0 | {
"file_path": "diffusers/examples/dreambooth/test_dreambooth.py",
"repo_id": "diffusers",
"token_count": 4466
} | 114 |
# Kandinsky2.2 text-to-image fine-tuning
Kandinsky 2.2 includes a prior pipeline that generates image embeddings from text prompts, and a decoder pipeline that generates the output image based on the image embeddings. We provide `train_text_to_image_prior.py` and `train_text_to_image_decoder.py` scripts to show you ho... | diffusers/examples/kandinsky2_2/text_to_image/README.md/0 | {
"file_path": "diffusers/examples/kandinsky2_2/text_to_image/README.md",
"repo_id": "diffusers",
"token_count": 4394
} | 115 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 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/research_projects/controlnet/train_controlnet_webdataset.py/0 | {
"file_path": "diffusers/examples/research_projects/controlnet/train_controlnet_webdataset.py",
"repo_id": "diffusers",
"token_count": 25995
} | 116 |
# InstructPix2Pix text-to-edit-image fine-tuning
This extended LoRA training script was authored by [Aiden-Frost](https://github.com/Aiden-Frost).
This is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py). This script... | diffusers/examples/research_projects/instructpix2pix_lora/README.md/0 | {
"file_path": "diffusers/examples/research_projects/instructpix2pix_lora/README.md",
"repo_id": "diffusers",
"token_count": 1124
} | 117 |
import argparse
import itertools
import json
import logging
import math
import uuid
import warnings
from os import environ, listdir, makedirs
from os.path import basename, join
from pathlib import Path
from typing import List
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.... | diffusers/examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py/0 | {
"file_path": "diffusers/examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py",
"repo_id": "diffusers",
"token_count": 21727
} | 118 |
# Show best practices for SDXL JAX
import time
import jax
import jax.numpy as jnp
import numpy as np
from flax.jax_utils import replicate
# Let's cache the model compilation, so that it doesn't take as long the next time around.
from jax.experimental.compilation_cache import compilation_cache as cc
from diffusers im... | diffusers/examples/research_projects/sdxl_flax/sdxl_single.py/0 | {
"file_path": "diffusers/examples/research_projects/sdxl_flax/sdxl_single.py",
"repo_id": "diffusers",
"token_count": 1341
} | 119 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 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_flax.py/0 | {
"file_path": "diffusers/examples/text_to_image/train_text_to_image_flax.py",
"repo_id": "diffusers",
"token_count": 10030
} | 120 |
import math
import os
import urllib
import warnings
from argparse import ArgumentParser
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub.utils import insecure_hashlib
from safetensors.torch import load_file as stl
from tqdm import tqdm
from diffusers import AutoencoderKL, Consis... | diffusers/scripts/convert_consistency_decoder.py/0 | {
"file_path": "diffusers/scripts/convert_consistency_decoder.py",
"repo_id": "diffusers",
"token_count": 21911
} | 121 |
# coding=utf-8
# Copyright 2024, Haofan Wang, Qixun Wang, 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 re... | diffusers/scripts/convert_lora_safetensor_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_lora_safetensor_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 2130
} | 122 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import onnx_graphsurgeon as gs
import torch
from onnx import shape_inference
from packaging import version
from polygraphy.backend.onnx.loader import fold_constants
from torch.onnx import export
from diffusers import (
ControlNetModel,
... | diffusers/scripts/convert_stable_diffusion_controlnet_to_onnx.py/0 | {
"file_path": "diffusers/scripts/convert_stable_diffusion_controlnet_to_onnx.py",
"repo_id": "diffusers",
"token_count": 8995
} | 123 |
# 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 applicabl... | diffusers/src/diffusers/loaders/lora.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/lora.py",
"repo_id": "diffusers",
"token_count": 28666
} | 124 |
# 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 applicabl... | diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py/0 | {
"file_path": "diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py",
"repo_id": "diffusers",
"token_count": 3208
} | 125 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | diffusers/src/diffusers/models/modeling_pytorch_flax_utils.py/0 | {
"file_path": "diffusers/src/diffusers/models/modeling_pytorch_flax_utils.py",
"repo_id": "diffusers",
"token_count": 3050
} | 126 |
# 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 applicabl... | diffusers/src/diffusers/models/unet_1d_blocks.py/0 | {
"file_path": "diffusers/src/diffusers/models/unet_1d_blocks.py",
"repo_id": "diffusers",
"token_count": 3632
} | 127 |
# 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 applicabl... | diffusers/src/diffusers/models/unets/unet_motion_model.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_motion_model.py",
"repo_id": "diffusers",
"token_count": 19370
} | 128 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
logger = logging.get_logger(__name__)
class MultiControlN... | diffusers/src/diffusers/pipelines/controlnet/multicontrolnet.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/controlnet/multicontrolnet.py",
"repo_id": "diffusers",
"token_count": 3924
} | 129 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ....utils import (
BaseOutput,
)
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt
class AltDiffusionPipelineOutput(BaseO... | diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 344
} | 130 |
# Copyright 2022 The Music Spectrogram Diffusion Authors.
# 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... | diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py",
"repo_id": "diffusers",
"token_count": 4996
} | 131 |
from typing import TYPE_CHECKING
from ....utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
... | diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/__init__.py",
"repo_id": "diffusers",
"token_count": 682
} | 132 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py",
"repo_id": "diffusers",
"token_count": 18694
} | 133 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
import torch.utils.checkpoint
from ...models import UNet2DModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscret... | diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py",
"repo_id": "diffusers",
"token_count": 3451
} | 134 |
# 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 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": 10182
} | 135 |
# Copyright 2024 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": 8369
} | 136 |
# 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 applicabl... | diffusers/src/diffusers/schedulers/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/__init__.py",
"repo_id": "diffusers",
"token_count": 4011
} | 137 |
# 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 applicabl... | diffusers/src/diffusers/utils/accelerate_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/accelerate_utils.py",
"repo_id": "diffusers",
"token_count": 558
} | 138 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | diffusers/src/diffusers/utils/dynamic_modules_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dynamic_modules_utils.py",
"repo_id": "diffusers",
"token_count": 7901
} | 139 |
# 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 applicabl... | diffusers/tests/fixtures/custom_pipeline/pipeline.py/0 | {
"file_path": "diffusers/tests/fixtures/custom_pipeline/pipeline.py",
"repo_id": "diffusers",
"token_count": 1738
} | 140 |
# 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 applicabl... | diffusers/tests/others/test_dependencies.py/0 | {
"file_path": "diffusers/tests/others/test_dependencies.py",
"repo_id": "diffusers",
"token_count": 775
} | 141 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/audioldm/test_audioldm.py/0 | {
"file_path": "diffusers/tests/pipelines/audioldm/test_audioldm.py",
"repo_id": "diffusers",
"token_count": 7498
} | 142 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/i2vgen_xl/test_i2vgenxl.py/0 | {
"file_path": "diffusers/tests/pipelines/i2vgen_xl/test_i2vgenxl.py",
"repo_id": "diffusers",
"token_count": 4240
} | 143 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_prior_emb2emb.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_prior_emb2emb.py",
"repo_id": "diffusers",
"token_count": 3478
} | 144 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/paint_by_example/test_paint_by_example.py/0 | {
"file_path": "diffusers/tests/pipelines/paint_by_example/test_paint_by_example.py",
"repo_id": "diffusers",
"token_count": 3653
} | 145 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_cascade/test_stable_cascade_prior.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_cascade/test_stable_cascade_prior.py",
"repo_id": "diffusers",
"token_count": 5151
} | 146 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax_inpaint.py",
"repo_id": "diffusers",
"token_count": 1259
} | 147 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_ldm3d/test_stable_diffusion_ldm3d.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_ldm3d/test_stable_diffusion_ldm3d.py",
"repo_id": "diffusers",
"token_count": 5569
} | 148 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLI... | diffusers/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py",
"repo_id": "diffusers",
"token_count": 5046
} | 149 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/unclip/test_unclip.py/0 | {
"file_path": "diffusers/tests/pipelines/unclip/test_unclip.py",
"repo_id": "diffusers",
"token_count": 7862
} | 150 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class DPMSolverMultistepSchedulerTest(SchedulerCommonTest):
scheduler_classes = (DP... | diffusers/tests/schedulers/test_scheduler_dpm_multi.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_dpm_multi.py",
"repo_id": "diffusers",
"token_count": 6455
} | 151 |
import torch
from diffusers import SASolverScheduler
from diffusers.utils.testing_utils import require_torchsde, torch_device
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class SASolverSchedulerTest(SchedulerCommonTest):
scheduler_classes = (SASolverScheduler,)
forward_default_kwargs =... | diffusers/tests/schedulers/test_scheduler_sasolver.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_sasolver.py",
"repo_id": "diffusers",
"token_count": 3629
} | 152 |
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
} | 153 |
# Introduction
<CourseFloatingBanner
unit={0}
classNames="absolute z-10 right-0 top-0"
/>
## Welcome to the course on diffusion models ๐ค !
## What to expect?
In this free course, you will:
- ๐ฉโ๐ Study the theory behind diffusion models
- ๐งจ Learn how to generate images and audio with the popular ๐ค Diffu... | diffusion-models-class/units/en/unit0/1.mdx/0 | {
"file_path": "diffusion-models-class/units/en/unit0/1.mdx",
"repo_id": "diffusion-models-class",
"token_count": 1359
} | 154 |
# Sprint Dreambooth en Keras
Cette paget rรฉsume toutes les informations pertinentes requises pour l'รฉvรฉnement. ๐.
## Introduction
Dreambooth est une technique de *finetuning* permettant d'enseigner de nouveaux concepts visuels ร des modรจles de diffusion conditionnรฉs par le texte en utilisant seulement 3 ร 5 images.... | diffusion-models-class/units/fr/events/3.mdx/0 | {
"file_path": "diffusion-models-class/units/fr/events/3.mdx",
"repo_id": "diffusion-models-class",
"token_count": 4273
} | 155 |
<jupyter_start><jupyter_text>Recherche sรฉmantique avec FAISS (PyTorch) Installez les bibliothรจques ๐ค Transformers et ๐ค Datasets pour exรฉcuter ce *notebook*.<jupyter_code>!pip install datasets evaluate transformers[sentencepiece]
!pip install faiss-gpu
from huggingface_hub import hf_hub_url
data_files = hf_hub_url(
... | notebooks/course/fr/chapter5/section6_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter5/section6_pt.ipynb",
"repo_id": "notebooks",
"token_count": 1233
} | 156 |
<jupyter_start><jupyter_text>Finetuner un modรจle de language masquรฉ (TensorFlow) Installez les bibliothรจques ๐ค *Datasets* et ๐ค *Transformers* pour exรฉcuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!apt install git-lfs<jupyter_output><empty_output><jupyter_text>Vous aurez besoin de... | notebooks/course/fr/chapter7/section3_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section3_tf.ipynb",
"repo_id": "notebooks",
"token_count": 2949
} | 157 |
<jupyter_start><jupyter_text>Comprendre la classe Interface Installez les bibliothรจques ๐ค Transformers et ๐ค Gradio pour exรฉcuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install gradio
import numpy as np
import gradio as gr
def reverse_audio(audio):
sr, data = audio
... | notebooks/course/fr/chapter9/section3.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter9/section3.ipynb",
"repo_id": "notebooks",
"token_count": 759
} | 158 |
<jupyter_start><jupyter_text>Image super-resolution using Latent Diffusion This colab notebook shows how to use the Latent Diffusion image super-resolution model using ๐งจ [diffusers](https://github.com/huggingface/diffusers) libray.The model was originally released in [Latent Diffusion repo](https://github.com/CompVis/... | notebooks/diffusers/latent_diffusion_upscaler.ipynb/0 | {
"file_path": "notebooks/diffusers/latent_diffusion_upscaler.ipynb",
"repo_id": "notebooks",
"token_count": 656
} | 159 |
# adapted from https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/pytorch/image_captioning.ipynb
# This example demonstrates normal finetuning (w/o peft) - for the sake of keeping the memory
# requirements small it freezes the original pre-trained text and image layers to keep the memory
# requirem... | notebooks/examples/idefics/finetune_image_captioning.py/0 | {
"file_path": "notebooks/examples/idefics/finetune_image_captioning.py",
"repo_id": "notebooks",
"token_count": 1670
} | 160 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install ๐ค Transformers as well as some other libraries. Uncomment the following cell and run it.<jupyter_code># Install
!pip install -q biopython transformers datasets huggingface_hub accelerate peft<jupyter_output>[2K ... | notebooks/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb/0 | {
"file_path": "notebooks/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb",
"repo_id": "notebooks",
"token_count": 8292
} | 161 |
<jupyter_start><jupyter_text>How to fine-tune a T5 model with ONNX RuntimeThis notebook is largely inspired by the summarization [notebook of Transformers](https://github.com/huggingface/notebooks/blob/main/examples/summarization.ipynb) which takes PyTorch as backend for fine tuning.Here you will use the `ORTSeq2SeqTra... | notebooks/examples/summarization_ort.ipynb/0 | {
"file_path": "notebooks/examples/summarization_ort.ipynb",
"repo_id": "notebooks",
"token_count": 6048
} | 162 |
<jupyter_start><jupyter_text>Getting started with Owl-ViTIn this notebook, we are going to run the [OWL-ViT](https://arxiv.org/abs/2205.06230) model (an open-vocabulary object detection model) by Google Research on scikit-image samples images. OWL-ViT: A Quick IntroOWL-ViT is an open-vocabulary object detector. Given ... | notebooks/examples/zeroshot_object_detection_with_owlvit.ipynb/0 | {
"file_path": "notebooks/examples/zeroshot_object_detection_with_owlvit.ipynb",
"repo_id": "notebooks",
"token_count": 4929
} | 163 |
import argparse
import logging
import os
import random
import sys
from datasets import load_from_disk
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import torch
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer
if __name__ == "__main_... | notebooks/sagemaker/06_sagemaker_metrics/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/06_sagemaker_metrics/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1415
} | 164 |
# SageMaker push to hf.co/models example | notebooks/sagemaker/14_train_and_push_to_hub/README.md/0 | {
"file_path": "notebooks/sagemaker/14_train_and_push_to_hub/README.md",
"repo_id": "notebooks",
"token_count": 12
} | 165 |
# 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": 1010
} | 166 |
<!--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/quantization.md/0 | {
"file_path": "peft/docs/source/developer_guides/quantization.md",
"repo_id": "peft",
"token_count": 2133
} | 167 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/package_reference/p_tuning.md/0 | {
"file_path": "peft/docs/source/package_reference/p_tuning.md",
"repo_id": "peft",
"token_count": 540
} | 168 |
<jupyter_start><jupyter_text>Training PEFT models with new tokens being added to the embedding layers and tokenizerIn this example, we will learn how to train a LoRA model when adding new tokens to the tokenizer and model. This is a common usecase when doing the following:1. Instruction finetuning with new tokens beind... | peft/examples/causal_language_modeling/peft_lora_clm_with_additional_tokens.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_lora_clm_with_additional_tokens.ipynb",
"repo_id": "peft",
"token_count": 4571
} | 169 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/examples/feature_extraction/peft_lora_embedding_semantic_search.py/0 | {
"file_path": "peft/examples/feature_extraction/peft_lora_embedding_semantic_search.py",
"repo_id": "peft",
"token_count": 8634
} | 170 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/examples/loftq_finetuning/train_gsm8k_llama.py/0 | {
"file_path": "peft/examples/loftq_finetuning/train_gsm8k_llama.py",
"repo_id": "peft",
"token_count": 14574
} | 171 |
<jupyter_start><jupyter_text>IntroductionIn this notebook, we will learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from ๐ค PEFT to fine-tune a SegFormer model variant for semantic segmentation by ONLY using **14%** of the original trainable parameters of the model. LoRA adds low-rank "update matrices" to cert... | peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb/0 | {
"file_path": "peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb",
"repo_id": "peft",
"token_count": 8322
} | 172 |
accelerate launch --config_file "configs/deepspeed_config.yaml" train.py \
--seed 100 \
--model_name_or_path "meta-llama/Llama-2-70b-hf" \
--dataset_name "smangrul/ultrachat-10k-chatml" \
--chat_template_format "chatml" \
--add_special_tokens False \
--append_concat_token False \
--splits "train,test" \
--max_seq_len ... | peft/examples/sft/run_peft_deepspeed.sh/0 | {
"file_path": "peft/examples/sft/run_peft_deepspeed.sh",
"repo_id": "peft",
"token_count": 454
} | 173 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/adalora/layer.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/layer.py",
"repo_id": "peft",
"token_count": 6986
} | 174 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/prefix_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/prefix_tuning/config.py",
"repo_id": "peft",
"token_count": 447
} | 175 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/regression/test_regression.py/0 | {
"file_path": "peft/tests/regression/test_regression.py",
"repo_id": "peft",
"token_count": 9655
} | 176 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_other.py/0 | {
"file_path": "peft/tests/test_other.py",
"repo_id": "peft",
"token_count": 892
} | 177 |
#!/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
} | 178 |
# 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
} | 179 |
# (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
} | 180 |
# 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
} | 181 |
# 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
} | 182 |
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
} | 183 |
""" Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from typing import Optional
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, KMNIST, FashionMNIST, ImageFolder
try:
from torchvision.datasets import Places365
has_places365 = True
except ImportError:
has_places3... | pytorch-image-models/timm/data/dataset_factory.py/0 | {
"file_path": "pytorch-image-models/timm/data/dataset_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 3864
} | 184 |
""" A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | pytorch-image-models/timm/data/readers/reader_image_tar.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_tar.py",
"repo_id": "pytorch-image-models",
"token_count": 1071
} | 185 |
""" Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers f... | pytorch-image-models/timm/layers/bottleneck_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/bottleneck_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 2907
} | 186 |
""" Filter Response Norm in PyTorch
Based on `Filter Response Normalization Layer` - https://arxiv.org/abs/1911.09737
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
from .create_act import create_act_layer
from .trace_utils import _assert
def inv_instance_rms(x, eps: float... | pytorch-image-models/timm/layers/filter_response_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/filter_response_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 1182
} | 187 |
""" Bilinear-Attention-Transform and Non-Local Attention
Paper: `Non-Local Neural Networks With Grouped Bilinear Attentional Transforms`
- https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.html
Adapted from original code:... | pytorch-image-models/timm/layers/non_local_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/non_local_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 3028
} | 188 |
""" Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
Code:... | pytorch-image-models/timm/layers/std_conv.py/0 | {
"file_path": "pytorch-image-models/timm/layers/std_conv.py",
"repo_id": "pytorch-image-models",
"token_count": 2483
} | 189 |
""" PyTorch FX Based Feature Extraction Helpers
Using https://pytorch.org/vision/stable/feature_extraction.html
"""
from typing import Callable, List, Dict, Union, Type
import torch
from torch import nn
from ._features import _get_feature_info, _get_return_layers
try:
from torchvision.models.feature_extraction i... | pytorch-image-models/timm/models/_features_fx.py/0 | {
"file_path": "pytorch-image-models/timm/models/_features_fx.py",
"repo_id": "pytorch-image-models",
"token_count": 1801
} | 190 |
"""
CoaT architecture.
Paper: Co-Scale Conv-Attentional Image Transformers - https://arxiv.org/abs/2104.06399
Official CoaT code at: https://github.com/mlpc-ucsd/CoaT
Modified from timm/models/vision_transformer.py
"""
from functools import partial
from typing import Tuple, List, Union
import torch
import torch.nn... | pytorch-image-models/timm/models/coat.py/0 | {
"file_path": "pytorch-image-models/timm/models/coat.py",
"repo_id": "pytorch-image-models",
"token_count": 15685
} | 191 |
""" EfficientViT (by MSRA)
Paper: `EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention`
- https://arxiv.org/abs/2305.07027
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/EfficientViT
"""
__all__ = ['EfficientVitMsra']
import itertools
from collections impor... | pytorch-image-models/timm/models/efficientvit_msra.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientvit_msra.py",
"repo_id": "pytorch-image-models",
"token_count": 11871
} | 192 |
""" 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
} | 193 |
""" Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | pytorch-image-models/timm/models/pvt_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/pvt_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 9047
} | 194 |
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... | pytorch-image-models/timm/models/swin_transformer_v2_cr.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2_cr.py",
"repo_id": "pytorch-image-models",
"token_count": 18939
} | 195 |
from .adabelief import AdaBelief
from .adafactor import Adafactor
from .adahessian import Adahessian
from .adamp import AdamP
from .adamw import AdamW
from .adan import Adan
from .lamb import Lamb
from .lars import Lars
from .lookahead import Lookahead
from .madgrad import MADGRAD
from .nadam import Nadam
from .nvnovog... | pytorch-image-models/timm/optim/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/optim/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 170
} | 196 |
"""RAdam Optimizer.
Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
def __init__(self, params, ... | pytorch-image-models/timm/optim/radam.py/0 | {
"file_path": "pytorch-image-models/timm/optim/radam.py",
"repo_id": "pytorch-image-models",
"token_count": 1967
} | 197 |
import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
""" Dispatch to gradient clipping method
Args:
parameters (Iterable): model parameters to clip
value (float): clipping value/factor/norm, m... | pytorch-image-models/timm/utils/clip_grad.py/0 | {
"file_path": "pytorch-image-models/timm/utils/clip_grad.py",
"repo_id": "pytorch-image-models",
"token_count": 306
} | 198 |
aml
target
server/transformers
server/flash-attention
| text-generation-inference/.dockerignore/0 | {
"file_path": "text-generation-inference/.dockerignore",
"repo_id": "text-generation-inference",
"token_count": 16
} | 199 |
install-server:
cd server && make install
install-custom-kernels:
if [ "$$BUILD_EXTENSIONS" = "True" ]; then cd server/custom_kernels && python setup.py install; else echo "Custom kernels are disabled, you need to set the BUILD_EXTENSIONS environment variable to 'True' in order to build them. (Please read the docs, ... | text-generation-inference/Makefile/0 | {
"file_path": "text-generation-inference/Makefile",
"repo_id": "text-generation-inference",
"token_count": 498
} | 200 |
# Serving Private & Gated Models
If the model you wish to serve is behind gated access or the model repository on Hugging Face Hub is private, and you have access to the model, you can provide your Hugging Face Hub access token. You can generate and copy a read token from [Hugging Face Hub tokens page](https://hugging... | text-generation-inference/docs/source/basic_tutorials/gated_model_access.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/gated_model_access.md",
"repo_id": "text-generation-inference",
"token_count": 320
} | 201 |
# Quick Tour
The easiest way of getting started is using the official Docker container. Install Docker following [their installation instructions](https://docs.docker.com/get-docker/).
Let's say you want to deploy [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model with... | text-generation-inference/docs/source/quicktour.md/0 | {
"file_path": "text-generation-inference/docs/source/quicktour.md",
"repo_id": "text-generation-inference",
"token_count": 1223
} | 202 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -10.0,
"text": "Test"
},
{
"id": 3853,... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma.json",
"repo_id": "text-generation-inference",
"token_count": 1049
} | 203 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 1724,
"logprob": -10.734375,
"text": "What"
},
{
"id": 33... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_simple.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_simple.json",
"repo_id": "text-generation-inference",
"token_count": 1227
} | 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": 17,
"prefill": [
{
"id": 1276,
"logprob": null,
"text": "What"
},
{
"id": 310,
"logprob": -1.5117188,
"text": " is"... | text-generation-inference/integration-tests/models/__snapshots__/test_mpt/test_mpt_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mpt/test_mpt_load.json",
"repo_id": "text-generation-inference",
"token_count": 7884
} | 206 |
import pytest
@pytest.fixture(scope="module")
def bloom_560m_sharded_handle(launcher):
with launcher("bigscience/bloom-560m", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def bloom_560m_sharded(bloom_560m_sharded_handle):
await bloom_560m_sharded_handle.health(240)
... | text-generation-inference/integration-tests/models/test_bloom_560m_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_bloom_560m_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 511
} | 207 |
import pytest
@pytest.fixture(scope="module")
def flash_starcoder2_handle(launcher):
with launcher("bigcode/starcoder2-3b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder2(flash_starcoder2_handle):
await flash_starcoder2_handle.health(300)
return f... | text-generation-inference/integration-tests/models/test_flash_starcoder2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_starcoder2.py",
"repo_id": "text-generation-inference",
"token_count": 601
} | 208 |
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