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# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)
[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.
The `train_dre... | diffusers/examples/research_projects/colossalai/README.md/0 | {
"file_path": "diffusers/examples/research_projects/colossalai/README.md",
"repo_id": "diffusers",
"token_count": 1659
} | 115 |
"""
The main idea for this code is to provide a way for users to not need to bother with the hassle of multiple tokens for a concept by typing
a photo of <concept>_0 <concept>_1 ... and so on
and instead just do
a photo of <concept>
which gets translated to the above. This needs to work for both inference and training.... | diffusers/examples/research_projects/multi_token_textual_inversion/multi_token_clip.py/0 | {
"file_path": "diffusers/examples/research_projects/multi_token_textual_inversion/multi_token_clip.py",
"repo_id": "diffusers",
"token_count": 1828
} | 116 |
# 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/t2i_adapter/test_t2i_adapter.py/0 | {
"file_path": "diffusers/examples/t2i_adapter/test_t2i_adapter.py",
"repo_id": "diffusers",
"token_count": 682
} | 117 |
## Textual Inversion fine-tuning example for SDXL
```
export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export DATA_DIR="./cat"
accelerate launch textual_inversion_sdxl.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_toke... | diffusers/examples/textual_inversion/README_sdxl.md/0 | {
"file_path": "diffusers/examples/textual_inversion/README_sdxl.md",
"repo_id": "diffusers",
"token_count": 294
} | 118 |
# 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/LICENSE-2.0
#
# Unless required by appl... | diffusers/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py/0 | {
"file_path": "diffusers/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py",
"repo_id": "diffusers",
"token_count": 16484
} | 119 |
# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# ========... | diffusers/scripts/convert_diffusers_to_original_sdxl.py/0 | {
"file_path": "diffusers/scripts/convert_diffusers_to_original_sdxl.py",
"repo_id": "diffusers",
"token_count": 6297
} | 120 |
# 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/scripts/convert_original_audioldm2_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_original_audioldm2_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 21165
} | 121 |
# Convert the original UniDiffuser checkpoints into diffusers equivalents.
import argparse
from argparse import Namespace
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
GPT2Tokenizer... | diffusers/scripts/convert_unidiffuser_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_unidiffuser_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 13871
} | 122 |
# 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/dependency_versions_check.py/0 | {
"file_path": "diffusers/src/diffusers/dependency_versions_check.py",
"repo_id": "diffusers",
"token_count": 381
} | 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/textual_inversion.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/textual_inversion.py",
"repo_id": "diffusers",
"token_count": 11973
} | 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/consistency_decoder_vae.py/0 | {
"file_path": "diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py",
"repo_id": "diffusers",
"token_count": 8597
} | 125 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
# `TemporalConvLayer` Copyright 2024 Alibaba DAMO-VILAB, The ModelScope 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.
#... | diffusers/src/diffusers/models/resnet.py/0 | {
"file_path": "diffusers/src/diffusers/models/resnet.py",
"repo_id": "diffusers",
"token_count": 14456
} | 126 |
from ...utils import is_flax_available, is_torch_available
if is_torch_available():
from .unet_1d import UNet1DModel
from .unet_2d import UNet2DModel
from .unet_2d_condition import UNet2DConditionModel
from .unet_3d_condition import UNet3DConditionModel
from .unet_i2vgen_xl import I2VGenXLUNet
... | diffusers/src/diffusers/models/unets/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/__init__.py",
"repo_id": "diffusers",
"token_count": 265
} | 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/upsampling.py/0 | {
"file_path": "diffusers/src/diffusers/models/upsampling.py",
"repo_id": "diffusers",
"token_count": 7671
} | 128 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_tr... | diffusers/src/diffusers/pipelines/audioldm2/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/audioldm2/__init__.py",
"repo_id": "diffusers",
"token_count": 637
} | 129 |
from typing import TYPE_CHECKING
from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
_import_structure = {"pipeline_latent_diffusion_uncond": ["LDMPipeline"]}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_latent_diffusion_uncond import LDMPipeline
else:
import sys
sys.modules[__name__]... | diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/__init__.py",
"repo_id": "diffusers",
"token_count": 190
} | 130 |
# 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/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py",
"repo_id": "diffusers",
"token_count": 18115
} | 131 |
# 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/free_init_utils.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/free_init_utils.py",
"repo_id": "diffusers",
"token_count": 3449
} | 132 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput
@dataclass
class LEditsPPDiffusionPipelineOutput(BaseOutput):
"""
Output class for LEdits++ Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` o... | diffusers/src/diffusers/pipelines/ledits_pp/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/ledits_pp/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 613
} | 133 |
import inspect
from itertools import repeat
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipelines.stable_diff... | diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 18046
} | 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/safety_checker.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker.py",
"repo_id": "diffusers",
"token_count": 2300
} | 135 |
# Copyright 2024 MultiDiffusion 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
#
# Un... | diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py",
"repo_id": "diffusers",
"token_count": 26369
} | 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/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py",
"repo_id": "diffusers",
"token_count": 12708
} | 137 |
import math
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
from ...models.attention import FeedForward
from ...models.attention_processor import Attention
from ...models.embeddings import Timestep... | diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py",
"repo_id": "diffusers",
"token_count": 24196
} | 138 |
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import SchedulerMixin
def gumbel_noise(t, generator=None):
device = generator.device ... | diffusers/src/diffusers/schedulers/scheduling_amused.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_amused.py",
"repo_id": "diffusers",
"token_count": 2780
} | 139 |
# Copyright 2024 ETH Zurich Computer Vision Lab 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... | diffusers/src/diffusers/schedulers/scheduling_repaint.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_repaint.py",
"repo_id": "diffusers",
"token_count": 6513
} | 140 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject):
_backends = ["flax", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax",... | diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py",
"repo_id": "diffusers",
"token_count": 957
} | 141 |
import os
from typing import Callable, Union
import PIL.Image
import PIL.ImageOps
import requests
def load_image(
image: Union[str, PIL.Image.Image], convert_method: Callable[[PIL.Image.Image], PIL.Image.Image] = None
) -> PIL.Image.Image:
"""
Loads `image` to a PIL Image.
Args:
image (`str`... | diffusers/src/diffusers/utils/loading_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/loading_utils.py",
"repo_id": "diffusers",
"token_count": 660
} | 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/lora/test_lora_layers_sdxl.py/0 | {
"file_path": "diffusers/tests/lora/test_lora_layers_sdxl.py",
"repo_id": "diffusers",
"token_count": 11919
} | 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/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
} | 144 |
import pickle as pkl
import unittest
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from diffusers.utils.outputs import BaseOutput
from diffusers.utils.testing_utils import require_torch
@dataclass
class CustomOutput(BaseOutput):
images: Union[List[PIL.Image... | diffusers/tests/others/test_outputs.py/0 | {
"file_path": "diffusers/tests/others/test_outputs.py",
"repo_id": "diffusers",
"token_count": 1506
} | 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_diffusion/test_onnx_stable_diffusion_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py",
"repo_id": "diffusers",
"token_count": 2242
} | 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_v_pred.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py",
"repo_id": "diffusers",
"token_count": 10288
} | 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_safe/test_safe_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py",
"repo_id": "diffusers",
"token_count": 7582
} | 148 |
# 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/test_pipelines.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipelines.py",
"repo_id": "diffusers",
"token_count": 39144
} | 149 |
import tempfile
import unittest
import torch
from diffusers import (
EDMDPMSolverMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class EDMDPMSolverMultistepSchedulerTest(SchedulerCommonTest):
scheduler_classes = (EDMDPMSolverMultistepScheduler,)
forward_default_kwargs = (("num_in... | diffusers/tests/schedulers/test_scheduler_edm_dpmsolver_multistep.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_edm_dpmsolver_multistep.py",
"repo_id": "diffusers",
"token_count": 5436
} | 150 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UniPCMultistepSchedulerTest(SchedulerCommonTest):
scheduler_classes = (UniPCM... | diffusers/tests/schedulers/test_scheduler_unipc.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_unipc.py",
"repo_id": "diffusers",
"token_count": 7482
} | 151 |
<jupyter_start><jupyter_text>IntroductionThis notebook is going to cover the basics of how to use Stable Diffusion to create and modify images using existing pipelines. We'll also take a brief look at the key components within the pipeline, while leaving further exploration of them to the deep dive notebook. Specifical... | diffusion-models-class/unit3/01_stable_diffusion_introduction.ipynb/0 | {
"file_path": "diffusion-models-class/unit3/01_stable_diffusion_introduction.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 6210
} | 152 |
<jupyter_start><jupyter_text>Que peuvent faire les *transformers* ? Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
from transformers import pipeline<jupyter_output><empty_output><jupyter_text>Analyse de sentiments<jupyter_code>classifier = ... | notebooks/course/fr/chapter1/section3.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter1/section3.ipynb",
"repo_id": "notebooks",
"token_count": 1607
} | 153 |
<jupyter_start><jupyter_text>Finetuner un modèle avec l'API Trainer Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce notebook.<jupyter_code>!pip install datasets transformers[sentencepiece]
from datasets import load_dataset
from transformers import AutoTokenizer, DataCollatorWithPadding
raw_... | notebooks/course/fr/chapter3/section3.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter3/section3.ipynb",
"repo_id": "notebooks",
"token_count": 754
} | 154 |
<jupyter_start><jupyter_text>Entraîner un modèle de langage causal de zéro (PyTorch)Ici nous entraînons un modèle à générer du code Python. Le Python utilisant des fonctions basées sur des mots anglais, nous gardons un gpt-2 anglais dans l'optique d'obtenir de meilleures performances que ce que l'on pourrait s'attendre... | notebooks/course/fr/chapter7/section6_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section6_pt.ipynb",
"repo_id": "notebooks",
"token_count": 4772
} | 155 |
<jupyter_start><jupyter_text>Dreambooth fine-tuning for Stable Diffusion using d🧨ffusers This notebook shows how to "teach" Stable Diffusion a new concept via Dreambooth using 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers). _By using just 3-5 images you can teach new concepts to Stabl... | notebooks/diffusers/sd_dreambooth_training.ipynb/0 | {
"file_path": "notebooks/diffusers/sd_dreambooth_training.ipynb",
"repo_id": "notebooks",
"token_count": 11907
} | 156 |
#!/bin/bash
#SBATCH --job-name=idefics_zero3_finetuning_multinode # name
#SBATCH --nodes=2 # nodes
#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
#SBATCH --cpus-per-task=96 # number of cores per tasks
#SBATCH --gres=gpu:8 # number of gp... | notebooks/examples/idefics/idefics_zero3_finetuning/slurm_script_idefics_zero3_finetuning_multinode.slurm/0 | {
"file_path": "notebooks/examples/idefics/idefics_zero3_finetuning/slurm_script_idefics_zero3_finetuning_multinode.slurm",
"repo_id": "notebooks",
"token_count": 389
} | 157 |
<jupyter_start><jupyter_text>Protein Folding with ESMFold and 🤗`transformers` ESMFold ([paper link](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2)) is a recently released protein folding model from FAIR. Unlike other protein folding models, it does not require external databases or search tools to predic... | notebooks/examples/protein_folding.ipynb/0 | {
"file_path": "notebooks/examples/protein_folding.ipynb",
"repo_id": "notebooks",
"token_count": 6321
} | 158 |
<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
} | 159 |
<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
} | 160 |
- title: Get started
sections:
- local: index
title: 🤗 PEFT
- local: quicktour
title: Quicktour
- local: install
title: Installation
- title: Tutorial
sections:
- local: tutorial/peft_model_config
title: Configurations and models
- local: tutorial/peft_integrations
title: Integration... | peft/docs/source/_toctree.yml/0 | {
"file_path": "peft/docs/source/_toctree.yml",
"repo_id": "peft",
"token_count": 1035
} | 161 |
<!--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/prefix_tuning.md/0 | {
"file_path": "peft/docs/source/package_reference/prefix_tuning.md",
"repo_id": "peft",
"token_count": 514
} | 162 |
# Fine-tuning for image classification using LoRA and 🤗 PEFT
## Vision Transformer model from transformers
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_peft_lora.ipyn... | peft/examples/image_classification/README.md/0 | {
"file_path": "peft/examples/image_classification/README.md",
"repo_id": "peft",
"token_count": 457
} | 163 |
<jupyter_start><jupyter_code>import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from pathlib import Path
from typing import Optional
import psutil
import json
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from tor... | peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb/0 | {
"file_path": "peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb",
"repo_id": "peft",
"token_count": 2282
} | 164 |
<jupyter_start><jupyter_code>import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from peft import (
get_peft_config,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
PeftType,
PrefixTuningConfig,
PromptEncoderConf... | peft/examples/sequence_classification/Prompt_Tuning.ipynb/0 | {
"file_path": "peft/examples/sequence_classification/Prompt_Tuning.ipynb",
"repo_id": "peft",
"token_count": 2018
} | 165 |
# 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/config.py/0 | {
"file_path": "peft/src/peft/config.py",
"repo_id": "peft",
"token_count": 4402
} | 166 |
# 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/adaption_prompt/layer.py/0 | {
"file_path": "peft/src/peft/tuners/adaption_prompt/layer.py",
"repo_id": "peft",
"token_count": 2468
} | 167 |
# 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/lora/__init__.py/0 | {
"file_path": "peft/src/peft/tuners/lora/__init__.py",
"repo_id": "peft",
"token_count": 413
} | 168 |
# 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/prompt_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/prompt_tuning/model.py",
"repo_id": "peft",
"token_count": 1482
} | 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/tests/test_config.py/0 | {
"file_path": "peft/tests/test_config.py",
"repo_id": "peft",
"token_count": 4091
} | 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/tests/testing_common.py/0 | {
"file_path": "peft/tests/testing_common.py",
"repo_id": "peft",
"token_count": 27060
} | 171 |
#!/bin/bash
NUM_PROC=$1
shift
torchrun --nproc_per_node=$NUM_PROC train.py "$@"
| pytorch-image-models/distributed_train.sh/0 | {
"file_path": "pytorch-image-models/distributed_train.sh",
"repo_id": "pytorch-image-models",
"token_count": 37
} | 172 |
# 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/docs/models/.templates/models/densenet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/densenet.md",
"repo_id": "pytorch-image-models",
"token_count": 3382
} | 173 |
# 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/docs/models/.templates/models/ig-resnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ig-resnext.md",
"repo_id": "pytorch-image-models",
"token_count": 2409
} | 174 |
# SWSL ResNeXt
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 transformations)... | pytorch-image-models/docs/models/.templates/models/swsl-resnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/swsl-resnext.md",
"repo_id": "pytorch-image-models",
"token_count": 2646
} | 175 |
- sections:
- local: index
title: Home
- local: quickstart
title: Quickstart
- local: installation
title: Installation
title: Get started
- sections:
- local: feature_extraction
title: Using Pretrained Models as Feature Extractors
- local: training_script
title: Training With The Offici... | pytorch-image-models/hfdocs/source/_toctree.yml/0 | {
"file_path": "pytorch-image-models/hfdocs/source/_toctree.yml",
"repo_id": "pytorch-image-models",
"token_count": 1686
} | 176 |
""" ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | pytorch-image-models/onnx_export.py/0 | {
"file_path": "pytorch-image-models/onnx_export.py",
"repo_id": "pytorch-image-models",
"token_count": 1811
} | 177 |
"""Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a ... | pytorch-image-models/tests/test_models.py/0 | {
"file_path": "pytorch-image-models/tests/test_models.py",
"repo_id": "pytorch-image-models",
"token_count": 9191
} | 178 |
""" Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from... | pytorch-image-models/timm/data/loader.py/0 | {
"file_path": "pytorch-image-models/timm/data/loader.py",
"repo_id": "pytorch-image-models",
"token_count": 6793
} | 179 |
""" Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabels... | pytorch-image-models/timm/data/real_labels.py/0 | {
"file_path": "pytorch-image-models/timm/data/real_labels.py",
"repo_id": "pytorch-image-models",
"token_count": 854
} | 180 |
""" Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to... | pytorch-image-models/timm/layers/config.py/0 | {
"file_path": "pytorch-image-models/timm/layers/config.py",
"repo_id": "pytorch-image-models",
"token_count": 1787
} | 181 |
from typing import Tuple
import torch
def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
"""generate N-D grid in dimension order.
The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
That is, the statement
[X1,X2,X3] = ndgrid(x1,x2,x3)
produc... | pytorch-image-models/timm/layers/grid.py/0 | {
"file_path": "pytorch-image-models/timm/layers/grid.py",
"repo_id": "pytorch-image-models",
"token_count": 565
} | 182 |
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
num_prefix_tokens: int = 1,
... | pytorch-image-models/timm/layers/patch_dropout.py/0 | {
"file_path": "pytorch-image-models/timm/layers/patch_dropout.py",
"repo_id": "pytorch-image-models",
"token_count": 842
} | 183 |
import torch
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_no... | pytorch-image-models/timm/layers/weight_init.py/0 | {
"file_path": "pytorch-image-models/timm/layers/weight_init.py",
"repo_id": "pytorch-image-models",
"token_count": 1838
} | 184 |
import copy
from collections import deque, defaultdict
from dataclasses import dataclass, field, replace, asdict
from typing import Any, Deque, Dict, Tuple, Optional, Union
__all__ = ['PretrainedCfg', 'filter_pretrained_cfg', 'DefaultCfg']
@dataclass
class PretrainedCfg:
"""
"""
# weight source location... | pytorch-image-models/timm/models/_pretrained.py/0 | {
"file_path": "pytorch-image-models/timm/models/_pretrained.py",
"repo_id": "pytorch-image-models",
"token_count": 1341
} | 185 |
""" CrossViT Model
@inproceedings{
chen2021crossvit,
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
booktitle={International Conference on Computer Vision (ICCV)},
year={2021}
}
Paper l... | pytorch-image-models/timm/models/crossvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/crossvit.py",
"repo_id": "pytorch-image-models",
"token_count": 12463
} | 186 |
""" MaxVit and CoAtNet Vision Transformer - CNN Hybrids in PyTorch
This is a from-scratch implementation of both CoAtNet and MaxVit in PyTorch.
99% of the implementation was done from papers, however last minute some adjustments were made
based on the (as yet unfinished?) public code release https://github.com/google... | pytorch-image-models/timm/models/maxxvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/maxxvit.py",
"repo_id": "pytorch-image-models",
"token_count": 42620
} | 187 |
""" RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | pytorch-image-models/timm/models/repvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/repvit.py",
"repo_id": "pytorch-image-models",
"token_count": 8357
} | 188 |
""" Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... | pytorch-image-models/timm/models/twins.py/0 | {
"file_path": "pytorch-image-models/timm/models/twins.py",
"repo_id": "pytorch-image-models",
"token_count": 9685
} | 189 |
"""
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... | pytorch-image-models/timm/optim/adamp.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adamp.py",
"repo_id": "pytorch-image-models",
"token_count": 1863
} | 190 |
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
from .scheduler_factory import create_scheduler, create_scheduler_v2, schedul... | pytorch-image-models/timm/scheduler/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 112
} | 191 |
""" JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT exectutor. These... | pytorch-image-models/timm/utils/jit.py/0 | {
"file_path": "pytorch-image-models/timm/utils/jit.py",
"repo_id": "pytorch-image-models",
"token_count": 1036
} | 192 |
[package]
name = "text-generation-benchmark"
description = "Text Generation Benchmarking tool"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[lib]
path = "src/lib.rs"
[[bin]]
name = "text-generation-benchmark"
path = "src/main.rs"
[dependencies]
average = "0.14"... | text-generation-inference/benchmark/Cargo.toml/0 | {
"file_path": "text-generation-inference/benchmark/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 381
} | 193 |
from text_generation.errors import (
parse_error,
GenerationError,
IncompleteGenerationError,
OverloadedError,
ValidationError,
BadRequestError,
ShardNotReadyError,
ShardTimeoutError,
NotFoundError,
RateLimitExceededError,
UnknownError,
)
def test_generation_error():
pa... | text-generation-inference/clients/python/tests/test_errors.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_errors.py",
"repo_id": "text-generation-inference",
"token_count": 598
} | 194 |
# Model safety.
[Pytorch uses pickle](https://pytorch.org/docs/master/generated/torch.load.html) by default meaning that for quite a long while
*Every* model using that format is potentially executing unintended code while purely loading the model.
There is a big red warning on Python's page for pickle [link](https:/... | text-generation-inference/docs/source/basic_tutorials/safety.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/safety.md",
"repo_id": "text-generation-inference",
"token_count": 465
} | 195 |
[
{
"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"
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_load.json",
"repo_id": "text-generation-inference",
"token_count": 4916
} | 196 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1041
} | 197 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6289062,
"text": " prin... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_load.json",
"repo_id": "text-generation-inference",
"token_count": 5176
} | 198 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 9,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": 0,
"tokens": [
{
"id": 16017,
"logprob": -0.30908203,
"spec... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 831
} | 199 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle_sharded(launcher):
with launcher(
"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
num_shard=2,
quantize="awq",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_ll... | text-generation-inference/integration-tests/models/test_flash_awq_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_awq_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 608
} | 200 |
import pytest
import json
from text_generation.types import GrammarType
@pytest.fixture(scope="module")
def non_flash_llama_grammar_handle(launcher):
with launcher(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
num_shard=1,
disable_grammar_support=False,
use_flash_attention=False,
) a... | text-generation-inference/integration-tests/models/test_grammar_llama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_grammar_llama.py",
"repo_id": "text-generation-inference",
"token_count": 1338
} | 201 |
use clap::{Parser, ValueEnum};
use nix::sys::signal::{self, Signal};
use nix::unistd::Pid;
use serde::Deserialize;
use std::env;
use std::ffi::OsString;
use std::io::{BufRead, BufReader, Lines};
use std::os::unix::process::{CommandExt, ExitStatusExt};
use std::path::Path;
use std::process::{Child, Command, ExitStatus, ... | text-generation-inference/launcher/src/main.rs/0 | {
"file_path": "text-generation-inference/launcher/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 20388
} | 202 |
//! A crate to extract and inject a OpenTelemetry context from and to a gRPC request.
//! Inspired by: https://github.com/open-telemetry/opentelemetry-rust gRPC examples
use opentelemetry::global;
use opentelemetry::propagation::{Extractor, Injector};
use tracing_opentelemetry::OpenTelemetrySpanExt;
/// Extract conte... | text-generation-inference/router/grpc-metadata/src/lib.rs/0 | {
"file_path": "text-generation-inference/router/grpc-metadata/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 889
} | 203 |
selective_scan_commit := 2a3704fd47ba817b415627b06fd796b971fdc137
causal-conv1d:
rm -rf causal-conv1d
git clone https://github.com/Dao-AILab/causal-conv1d.git
build-causal-conv1d: causal-conv1d
cd causal-conv1d/ && git checkout v1.1.1 # known latest working version tag
cd causal-conv1d/ && CAUSAL_CONV1D_FORCE_BUI... | text-generation-inference/server/Makefile-selective-scan/0 | {
"file_path": "text-generation-inference/server/Makefile-selective-scan",
"repo_id": "text-generation-inference",
"token_count": 351
} | 204 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _hip_compat_cuh
#define _hip_compat_cuh
// Workaround for a bug in hipamd, backported from upstream, this is fixed in ROCm 5.6.
__device__ __forceinline__ __half __compat_hrcp(__half x) {
return __half_raw{
static_cast<_Float1... | text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh",
"repo_id": "text-generation-inference",
"token_count": 1708
} | 205 |
#ifndef _qdq_3_cuh
#define _qdq_3_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_3BIT == 1
// Permutation:
//
// v9997775 55333111 u8886664 44222000 (u, v lsb)
// vjjjhhhf ffdddbbb uiiiggge eecccaaa
// vtttrrrp ppnnnlll usssqqqo oommmkkk
__forceinline__ __device__ void shuffle_3bit_32
(
uin... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh",
"repo_id": "text-generation-inference",
"token_count": 3335
} | 206 |
import pytest
import torch
from transformers import AutoTokenizer
from text_generation_server.models import Model
def get_test_model():
class TestModel(Model):
def batch_type(self):
raise NotImplementedError
def generate_token(self, batch):
raise NotImplementedError
... | text-generation-inference/server/tests/models/test_model.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_model.py",
"repo_id": "text-generation-inference",
"token_count": 829
} | 207 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 28490
} | 208 |
import torch
from typing import Optional
from text_generation_server.models.flash_mistral import BaseFlashMistral
from text_generation_server.models.custom_modeling.flash_mixtral_modeling import (
MixtralConfig,
FlashMixtralForCausalLM,
)
class FlashMixtral(BaseFlashMistral):
def __init__(
self,... | text-generation-inference/server/text_generation_server/models/flash_mixtral.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_mixtral.py",
"repo_id": "text-generation-inference",
"token_count": 421
} | 209 |
import torch
import torch.distributed
from transformers import AutoConfig, AutoTokenizer
from typing import Optional, List, Tuple
from text_generation_server.models import CausalLM
from text_generation_server.models.custom_modeling.phi_modeling import (
PhiConfig,
PhiForCausalLM,
)
from text_generation_server... | text-generation-inference/server/text_generation_server/models/phi.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/phi.py",
"repo_id": "text-generation-inference",
"token_count": 1000
} | 210 |
import torch
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
none_tensor = torch.empty((1, 1), device="meta")
def ext_make_q4(qweight, qzeros, scales, g_idx, device):
"""Construct Q4... | text-generation-inference/server/text_generation_server/utils/gptq/exllama.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/gptq/exllama.py",
"repo_id": "text-generation-inference",
"token_count": 1833
} | 211 |
## How to release
# Before the release
Simple checklist on how to make releases for `tokenizers`.
- Freeze `master` branch.
- Run all tests (Check CI has properly run)
- If any significant work, check benchmarks:
- `cd tokenizers && cargo bench` (needs to be run on latest release tag to measure difference if it's ... | tokenizers/RELEASE.md/0 | {
"file_path": "tokenizers/RELEASE.md",
"repo_id": "tokenizers",
"token_count": 1519
} | 212 |
/* eslint-disable */
var globRequire = require
console.log = (..._args: any[]) => {}
describe('quicktourExample', () => {
function require(mod: string) {
if (mod.startsWith('tokenizers')) {
return globRequire('../../')
} else {
return globRequire(mod)
}
}
it.skip('trains the tokenizer',... | tokenizers/bindings/node/examples/documentation/quicktour.test.ts/0 | {
"file_path": "tokenizers/bindings/node/examples/documentation/quicktour.test.ts",
"repo_id": "tokenizers",
"token_count": 2324
} | 213 |
{
"name": "tokenizers-android-arm-eabi",
"version": "0.13.4-rc1",
"os": [
"android"
],
"cpu": [
"arm"
],
"main": "tokenizers.android-arm-eabi.node",
"files": [
"tokenizers.android-arm-eabi.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
... | tokenizers/bindings/node/npm/android-arm-eabi/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/android-arm-eabi/package.json",
"repo_id": "tokenizers",
"token_count": 269
} | 214 |
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