text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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 ... | datasets/metrics/meteor/meteor.py/0 | {
"file_path": "datasets/metrics/meteor/meteor.py",
"repo_id": "datasets",
"token_count": 1898
} | 57 |
# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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 ... | datasets/metrics/sacrebleu/sacrebleu.py/0 | {
"file_path": "datasets/metrics/sacrebleu/sacrebleu.py",
"repo_id": "datasets",
"token_count": 3057
} | 58 |
# Metric Card for TER
## Metric Description
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a hypothesis requires to match a reference translation. We use the implementation that is already present in [sacrebleu](https://github.com/mjpost/sacreBLEU#ter),... | datasets/metrics/ter/README.md/0 | {
"file_path": "datasets/metrics/ter/README.md",
"repo_id": "datasets",
"token_count": 2596
} | 59 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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
#
# U... | datasets/src/datasets/arrow_reader.py/0 | {
"file_path": "datasets/src/datasets/arrow_reader.py",
"repo_id": "datasets",
"token_count": 11372
} | 60 |
import copy
import warnings
from dataclasses import InitVar, dataclass, field
from pathlib import Path
from typing import Any, Dict, Optional, Union
from .. import config
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*... | datasets/src/datasets/download/download_config.py/0 | {
"file_path": "datasets/src/datasets/download/download_config.py",
"repo_id": "datasets",
"token_count": 1880
} | 61 |
# Copyright 2021 The HuggingFace Authors.
#
# 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 to... | datasets/src/datasets/formatting/jax_formatter.py/0 | {
"file_path": "datasets/src/datasets/formatting/jax_formatter.py",
"repo_id": "datasets",
"token_count": 2858
} | 62 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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
#
# U... | datasets/src/datasets/keyhash.py/0 | {
"file_path": "datasets/src/datasets/keyhash.py",
"repo_id": "datasets",
"token_count": 1378
} | 63 |
from dataclasses import dataclass
from typing import Callable, Optional
import datasets
@dataclass
class GeneratorConfig(datasets.BuilderConfig):
generator: Optional[Callable] = None
gen_kwargs: Optional[dict] = None
features: Optional[datasets.Features] = None
def __post_init__(self):
asser... | datasets/src/datasets/packaged_modules/generator/generator.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/generator/generator.py",
"repo_id": "datasets",
"token_count": 351
} | 64 |
#
# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
# This file coems from the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style).
#
"""
Binary tensor encodings for PyTorch and NumPy.
This defines efficient binary encodings for tensors. The format is 8 byte
aligned and can be ... | datasets/src/datasets/packaged_modules/webdataset/_tenbin.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/webdataset/_tenbin.py",
"repo_id": "datasets",
"token_count": 3409
} | 65 |
import re
import textwrap
from collections import Counter
from itertools import groupby
from operator import itemgetter
from pathlib import Path
from typing import Any, ClassVar, Dict, List, Optional, Tuple, Union
import yaml
from huggingface_hub import DatasetCardData
from ..config import METADATA_CONFIGS_FIELD
from... | datasets/src/datasets/utils/metadata.py/0 | {
"file_path": "datasets/src/datasets/utils/metadata.py",
"repo_id": "datasets",
"token_count": 5993
} | 66 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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
#
# U... | datasets/src/datasets/utils/version.py/0 | {
"file_path": "datasets/src/datasets/utils/version.py",
"repo_id": "datasets",
"token_count": 1291
} | 67 |
import contextlib
import copy
import itertools
import json
import os
import pickle
import re
import sys
import tempfile
from functools import partial
from pathlib import Path
from unittest import TestCase
from unittest.mock import MagicMock, patch
import numpy as np
import numpy.testing as npt
import pandas as pd
impo... | datasets/tests/test_arrow_dataset.py/0 | {
"file_path": "datasets/tests/test_arrow_dataset.py",
"repo_id": "datasets",
"token_count": 123706
} | 68 |
import datetime
from pathlib import Path
from unittest import TestCase
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from datasets import Audio, Features, Image, IterableDataset
from datasets.formatting import NumpyFormatter, PandasFormatter, PythonFormatter, query_table
from datasets.form... | datasets/tests/test_formatting.py/0 | {
"file_path": "datasets/tests/test_formatting.py",
"repo_id": "datasets",
"token_count": 19116
} | 69 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_... | datasets/tests/test_search.py/0 | {
"file_path": "datasets/tests/test_search.py",
"repo_id": "datasets",
"token_count": 4372
} | 70 |
# Live 1: How the course work, Q&A, and playing with Huggy
In this first live stream, we explained how the course work (scope, units, challenges, and more) and answered your questions.
And finally, we saw some LunarLander agents you've trained and play with your Huggies 🐶
<Youtube id="JeJIswxyrsM" />
To know when ... | deep-rl-class/units/en/live1/live1.mdx/0 | {
"file_path": "deep-rl-class/units/en/live1/live1.mdx",
"repo_id": "deep-rl-class",
"token_count": 131
} | 71 |
# What is Reinforcement Learning? [[what-is-reinforcement-learning]]
To understand Reinforcement Learning, let’s start with the big picture.
## The big picture [[the-big-picture]]
The idea behind Reinforcement Learning is that an agent (an AI) will learn from the environment by **interacting with it** (through trial... | deep-rl-class/units/en/unit1/what-is-rl.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit1/what-is-rl.mdx",
"repo_id": "deep-rl-class",
"token_count": 624
} | 72 |
# Additional Readings [[additional-readings]]
These are **optional readings** if you want to go deeper.
- [Foundations of Deep RL Series, L2 Deep Q-Learning by Pieter Abbeel](https://youtu.be/Psrhxy88zww)
- [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/abs/1312.5602)
- [Double Deep Q-Learning](ht... | deep-rl-class/units/en/unit3/additional-readings.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit3/additional-readings.mdx",
"repo_id": "deep-rl-class",
"token_count": 163
} | 73 |
# Diving deeper into policy-gradient methods
## Getting the big picture
We just learned that policy-gradient methods aim to find parameters \\( \theta \\) that **maximize the expected return**.
The idea is that we have a *parameterized stochastic policy*. In our case, a neural network outputs a probability distribut... | deep-rl-class/units/en/unit4/policy-gradient.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit4/policy-gradient.mdx",
"repo_id": "deep-rl-class",
"token_count": 2364
} | 74 |
# Introduction [[introduction]]
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/thumbnail.png" alt="Thumbnail"/>
In unit 4, we learned about our first Policy-Based algorithm called **Reinforce**.
In Policy-Based methods, **we aim to optimize the policy direc... | deep-rl-class/units/en/unit6/introduction.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit6/introduction.mdx",
"repo_id": "deep-rl-class",
"token_count": 427
} | 75 |
# Hands-on: advanced Deep Reinforcement Learning. Using Sample Factory to play Doom from pixels
<CourseFloatingBanner classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit8/unit8_part2.ipynb"}
... | deep-rl-class/units/en/unit8/hands-on-sf.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit8/hands-on-sf.mdx",
"repo_id": "deep-rl-class",
"token_count": 5955
} | 76 |
# Generalization in Reinforcement Learning
Generalization plays a pivotal role in the realm of Reinforcement Learning. While **RL algorithms demonstrate good performance in controlled environments**, the real world presents a **unique challenge due to its non-stationary and open-ended nature**.
As a result, the devel... | deep-rl-class/units/en/unitbonus3/generalisation.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus3/generalisation.mdx",
"repo_id": "deep-rl-class",
"token_count": 250
} | 77 |
import argparse
import csv
import gc
import os
from dataclasses import dataclass
from typing import Dict, List, Union
import torch
import torch.utils.benchmark as benchmark
GITHUB_SHA = os.getenv("GITHUB_SHA", None)
BENCHMARK_FIELDS = [
"pipeline_cls",
"ckpt_id",
"batch_size",
"num_inference_steps",
... | diffusers/benchmarks/utils.py/0 | {
"file_path": "diffusers/benchmarks/utils.py",
"repo_id": "diffusers",
"token_count": 1254
} | 78 |
<!--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... | diffusers/docs/source/en/api/image_processor.md/0 | {
"file_path": "diffusers/docs/source/en/api/image_processor.md",
"repo_id": "diffusers",
"token_count": 448
} | 79 |
<!--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... | diffusers/docs/source/en/api/models/transformer2d.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/transformer2d.md",
"repo_id": "diffusers",
"token_count": 468
} | 80 |
<!--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... | diffusers/docs/source/en/api/pipelines/auto_pipeline.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/auto_pipeline.md",
"repo_id": "diffusers",
"token_count": 715
} | 81 |
<!--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... | diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md",
"repo_id": "diffusers",
"token_count": 681
} | 82 |
<!--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... | diffusers/docs/source/en/optimization/xformers.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/xformers.md",
"repo_id": "diffusers",
"token_count": 448
} | 83 |
<!--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... | diffusers/docs/source/en/training/t2i_adapters.md/0 | {
"file_path": "diffusers/docs/source/en/training/t2i_adapters.md",
"repo_id": "diffusers",
"token_count": 3503
} | 84 |
<!--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... | diffusers/docs/source/en/using-diffusers/custom_pipeline_examples.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/custom_pipeline_examples.md",
"repo_id": "diffusers",
"token_count": 1897
} | 85 |
<!--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... | diffusers/docs/source/en/using-diffusers/pipeline_overview.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/pipeline_overview.md",
"repo_id": "diffusers",
"token_count": 328
} | 86 |
<!--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... | diffusers/docs/source/ja/index.md/0 | {
"file_path": "diffusers/docs/source/ja/index.md",
"repo_id": "diffusers",
"token_count": 2032
} | 87 |
<!--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... | diffusers/docs/source/ko/optimization/open_vino.md/0 | {
"file_path": "diffusers/docs/source/ko/optimization/open_vino.md",
"repo_id": "diffusers",
"token_count": 921
} | 88 |
<!--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... | diffusers/docs/source/ko/training/text2image.md/0 | {
"file_path": "diffusers/docs/source/ko/training/text2image.md",
"repo_id": "diffusers",
"token_count": 6016
} | 89 |
<!--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... | 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": 6828
} | 90 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/examples/community/ddim_noise_comparative_analysis.py/0 | {
"file_path": "diffusers/examples/community/ddim_noise_comparative_analysis.py",
"repo_id": "diffusers",
"token_count": 3417
} | 91 |
## ----------------------------------------------------------
# A SDXL pipeline can take unlimited weighted prompt
#
# Author: Andrew Zhu
# Github: https://github.com/xhinker
# Medium: https://medium.com/@xhinker
## -----------------------------------------------------------
import inspect
import os
from typing import... | diffusers/examples/community/lpw_stable_diffusion_xl.py/0 | {
"file_path": "diffusers/examples/community/lpw_stable_diffusion_xl.py",
"repo_id": "diffusers",
"token_count": 49571
} | 92 |
# Copyright 2023 TencentARC and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | diffusers/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py/0 | {
"file_path": "diffusers/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py",
"repo_id": "diffusers",
"token_count": 34162
} | 93 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | diffusers/examples/controlnet/train_controlnet.py/0 | {
"file_path": "diffusers/examples/controlnet/train_controlnet.py",
"repo_id": "diffusers",
"token_count": 19700
} | 94 |
import argparse
import logging
import math
import os
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
import optax
import torch
import torch.utils.checkpoint
import transformers
from flax import jax_utils
from flax.training import train_state
from flax.training.common_utils import shard
f... | diffusers/examples/dreambooth/train_dreambooth_flax.py/0 | {
"file_path": "diffusers/examples/dreambooth/train_dreambooth_flax.py",
"repo_id": "diffusers",
"token_count": 11966
} | 95 |
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion... | diffusers/examples/research_projects/intel_opts/textual_inversion/README.md/0 | {
"file_path": "diffusers/examples/research_projects/intel_opts/textual_inversion/README.md",
"repo_id": "diffusers",
"token_count": 1014
} | 96 |
## [Deprecated] Multi Token Textual Inversion
**IMPORTART: This research project is deprecated. Multi Token Textual Inversion is now supported natively in [the official textual inversion example](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion#running-locally-with-pytorch).**
The author ... | diffusers/examples/research_projects/multi_token_textual_inversion/README.md/0 | {
"file_path": "diffusers/examples/research_projects/multi_token_textual_inversion/README.md",
"repo_id": "diffusers",
"token_count": 1842
} | 97 |
## Diffusers examples with ONNXRuntime optimizations
**This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Isamu Isozaki(isamu-isozaki) on github with any questions.**
The aim of this project is to provide retrieval augmented diffusion models to diffus... | diffusers/examples/research_projects/rdm/README.md/0 | {
"file_path": "diffusers/examples/research_projects/rdm/README.md",
"repo_id": "diffusers",
"token_count": 75
} | 98 |
# Stable Diffusion text-to-image fine-tuning
The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.
___Note___:
___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. I... | diffusers/examples/text_to_image/README.md/0 | {
"file_path": "diffusers/examples/text_to_image/README.md",
"repo_id": "diffusers",
"token_count": 4855
} | 99 |
import argparse
import re
import torch
import yaml
from transformers import (
CLIPProcessor,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionGLIGENPipeline,
StableDiffusionGLIGENTextImagePipeline,
U... | diffusers/scripts/convert_gligen_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_gligen_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 11153
} | 100 |
# Run inside root directory of official source code: https://github.com/dome272/wuerstchen/
import os
import torch
from transformers import AutoTokenizer, CLIPTextModel
from vqgan import VQModel
from diffusers import (
DDPMWuerstchenScheduler,
WuerstchenCombinedPipeline,
WuerstchenDecoderPipeline,
Wue... | diffusers/scripts/convert_wuerstchen.py/0 | {
"file_path": "diffusers/scripts/convert_wuerstchen.py",
"repo_id": "diffusers",
"token_count": 2171
} | 101 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/image_processor.py/0 | {
"file_path": "diffusers/src/diffusers/image_processor.py",
"repo_id": "diffusers",
"token_count": 16586
} | 102 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/adapter.py/0 | {
"file_path": "diffusers/src/diffusers/models/adapter.py",
"repo_id": "diffusers",
"token_count": 10101
} | 103 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/embeddings_flax.py/0 | {
"file_path": "diffusers/src/diffusers/models/embeddings_flax.py",
"repo_id": "diffusers",
"token_count": 1402
} | 104 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
from ...utils import BaseOutput
from ..at... | diffusers/src/diffusers/models/transformers/prior_transformer.py/0 | {
"file_path": "diffusers/src/diffusers/models/transformers/prior_transformer.py",
"repo_id": "diffusers",
"token_count": 7388
} | 105 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py",
"repo_id": "diffusers",
"token_count": 9817
} | 106 |
# Copyright 2023 Salesforce.com, inc.
# 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... | diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py",
"repo_id": "diffusers",
"token_count": 6484
} | 107 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py",
"repo_id": "diffusers",
"token_count": 2749
} | 108 |
from typing import TYPE_CHECKING
from ....utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and i... | diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/__init__.py",
"repo_id": "diffusers",
"token_count": 685
} | 109 |
# flake8: noqa
from typing import TYPE_CHECKING
from ....utils import (
DIFFUSERS_SLOW_IMPORT,
_LazyModule,
is_note_seq_available,
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
get_objects_from_module,
)
_dummy_objects = {}
_import_structure = {}
try:
i... | diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/__init__.py",
"repo_id": "diffusers",
"token_count": 985
} | 110 |
import inspect
from typing import Callable, List, Optional, Union
import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel
from ....models import AutoencoderKL, UNet2DConditionModel
from ....schedulers import KarrasDiffusionSchedulers
from ....utils impo... | diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py",
"repo_id": "diffusers",
"token_count": 9160
} | 111 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class MCLIPConfig(XLMRobertaConfig):
model_type = "M-CLIP"
def __init__(self, transformerDimSize=1024, imageDimSize=768, **kwargs):
self.transformerDimensions = transformerDimSize
self.numDims = imageDimS... | diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py",
"repo_id": "diffusers",
"token_count": 405
} | 112 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/pipelines/stable_diffusion/stable_unclip_image_normalizer.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/stable_unclip_image_normalizer.py",
"repo_id": "diffusers",
"token_count": 675
} | 113 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...utils import (
BaseOutput,
)
@dataclass
class StableDiffusionSafePipelineOutput(BaseOutput):
"""
Output class for Safe Stable Diffusion pipelines.
Args:
images (`List[PIL.I... | diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 527
} | 114 |
# Copyright 2023 TencentARC and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py",
"repo_id": "diffusers",
"token_count": 20713
} | 115 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is... | diffusers/src/diffusers/pipelines/wuerstchen/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/__init__.py",
"repo_id": "diffusers",
"token_count": 849
} | 116 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/schedulers/scheduling_consistency_models.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_consistency_models.py",
"repo_id": "diffusers",
"token_count": 8103
} | 117 |
# Copyright 2023 Kakao Brain and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | diffusers/src/diffusers/schedulers/scheduling_unclip.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_unclip.py",
"repo_id": "diffusers",
"token_count": 6255
} | 118 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class AudioDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "librosa"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "librosa"])
... | diffusers/src/diffusers/utils/dummy_torch_and_librosa_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_torch_and_librosa_objects.py",
"repo_id": "diffusers",
"token_count": 397
} | 119 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/utils/state_dict_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/state_dict_utils.py",
"repo_id": "diffusers",
"token_count": 5702
} | 120 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/models/autoencoders/test_models_vq.py/0 | {
"file_path": "diffusers/tests/models/autoencoders/test_models_vq.py",
"repo_id": "diffusers",
"token_count": 1281
} | 121 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/models/unets/test_unet_2d_blocks.py/0 | {
"file_path": "diffusers/tests/models/unets/test_unet_2d_blocks.py",
"repo_id": "diffusers",
"token_count": 5187
} | 122 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/amused/test_amused_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/amused/test_amused_inpaint.py",
"repo_id": "diffusers",
"token_count": 4714
} | 123 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/controlnet/test_controlnet_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/controlnet/test_controlnet_inpaint.py",
"repo_id": "diffusers",
"token_count": 10794
} | 124 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py",
"repo_id": "diffusers",
"token_count": 4263
} | 125 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/musicldm/test_musicldm.py/0 | {
"file_path": "diffusers/tests/pipelines/musicldm/test_musicldm.py",
"repo_id": "diffusers",
"token_count": 7999
} | 126 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 6722
} | 127 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_latent_upscale.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_latent_upscale.py",
"repo_id": "diffusers",
"token_count": 4990
} | 128 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_panorama/test_stable_diffusion_panorama.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_panorama/test_stable_diffusion_panorama.py",
"repo_id": "diffusers",
"token_count": 7597
} | 129 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
import diffusers
from diffusers import (
AutoencoderKLTemporalDecoder,
EulerDiscreteScheduler,
StableVideoDi... | diffusers/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py",
"repo_id": "diffusers",
"token_count": 9425
} | 130 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils.testing_utils import require_torchsde, torch_device
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class DPMSolverSDESchedulerTest(SchedulerCommonTest):
scheduler_classes = (DPMSolverSDEScheduler,)
num_inferenc... | diffusers/tests/schedulers/test_scheduler_dpm_sde.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_dpm_sde.py",
"repo_id": "diffusers",
"token_count": 3050
} | 131 |
import torch
import torch.nn.functional as F
from diffusers import VQDiffusionScheduler
from .test_schedulers import SchedulerCommonTest
class VQDiffusionSchedulerTest(SchedulerCommonTest):
scheduler_classes = (VQDiffusionScheduler,)
def get_scheduler_config(self, **kwargs):
config = {
... | diffusers/tests/schedulers/test_scheduler_vq_diffusion.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_vq_diffusion.py",
"repo_id": "diffusers",
"token_count": 694
} | 132 |
<jupyter_start><jupyter_text>Introduction à 🤗 Diffusers Dans ce *notebook*, vous allez entraîner votre premier modèle de diffusion pour générer des images de mignons papillons 🦋. En cours de route, vous apprendrez les composants de base de la bibliothèque 🤗 *Diffusers*, qui fournira une bonne assise pour les applica... | diffusion-models-class/units/fr/unit1/introduction_to_diffusers.ipynb/0 | {
"file_path": "diffusion-models-class/units/fr/unit1/introduction_to_diffusers.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 11144
} | 133 |
<jupyter_start><jupyter_text>Modèles (PyTorch) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
from transformers import CamembertConfig, CamembertModel
# Construire la configuration
config = CamembertConfig()
# Construire le modèle à parti... | notebooks/course/fr/chapter2/section3_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section3_pt.ipynb",
"repo_id": "notebooks",
"token_count": 341
} | 134 |
<jupyter_start><jupyter_text>Utilisation de modèles pré-entraînés (TensorFlow) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert-base")
re... | notebooks/course/fr/chapter4/section2_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter4/section2_tf.ipynb",
"repo_id": "notebooks",
"token_count": 270
} | 135 |
<jupyter_start><jupyter_text>Tokenisation *Byte-Pair Encoding* Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
corpus = [
"C'est le cours d'Hugging Face.",
"Ce chapitre traite de la tokenisation.",
"Ce... | notebooks/course/fr/chapter6/section5.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter6/section5.ipynb",
"repo_id": "notebooks",
"token_count": 1572
} | 136 |
<jupyter_start><jupyter_text>Que faire quand vous obtenez une erreurCe chapitre portant sur le débogage, la langue nous importe peu ici. Nous nous intéressons surtout à la logique du code pour comprendre d'où provient l'erreur. Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*.<jupy... | notebooks/course/fr/chapter8/section2.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter8/section2.ipynb",
"repo_id": "notebooks",
"token_count": 1365
} | 137 |
<jupyter_start><jupyter_text>Running IF with 🧨 diffusers on a Free Tier Google Colab_**TL;DR**: We show how to run one of the most powerful open-source text to image models **IF** on a free-tier Google Colab with 🧨 diffusers._*by DeepFloyd &* 🤗 *HuggingFace* *Image taken from official IF GitHub repo [here](https://... | notebooks/diffusers/deepfloyd_if_free_tier_google_colab.ipynb/0 | {
"file_path": "notebooks/diffusers/deepfloyd_if_free_tier_google_colab.ipynb",
"repo_id": "notebooks",
"token_count": 9958
} | 138 |
<jupyter_start><jupyter_text>🧨 Stable Diffusion in JAX / Flax ! 🤗 Hugging Face [Diffusers](https://github.com/huggingface/diffusers) supports Flax since version `0.5.1`! This allows for super fast inference on Google TPUs, such as those available in Colab, Kaggle or Google Cloud Platform.This notebook shows how to ru... | notebooks/diffusers/stable_diffusion_jax_how_to.ipynb/0 | {
"file_path": "notebooks/diffusers/stable_diffusion_jax_how_to.ipynb",
"repo_id": "notebooks",
"token_count": 3380
} | 139 |
<jupyter_start><jupyter_text>The Annotated Diffusion Model nielsr Niels Rogge kashif Kashif Rasul In this blog post, we'll take a deeper look into **Denoising Diffusion Probabilistic Models** (also known as D... | notebooks/examples/annotated_diffusion.ipynb/0 | {
"file_path": "notebooks/examples/annotated_diffusion.ipynb",
"repo_id": "notebooks",
"token_count": 16579
} | 140 |
<jupyter_start><jupyter_text>**Fine-tuning for Image Classification with 🤗 Transformers**This notebook shows how to fine-tune any pretrained Vision model for Image Classification on a custom dataset. The idea is to add a randomly initialized classification head on top of a pre-trained encoder, and fine-tune the model ... | notebooks/examples/image_classification-tf.ipynb/0 | {
"file_path": "notebooks/examples/image_classification-tf.ipynb",
"repo_id": "notebooks",
"token_count": 9526
} | 141 |
<jupyter_start><jupyter_text>Fine-tunining DeBERTa model on a question answering task with ORTTrainer In this notebook, we will see how to fine-tune the [DeBERTa base](https://huggingface.co/microsoft/deberta-base/tree/main) model to a question answering task, which is the task of extracting the answer to a question fr... | notebooks/examples/question_answering_ort.ipynb/0 | {
"file_path": "notebooks/examples/question_answering_ort.ipynb",
"repo_id": "notebooks",
"token_count": 10005
} | 142 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. We will also use the `seqeval` library to compute some evaluation metrics. Uncomment the following cell and run it.<jupyter_code>#! pip install transformers
#! pip install datasets
#... | notebooks/examples/token_classification-tf.ipynb/0 | {
"file_path": "notebooks/examples/token_classification-tf.ipynb",
"repo_id": "notebooks",
"token_count": 9592
} | 143 |
accelerate launch --config_file accelerate_config.yaml run_seq2seq_no_trainer.py \
--dataset_name "smangrul/MuDoConv" \
--max_source_length 128 \
--source_prefix "chatbot: " \
--max_target_length 64 \
--val_max_target_length 64 \
--val_min_target_length 20 \
--n_val_batch_generations 5 \
... | notebooks/sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/launch.sh/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/launch.sh",
"repo_id": "notebooks",
"token_count": 355
} | 144 |
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
def model_fn(model_dir):
# load model and processor from model_dir
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
return model,... | notebooks/sagemaker/24_train_bloom_peft_lora/scripts/inference.py/0 | {
"file_path": "notebooks/sagemaker/24_train_bloom_peft_lora/scripts/inference.py",
"repo_id": "notebooks",
"token_count": 348
} | 145 |
<!--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": 1937
} | 146 |
<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Models
[`PeftModel`] is the base model class for specifying the base Transformer model and configuration to apply a PEFT method to. The base `Peft... | peft/docs/source/package_reference/peft_model.md/0 | {
"file_path": "peft/docs/source/package_reference/peft_model.md",
"repo_id": "peft",
"token_count": 486
} | 147 |
<jupyter_start><jupyter_code>import os
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import get_peft_model, PromptTuningConfig, TaskType, PromptTuningInit
from torch.utils.data import DataLoader
from tqdm import tqdm
from da... | peft/examples/conditional_generation/peft_prompt_tuning_seq2seq.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_prompt_tuning_seq2seq.ipynb",
"repo_id": "peft",
"token_count": 2336
} | 148 |
# LoftQ: LoRA-fine-tuning-aware Quantization
## Introduction
LoftQ finds quantized LoRA initialization: quantized backbone Q and LoRA adapters A and B, given a pre-trained weight W.
## Quick Start
Steps:
1. Apply LoftQ to a full-precision pre-trained weight and save.
2. Load LoftQ initialization and train.
For ste... | peft/examples/loftq_finetuning/README.md/0 | {
"file_path": "peft/examples/loftq_finetuning/README.md",
"repo_id": "peft",
"token_count": 1880
} | 149 |
<jupyter_start><jupyter_text>IntroductionIn this notebook, we will learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 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
} | 150 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/adalora/layer.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/layer.py",
"repo_id": "peft",
"token_count": 6963
} | 151 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/multitask_prompt_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/multitask_prompt_tuning/model.py",
"repo_id": "peft",
"token_count": 2100
} | 152 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/tests/test_config.py/0 | {
"file_path": "peft/tests/test_config.py",
"repo_id": "peft",
"token_count": 4241
} | 153 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/tests/testing_utils.py/0 | {
"file_path": "peft/tests/testing_utils.py",
"repo_id": "peft",
"token_count": 1141
} | 154 |
# Recent Changes
### Dec 23, 2022 🎄☃
* Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013)
* NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP
* Many more models updated to ... | pytorch-image-models/docs/changes.md/0 | {
"file_path": "pytorch-image-models/docs/changes.md",
"repo_id": "pytorch-image-models",
"token_count": 23289
} | 155 |
# Dual Path Network (DPN)
A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while DenseNet enables new feature exploration, and both are importa... | pytorch-image-models/docs/models/.templates/models/dpn.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/dpn.md",
"repo_id": "pytorch-image-models",
"token_count": 2889
} | 156 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.