text stringlengths 7 328k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 459 |
|---|---|---|---|
# Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.
# Copyright 2024 The ModelScope 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.apa... | diffusers/src/diffusers/models/unets/unet_3d_condition.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_3d_condition.py",
"repo_id": "diffusers",
"token_count": 15230
} | 123 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
_LazyModule,
)
_import_structure = {
"pipeline_consistency_models": ["ConsistencyModelPipeline"],
}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_consistency_models import ConsistencyModelPipeline
else:
i... | diffusers/src/diffusers/pipelines/consistency_models/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/consistency_models/__init__.py",
"repo_id": "diffusers",
"token_count": 209
} | 124 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
_LazyModule,
)
_import_structure = {"pipeline_ddpm": ["DDPMPipeline"]}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_ddpm import DDPMPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
... | diffusers/src/diffusers/pipelines/ddpm/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/ddpm/__init__.py",
"repo_id": "diffusers",
"token_count": 193
} | 125 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class TransformationModelOutput(ModelOutput):
"""
Base class for text... | diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py",
"repo_id": "diffusers",
"token_count": 2332
} | 126 |
# Copyright 2022 The Music Spectrogram Diffusion Authors.
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache... | diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py",
"repo_id": "diffusers",
"token_count": 1329
} | 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/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py",
"repo_id": "diffusers",
"token_count": 11560
} | 128 |
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
} | 129 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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.a... | diffusers/src/diffusers/pipelines/pipeline_flax_utils.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/pipeline_flax_utils.py",
"repo_id": "diffusers",
"token_count": 12211
} | 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/stable_cascade/pipeline_stable_cascade_prior.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py",
"repo_id": "diffusers",
"token_count": 14207
} | 131 |
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput, is_flax_available
@dataclass
class StableDiffusionXLPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Im... | diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 401
} | 132 |
import copy
import inspect
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from torch.nn.functional import grid_sample
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
... | diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py",
"repo_id": "diffusers",
"token_count": 28438
} | 133 |
# 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/wuerstchen/pipeline_wuerstchen_prior.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py",
"repo_id": "diffusers",
"token_count": 10629
} | 134 |
# Copyright 2024 Microsoft 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 require... | diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py",
"repo_id": "diffusers",
"token_count": 12487
} | 135 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class OnnxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["... | diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py",
"repo_id": "diffusers",
"token_count": 1270
} | 136 |
# Copyright 2020 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/versions.py/0 | {
"file_path": "diffusers/src/diffusers/utils/versions.py",
"repo_id": "diffusers",
"token_count": 1699
} | 137 |
# 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/test_layers_utils.py/0 | {
"file_path": "diffusers/tests/models/test_layers_utils.py",
"repo_id": "diffusers",
"token_count": 10674
} | 138 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/tests/others/test_check_copies.py/0 | {
"file_path": "diffusers/tests/others/test_check_copies.py",
"repo_id": "diffusers",
"token_count": 2032
} | 139 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AnimateDiffPipeline,
AutoencoderKL,
DDIMScheduler,
MotionAdapter,
UNet2DConditionModel,
UNetMotionModel,
)
from diffusers.uti... | diffusers/tests/pipelines/animatediff/test_animatediff.py/0 | {
"file_path": "diffusers/tests/pipelines/animatediff/test_animatediff.py",
"repo_id": "diffusers",
"token_count": 6157
} | 140 |
# 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/controlnet/test_controlnet_sdxl.py/0 | {
"file_path": "diffusers/tests/pipelines/controlnet/test_controlnet_sdxl.py",
"repo_id": "diffusers",
"token_count": 22099
} | 141 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_img2img.py",
"repo_id": "diffusers",
"token_count": 4352
} | 142 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py",
"repo_id": "diffusers",
"token_count": 11021
} | 143 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_k_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_k_diffusion.py",
"repo_id": "diffusers",
"token_count": 2096
} | 144 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero_sdxl.py/0 | {
"file_path": "diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero_sdxl.py",
"repo_id": "diffusers",
"token_count": 7187
} | 145 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class DDPMSchedulerTest(SchedulerCommonTest):
scheduler_classes = (DDPMScheduler,)
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1000,
"beta_start"... | diffusers/tests/schedulers/test_scheduler_ddpm.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_ddpm.py",
"repo_id": "diffusers",
"token_count": 3860
} | 146 |
import tempfile
from typing import Dict, List, Tuple
import torch
from diffusers import LCMScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class LCMSchedulerTest(SchedulerCommonTest):
scheduler_classes = (LCMScheduler,)
forward_default_kwarg... | diffusers/tests/schedulers/test_scheduler_lcm.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_lcm.py",
"repo_id": "diffusers",
"token_count": 5668
} | 147 |
# 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/utils/check_table.py/0 | {
"file_path": "diffusers/utils/check_table.py",
"repo_id": "diffusers",
"token_count": 3010
} | 148 |
# Keras Dreambooth event! 🤗
This document summarises all the relevant information required for the event 📋.
## Introduction
Dreambooth is a fine-tuning technique to teach new visual concepts to text-conditioned Diffusion models with just 3-5 images. With Dreambooth, you could generate funny and realistic images ... | diffusion-models-class/units/en/events/3.mdx/0 | {
"file_path": "diffusion-models-class/units/en/events/3.mdx",
"repo_id": "diffusion-models-class",
"token_count": 3063
} | 149 |
- title: Introduction au cours
sections:
- local: unit0/1
title: Introduction
- title: 1. Introduction aux modèles de diffusion
sections:
- local: unit1/1
title: Vue d'ensemble
- local: unit1/2
title: Introduction à 🤗 Diffusers
- local: unit1/3
title: Implémentation à partir de 0
- title:... | diffusion-models-class/units/fr/_toctree.yml/0 | {
"file_path": "diffusion-models-class/units/fr/_toctree.yml",
"repo_id": "diffusion-models-class",
"token_count": 494
} | 150 |
# notebooks
Notebooks using the Hugging Face libraries 🤗
| notebooks/README.md/0 | {
"file_path": "notebooks/README.md",
"repo_id": "notebooks",
"token_count": 15
} | 151 |
<jupyter_start><jupyter_text>Manipulation de plusieurs séquences (PyTorch) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
checkpoint = "tblard/tf-alloc... | notebooks/course/fr/chapter2/section5_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section5_pt.ipynb",
"repo_id": "notebooks",
"token_count": 814
} | 152 |
<jupyter_start><jupyter_text>Il est temps de trancher et de découper Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*.<jupyter_code>!pip install datasets evaluate transformers[sentencepiece]
!wget "https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip"
!u... | notebooks/course/fr/chapter5/section3.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter5/section3.ipynb",
"repo_id": "notebooks",
"token_count": 1912
} | 153 |
<jupyter_start><jupyter_text>Classification de token (PyTorch) Installez les bibliothèques 🤗 *Datasets*, 🤗 *Transformers* et 🤗 *Accelerate* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install accelerate
# Pour exécuter l'entraînement sur TPU, vous devrez décommen... | notebooks/course/fr/chapter7/section2_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section2_pt.ipynb",
"repo_id": "notebooks",
"token_count": 3899
} | 154 |
<jupyter_start><jupyter_text>IntroductionThis colab is design to run the pretrained models from [GeoDiff](https://github.com/MinkaiXu/GeoDiff).The visualization code is inspired by this PyMol [colab](https://colab.research.google.com/gist/iwatobipen/2ec7faeafe5974501e69fcc98c122922/pymol.ipynbscrollTo=Hm4kY7CaZSlw).The... | notebooks/diffusers/geodiff_molecule_conformation.ipynb/0 | {
"file_path": "notebooks/diffusers/geodiff_molecule_conformation.ipynb",
"repo_id": "notebooks",
"token_count": 18632
} | 155 |
<jupyter_start><jupyter_text>**How to benchmark models with Transformers**With ever-larger language models, it is no longer enough to just compare models on their performance on a specific task. One should always be aware of the computational cost that is attached to a specific model. For a given computation environmen... | notebooks/examples/benchmark.ipynb/0 | {
"file_path": "notebooks/examples/benchmark.ipynb",
"repo_id": "notebooks",
"token_count": 12105
} | 156 |
<jupyter_start><jupyter_text>**Building an Image Similarity System with 🤗 Transformers**In this notebook, you'll learn to build an image similarity system with 🤗 Transformers. Finding out the similarity between a query image and potential candidates is an important use case for information retrieval systems, reverse ... | notebooks/examples/image_similarity.ipynb/0 | {
"file_path": "notebooks/examples/image_similarity.ipynb",
"repo_id": "notebooks",
"token_count": 8098
} | 157 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.<jupyter_code>#! pip install datasets transformers<jupyter_output><empty_output><jupyter_text>If you're opening this notebook locally, make su... | notebooks/examples/multiple_choice.ipynb/0 | {
"file_path": "notebooks/examples/multiple_choice.ipynb",
"repo_id": "notebooks",
"token_count": 6252
} | 158 |
<jupyter_start><jupyter_text>**Fine-tuning Speech Model with 🤗 Transformers** This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition. This notebook is built to run on the [TIMIT dataset](https://huggingface.co/datasets/timit) with any speech model checkpoint from t... | notebooks/examples/speech_recognition.ipynb/0 | {
"file_path": "notebooks/examples/speech_recognition.ipynb",
"repo_id": "notebooks",
"token_count": 9428
} | 159 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it. We also use the `sacrebleu` and `sentencepiece` libraries - you may need to install these even if you already have 🤗 Transformers!<jupyter_c... | notebooks/examples/translation-tf.ipynb/0 | {
"file_path": "notebooks/examples/translation-tf.ipynb",
"repo_id": "notebooks",
"token_count": 8046
} | 160 |
<jupyter_start><jupyter_text>Spot Instances - Amazon SageMaker x Hugging Face Transformers Learn how to use Spot Instances and Checkpointing and save up to 90% training cost [Amazon EC2 Spot Instances](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html) are a way to take advantage of unused E... | notebooks/sagemaker/05_spot_instances/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/05_spot_instances/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3523
} | 161 |
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
# Helper: Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embedd... | notebooks/sagemaker/17_custom_inference_script/code/inference.py/0 | {
"file_path": "notebooks/sagemaker/17_custom_inference_script/code/inference.py",
"repo_id": "notebooks",
"token_count": 487
} | 162 |
base_job_name: accelerate-sagemaker-1
compute_environment: AMAZON_SAGEMAKER
distributed_type: DATA_PARALLEL
ec2_instance_type: ml.p3.16xlarge
iam_role_name: xxxxx
image_uri: null
mixed_precision: fp16
num_machines: 1
profile: xxxxx
py_version: py38
pytorch_version: 1.10.2
region: us-east-1
sagemaker_inputs_file: sagema... | notebooks/sagemaker/22_accelerate_sagemaker_examples/src/text-classification/accelerate_config.yaml/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/src/text-classification/accelerate_config.yaml",
"repo_id": "notebooks",
"token_count": 177
} | 163 |
<jupyter_start><jupyter_text>How to scale LLM workloads to 20B+ with multi-node clusters on Amazon SageMaker using Hugging Face and PyTorch FSDPIn this tutorial, we will fine-tune the new [GPT-NeoXT-Chat-Base-20B](https://huggingface.co/togethercomputer/GPT-NeoXT-Chat-Base-20B) on the [ELI5](https://huggingface.co/data... | notebooks/sagemaker/25_pytorch_fsdp_model_parallelism/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/25_pytorch_fsdp_model_parallelism/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3866
} | 164 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/developer_guides/low_level_api.md/0 | {
"file_path": "peft/docs/source/developer_guides/low_level_api.md",
"repo_id": "peft",
"token_count": 1262
} | 165 |
<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
} | 166 |
<jupyter_start><jupyter_text>Initializing weights with LoftQ by replacing LoRA weights in-place This notebook shows how to apply [LoftQ](https://arxiv.org/abs/2310.08659) initialization on our QLoRA model.In short, the idea behind LoftQ is the following. When we use QLoRA, i.e. we quantize the base model with bitsandby... | peft/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb/0 | {
"file_path": "peft/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb",
"repo_id": "peft",
"token_count": 2206
} | 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/adalora/bnb.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/bnb.py",
"repo_id": "peft",
"token_count": 2713
} | 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/lycoris_utils.py/0 | {
"file_path": "peft/src/peft/tuners/lycoris_utils.py",
"repo_id": "peft",
"token_count": 7188
} | 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/src/peft/tuners/poly/model.py/0 | {
"file_path": "peft/src/peft/tuners/poly/model.py",
"repo_id": "peft",
"token_count": 2924
} | 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/src/peft/utils/save_and_load.py/0 | {
"file_path": "peft/src/peft/utils/save_and_load.py",
"repo_id": "peft",
"token_count": 6629
} | 171 |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | peft/tests/test_low_level_api.py/0 | {
"file_path": "peft/tests/test_low_level_api.py",
"repo_id": "peft",
"token_count": 1280
} | 172 |
# PyTorch Image Models
- [What's New](#whats-new)
- [Introduction](#introduction)
- [Models](#models)
- [Features](#features)
- [Results](#results)
- [Getting Started (Documentation)](#getting-started-documentation)
- [Train, Validation, Inference Scripts](#train-validation-inference-scripts)
- [Awesome PyTorch Resourc... | pytorch-image-models/README.md/0 | {
"file_path": "pytorch-image-models/README.md",
"repo_id": "pytorch-image-models",
"token_count": 13284
} | 173 |
"""
Run this script to generate the model-index files in `models` from the templates in `.templates/models`.
"""
import argparse
from pathlib import Path
from jinja2 import Environment, FileSystemLoader
import modelindex
def generate_readmes(templates_path: Path, dest_path: Path):
"""Add the code snippet templ... | pytorch-image-models/docs/models/.templates/generate_readmes.py/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/generate_readmes.py",
"repo_id": "pytorch-image-models",
"token_count": 725
} | 174 |
# (Gluon) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswit... | pytorch-image-models/docs/models/.templates/models/gloun-inception-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-inception-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1073
} | 175 |
# MobileNet v2
**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expa... | pytorch-image-models/docs/models/.templates/models/mobilenet-v2.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/mobilenet-v2.md",
"repo_id": "pytorch-image-models",
"token_count": 2583
} | 176 |
# SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
{% include 'code_snippets.md'... | pytorch-image-models/docs/models/.templates/models/seresnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/seresnext.md",
"repo_id": "pytorch-image-models",
"token_count": 1929
} | 177 |
# Wide ResNet
**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).
{% include 'code_snippet... | pytorch-image-models/docs/models/.templates/models/wide-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/wide-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1220
} | 178 |
# CSP-DarkNet
**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The u... | pytorch-image-models/hfdocs/source/models/csp-darknet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/csp-darknet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1756
} | 179 |
# (Gluon) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this... | pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2538
} | 180 |
# PNASNet
**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to comple... | pytorch-image-models/hfdocs/source/models/pnasnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/pnasnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1622
} | 181 |
# SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual b... | pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2425
} | 182 |
# Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in `timm`.
## Schedulers
### Factory functions
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2
### Scheduler Classes
[[... | pytorch-image-models/hfdocs/source/reference/schedulers.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/reference/schedulers.mdx",
"repo_id": "pytorch-image-models",
"token_count": 242
} | 183 |
import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg... | pytorch-image-models/timm/data/config.py/0 | {
"file_path": "pytorch-image-models/timm/data/config.py",
"repo_id": "pytorch-image-models",
"token_count": 1927
} | 184 |
""" Dataset reader for HF IterableDataset
"""
import math
import os
from itertools import repeat, chain
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
from datasets.distributed import split_dataset_by_node
from datasets.splits import Sp... | pytorch-image-models/timm/data/readers/reader_hfids.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_hfids.py",
"repo_id": "pytorch-image-models",
"token_count": 3722
} | 185 |
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import use_fused_attn
from .mlp import Mlp
from .weight_init import trunc_normal_tf_
class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[boo... | pytorch-image-models/timm/layers/attention_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 1758
} | 186 |
"""
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by Chris Ha https://github.com/V... | pytorch-image-models/timm/layers/eca.py/0 | {
"file_path": "pytorch-image-models/timm/layers/eca.py",
"repo_id": "pytorch-image-models",
"token_count": 2411
} | 187 |
""" PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(num_chan, num_groups):
split = [nu... | pytorch-image-models/timm/layers/mixed_conv2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/mixed_conv2d.py",
"repo_id": "pytorch-image-models",
"token_count": 834
} | 188 |
""" Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
import torch
impor... | pytorch-image-models/timm/layers/split_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/split_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 1533
} | 189 |
""" EfficientNet, MobileNetV3, etc Builder
Assembles EfficieNet and related network feature blocks from string definitions.
Handles stride, dilation calculations, and selects feature extraction points.
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import math
import re
from copy import deepco... | pytorch-image-models/timm/models/_efficientnet_builder.py/0 | {
"file_path": "pytorch-image-models/timm/models/_efficientnet_builder.py",
"repo_id": "pytorch-image-models",
"token_count": 9013
} | 190 |
""" Bring-Your-Own-Attention Network
A flexible network w/ dataclass based config for stacking NN blocks including
self-attention (or similar) layers.
Currently used to implement experimental variants of:
* Bottleneck Transformers
* Lambda ResNets
* HaloNets
Consider all of the models definitions here as exper... | pytorch-image-models/timm/models/byoanet.py/0 | {
"file_path": "pytorch-image-models/timm/models/byoanet.py",
"repo_id": "pytorch-image-models",
"token_count": 9703
} | 191 |
""" EfficientFormer-V2
@article{
li2022rethinking,
title={Rethinking Vision Transformers for MobileNet Size and Speed},
author={Li, Yanyu and Hu, Ju and Wen, Yang and Evangelidis, Georgios and Salahi, Kamyar and Wang, Yanzhi and Tulyakov, Sergey and Ren, Jian},
journal={arXiv preprint arXiv:2212.08059}... | pytorch-image-models/timm/models/efficientformer_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 12721
} | 192 |
""" Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... | pytorch-image-models/timm/models/nfnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/nfnet.py",
"repo_id": "pytorch-image-models",
"token_count": 19131
} | 193 |
""" Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... | pytorch-image-models/timm/models/sknet.py/0 | {
"file_path": "pytorch-image-models/timm/models/sknet.py",
"repo_id": "pytorch-image-models",
"token_count": 3801
} | 194 |
"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | pytorch-image-models/timm/models/xception.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception.py",
"repo_id": "pytorch-image-models",
"token_count": 3973
} | 195 |
""" NAdamW Optimizer
Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
Added multi-tensor (foreach) path.
"""
import math
from typing import List, Optional
import torch
from torch import Tensor
# Modified from github.com/pytorch/pytorch/blob/v1.12.1/tor... | pytorch-image-models/timm/optim/nadamw.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nadamw.py",
"repo_id": "pytorch-image-models",
"token_count": 5958
} | 196 |
from .agc import adaptive_clip_grad
from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .decay_batch import decay_batch_step, check_batch_size_retry
from .distributed import distribute_bn, reduce_tensor, init_distributed_device,\
wo... | pytorch-image-models/timm/utils/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/utils/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 252
} | 197 |
__version__ = '0.9.16'
| pytorch-image-models/timm/version.py/0 | {
"file_path": "pytorch-image-models/timm/version.py",
"repo_id": "pytorch-image-models",
"token_count": 12
} | 198 |
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.75 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef as planner
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN ca... | text-generation-inference/Dockerfile/0 | {
"file_path": "text-generation-inference/Dockerfile",
"repo_id": "text-generation-inference",
"token_count": 3374
} | 199 |
{
"openapi": "3.0.3",
"info": {
"title": "Text Generation Inference",
"description": "Text Generation Webserver",
"contact": {
"name": "Olivier Dehaene"
},
"license": {
"name": "Apache 2.0",
"url": "https://www.apache.org/licenses/LICENSE-2.0"
},
"version": "1.4.3"
},... | text-generation-inference/docs/openapi.json/0 | {
"file_path": "text-generation-inference/docs/openapi.json",
"repo_id": "text-generation-inference",
"token_count": 23750
} | 200 |
# Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
![Text Generation Inference](https://hugging... | text-generation-inference/docs/source/index.md/0 | {
"file_path": "text-generation-inference/docs/source/index.md",
"repo_id": "text-generation-inference",
"token_count": 544
} | 201 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50,
"logprob": null,
"text": "G"
},
{
"id": 330,
"logprob": -5.96875,
"text": "ir"
},
{
"id": 1622,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json",
"repo_id": "text-generation-inference",
"token_count": 4604
} | 202 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.828125,
"text": "Test"
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_load.json",
"repo_id": "text-generation-inference",
"token_count": 4903
} | 203 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2271,
"logprob": null,
"text": "Test"
},
{
"id": 1681,
"logprob": -8.8515625,
"text": " re... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_load.json",
"repo_id": "text-generation-inference",
"token_count": 4624
} | 204 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2502,
"logprob": null,
"text": " red"
},
{
"id": 13,
"logprob": -2.734375,
"text": ","
},
{
"id": 8862... | text-generation-inference/integration-tests/models/__snapshots__/test_mamba/test_mamba_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mamba/test_mamba_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1156
} | 205 |
{
"choices": [
{
"finish_reason": "eos_token",
"index": 0,
"logprobs": null,
"message": {
"content": null,
"name": null,
"role": "assistant",
"tool_calls": {
"function": {
"description": null,
"name": "tools",
"p... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json",
"repo_id": "text-generation-inference",
"token_count": 449
} | 206 |
import pytest
@pytest.fixture(scope="module")
def flash_qwen2_handle(launcher):
with launcher("Qwen/Qwen1.5-0.5B") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_qwen2(flash_qwen2_handle):
await flash_qwen2_handle.health(300)
return flash_qwen2_handle.client
@pytest.ma... | text-generation-inference/integration-tests/models/test_flash_qwen2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_qwen2.py",
"repo_id": "text-generation-inference",
"token_count": 723
} | 207 |
[pytest]
addopts = --snapshot-warn-unused
asyncio_mode = auto
markers =
private: marks tests as requiring an admin hf token (deselect with '-m "not private"')
| text-generation-inference/integration-tests/pytest.ini/0 | {
"file_path": "text-generation-inference/integration-tests/pytest.ini",
"repo_id": "text-generation-inference",
"token_count": 58
} | 208 |
/// Single shard Client
use crate::pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
use crate::pb::generate::v2::*;
use crate::Result;
use grpc_metadata::InjectTelemetryContext;
use std::cmp::min;
use std::time::Duration;
use tonic::transport::{Channel, Uri};
use tracing::instrument;
/// ... | text-generation-inference/router/client/src/client.rs/0 | {
"file_path": "text-generation-inference/router/client/src/client.rs",
"repo_id": "text-generation-inference",
"token_count": 3833
} | 209 |
include Makefile-flash-att
include Makefile-flash-att-v2
include Makefile-vllm
include Makefile-awq
include Makefile-eetq
include Makefile-selective-scan
unit-tests:
pytest -s -vv -m "not private" tests
gen-server:
# Compile protos
pip install grpcio-tools==1.51.1 mypy-protobuf==3.4.0 'types-protobuf>=3.20.4' --no... | text-generation-inference/server/Makefile/0 | {
"file_path": "text-generation-inference/server/Makefile",
"repo_id": "text-generation-inference",
"token_count": 497
} | 210 |
#include "q4_matmul.cuh"
#include "column_remap.cuh"
#include <ATen/cuda/CUDAContext.h>
#include "../util.cuh"
#include "../matrix.cuh"
#include "../cu_compat.cuh"
#include "../cuda_buffers.cuh"
#if defined(USE_ROCM)
#include "../hip_compat.cuh"
#endif
const int THREADS_X = 32; // Block size and thread count alo... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cu",
"repo_id": "text-generation-inference",
"token_count": 4211
} | 211 |
#include "compat.cuh"
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result, const half qs_h)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __hfm... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel.cuh",
"repo_id": "text-generation-inference",
"token_count": 11459
} | 212 |
import os
import sys
import typer
from pathlib import Path
from loguru import logger
from typing import Optional
from enum import Enum
from huggingface_hub import hf_hub_download
app = typer.Typer()
class Quantization(str, Enum):
bitsandbytes = "bitsandbytes"
bitsandbytes_nf4 = "bitsandbytes-nf4"
bitsa... | text-generation-inference/server/text_generation_server/cli.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/cli.py",
"repo_id": "text-generation-inference",
"token_count": 4742
} | 213 |
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
TensorParallelRowLinear,
TensorParallelColumnLi... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 8199
} | 214 |
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer
from transformers.models.llama import LlamaTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_lla... | text-generation-inference/server/text_generation_server/models/flash_llama.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_llama.py",
"repo_id": "text-generation-inference",
"token_count": 1271
} | 215 |
import torch
import torch.distributed
from pathlib import Path
from typing import Optional, Type
from opentelemetry import trace
from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerBase
from huggingface_hub import hf_hub_download
import json
from text_generation_server.models import CausalLM
... | text-generation-inference/server/text_generation_server/models/mpt.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/mpt.py",
"repo_id": "text-generation-inference",
"token_count": 1479
} | 216 |
import os
import torch
from loguru import logger
from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
raise ImportError("`USE_FLASH_ATTENTION` is false.")
if not torch.cuda.is_available():
raise ImportError("CUDA is ... | text-generation-inference/server/text_generation_server/utils/flash_attn.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/flash_attn.py",
"repo_id": "text-generation-inference",
"token_count": 2911
} | 217 |
import os
from pathlib import Path
from typing import List, Dict, Optional, Tuple
from safetensors import safe_open, SafetensorError
import torch
from loguru import logger
from huggingface_hub import hf_hub_download
import json
from text_generation_server.utils.log import log_once
class Weights:
def __init__(
... | text-generation-inference/server/text_generation_server/utils/weights.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/weights.py",
"repo_id": "text-generation-inference",
"token_count": 9541
} | 218 |
extern crate napi_build;
fn main() {
napi_build::setup();
}
| tokenizers/bindings/node/build.rs/0 | {
"file_path": "tokenizers/bindings/node/build.rs",
"repo_id": "tokenizers",
"token_count": 26
} | 219 |
// import { promisify } from 'util'
import { BPE, Tokenizer, mergeEncodings, slice } from '../../'
describe('slice', () => {
const text = 'My name is John 👋'
const sliceText = slice.bind({}, text)
it('returns the full text when no params', () => {
const sliced = sliceText()
expect(sliced).toEqual(text... | tokenizers/bindings/node/lib/bindings/utils.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/utils.test.ts",
"repo_id": "tokenizers",
"token_count": 1866
} | 220 |
{
"name": "tokenizers-linux-arm64-musl",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"arm64"
],
"main": "tokenizers.linux-arm64-musl.node",
"files": [
"tokenizers.linux-arm64-musl.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
... | tokenizers/bindings/node/npm/linux-arm64-musl/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-musl/package.json",
"repo_id": "tokenizers",
"token_count": 291
} | 221 |
#![deny(clippy::all)]
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
mod arc_rwlock_serde;
pub mod decoders;
pub mod encoding;
pub mod models;
pub mod normalizers;
pub mod pre_tokenizers;
pub mod processors;
pub mod tasks;
pub mod tokenizer;
pub mod trainers;
pub mod utils;
| tokenizers/bindings/node/src/lib.rs/0 | {
"file_path": "tokenizers/bindings/node/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 102
} | 222 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.