text stringlengths 7 328k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 459 |
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<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/language_modeling.ipynb/0 | {
"file_path": "notebooks/examples/language_modeling.ipynb",
"repo_id": "notebooks",
"token_count": 7093
} | 158 |
<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 transformers datasets huggingface_hub<jupyter_output><empty_output><jupyter_text>If you're opening this notebook ... | notebooks/examples/question_answering-tf.ipynb/0 | {
"file_path": "notebooks/examples/question_answering-tf.ipynb",
"repo_id": "notebooks",
"token_count": 17339
} | 159 |
<jupyter_start><jupyter_text>Probabilistic Time Series Forecasting with 🤗 Transformers IntroductionTime series forecasting is an essential scientific and business problem and as such has also seen a lot of innovation recently with the use of [deep learning based](https://dl.acm.org/doi/abs/10.1145/3533382) models in a... | notebooks/examples/time-series-transformers.ipynb/0 | {
"file_path": "notebooks/examples/time-series-transformers.ipynb",
"repo_id": "notebooks",
"token_count": 13676
} | 160 |
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from datasets import load_from_disk
import random
import logging
import sys
import argparse
import os
import torch
if __name__ == "__main__"... | notebooks/sagemaker/01_getting_started_pytorch/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/01_getting_started_pytorch/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1418
} | 161 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Deploy 🤗 Transformers for inference Welcome to this getting started guide, we will use the new Hugging Face Inference DLCs and Amazon SageMaker Python SDK to deploy a transformer model for inference. In this example we directly deploy one of the 10 000+ Hugging... | notebooks/sagemaker/11_deploy_model_from_hf_hub/deploy_transformer_model_from_hf_hub.ipynb/0 | {
"file_path": "notebooks/sagemaker/11_deploy_model_from_hf_hub/deploy_transformer_model_from_hf_hub.ipynb",
"repo_id": "notebooks",
"token_count": 1196
} | 162 |
<jupyter_start><jupyter_text>Semantic Segmantion with Hugging Face's Transformers & Amazon SageMaker Transformer models are changing are changing the world of machine learning, starting with natural language processing, and now, with audio and computer vision. Hugging Face's mission is to democratize good machine learn... | notebooks/sagemaker/21_image_segmantation/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/21_image_segmantation/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 2831
} | 163 |
<!--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/conceptual_guides/adapter.md/0 | {
"file_path": "peft/docs/source/conceptual_guides/adapter.md",
"repo_id": "peft",
"token_count": 2203
} | 164 |
<!--⚠️ 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.
-->
# Configuration
[`PeftConfigMixin`] is the base configuration class for storing the adapter configuration of a [`PeftModel`], and [`PromptLearningCo... | peft/docs/source/package_reference/config.md/0 | {
"file_path": "peft/docs/source/package_reference/config.md",
"repo_id": "peft",
"token_count": 224
} | 165 |
<!--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/quicktour.md/0 | {
"file_path": "peft/docs/source/quicktour.md",
"repo_id": "peft",
"token_count": 2384
} | 166 |
<jupyter_start><jupyter_code>from transformers import AutoModelForSeq2SeqLM
import peft
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, IA3Config, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from transformers import AutoT... | peft/examples/conditional_generation/peft_ia3_seq2seq.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_ia3_seq2seq.ipynb",
"repo_id": "peft",
"token_count": 2685
} | 167 |
<jupyter_start><jupyter_text>Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets... | peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb/0 | {
"file_path": "peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb",
"repo_id": "peft",
"token_count": 4290
} | 168 |
<jupyter_start><jupyter_code>import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
from peft import PeftConfig, PeftModel
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
import torch
import random
peft_model_id = "smangrul/tinyllama_lo... | peft/examples/multi_adapter_examples/Lora_Merging.ipynb/0 | {
"file_path": "peft/examples/multi_adapter_examples/Lora_Merging.ipynb",
"repo_id": "peft",
"token_count": 1305
} | 169 |
import os
from enum import Enum
import torch
from datasets import DatasetDict, load_dataset, load_from_disk
from datasets.builder import DatasetGenerationError
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
from peft import LoraConfig
DEFAULT_CHATML_CHAT_TEMPLATE =... | peft/examples/sft/utils.py/0 | {
"file_path": "peft/examples/sft/utils.py",
"repo_id": "peft",
"token_count": 3277
} | 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/mapping.py/0 | {
"file_path": "peft/src/peft/mapping.py",
"repo_id": "peft",
"token_count": 2265
} | 171 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/lora/bnb.py/0 | {
"file_path": "peft/src/peft/tuners/lora/bnb.py",
"repo_id": "peft",
"token_count": 11452
} | 172 |
# 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/constants.py/0 | {
"file_path": "peft/src/peft/utils/constants.py",
"repo_id": "peft",
"token_count": 2721
} | 173 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_encoder_decoder_models.py/0 | {
"file_path": "peft/tests/test_encoder_decoder_models.py",
"repo_id": "peft",
"token_count": 4631
} | 174 |
# ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https:/... | pytorch-image-models/docs/models/.templates/models/ecaresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ecaresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 2832
} | 175 |
# Inception v4
**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
{% include 'code_snippets.md' %}
## How do I t... | pytorch-image-models/docs/models/.templates/models/inception-v4.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/inception-v4.md",
"repo_id": "pytorch-image-models",
"token_count": 816
} | 176 |
# ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the [1×1 convolution](https://paperswithcode.co... | pytorch-image-models/docs/models/.templates/models/resnet-d.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/resnet-d.md",
"repo_id": "pytorch-image-models",
"token_count": 3126
} | 177 |
# (Tensorflow) EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scal... | pytorch-image-models/docs/models/.templates/models/tf-efficientnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/tf-efficientnet.md",
"repo_id": "pytorch-image-models",
"token_count": 7172
} | 178 |
# 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/dpn.md/0 | {
"file_path": "pytorch-image-models/docs/models/dpn.md",
"repo_id": "pytorch-image-models",
"token_count": 3689
} | 179 |
# 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://paperswithcode.co... | pytorch-image-models/docs/models/inception-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/inception-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1888
} | 180 |
# ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V... | pytorch-image-models/docs/models/resnest.md/0 | {
"file_path": "pytorch-image-models/docs/models/resnest.md",
"repo_id": "pytorch-image-models",
"token_count": 5449
} | 181 |
# (Tensorflow) EfficientNet Lite
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly... | pytorch-image-models/docs/models/tf-efficientnet-lite.md/0 | {
"file_path": "pytorch-image-models/docs/models/tf-efficientnet-lite.md",
"repo_id": "pytorch-image-models",
"token_count": 3355
} | 182 |
# timm
<img class="float-left !m-0 !border-0 !dark:border-0 !shadow-none !max-w-lg w-[150px]" src="https://huggingface.co/front/thumbnails/docs/timm.png"/>
`timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation script... | pytorch-image-models/hfdocs/source/index.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/index.mdx",
"repo_id": "pytorch-image-models",
"token_count": 560
} | 183 |
# ESE-VoVNet
**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel.
Read about [one-shot aggregatio... | pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1951
} | 184 |
# MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
## How do I use this model on an image?
To load a pretrained mo... | pytorch-image-models/hfdocs/source/models/mixnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/mixnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2684
} | 185 |
# 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).
## How do I use this mod... | pytorch-image-models/hfdocs/source/models/wide-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/wide-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2035
} | 186 |
import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv',
],
'results-imagenet-r-clean.csv... | pytorch-image-models/results/generate_csv_results.py/0 | {
"file_path": "pytorch-image-models/results/generate_csv_results.py",
"repo_id": "pytorch-image-models",
"token_count": 1346
} | 187 |
from .version import __version__
from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable
from .models import create_model, list_models, list_pretrained, is_model, list_modules, model_entrypoint, \
is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value
| pytorch-image-models/timm/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 91
} | 188 |
from .reader_factory import create_reader
from .img_extensions import *
| pytorch-image-models/timm/data/readers/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 20
} | 189 |
""" Transforms Factory
Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
Hacked together by / Copyright 2019, Ross Wightman
"""
import math
from typing import Optional, Tuple, Union
import torch
from torchvision import transforms
from timm.data.constants import IMAGENET_DEFAULT_M... | pytorch-image-models/timm/data/transforms_factory.py/0 | {
"file_path": "pytorch-image-models/timm/data/transforms_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 8112
} | 190 |
""" Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Union, Callable, Type
from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit
# PyTorch has an optimized, native 'silu' (aka 'swis... | pytorch-image-models/timm/layers/create_act.py/0 | {
"file_path": "pytorch-image-models/timm/layers/create_act.py",
"repo_id": "pytorch-image-models",
"token_count": 2445
} | 191 |
""" Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple... | pytorch-image-models/timm/layers/helpers.py/0 | {
"file_path": "pytorch-image-models/timm/layers/helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 462
} | 192 |
""" Position Embedding Utilities
Hacked together by / Copyright 2022 Ross Wightman
"""
import logging
import math
from typing import List, Tuple, Optional, Union
import torch
import torch.nn.functional as F
from .helpers import to_2tuple
_logger = logging.getLogger(__name__)
def resample_abs_pos_embed(
po... | pytorch-image-models/timm/layers/pos_embed.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pos_embed.py",
"repo_id": "pytorch-image-models",
"token_count": 1127
} | 193 |
""" Binary Cross Entropy w/ a few extras
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryCrossEntropy(nn.Module):
""" BCE with optional one-hot from dense targets, label smoothing, thresholdin... | pytorch-image-models/timm/loss/binary_cross_entropy.py/0 | {
"file_path": "pytorch-image-models/timm/loss/binary_cross_entropy.py",
"repo_id": "pytorch-image-models",
"token_count": 1082
} | 194 |
""" DeiT - Data-efficient Image Transformers
DeiT model defs and weights from https://github.com/facebookresearch/deit, original copyright below
paper: `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
paper: `DeiT III: Revenge of the ViT` - https://arxiv.org/abs/2204.07118
Modifications ... | pytorch-image-models/timm/models/deit.py/0 | {
"file_path": "pytorch-image-models/timm/models/deit.py",
"repo_id": "pytorch-image-models",
"token_count": 8300
} | 195 |
""" Global Context ViT
From scratch implementation of GCViT in the style of timm swin_transformer_v2_cr.py
Global Context Vision Transformers -https://arxiv.org/abs/2206.09959
@article{hatamizadeh2022global,
title={Global Context Vision Transformers},
author={Hatamizadeh, Ali and Yin, Hongxu and Kautz, Jan and M... | pytorch-image-models/timm/models/gcvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/gcvit.py",
"repo_id": "pytorch-image-models",
"token_count": 10789
} | 196 |
""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import Callable, List, Optional, Tuple
import torch
imp... | pytorch-image-models/timm/models/mobilenetv3.py/0 | {
"file_path": "pytorch-image-models/timm/models/mobilenetv3.py",
"repo_id": "pytorch-image-models",
"token_count": 17103
} | 197 |
"""PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
fro... | pytorch-image-models/timm/models/resnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/resnet.py",
"repo_id": "pytorch-image-models",
"token_count": 44237
} | 198 |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https:... | pytorch-image-models/timm/models/vision_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 59568
} | 199 |
""" PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb
This optimizer code was adapted from the following (starting with latest)
* https://github.com/HabanaAI/Model-References/blob/2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
* https://github.com/NVIDIA/DeepLearningExample... | pytorch-image-models/timm/optim/lamb.py/0 | {
"file_path": "pytorch-image-models/timm/optim/lamb.py",
"repo_id": "pytorch-image-models",
"token_count": 3768
} | 200 |
""" Plateau Scheduler
Adapts PyTorch plateau scheduler and allows application of noise, warmup.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from .scheduler import Scheduler
class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation loss plateaus."""
d... | pytorch-image-models/timm/scheduler/plateau_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/plateau_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1800
} | 201 |
""" Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, nam... | pytorch-image-models/timm/utils/misc.py/0 | {
"file_path": "pytorch-image-models/timm/utils/misc.py",
"repo_id": "pytorch-image-models",
"token_count": 451
} | 202 |
/// Inspired by https://github.com/orhun/rust-tui-template/blob/472aa515119d4c94903eac12d9784417281dc7f5/src/event.rs
use crossterm::event;
use std::time::{Duration, Instant};
use tokio::sync::{broadcast, mpsc};
/// Events
#[derive(Debug)]
pub(crate) enum Event {
/// Terminal tick.
Tick,
/// Key press.
... | text-generation-inference/benchmark/src/event.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/event.rs",
"repo_id": "text-generation-inference",
"token_count": 922
} | 203 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
},
{
"id": 49833,
"logprob": -10.5390625,
"text": "... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m_sharded/test_bloom_560m_sharded_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m_sharded/test_bloom_560m_sharded_load.json",
"repo_id": "text-generation-inference",
"token_count": 7258
} | 204 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 29896,
"logprob": -0.7685547,
"special": false,
"text": "1"
},
{
"id": 29906,
"logprob... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_single_load_instance.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_single_load_instance.json",
"repo_id": "text-generation-inference",
"token_count": 866
} | 205 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|prompter|>"
},
{
"id": 1276,
"logprob": -8.03125,
"text... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox_sharded/test_flash_neox_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox_sharded/test_flash_neox_load.json",
"repo_id": "text-generation-inference",
"token_count": 9176
} | 206 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 20,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 3226,
"logprob": -8.5859375,
"text": " ge"
},
{
"id": 2... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq_default_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq_default_params.json",
"repo_id": "text-generation-inference",
"token_count": 2310
} | 207 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|prompter|>"
},
{
"id": 1276,
"logprob": -8.0234375,
"te... | text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox_load.json",
"repo_id": "text-generation-inference",
"token_count": 9164
} | 208 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_gptq_handle(launcher):
with launcher("huggingface/llama-7b-gptq", num_shard=2, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_gptq(flash_llama_gptq_handle):
await flash_llama_gptq_handle.hea... | text-generation-inference/integration-tests/models/test_flash_llama_gptq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_gptq.py",
"repo_id": "text-generation-inference",
"token_count": 723
} | 209 |
import pytest
@pytest.fixture(scope="module")
def neox_handle(launcher):
with launcher(
"stabilityai/stablelm-tuned-alpha-3b", num_shard=1, use_flash_attention=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def neox(neox_handle):
await neox_handle.health(300)
r... | text-generation-inference/integration-tests/models/test_neox.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_neox.py",
"repo_id": "text-generation-inference",
"token_count": 499
} | 210 |
syntax = "proto3";
package generate.v2;
service TextGenerationService {
/// Model Info
rpc Info (InfoRequest) returns (InfoResponse) {}
/// Service discovery
rpc ServiceDiscovery (ServiceDiscoveryRequest) returns (ServiceDiscoveryResponse) {}
/// Empties batch cache
rpc ClearCache (ClearCacheR... | text-generation-inference/proto/generate.proto/0 | {
"file_path": "text-generation-inference/proto/generate.proto",
"repo_id": "text-generation-inference",
"token_count": 2074
} | 211 |
use crate::infer::InferError;
use crate::infer::InferStreamResponse;
use crate::validation::ValidGenerateRequest;
use nohash_hasher::{BuildNoHashHasher, IntMap};
use std::cmp::min;
use std::collections::VecDeque;
use text_generation_client::{Batch, Request};
use tokio::sync::{mpsc, oneshot};
use tokio::time::Instant;
u... | text-generation-inference/router/src/queue.rs/0 | {
"file_path": "text-generation-inference/router/src/queue.rs",
"repo_id": "text-generation-inference",
"token_count": 9950
} | 212 |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import torch
extra_compile_args = ["-std=c++17"]
if not torch.version.hip:
extra_compile_args.append("-arch=compute_80")
setup(
name="custom_kernels",
ext_modules=[
CUDAExtension(
name="cus... | text-generation-inference/server/custom_kernels/setup.py/0 | {
"file_path": "text-generation-inference/server/custom_kernels/setup.py",
"repo_id": "text-generation-inference",
"token_count": 342
} | 213 |
#ifndef _config_h
#define _config_h
#define MAX_Q_GEMM_ROWS 50
#define MAX_Q_GEMM_WEIGHTS 4 // must be <= MAX_Q_GEMM_ROWS
#define QMODE_2BIT 1
#define QMODE_3BIT 1
#define QMODE_4BIT 1
#define QMODE_5BIT 1
#define QMODE_6BIT 0
#define QMODE_8BIT 0
#endif
| text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h",
"repo_id": "text-generation-inference",
"token_count": 119
} | 214 |
#ifndef _qdq_util_cuh
#define _qdq_util_cuh
union half2_uint32
{
uint32_t as_uint32;
half2 as_half2;
__device__ half2_uint32(uint32_t val) : as_uint32(val) {}
__device__ half2_uint32(half2 val) : as_half2(val) {}
__device__ half2_uint32() : as_uint32(0) {}
};
union half_uint16
{
uint16_t as_ui... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh",
"repo_id": "text-generation-inference",
"token_count": 602
} | 215 |
import os
import requests
import tempfile
import pytest
import huggingface_hub.constants
from huggingface_hub import hf_api
import text_generation_server.utils.hub
from text_generation_server.utils.hub import (
weight_hub_files,
download_weights,
weight_files,
EntryNotFoundError,
LocalEntryNotFou... | text-generation-inference/server/tests/utils/test_hub.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_hub.py",
"repo_id": "text-generation-inference",
"token_count": 1264
} | 216 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 7562
} | 217 |
# coding=utf-8
# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | text-generation-inference/server/text_generation_server/models/custom_modeling/neox_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/neox_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 14336
} | 218 |
import torch
import os
MEM_POOL = torch.cuda.graph_pool_handle()
# This is overridden by the cli
ENABLE_CUDA_GRAPHS = os.getenv("ENABLE_CUDA_GRAPHS", "false").lower() in {"1", "true"}
| text-generation-inference/server/text_generation_server/models/globals.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/globals.py",
"repo_id": "text-generation-inference",
"token_count": 73
} | 219 |
import grpc
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.grpc._aio_server import (
OpenTelemetryAioServerInterceptor,
)
from opentelemetry.semconv.trace import SpanAttributes
from opentelemetry.sdk.resources im... | text-generation-inference/server/text_generation_server/tracing.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/tracing.py",
"repo_id": "text-generation-inference",
"token_count": 985
} | 220 |
import math
import torch
from loguru import logger
from typing import Dict, Union
from text_generation_server.pb.generate_pb2 import GrammarType
from outlines.fsm.fsm import RegexFSM
from outlines.fsm.json_schema import build_regex_from_object
from functools import lru_cache
from typing import List, Optional, Default... | text-generation-inference/server/text_generation_server/utils/logits_process.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/logits_process.py",
"repo_id": "text-generation-inference",
"token_count": 9502
} | 221 |
import {
PaddingDirection,
WordPiece,
punctuationPreTokenizer,
sequencePreTokenizer,
whitespacePreTokenizer,
Encoding,
EncodeOptions,
Tokenizer,
} from '../../'
import { InputSequence } from '../../types'
const MOCKS_DIR = __dirname + '/__mocks__'
describe('Can modify pretokenizers on the fly', () => ... | tokenizers/bindings/node/lib/bindings/encoding.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/encoding.test.ts",
"repo_id": "tokenizers",
"token_count": 3021
} | 222 |
{
"name": "tokenizers-freebsd-x64",
"version": "0.13.4-rc1",
"os": [
"freebsd"
],
"cpu": [
"x64"
],
"main": "tokenizers.freebsd-x64.node",
"files": [
"tokenizers.freebsd-x64.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",
"N... | tokenizers/bindings/node/npm/freebsd-x64/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/freebsd-x64/package.json",
"repo_id": "tokenizers",
"token_count": 272
} | 223 |
{
"name": "tokenizers-win32-x64-msvc",
"version": "0.13.4-rc1",
"os": [
"win32"
],
"cpu": [
"x64"
],
"main": "tokenizers.win32-x64-msvc.node",
"files": [
"tokenizers.win32-x64-msvc.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",... | tokenizers/bindings/node/npm/win32-x64-msvc/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-x64-msvc/package.json",
"repo_id": "tokenizers",
"token_count": 277
} | 224 |
use napi::bindgen_prelude::*;
use napi_derive::napi;
use tokenizers as tk;
use tokenizers::Encoding;
use crate::encoding::JsEncoding;
#[napi]
pub fn slice(s: String, begin_index: Option<i32>, end_index: Option<i32>) -> Result<String> {
let len = s.chars().count();
let get_index = |x: i32| -> usize {
if x >= ... | tokenizers/bindings/node/src/utils.rs/0 | {
"file_path": "tokenizers/bindings/node/src/utils.rs",
"repo_id": "tokenizers",
"token_count": 503
} | 225 |
import datasets
from tokenizers import Tokenizer, models, normalizers, pre_tokenizers
# Build a tokenizer
bpe_tokenizer = Tokenizer(models.BPE())
bpe_tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
bpe_tokenizer.normalizer = normalizers.Lowercase()
# Initialize a dataset
dataset = datasets.load_dataset("wikit... | tokenizers/bindings/python/examples/train_with_datasets.py/0 | {
"file_path": "tokenizers/bindings/python/examples/train_with_datasets.py",
"repo_id": "tokenizers",
"token_count": 207
} | 226 |
# Generated content DO NOT EDIT
class Normalizer:
"""
Base class for all normalizers
This class is not supposed to be instantiated directly. Instead, any implementation of a
Normalizer will return an instance of this class when instantiated.
"""
def normalize(self, normalized):
"""
... | tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 8053
} | 227 |
use std::sync::{Arc, RwLock};
use crate::utils::PyChar;
use crate::utils::PyPattern;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::de::Error;
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use tk::decoders::bpe::BPEDecoder;
use tk::decoders::byte_fallback::ByteFallback;
use... | tokenizers/bindings/python/src/decoders.rs/0 | {
"file_path": "tokenizers/bindings/python/src/decoders.rs",
"repo_id": "tokenizers",
"token_count": 9016
} | 228 |
import argparse
import inspect
import os
from pathlib import Path
INDENT = " " * 4
GENERATED_COMMENT = "# Generated content DO NOT EDIT\n"
def do_indent(text: str, indent: str):
return text.replace("\n", f"\n{indent}")
def function(obj, indent, text_signature=None):
if text_signature is None:
text... | tokenizers/bindings/python/stub.py/0 | {
"file_path": "tokenizers/bindings/python/stub.py",
"repo_id": "tokenizers",
"token_count": 2395
} | 229 |
# Models
<tokenizerslangcontent>
<python>
## BPE
[[autodoc]] tokenizers.models.BPE
## Model
[[autodoc]] tokenizers.models.Model
## Unigram
[[autodoc]] tokenizers.models.Unigram
## WordLevel
[[autodoc]] tokenizers.models.WordLevel
## WordPiece
[[autodoc]] tokenizers.models.WordPiece
</python>
<rust>
The Rust A... | tokenizers/docs/source-doc-builder/api/models.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/models.mdx",
"repo_id": "tokenizers",
"token_count": 179
} | 230 |
Installation with npm
----------------------------------------------------------------------------------------------------
You can simply install 🤗 Tokenizers with npm using::
npm install tokenizers
| tokenizers/docs/source/installation/node.inc/0 | {
"file_path": "tokenizers/docs/source/installation/node.inc",
"repo_id": "tokenizers",
"token_count": 31
} | 231 |
#[macro_use]
extern crate criterion;
use criterion::Criterion;
use std::collections::HashMap;
use std::fs::read_to_string;
use std::time::{Duration, Instant};
use tokenizers::models::unigram::Unigram;
use tokenizers::models::unigram::UnigramTrainer;
pub fn bench_train(c: &mut Criterion) {
let trainer = UnigramTra... | tokenizers/tokenizers/benches/unigram_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/unigram_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 1174
} | 232 |
import * as wasm from "unstable_wasm";
console.log(wasm.tokenize("ab"));
console.log(wasm.tokenize("abc"));
| tokenizers/tokenizers/examples/unstable_wasm/www/index.js/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/index.js",
"repo_id": "tokenizers",
"token_count": 43
} | 233 |
use super::{super::OrderedVocabIter, convert_merges_to_hashmap, BpeBuilder, Pair, BPE};
use serde::{
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashMap;
impl Serialize for BPE {
fn serialize<S>(&self, serializer: S) ... | tokenizers/tokenizers/src/models/bpe/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/serialization.rs",
"repo_id": "tokenizers",
"token_count": 2739
} | 234 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use serde::{Deserialize, Serialize};
use unicode_categories::UnicodeCategories;
/// Checks whether a character is whitespace
fn is_whitespace(c: char) -> bool {
// These are technically control characters but we count them as whitespace
match c {
... | tokenizers/tokenizers/src/normalizers/bert.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/bert.rs",
"repo_id": "tokenizers",
"token_count": 1856
} | 235 |
use crate::utils::SysRegex;
use serde::{Deserialize, Deserializer, Serialize};
use crate::tokenizer::{
pattern::Invert, PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior,
};
/// Represents the different patterns that `Split` can use
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq)]
pub... | tokenizers/tokenizers/src/pre_tokenizers/split.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/split.rs",
"repo_id": "tokenizers",
"token_count": 4038
} | 236 |
use std::marker::PhantomData;
use serde::{
self,
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use super::{added_vocabulary::AddedTokenWithId, TokenizerImpl};
use crate::{Decoder, Model, Normalizer, PostProcessor, PreTokenizer, TokenizerBui... | tokenizers/tokenizers/src/tokenizer/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/serialization.rs",
"repo_id": "tokenizers",
"token_count": 3618
} | 237 |
mod common;
use common::*;
use tokenizers::decoders::byte_level::ByteLevel;
use tokenizers::decoders::DecoderWrapper;
use tokenizers::models::bpe::BPE;
use tokenizers::models::wordlevel::WordLevel;
use tokenizers::models::wordpiece::WordPiece;
use tokenizers::models::ModelWrapper;
use tokenizers::normalizers::bert::Be... | tokenizers/tokenizers/tests/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/serialization.rs",
"repo_id": "tokenizers",
"token_count": 3683
} | 238 |
FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
LABEL maintainer="Hugging Face"
LABEL repository="transformers"
RUN apt update && \
apt install -y bash \
build-essential \
git \
curl \
ca-certificates \
python3 \
... | transformers/docker/transformers-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 397
} | 239 |
<!--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 applicable law or agreed... | transformers/docs/source/de/quicktour.md/0 | {
"file_path": "transformers/docs/source/de/quicktour.md",
"repo_id": "transformers",
"token_count": 7330
} | 240 |
<!--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 applicable law or agreed... | transformers/docs/source/en/big_models.md/0 | {
"file_path": "transformers/docs/source/en/big_models.md",
"repo_id": "transformers",
"token_count": 1722
} | 241 |
<!---
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 applicable law or ... | transformers/docs/source/en/installation.md/0 | {
"file_path": "transformers/docs/source/en/installation.md",
"repo_id": "transformers",
"token_count": 2901
} | 242 |
<!--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 applicable law or agreed... | transformers/docs/source/en/main_classes/data_collator.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/data_collator.md",
"repo_id": "transformers",
"token_count": 681
} | 243 |
<!--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 applicable law or agreed... | transformers/docs/source/en/model_doc/albert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/albert.md",
"repo_id": "transformers",
"token_count": 3405
} | 244 |
<!--Copyright 2021 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... | transformers/docs/source/en/model_doc/clip.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/clip.md",
"repo_id": "transformers",
"token_count": 2696
} | 245 |
<!--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 applicable law or agreed... | transformers/docs/source/en/model_doc/deberta.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/deberta.md",
"repo_id": "transformers",
"token_count": 2499
} | 246 |
<!--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 applicable law or agreed... | transformers/docs/source/en/model_doc/efficientformer.md/0 | {
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