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# accelerate-aws-sagemaker
Examples showcasing AWS SageMaker integration of π€ Accelerate. Just give the `accelerate config` and do `accelerate launch` π. It's as simple as that!
1. Set up the accelerate config by running `accelerate config --config_file accelerate_config.yaml` and answer the SageMaker questions.
2.... | notebooks/sagemaker/22_accelerate_sagemaker_examples/README.md/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/README.md",
"repo_id": "notebooks",
"token_count": 3628
} | 163 |
<jupyter_start><jupyter_text>Stable Diffusion on Amazon SageMakerWelcome to this Amazon SageMaker guide on how to use the [Stable Diffusion](https://huggingface.co/blog/stable_diffusion) to generate image for a given input prompt. We will deploy [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diff... | notebooks/sagemaker/23_stable_diffusion_inference/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/23_stable_diffusion_inference/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 4469
} | 164 |
import os
import argparse
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
set_seed,
default_data_collator,
BitsAndBytesConfig,
Trainer,
TrainingArguments,
)
from datasets import load_from_disk
import torch
from peft import PeftConfig, PeftModel
def parse_arge():
"""Pars... | notebooks/sagemaker/28_train_llms_with_qlora/scripts/run_clm.py/0 | {
"file_path": "notebooks/sagemaker/28_train_llms_with_qlora/scripts/run_clm.py",
"repo_id": "notebooks",
"token_count": 2378
} | 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/conceptual_guides/adapter.md/0 | {
"file_path": "peft/docs/source/conceptual_guides/adapter.md",
"repo_id": "peft",
"token_count": 2203
} | 166 |
<!--β οΈ 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
} | 167 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/quicktour.md/0 | {
"file_path": "peft/docs/source/quicktour.md",
"repo_id": "peft",
"token_count": 2384
} | 168 |
<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
} | 169 |
<jupyter_start><jupyter_text>Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` π€ 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
} | 170 |
<jupyter_start><jupyter_code>!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q git+https://github.com/huggingface/peft.git
!pip install -q git+https://github.com/huggingface/accelerate.git@main
!pip install huggingface_hub
!pip install bitsandbytes
!pip install SentencePiece
import os
... | peft/examples/multi_adapter_examples/PEFT_Multi_LoRA_Inference.ipynb/0 | {
"file_path": "peft/examples/multi_adapter_examples/PEFT_Multi_LoRA_Inference.ipynb",
"repo_id": "peft",
"token_count": 1344
} | 171 |
# Supervised Fine-tuning (SFT) with PEFT
In this example, we'll see how to use [PEFT](https://github.com/huggingface/peft) to perform SFT using PEFT on various distributed setups.
## Single GPU SFT with QLoRA
QLoRA uses 4-bit quantization of the base model to drastically reduce the GPU memory consumed by the base mode... | peft/examples/sft/README.md/0 | {
"file_path": "peft/examples/sft/README.md",
"repo_id": "peft",
"token_count": 807
} | 172 |
import argparse
import json
import logging
import os
from collections import Counter
from dataclasses import dataclass
from operator import attrgetter
from typing import Dict, List, Optional, Union
import safetensors
import torch
import torch.nn as nn
from diffusers import UNet2DConditionModel
from transformers import... | peft/examples/stable_diffusion/convert_sd_adapter_to_peft.py/0 | {
"file_path": "peft/examples/stable_diffusion/convert_sd_adapter_to_peft.py",
"repo_id": "peft",
"token_count": 10390
} | 173 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/mixed_model.py/0 | {
"file_path": "peft/src/peft/mixed_model.py",
"repo_id": "peft",
"token_count": 6797
} | 174 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/ia3/bnb.py/0 | {
"file_path": "peft/src/peft/tuners/ia3/bnb.py",
"repo_id": "peft",
"token_count": 2193
} | 175 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/lora/config.py/0 | {
"file_path": "peft/src/peft/tuners/lora/config.py",
"repo_id": "peft",
"token_count": 6552
} | 176 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/p_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/p_tuning/config.py",
"repo_id": "peft",
"token_count": 732
} | 177 |
# 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/integrations.py/0 | {
"file_path": "peft/src/peft/utils/integrations.py",
"repo_id": "peft",
"token_count": 890
} | 178 |
# 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_feature_extraction_models.py/0 | {
"file_path": "peft/tests/test_feature_extraction_models.py",
"repo_id": "peft",
"token_count": 3356
} | 179 |
# Archived Changes
### Nov 22, 2021
* A number of updated weights anew new model defs
* `eca_halonext26ts` - 79.5 @ 256
* `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288
* `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, ... | pytorch-image-models/docs/archived_changes.md/0 | {
"file_path": "pytorch-image-models/docs/archived_changes.md",
"repo_id": "pytorch-image-models",
"token_count": 9335
} | 180 |
# Deep Layer Aggregation
Extending βshallowβ skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through ... | pytorch-image-models/docs/models/.templates/models/dla.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/dla.md",
"repo_id": "pytorch-image-models",
"token_count": 5955
} | 181 |
# Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
{% include ... | pytorch-image-models/docs/models/.templates/models/inception-resnet-v2.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/inception-resnet-v2.md",
"repo_id": "pytorch-image-models",
"token_count": 864
} | 182 |
# Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-li... | pytorch-image-models/docs/models/.templates/models/res2next.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/res2next.md",
"repo_id": "pytorch-image-models",
"token_count": 905
} | 183 |
# (Tensorflow) EfficientNet CondConv
**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 unifo... | pytorch-image-models/docs/models/.templates/models/tf-efficientnet-condconv.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/tf-efficientnet-condconv.md",
"repo_id": "pytorch-image-models",
"token_count": 2457
} | 184 |
# Feature Extraction
All of the models in `timm` have consistent mechanisms for obtaining various types of features from the model for tasks besides classification.
## Penultimate Layer Features (Pre-Classifier Features)
The features from the penultimate model layer can be obtained in several ways without requiring ... | pytorch-image-models/hfdocs/source/feature_extraction.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/feature_extraction.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2004
} | 185 |
# 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 scales network wi... | pytorch-image-models/hfdocs/source/models/efficientnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/efficientnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4915
} | 186 |
# (Legacy) 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.
## How do I use this... | pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2730
} | 187 |
# ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) \\( ... | pytorch-image-models/hfdocs/source/models/resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3056
} | 188 |
""" ONNX-runtime validation script
This script was created to verify accuracy and performance of exported ONNX
models running with the onnxruntime. It utilizes the PyTorch dataloader/processing
pipeline for a fair comparison against the originals.
Copyright 2020 Ross Wightman
"""
import argparse
import numpy as np
im... | pytorch-image-models/onnx_validate.py/0 | {
"file_path": "pytorch-image-models/onnx_validate.py",
"repo_id": "pytorch-image-models",
"token_count": 1960
} | 189 |
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops.misc import FrozenBatchNorm2d
import timm
from timm.utils.model import freeze, unfreeze
def test_freeze_unfreeze():
model = timm.create_model('resnet18')
# Freeze all
freeze(model)
# Check top level module
assert model.fc.we... | pytorch-image-models/tests/test_utils.py/0 | {
"file_path": "pytorch-image-models/tests/test_utils.py",
"repo_id": "pytorch-image-models",
"token_count": 776
} | 190 |
""" Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | pytorch-image-models/timm/data/random_erasing.py/0 | {
"file_path": "pytorch-image-models/timm/data/random_erasing.py",
"repo_id": "pytorch-image-models",
"token_count": 2258
} | 191 |
import math
import numbers
import random
import warnings
from typing import List, Sequence, Tuple, Union
import torch
import torchvision.transforms.functional as F
try:
from torchvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
except ImportError:
has_interpolation_mode =... | pytorch-image-models/timm/data/transforms.py/0 | {
"file_path": "pytorch-image-models/timm/data/transforms.py",
"repo_id": "pytorch-image-models",
"token_count": 8644
} | 192 |
""" Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_... | pytorch-image-models/timm/layers/conv_bn_act.py/0 | {
"file_path": "pytorch-image-models/timm/layers/conv_bn_act.py",
"repo_id": "pytorch-image-models",
"token_count": 1885
} | 193 |
""" Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon Shlens},
Title = {Scaling Local Self... | pytorch-image-models/timm/layers/halo_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/halo_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 4601
} | 194 |
""" AvgPool2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
from .helpers import to_2tuple
from .padding import pad_same, get_padding_value
def avg_pool2d_same(x, kernel_size: List[int... | pytorch-image-models/timm/layers/pool2d_same.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pool2d_same.py",
"repo_id": "pytorch-image-models",
"token_count": 1294
} | 195 |
import torch
import torch.nn as nn
class AsymmetricLossMultiLabel(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
super(AsymmetricLossMultiLabel, self).__init__()
self.gamma_neg = gamma_neg
self.gamma_pos = gamma_pos
... | pytorch-image-models/timm/loss/asymmetric_loss.py/0 | {
"file_path": "pytorch-image-models/timm/loss/asymmetric_loss.py",
"repo_id": "pytorch-image-models",
"token_count": 1620
} | 196 |
""" DaViT: Dual Attention Vision Transformers
As described in https://arxiv.org/abs/2204.03645
Input size invariant transformer architecture that combines channel and spacial
attention in each block. The attention mechanisms used are linear in complexity.
DaViT model defs and weights adapted from https://github.com/... | pytorch-image-models/timm/models/davit.py/0 | {
"file_path": "pytorch-image-models/timm/models/davit.py",
"repo_id": "pytorch-image-models",
"token_count": 11881
} | 197 |
""" MLP-Mixer, ResMLP, and gMLP in PyTorch
This impl originally based on MLP-Mixer paper.
Official JAX impl: https://github.com/google-research/vision_transformer/blob/linen/vit_jax/models_mixer.py
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
@article{tolstikhin2021,
t... | pytorch-image-models/timm/models/mlp_mixer.py/0 | {
"file_path": "pytorch-image-models/timm/models/mlp_mixer.py",
"repo_id": "pytorch-image-models",
"token_count": 11661
} | 198 |
""" ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
from torch import nn
from timm.data... | pytorch-image-models/timm/models/resnest.py/0 | {
"file_path": "pytorch-image-models/timm/models/resnest.py",
"repo_id": "pytorch-image-models",
"token_count": 4439
} | 199 |
""" Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... | pytorch-image-models/timm/models/visformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/visformer.py",
"repo_id": "pytorch-image-models",
"token_count": 10132
} | 200 |
""" Adan Optimizer
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Implementation adapted from https://github.com/sail-sg/Adan
"""
import math
import torch
from torch.optim import Optimizer
class Adan(Opt... | pytorch-image-models/timm/optim/adan.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adan.py",
"repo_id": "pytorch-image-models",
"token_count": 2501
} | 201 |
""" MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List
class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
... | pytorch-image-models/timm/scheduler/multistep_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/multistep_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1029
} | 202 |
""" Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | pytorch-image-models/timm/utils/metrics.py/0 | {
"file_path": "pytorch-image-models/timm/utils/metrics.py",
"repo_id": "pytorch-image-models",
"token_count": 374
} | 203 |
import pytest
from text_generation import Client, AsyncClient
from text_generation.errors import NotFoundError, ValidationError
from text_generation.types import FinishReason, InputToken
def test_generate(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
response = client.generate("t... | text-generation-inference/clients/python/tests/test_client.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_client.py",
"repo_id": "text-generation-inference",
"token_count": 2116
} | 204 |
# Preparing the Model
Text Generation Inference improves the model in several aspects.
## Quantization
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference wit... | text-generation-inference/docs/source/basic_tutorials/preparing_model.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/preparing_model.md",
"repo_id": "text-generation-inference",
"token_count": 548
} | 205 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
},
{
"id": 49833,
"logprob": -10.5625,
"text": " dΓ©g"
},
{
"id"... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m.json",
"repo_id": "text-generation-inference",
"token_count": 1544
} | 206 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -13.90625,
"text": "Test"
},
{
"id": 200... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar.json",
"repo_id": "text-generation-inference",
"token_count": 1048
} | 207 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_load.json",
"repo_id": "text-generation-inference",
"token_count": 4897
} | 208 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 610,
"logprob": null,
"text": "def"
},
{
"id": 1489,
"logprob": -5.2617188,
"text": " print"
},
{
"id"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2.json",
"repo_id": "text-generation-inference",
"token_count": 1124
} | 209 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 6,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": null,
"tokens": [
{
"id": 259,
... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json",
"repo_id": "text-generation-inference",
"token_count": 2874
} | 210 |
import pytest
@pytest.fixture(scope="module")
def flash_falcon_handle(launcher):
with launcher("tiiuae/falcon-7b", trust_remote_code=True) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_falcon(flash_falcon_handle):
await flash_falcon_handle.health(300)
return flash_falco... | text-generation-inference/integration-tests/models/test_flash_falcon.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_falcon.py",
"repo_id": "text-generation-inference",
"token_count": 884
} | 211 |
import pytest
@pytest.fixture(scope="module")
def idefics_handle(launcher):
with launcher(
"HuggingFaceM4/idefics-9b-instruct", num_shard=2, dtype="float16"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def idefics(idefics_handle):
await idefics_handle.health(300)
r... | text-generation-inference/integration-tests/models/test_idefics.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_idefics.py",
"repo_id": "text-generation-inference",
"token_count": 552
} | 212 |
import { check, randomSeed } from 'k6';
import http from 'k6/http';
import { Trend, Counter } from 'k6/metrics';
import { randomItem } from 'https://jslib.k6.io/k6-utils/1.2.0/index.js';
const seed = 0;
const host = __ENV.HOST || '127.0.0.1:8000';
const timePerToken = new Trend('time_per_token', true);
const tokens =... | text-generation-inference/load_tests/common.js/0 | {
"file_path": "text-generation-inference/load_tests/common.js",
"repo_id": "text-generation-inference",
"token_count": 1025
} | 213 |
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;
use text_generation_client::GrammarType as ProtoGrammarType;
use text_generation_client::{
Batch, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
};
// Note: Request ids and batch ids cannot collide.
const LIVENESS_I... | text-generation-inference/router/src/health.rs/0 | {
"file_path": "text-generation-inference/router/src/health.rs",
"repo_id": "text-generation-inference",
"token_count": 1307
} | 214 |
vllm-cuda:
# Clone vllm
pip install -U ninja packaging --no-cache-dir
git clone https://github.com/vllm-project/vllm.git vllm
build-vllm-cuda: vllm-cuda
cd vllm && git fetch && git checkout f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
cd vllm && python setup.py build
install-vllm-cuda: build-vllm-cuda
pip uninst... | text-generation-inference/server/Makefile-vllm/0 | {
"file_path": "text-generation-inference/server/Makefile-vllm",
"repo_id": "text-generation-inference",
"token_count": 332
} | 215 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _matrix_cuh
#define _matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
class MatrixView_half
{
public:
const half* data;
const int height;
const int width;
__device__ __forceinline__ MatrixView_half(const half*... | text-generation-inference/server/exllama_kernels/exllama_kernels/matrix.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/matrix.cuh",
"repo_id": "text-generation-inference",
"token_count": 5380
} | 216 |
#ifndef _qdq_4_cuh
#define _qdq_4_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_4BIT == 1
// Permutation:
//
// 77775555 33331111 66664444 22220000
__forceinline__ __device__ void shuffle_4bit_8
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unroll... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_4.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_4.cuh",
"repo_id": "text-generation-inference",
"token_count": 3279
} | 217 |
import pytest
import torch
from transformers import AutoTokenizer
from text_generation_server.models import Model
def get_test_model():
class TestModel(Model):
def batch_type(self):
raise NotImplementedError
def generate_token(self, batch):
raise NotImplementedError
... | text-generation-inference/server/tests/models/test_model.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_model.py",
"repo_id": "text-generation-inference",
"token_count": 829
} | 218 |
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_rw_modeling import (
RWConfig,
FlashRWForCausalLM,
)
from te... | text-generation-inference/server/text_generation_server/models/flash_rw.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_rw.py",
"repo_id": "text-generation-inference",
"token_count": 1197
} | 219 |
import torch
import torch.distributed
from typing import List, Optional, Tuple
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models import Seq2SeqLM
from text_generation_server.models.custom_modeling.t5_modeling import (
T5ForConditionalGeneration,
)
from text_genera... | text-generation-inference/server/text_generation_server/models/t5.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/t5.py",
"repo_id": "text-generation-inference",
"token_count": 1678
} | 220 |
import time
import os
from datetime import timedelta
from loguru import logger
from pathlib import Path
from typing import Optional, List
from huggingface_hub import file_download, hf_api, HfApi, hf_hub_download
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub.utils import (
LocalE... | text-generation-inference/server/text_generation_server/utils/hub.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/hub.py",
"repo_id": "text-generation-inference",
"token_count": 3480
} | 221 |
parser: '@typescript-eslint/parser'
parserOptions:
ecmaFeatures:
jsx: true
ecmaVersion: latest
sourceType: module
project: ./tsconfig.json
env:
browser: true
es6: true
node: true
jest: true
ignorePatterns: ['index.js', 'target/']
plugins:
- import
- '@typescript-eslint'
extends:
- eslint:... | tokenizers/bindings/node/.eslintrc.yml/0 | {
"file_path": "tokenizers/bindings/node/.eslintrc.yml",
"repo_id": "tokenizers",
"token_count": 2733
} | 222 |
/* eslint-disable prettier/prettier */
// For a detailed explanation regarding each configuration property, visit:
// https://jestjs.io/docs/en/configuration.html
module.exports = {
// All imported modules in your tests should be mocked automatically
// automock: false,
// Stop running tests after `n` failures
... | tokenizers/bindings/node/jest.config.js/0 | {
"file_path": "tokenizers/bindings/node/jest.config.js",
"repo_id": "tokenizers",
"token_count": 1715
} | 223 |
# `tokenizers-darwin-arm64`
This is the **aarch64-apple-darwin** binary for `tokenizers`
| tokenizers/bindings/node/npm/darwin-arm64/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/darwin-arm64/README.md",
"repo_id": "tokenizers",
"token_count": 33
} | 224 |
# `tokenizers-win32-arm64-msvc`
This is the **aarch64-pc-windows-msvc** binary for `tokenizers`
| tokenizers/bindings/node/npm/win32-arm64-msvc/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-arm64-msvc/README.md",
"repo_id": "tokenizers",
"token_count": 38
} | 225 |
pub mod models;
pub mod tokenizer;
| tokenizers/bindings/node/src/tasks/mod.rs/0 | {
"file_path": "tokenizers/bindings/node/src/tasks/mod.rs",
"repo_id": "tokenizers",
"token_count": 11
} | 226 |
import pytest
def pytest_addoption(parser):
parser.addoption("--runslow", action="store_true", default=False, help="run slow tests")
def pytest_configure(config):
config.addinivalue_line("markers", "slow: mark test as slow to run")
def pytest_collection_modifyitems(config, items):
if config.getoption(... | tokenizers/bindings/python/conftest.py/0 | {
"file_path": "tokenizers/bindings/python/conftest.py",
"repo_id": "tokenizers",
"token_count": 217
} | 227 |
from typing import Dict, Iterator, List, Optional, Tuple, Union
from tokenizers import AddedToken, Tokenizer, decoders, pre_tokenizers, trainers
from tokenizers.models import BPE
from tokenizers.normalizers import NFKC
from .base_tokenizer import BaseTokenizer
class SentencePieceBPETokenizer(BaseTokenizer):
"""... | tokenizers/bindings/python/py_src/tokenizers/implementations/sentencepiece_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/sentencepiece_bpe.py",
"repo_id": "tokenizers",
"token_count": 1655
} | 228 |
stable
| tokenizers/bindings/python/rust-toolchain/0 | {
"file_path": "tokenizers/bindings/python/rust-toolchain",
"repo_id": "tokenizers",
"token_count": 2
} | 229 |
use pyo3::prelude::*;
use std::collections::VecDeque;
/// An simple iterator that can be instantiated with a specified length.
/// We use this with iterators that don't have a size_hint but we might
/// know its size. This is useful with progress bars for example.
pub struct MaybeSizedIterator<I> {
length: Option<... | tokenizers/bindings/python/src/utils/iterators.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/iterators.rs",
"repo_id": "tokenizers",
"token_count": 1797
} | 230 |
import copy
import os
import pickle
import pytest
from tokenizers import (
AddedToken,
SentencePieceUnigramTokenizer,
Tokenizer,
models,
normalizers,
pre_tokenizers,
trainers,
)
from ..utils import data_dir, train_files
class TestBpeTrainer:
def test_can_modify(self):
traine... | tokenizers/bindings/python/tests/bindings/test_trainers.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_trainers.py",
"repo_id": "tokenizers",
"token_count": 4957
} | 231 |
# Added Tokens
<tokenizerslangcontent>
<python>
## AddedToken
[[autodoc]] tokenizers.AddedToken
- content
- lstrip
- normalized
- rstrip
- single_word
</python>
<rust>
The Rust API Reference is available directly on the [Docs.rs](https://docs.rs/tokenizers/latest/tokenizers/) website.
</rust>
<nod... | tokenizers/docs/source-doc-builder/api/added-tokens.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/added-tokens.mdx",
"repo_id": "tokenizers",
"token_count": 134
} | 232 |
# Quicktour
Let's have a quick look at the π€ Tokenizers library features. The
library provides an implementation of today's most used tokenizers that
is both easy to use and blazing fast.
## Build a tokenizer from scratch
To illustrate how fast the π€ Tokenizers library is, let's train a new
tokenizer on [wikitext-... | tokenizers/docs/source-doc-builder/quicktour.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/quicktour.mdx",
"repo_id": "tokenizers",
"token_count": 7936
} | 233 |
Components
====================================================================================================
When building a Tokenizer, you can attach various types of components to this Tokenizer in order
to customize its behavior. This page lists most provided components.
.. _normalizers:
.. entities:: python
... | tokenizers/docs/source/components.rst/0 | {
"file_path": "tokenizers/docs/source/components.rst",
"repo_id": "tokenizers",
"token_count": 4236
} | 234 |
<p align="center">
<br>
<img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/>
<br>
<p>
<p align="center">
<img alt="Build" src="https://github.com/huggingface/tokenizers/workflows/Rust/badge.svg">
<a href="https://github.com/huggingface/tokenizers/blob/master/... | tokenizers/tokenizers/README.tpl/0 | {
"file_path": "tokenizers/tokenizers/README.tpl",
"repo_id": "tokenizers",
"token_count": 259
} | 235 |
use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize, Default)]
/// Strip is a simple trick which converts tokens looking like `<0x61>`
/// to pure bytes, and attempts to make them into a string. If the tokens
/// cannot be decoded you will get οΏ½ ... | tokenizers/tokenizers/src/decoders/strip.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/strip.rs",
"repo_id": "tokenizers",
"token_count": 1217
} | 236 |
use super::{super::OrderedVocabIter, WordLevel, WordLevelBuilder};
use serde::{
de::{MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashSet;
impl Serialize for WordLevel {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Er... | tokenizers/tokenizers/src/models/wordlevel/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordlevel/serialization.rs",
"repo_id": "tokenizers",
"token_count": 2084
} | 237 |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
/// Pre tokenizes the numbers into single tokens. If individual_digits is set
/// to true, then all digits are ... | tokenizers/tokenizers/src/pre_tokenizers/digits.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/digits.rs",
"repo_id": "tokenizers",
"token_count": 1667
} | 238 |
use crate::parallelism::*;
use crate::tokenizer::{Offsets, Token};
use crate::utils::padding::PaddingDirection;
use crate::utils::truncation::TruncationDirection;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::ops::Range;
/// Represents the output of a `Tokenizer`.
#[derive(Default, Parti... | tokenizers/tokenizers/src/tokenizer/encoding.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/encoding.rs",
"repo_id": "tokenizers",
"token_count": 17197
} | 239 |
mod common;
use common::*;
use tokenizers::tokenizer::AddedToken;
#[test]
fn add_tokens() {
let mut tokenizer = get_empty();
assert_eq!(
tokenizer.add_special_tokens(&[
AddedToken::from("<cls>", true),
AddedToken::from("<sep>", true)
]),
2
);
assert_eq!... | tokenizers/tokenizers/tests/added_tokens.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/added_tokens.rs",
"repo_id": "tokenizers",
"token_count": 1770
} | 240 |
cff-version: "1.2.0"
date-released: 2020-10
message: "If you use this software, please cite it using these metadata."
title: "Transformers: State-of-the-Art Natural Language Processing"
url: "https://github.com/huggingface/transformers"
authors:
- family-names: Wolf
given-names: Thomas
- family-names: Debut
... | transformers/CITATION.cff/0 | {
"file_path": "transformers/CITATION.cff",
"repo_id": "transformers",
"token_count": 824
} | 241 |
local base = import 'templates/base.libsonnet';
local tpus = import 'templates/tpus.libsonnet';
local utils = import "templates/utils.libsonnet";
local volumes = import "templates/volumes.libsonnet";
local bertBaseCased = base.BaseTest {
frameworkPrefix: "hf",
modelName: "bert-base-cased",
mode: "example",
con... | transformers/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet/0 | {
"file_path": "transformers/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet",
"repo_id": "transformers",
"token_count": 371
} | 242 |
<!--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/accelerate.md/0 | {
"file_path": "transformers/docs/source/en/accelerate.md",
"repo_id": "transformers",
"token_count": 1516
} | 243 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/deepspeed.md/0 | {
"file_path": "transformers/docs/source/en/deepspeed.md",
"repo_id": "transformers",
"token_count": 18766
} | 244 |
<!--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/internal/tokenization_utils.md/0 | {
"file_path": "transformers/docs/source/en/internal/tokenization_utils.md",
"repo_id": "transformers",
"token_count": 428
} | 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/barthez.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/barthez.md",
"repo_id": "transformers",
"token_count": 818
} | 246 |
<!--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/bort.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bort.md",
"repo_id": "transformers",
"token_count": 867
} | 247 |
<!--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/dinat.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/dinat.md",
"repo_id": "transformers",
"token_count": 1371
} | 248 |
<!--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/gpt_neo.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/gpt_neo.md",
"repo_id": "transformers",
"token_count": 1582
} | 249 |
<!--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/layoutlm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/layoutlm.md",
"repo_id": "transformers",
"token_count": 2088
} | 250 |
<!--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/mobilebert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mobilebert.md",
"repo_id": "transformers",
"token_count": 1548
} | 251 |
<!--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/nystromformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/nystromformer.md",
"repo_id": "transformers",
"token_count": 907
} | 252 |
<!--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/plbart.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/plbart.md",
"repo_id": "transformers",
"token_count": 1586
} | 253 |
<!--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
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