text stringlengths 7 328k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 459 |
|---|---|---|---|
<jupyter_start><jupyter_text>Pre-Training a ๐ค Transformers model on TPU with **Flax/JAX**In this notebook, we will see how to pretrain one of the [๐ค Transformers](https://github.com/huggingface/transformers) models on TPU using [**Flax**](https://flax.readthedocs.io/en/latest/index.html). GPT2's causal language model... | notebooks/examples/causal_language_modeling_flax.ipynb/0 | {
"file_path": "notebooks/examples/causal_language_modeling_flax.ipynb",
"repo_id": "notebooks",
"token_count": 8784
} | 158 |
<jupyter_start><jupyter_text>Multivariate Probabilistic Time Series Forecasting with Informer IntroductionA few months ago we introduced the [Time Series Transformer](https://huggingface.co/blog/time-series-transformers), which is the vanilla Transformer ([Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)) applie... | notebooks/examples/multivariate_informer.ipynb/0 | {
"file_path": "notebooks/examples/multivariate_informer.ipynb",
"repo_id": "notebooks",
"token_count": 15125
} | 159 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install ๐ค Transformers and ๐ค Datasets as well as other dependencies. Uncomment the following cell and run it. Note the `rouge-score` and `nltk` dependencies - even if you've used ๐ค Transformers before, you may not have t... | notebooks/examples/summarization-tf.ipynb/0 | {
"file_path": "notebooks/examples/summarization-tf.ipynb",
"repo_id": "notebooks",
"token_count": 8798
} | 160 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install ๐ค Transformers and ๐ค Datasets. Uncomment the following cell and run it.<jupyter_code>#! pip install datasets transformers[sentencepiece] sacrebleu<jupyter_output><empty_output><jupyter_text>If you're opening this ... | notebooks/examples/translation.ipynb/0 | {
"file_path": "notebooks/examples/translation.ipynb",
"repo_id": "notebooks",
"token_count": 5285
} | 161 |
<jupyter_start><jupyter_text>Sentence Embeddings with Hugging Face Transformers, Sentence Transformers and Amazon SageMaker - Custom Inference for creating document embeddings with Hugging Face's Transformers Welcome to this getting started guide. We will use the Hugging Face Inference DLCs and Amazon SageMaker Python ... | notebooks/sagemaker/17_custom_inference_script/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/17_custom_inference_script/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3804
} | 162 |
accelerate launch --config_file accelerate_config.yaml train_using_s3_data.py \
--mixed_precision "fp16" | notebooks/sagemaker/22_accelerate_sagemaker_examples/src/text-classification/launch.sh/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/src/text-classification/launch.sh",
"repo_id": "notebooks",
"token_count": 40
} | 163 |
# Builds GPU docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.8
# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/acc... | peft/docker/peft-gpu-bnb-latest/Dockerfile/0 | {
"file_path": "peft/docker/peft-gpu-bnb-latest/Dockerfile",
"repo_id": "peft",
"token_count": 816
} | 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/mixed_models.md/0 | {
"file_path": "peft/docs/source/developer_guides/mixed_models.md",
"repo_id": "peft",
"token_count": 770
} | 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/package_reference/multitask_prompt_tuning.md/0 | {
"file_path": "peft/docs/source/package_reference/multitask_prompt_tuning.md",
"repo_id": "peft",
"token_count": 533
} | 166 |
<jupyter_start><jupyter_code>from transformers import AutoModelForCausalLM
from peft import PeftModel, PeftConfig
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_wi... | peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb",
"repo_id": "peft",
"token_count": 2945
} | 167 |
<jupyter_start><jupyter_code>import os
import torch
from transformers import (
AutoTokenizer,
default_data_collator,
AutoModelForSeq2SeqLM,
Seq2SeqTrainingArguments,
Seq2SeqTrainer,
GenerationConfig,
)
from peft import get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType
from datasets... | peft/examples/conditional_generation/peft_prompt_tuning_seq2seq_with_generate.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_prompt_tuning_seq2seq_with_generate.ipynb",
"repo_id": "peft",
"token_count": 2021
} | 168 |
# LoftQ: LoRA-fine-tuning-aware Quantization
## Introduction
LoftQ finds quantized LoRA initialization: quantized backbone Q and LoRA adapters A and B, given a pre-trained weight W.
## Quick Start
Steps:
1. Apply LoftQ to a full-precision pre-trained weight and save.
2. Load LoftQ initialization and train.
For ste... | peft/examples/loftq_finetuning/README.md/0 | {
"file_path": "peft/examples/loftq_finetuning/README.md",
"repo_id": "peft",
"token_count": 1978
} | 169 |
<jupyter_start><jupyter_code>%env CUDA_VISIBLE_DEVICES=0
%env TOKENIZERS_PARALLELISM=false<jupyter_output>env: CUDA_VISIBLE_DEVICES=0
env: TOKENIZERS_PARALLELISM=false<jupyter_text>Initialize PolyModel<jupyter_code>import torch
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer,
default_data_co... | peft/examples/poly/peft_poly_seq2seq_with_generate.ipynb/0 | {
"file_path": "peft/examples/poly/peft_poly_seq2seq_with_generate.ipynb",
"repo_id": "peft",
"token_count": 4104
} | 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/scripts/launch_notebook_mp.py/0 | {
"file_path": "peft/scripts/launch_notebook_mp.py",
"repo_id": "peft",
"token_count": 474
} | 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/adalora/config.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/config.py",
"repo_id": "peft",
"token_count": 860
} | 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/tuners/loha/layer.py/0 | {
"file_path": "peft/src/peft/tuners/loha/layer.py",
"repo_id": "peft",
"token_count": 7471
} | 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/tuners/poly/router.py/0 | {
"file_path": "peft/src/peft/tuners/poly/router.py",
"repo_id": "peft",
"token_count": 1117
} | 174 |
import os
if os.environ.get("PEFT_DEBUG_WITH_TORCH_COMPILE") == "1":
# This is a hack purely for debugging purposes. If the environment variable PEFT_DEBUG_WITH_TORCH_COMPILE is set to
# 1, get_peft_model() will return a compiled model. This way, all unit tests that use peft.get_peft_model() will
# use a ... | peft/tests/__init__.py/0 | {
"file_path": "peft/tests/__init__.py",
"repo_id": "peft",
"token_count": 302
} | 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/tests/test_mixed.py/0 | {
"file_path": "peft/tests/test_mixed.py",
"repo_id": "peft",
"token_count": 17543
} | 176 |
#!/usr/bin/env python3
""" Checkpoint Averaging Script
This script averages all model weights for checkpoints in specified path that match
the specified filter wildcard. All checkpoints must be from the exact same model.
For any hope of decent results, the checkpoints should be from the same or child
(via resumes) tr... | pytorch-image-models/avg_checkpoints.py/0 | {
"file_path": "pytorch-image-models/avg_checkpoints.py",
"repo_id": "pytorch-image-models",
"token_count": 2377
} | 177 |
# Adversarial 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://paper... | pytorch-image-models/docs/models/.templates/models/adversarial-inception-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/adversarial-inception-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1432
} | 178 |
# (Gluon) 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 [residu... | pytorch-image-models/docs/models/.templates/models/gloun-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 6383
} | 179 |
# MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in... | pytorch-image-models/docs/models/.templates/models/mobilenet-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/mobilenet-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1755
} | 180 |
# SK-ResNet
**SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNet are replaced by the proposed [SK convo... | pytorch-image-models/docs/models/.templates/models/skresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/skresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1276
} | 181 |
# Xception
**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
{% include ... | pytorch-image-models/docs/models/.templates/models/xception.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/xception.md",
"repo_id": "pytorch-image-models",
"token_count": 1874
} | 182 |
# Results
CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository [results folder](https://github.com/rwightman/pytorch-image-models/tree/master/results).
## Self-trained Weights
The table below includes ImageNe... | pytorch-image-models/hfdocs/source/results.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/results.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2259
} | 183 |
DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 *... | pytorch-image-models/timm/data/constants.py/0 | {
"file_path": "pytorch-image-models/timm/data/constants.py",
"repo_id": "pytorch-image-models",
"token_count": 236
} | 184 |
""" A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | pytorch-image-models/timm/data/readers/reader_image_folder.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_folder.py",
"repo_id": "pytorch-image-models",
"token_count": 1510
} | 185 |
""" Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... | pytorch-image-models/timm/layers/attention_pool2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool2d.py",
"repo_id": "pytorch-image-models",
"token_count": 2301
} | 186 |
""" EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato ... | pytorch-image-models/timm/layers/evo_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/evo_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 6684
} | 187 |
from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identi... | pytorch-image-models/timm/layers/ml_decoder.py/0 | {
"file_path": "pytorch-image-models/timm/layers/ml_decoder.py",
"repo_id": "pytorch-image-models",
"token_count": 3177
} | 188 |
""" Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
Thi... | pytorch-image-models/timm/layers/split_batchnorm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/split_batchnorm.py",
"repo_id": "pytorch-image-models",
"token_count": 1394
} | 189 |
import os
from typing import Any, Dict, Optional, Union
from urllib.parse import urlsplit
from timm.layers import set_layer_config
from ._helpers import load_checkpoint
from ._hub import load_model_config_from_hf
from ._pretrained import PretrainedCfg
from ._registry import is_model, model_entrypoint, split_model_name... | pytorch-image-models/timm/models/_factory.py/0 | {
"file_path": "pytorch-image-models/timm/models/_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 1944
} | 190 |
""" Bring-Your-Own-Blocks Network
A flexible network w/ dataclass based config for stacking those NN blocks.
This model is currently used to implement the following networks:
GPU Efficient (ResNets) - gernet_l/m/s (original versions called genet, but this was already used (by SENet author)).
Paper: `Neural Architect... | pytorch-image-models/timm/models/byobnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/byobnet.py",
"repo_id": "pytorch-image-models",
"token_count": 42793
} | 191 |
""" The EfficientNet Family in PyTorch
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* EfficientNet-V2
- `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/A... | pytorch-image-models/timm/models/efficientnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientnet.py",
"repo_id": "pytorch-image-models",
"token_count": 47473
} | 192 |
"""
InceptionNeXt paper: https://arxiv.org/abs/2303.16900
Original implementation & weights from: https://github.com/sail-sg/inceptionnext
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, Drop... | pytorch-image-models/timm/models/inception_next.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_next.py",
"repo_id": "pytorch-image-models",
"token_count": 7709
} | 193 |
""" Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | pytorch-image-models/timm/models/pit.py/0 | {
"file_path": "pytorch-image-models/timm/models/pit.py",
"repo_id": "pytorch-image-models",
"token_count": 7347
} | 194 |
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | pytorch-image-models/timm/models/swin_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 16908
} | 195 |
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partia... | pytorch-image-models/timm/models/xception_aligned.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception_aligned.py",
"repo_id": "pytorch-image-models",
"token_count": 7719
} | 196 |
""" Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... | pytorch-image-models/timm/optim/nvnovograd.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nvnovograd.py",
"repo_id": "pytorch-image-models",
"token_count": 2415
} | 197 |
""" Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021... | pytorch-image-models/timm/utils/agc.py/0 | {
"file_path": "pytorch-image-models/timm/utils/agc.py",
"repo_id": "pytorch-image-models",
"token_count": 661
} | 198 |
#!/usr/bin/env python3
""" ImageNet Training Script
This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
training results with some of the latest networks and training techniques. It favours canonical PyTorch
and standard Python style over trying to be able to 'do it al... | pytorch-image-models/train.py/0 | {
"file_path": "pytorch-image-models/train.py",
"repo_id": "pytorch-image-models",
"token_count": 24460
} | 199 |
# 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_amd/0 | {
"file_path": "text-generation-inference/Dockerfile_amd",
"repo_id": "text-generation-inference",
"token_count": 2129
} | 200 |
unit-tests:
python -m pytest --cov=text_generation tests
install:
pip install pip --upgrade
pip install -e .
| text-generation-inference/clients/python/Makefile/0 | {
"file_path": "text-generation-inference/clients/python/Makefile",
"repo_id": "text-generation-inference",
"token_count": 41
} | 201 |
- sections:
- local: index
title: Text Generation Inference
- local: quicktour
title: Quick Tour
- local: installation
title: Installation
- local: supported_models
title: Supported Models and Hardware
- local: messages_api
title: Messages API
title: Getting started
- sections:
- local... | text-generation-inference/docs/source/_toctree.yml/0 | {
"file_path": "text-generation-inference/docs/source/_toctree.yml",
"repo_id": "text-generation-inference",
"token_count": 434
} | 202 |
# Installation
This section explains how to install the CLI tool as well as installing TGI from source. **The strongly recommended approach is to use Docker, as it does not require much setup. Check [the Quick Tour](./quicktour) to learn how to run TGI with Docker.**
## Install CLI
You can use TGI command-line inter... | text-generation-inference/docs/source/installation.md/0 | {
"file_path": "text-generation-inference/docs/source/installation.md",
"repo_id": "text-generation-inference",
"token_count": 700
} | 203 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 330,
"logprob": null,
"text": "ir"
},
{
"id": 1622,
"logprob": -7.8125,
"text": "af"
},
{
"id": 249,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1204
} | 204 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 338,
"logprob": -10.0078125,
"text": "is"
},
{
"id": 2178... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1153
} | 205 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
"text": " print"
},
{
"id":... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder.json",
"repo_id": "text-generation-inference",
"token_count": 1111
} | 206 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1276,
"logprob": null,
"text": "What"
},
{
"id": 310,
"logprob": -0.83984375,
"text": " is... | text-generation-inference/integration-tests/models/__snapshots__/test_mamba/test_mamba_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mamba/test_mamba_load.json",
"repo_id": "text-generation-inference",
"token_count": 5458
} | 207 |
{
"choices": [
{
"delta": {
"content": null,
"role": "assistant",
"tool_calls": {
"function": {
"arguments": "</s>",
"name": null
},
"id": "",
"index": 20,
"type": "function"
}
},
"finish_re... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json",
"repo_id": "text-generation-inference",
"token_count": 319
} | 208 |
import pytest
@pytest.fixture(scope="module")
def flash_santacoder_handle(launcher):
with launcher("bigcode/santacoder") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_santacoder(flash_santacoder_handle):
await flash_santacoder_handle.health(300)
return flash_santacoder_... | text-generation-inference/integration-tests/models/test_flash_santacoder.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_santacoder.py",
"repo_id": "text-generation-inference",
"token_count": 387
} | 209 |
//! Text Generation gRPC client library
mod client;
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod sharded_client;
pub use client::Client;
pub use pb::generate::v2::HealthResponse;
pub use pb::generate::v2::InfoResponse as ShardInfo;
pub use pb::generate::v2::{
Batch, CachedBatch, FinishReason, Genera... | text-generation-inference/router/client/src/lib.rs/0 | {
"file_path": "text-generation-inference/router/client/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 464
} | 210 |
# Fork that adds only the correct stream to this kernel in order
# to make cuda graphs work.
awq_commit := bd1dc2d5254345cc76ab71894651fb821275bdd4
awq:
rm -rf llm-awq
git clone https://github.com/huggingface/llm-awq
build-awq: awq
cd llm-awq/ && git fetch && git checkout $(awq_commit)
cd llm-awq/awq/kernels && p... | text-generation-inference/server/Makefile-awq/0 | {
"file_path": "text-generation-inference/server/Makefile-awq",
"repo_id": "text-generation-inference",
"token_count": 183
} | 211 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matmul_cuh
#define _q4_matmul_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#include "q4_matrix.cuh"
#include "../tuning.h"
void q4_matmul_cuda
(
ExL... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cuh",
"repo_id": "text-generation-inference",
"token_count": 322
} | 212 |
#include "compat.cuh"
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result)
{
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 __hadd2(result, g_resu... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel_gptq.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel_gptq.cuh",
"repo_id": "text-generation-inference",
"token_count": 4839
} | 213 |
import torch
import grpc
from google.rpc import status_pb2, code_pb2
from grpc_status import rpc_status
from grpc_interceptor.server import AsyncServerInterceptor
from loguru import logger
from typing import Callable, Any
class ExceptionInterceptor(AsyncServerInterceptor):
async def intercept(
self,
... | text-generation-inference/server/text_generation_server/interceptor.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/interceptor.py",
"repo_id": "text-generation-inference",
"token_count": 449
} | 214 |
import math
import torch
import torch.distributed
import numpy as np
from dataclasses import dataclass
from opentelemetry import trace
from transformers import PreTrainedTokenizerBase
from transformers.models.llama import LlamaTokenizerFast
from typing import Optional, Tuple, Type
from text_generation_server.pb impo... | text-generation-inference/server/text_generation_server/models/flash_mistral.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_mistral.py",
"repo_id": "text-generation-inference",
"token_count": 10224
} | 215 |
import torch
import torch.distributed
from typing import Optional
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
from text_generation_server.models import CausalLM
from text_generation_server.utils import (
init... | text-generation-inference/server/text_generation_server/models/opt.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/opt.py",
"repo_id": "text-generation-inference",
"token_count": 1210
} | 216 |
# https://github.com/fpgaminer/GPTQ-triton
"""
Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
"""
import builtins
import math
import time
from typing import Dict
import triton
class Autotuner(triton.KernelInterface):
def __init__(
self,
fn,
... | text-generation-inference/server/text_generation_server/utils/gptq/custom_autotune.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/gptq/custom_autotune.py",
"repo_id": "text-generation-inference",
"token_count": 5116
} | 217 |
import subprocess
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--check", action="store_true")
args = parser.parse_args()
output = subprocess.check_output(["text-generation-launcher", "--help"]).decode(
"utf-8"
)
wrap_code_blocks_flag = "<!-- WR... | text-generation-inference/update_doc.py/0 | {
"file_path": "text-generation-inference/update_doc.py",
"repo_id": "text-generation-inference",
"token_count": 991
} | 218 |
<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/main/LI... | tokenizers/README.md/0 | {
"file_path": "tokenizers/README.md",
"repo_id": "tokenizers",
"token_count": 945
} | 219 |
/* eslint-disable */
var globRequire = require;
describe("pipelineExample", () => {
// This is a hack to let us require using path similar to what the user has to use
function require(mod: string) {
if (mod.startsWith("tokenizers")) {
// let path = mod.slice("tokenizers".length);
... | tokenizers/bindings/node/examples/documentation/pipeline.test.ts/0 | {
"file_path": "tokenizers/bindings/node/examples/documentation/pipeline.test.ts",
"repo_id": "tokenizers",
"token_count": 2710
} | 220 |
# `tokenizers-android-arm-eabi`
This is the **armv7-linux-androideabi** binary for `tokenizers`
| tokenizers/bindings/node/npm/android-arm-eabi/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/android-arm-eabi/README.md",
"repo_id": "tokenizers",
"token_count": 35
} | 221 |
# `tokenizers-linux-x64-gnu`
This is the **x86_64-unknown-linux-gnu** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-x64-gnu/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-x64-gnu/README.md",
"repo_id": "tokenizers",
"token_count": 36
} | 222 |
use crate::arc_rwlock_serde;
use crate::tasks::models::{BPEFromFilesTask, WordLevelFromFilesTask, WordPieceFromFilesTask};
use crate::trainers::Trainer;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::sync:... | tokenizers/bindings/node/src/models.rs/0 | {
"file_path": "tokenizers/bindings/node/src/models.rs",
"repo_id": "tokenizers",
"token_count": 3681
} | 223 |
[package]
name = "tokenizers-python"
version = "0.15.3-dev.0"
authors = ["Anthony MOI <m.anthony.moi@gmail.com>"]
edition = "2021"
[lib]
name = "tokenizers"
crate-type = ["cdylib"]
[dependencies]
rayon = "1.8"
serde = { version = "1.0", features = [ "rc", "derive" ]}
serde_json = "1.0"
libc = "0.2"
env_logger = "0.10... | tokenizers/bindings/python/Cargo.toml/0 | {
"file_path": "tokenizers/bindings/python/Cargo.toml",
"repo_id": "tokenizers",
"token_count": 302
} | 224 |
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import AddedToken, EncodeInput, Encoding, InputSequence, Tokenizer
from tokenizers.decoders import Decoder
from tokenizers.models import Model
from tokenizers.normalizers import Normalizer
from tokenizers.pre_tokenizers import PreTokenizer
from toke... | tokenizers/bindings/python/py_src/tokenizers/implementations/base_tokenizer.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/base_tokenizer.py",
"repo_id": "tokenizers",
"token_count": 6036
} | 225 |
import itertools
import os
import re
from string import Template
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple
from tokenizers import Encoding, Tokenizer
dirname = os.path.dirname(__file__)
css_filename = os.path.join(dirname, "visualizer-styles.css")
with open(css_filename) as f:
css... | tokenizers/bindings/python/py_src/tokenizers/tools/visualizer.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/tools/visualizer.py",
"repo_id": "tokenizers",
"token_count": 6754
} | 226 |
use std::convert::TryInto;
use std::sync::Arc;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use crate::encoding::PyEncoding;
use crate::error::ToPyResult;
use serde::{Deserialize, Serialize};
use tk::processors::bert::BertProcessing;
use tk::processors::byte_level::ByteLevel;
use tk::processors::ro... | tokenizers/bindings/python/src/processors.rs/0 | {
"file_path": "tokenizers/bindings/python/src/processors.rs",
"repo_id": "tokenizers",
"token_count": 7873
} | 227 |
import pickle
import pytest
from tokenizers import NormalizedString
from tokenizers.normalizers import BertNormalizer, Lowercase, Normalizer, Sequence, Strip, Prepend
class TestBertNormalizer:
def test_instantiate(self):
assert isinstance(BertNormalizer(), Normalizer)
assert isinstance(BertNorma... | tokenizers/bindings/python/tests/bindings/test_normalizers.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_normalizers.py",
"repo_id": "tokenizers",
"token_count": 2342
} | 228 |
import multiprocessing as mp
import os
import pytest
import requests
DATA_PATH = os.path.join("tests", "data")
def download(url, with_filename=None):
filename = with_filename if with_filename is not None else url.rsplit("/")[-1]
filepath = os.path.join(DATA_PATH, filename)
if not os.path.exists(filepa... | tokenizers/bindings/python/tests/utils.py/0 | {
"file_path": "tokenizers/bindings/python/tests/utils.py",
"repo_id": "tokenizers",
"token_count": 1569
} | 229 |
Documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The node API has not been documented yet.
| tokenizers/docs/source/api/node.inc/0 | {
"file_path": "tokenizers/docs/source/api/node.inc",
"repo_id": "tokenizers",
"token_count": 22
} | 230 |
[package]
authors = ["Anthony MOI <m.anthony.moi@gmail.com>", "Nicolas Patry <patry.nicolas@protonmail.com>"]
edition = "2018"
name = "tokenizers"
version = "0.15.3-dev.0"
homepage = "https://github.com/huggingface/tokenizers"
repository = "https://github.com/huggingface/tokenizers"
documentation = "https://docs.rs/tok... | tokenizers/tokenizers/Cargo.toml/0 | {
"file_path": "tokenizers/tokenizers/Cargo.toml",
"repo_id": "tokenizers",
"token_count": 838
} | 231 |
//! Test suite for the Web and headless browsers.
#![cfg(target_arch = "wasm32")]
extern crate wasm_bindgen_test;
use wasm_bindgen_test::*;
wasm_bindgen_test_configure!(run_in_browser);
#[wasm_bindgen_test]
fn pass() {
assert_eq!(1 + 1, 2);
}
| tokenizers/tokenizers/examples/unstable_wasm/tests/web.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/tests/web.rs",
"repo_id": "tokenizers",
"token_count": 109
} | 232 |
use super::model::Unigram;
use serde::{
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
impl Serialize for Unigram {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
{
let mut model ... | tokenizers/tokenizers/src/models/unigram/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/serialization.rs",
"repo_id": "tokenizers",
"token_count": 1824
} | 233 |
use serde::{Deserialize, Serialize};
use crate::normalizers::NormalizerWrapper;
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Deserialize, Debug, Serialize)]
#[serde(tag = "type")]
/// Allows concatenating multiple other Normalizer as a Sequence... | tokenizers/tokenizers/src/normalizers/utils.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/utils.rs",
"repo_id": "tokenizers",
"token_count": 478
} | 234 |
use crate::processors::byte_level::process_offsets;
use crate::tokenizer::{Encoding, PostProcessor, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::iter::FromIterator;
#[derive(Serialize, Deserialize, Debug, Clone, PartialEq, Eq)]
#[serde(tag = "type")]
pub struct RobertaProcessin... | tokenizers/tokenizers/src/processors/roberta.rs/0 | {
"file_path": "tokenizers/tokenizers/src/processors/roberta.rs",
"repo_id": "tokenizers",
"token_count": 8419
} | 235 |
use crate::parallelism::*;
use crate::tokenizer::{Encoding, Result};
use serde::{Deserialize, Serialize};
/// The various possible padding directions.
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum PaddingDirection {
Left,
Right,
}
impl std::convert::AsRef<str> for PaddingDirection {
fn as... | tokenizers/tokenizers/src/utils/padding.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/padding.rs",
"repo_id": "tokenizers",
"token_count": 2049
} | 236 |
FROM google/cloud-sdk:slim
# Build args.
ARG GITHUB_REF=refs/heads/main
# TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7
# wheels available; see below.
ENV PYTHON_VERSION=3.6
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
cmake \
... | transformers/docker/transformers-pytorch-tpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-pytorch-tpu/Dockerfile",
"repo_id": "transformers",
"token_count": 1235
} | 237 |
<!---
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 ... | transformers/docs/source/de/contributing.md/0 | {
"file_path": "transformers/docs/source/de/contributing.md",
"repo_id": "transformers",
"token_count": 8257
} | 238 |
- sections:
- local: index
title: ๐ค Transformers
- local: quicktour
title: Quick tour
- local: installation
title: Installation
title: Get started
- sections:
- local: pipeline_tutorial
title: Run inference with pipelines
- local: autoclass_tutorial
title: Write portable code with AutoC... | transformers/docs/source/en/_toctree.yml/0 | {
"file_path": "transformers/docs/source/en/_toctree.yml",
"repo_id": "transformers",
"token_count": 11121
} | 239 |
<!--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/debugging.md/0 | {
"file_path": "transformers/docs/source/en/debugging.md",
"repo_id": "transformers",
"token_count": 6482
} | 240 |
<!--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/onnx.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/onnx.md",
"repo_id": "transformers",
"token_count": 523
} | 241 |
<!--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/bart.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bart.md",
"repo_id": "transformers",
"token_count": 3297
} | 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/model_doc/bloom.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bloom.md",
"repo_id": "transformers",
"token_count": 1158
} | 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/convbert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/convbert.md",
"repo_id": "transformers",
"token_count": 1393
} | 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/detr.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/detr.md",
"repo_id": "transformers",
"token_count": 4104
} | 245 |
<!--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/esm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/esm.md",
"repo_id": "transformers",
"token_count": 1906
} | 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/gpt2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/gpt2.md",
"repo_id": "transformers",
"token_count": 2619
} | 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
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