text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not ... | peft/src/peft/__init__.py/0 | {
"file_path": "peft/src/peft/__init__.py",
"repo_id": "peft",
"token_count": 952
} | 171 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/lokr/layer.py/0 | {
"file_path": "peft/src/peft/tuners/lokr/layer.py",
"repo_id": "peft",
"token_count": 7543
} | 172 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/oft/config.py/0 | {
"file_path": "peft/src/peft/tuners/oft/config.py",
"repo_id": "peft",
"token_count": 2090
} | 173 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/src/peft/tuners/prompt_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/prompt_tuning/model.py",
"repo_id": "peft",
"token_count": 1444
} | 174 |
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | peft/tests/test_decoder_models.py/0 | {
"file_path": "peft/tests/test_decoder_models.py",
"repo_id": "peft",
"token_count": 6340
} | 175 |
# Getting Started
## Welcome
Welcome to the `timm` documentation, a lean set of docs that covers the basics of `timm`.
For a more comprehensive set of docs (currently under development), please visit [timmdocs](http://timm.fast.ai) by [Aman Arora](https://github.com/amaarora).
## Install
The library can be install... | pytorch-image-models/docs/index.md/0 | {
"file_path": "pytorch-image-models/docs/index.md",
"repo_id": "pytorch-image-models",
"token_count": 736
} | 176 |
# EfficientNet (Knapsack Pruned)
**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/.templates/models/efficientnet-pruned.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/efficientnet-pruned.md",
"repo_id": "pytorch-image-models",
"token_count": 1945
} | 177 |
# (Legacy) SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
{% include 'code_snippet... | pytorch-image-models/docs/models/.templates/models/legacy-se-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/legacy-se-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 2886
} | 178 |
# ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual block... | pytorch-image-models/docs/models/.templates/models/resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 4271
} | 179 |
# (Tensorflow) 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://pape... | pytorch-image-models/docs/models/.templates/models/tf-inception-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/tf-inception-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1141
} | 180 |
# 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/ecaresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/ecaresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 3638
} | 181 |
# 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).
## How do I use this model on an image?
To load... | pytorch-image-models/docs/models/inception-v4.md/0 | {
"file_path": "pytorch-image-models/docs/models/inception-v4.md",
"repo_id": "pytorch-image-models",
"token_count": 1622
} | 182 |
# 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/resnet-d.md/0 | {
"file_path": "pytorch-image-models/docs/models/resnet-d.md",
"repo_id": "pytorch-image-models",
"token_count": 3929
} | 183 |
# Installation
Before you start, you'll need to setup your environment and install the appropriate packages. `timm` is tested on **Python 3+**.
## Virtual Environment
You should install `timm` in a [virtual environment](https://docs.python.org/3/library/venv.html) to keep things tidy and avoid dependency conflicts.
... | pytorch-image-models/hfdocs/source/installation.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/installation.mdx",
"repo_id": "pytorch-image-models",
"token_count": 619
} | 184 |
# FBNet
**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and... | pytorch-image-models/hfdocs/source/models/fbnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/fbnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1705
} | 185 |
# MnasNet
**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and late... | pytorch-image-models/hfdocs/source/models/mnasnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/mnasnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2101
} | 186 |
# SelecSLS
**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('selecsl... | pytorch-image-models/hfdocs/source/models/selecsls.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/selecsls.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2420
} | 187 |
# 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).
## How do I... | pytorch-image-models/hfdocs/source/models/xception.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/xception.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2674
} | 188 |
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
} | 189 |
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
} | 190 |
""" 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
} | 191 |
""" 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": 2443
} | 192 |
""" 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
} | 193 |
""" 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
} | 194 |
""" 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
} | 195 |
""" 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
} | 196 |
""" 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
} | 197 |
""" MobileViT
Paper:
V1: `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
V2: `Separable Self-attention for Mobile Vision Transformers` - https://arxiv.org/abs/2206.02680
MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets... | pytorch-image-models/timm/models/mobilevit.py/0 | {
"file_path": "pytorch-image-models/timm/models/mobilevit.py",
"repo_id": "pytorch-image-models",
"token_count": 12812
} | 198 |
""" ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... | pytorch-image-models/timm/models/rexnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/rexnet.py",
"repo_id": "pytorch-image-models",
"token_count": 5784
} | 199 |
""" Relative Position Vision Transformer (ViT) in PyTorch
NOTE: these models are experimental / WIP, expect changes
Hacked together by / Copyright 2022, Ross Wightman
"""
import logging
import math
from functools import partial
from typing import Optional, Tuple
import torch
import torch.nn as nn
from torch.jit impo... | pytorch-image-models/timm/models/vision_transformer_relpos.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer_relpos.py",
"repo_id": "pytorch-image-models",
"token_count": 11278
} | 200 |
""" Lion Optimizer
Paper: `Symbolic Discovery of Optimization Algorithms` - https://arxiv.org/abs/2302.06675
Original Impl: https://github.com/google/automl/tree/master/lion
"""
# Copyright 2023 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use t... | pytorch-image-models/timm/optim/lion.py/0 | {
"file_path": "pytorch-image-models/timm/optim/lion.py",
"repo_id": "pytorch-image-models",
"token_count": 3257
} | 201 |
import abc
from abc import ABC
from typing import Any, Dict, Optional
import torch
class Scheduler(ABC):
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently calle... | pytorch-image-models/timm/scheduler/scheduler.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/scheduler.py",
"repo_id": "pytorch-image-models",
"token_count": 2361
} | 202 |
""" Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model Exponential Moving Average ... | pytorch-image-models/timm/utils/model_ema.py/0 | {
"file_path": "pytorch-image-models/timm/utils/model_ema.py",
"repo_id": "pytorch-image-models",
"token_count": 2112
} | 203 |
use crate::app::Data;
use tabled::settings::Merge;
use tabled::{builder::Builder, settings::Style, Table};
#[allow(clippy::too_many_arguments)]
pub(crate) fn parameters_table(
tokenizer_name: String,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: us... | text-generation-inference/benchmark/src/table.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/table.rs",
"repo_id": "text-generation-inference",
"token_count": 2255
} | 204 |
from enum import Enum
from pydantic import BaseModel, validator
from typing import Optional, List
from text_generation.errors import ValidationError
class Parameters(BaseModel):
# Activate logits sampling
do_sample: bool = False
# Maximum number of generated tokens
max_new_tokens: int = 20
# The ... | text-generation-inference/clients/python/text_generation/types.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/types.py",
"repo_id": "text-generation-inference",
"token_count": 2991
} | 205 |
# Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
![Text Generation Inference](https://hugging... | text-generation-inference/docs/source/index.md/0 | {
"file_path": "text-generation-inference/docs/source/index.md",
"repo_id": "text-generation-inference",
"token_count": 544
} | 206 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50,
"logprob": null,
"text": "G"
},
{
"id": 330,
"logprob": -5.96875,
"text": "ir"
},
{
"id": 1622,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json",
"repo_id": "text-generation-inference",
"token_count": 4604
} | 207 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|USER|>"
},
{
"id": 1276,
"logprob": -4.5546875,
"text":... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox_load.json",
"repo_id": "text-generation-inference",
"token_count": 6308
} | 208 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 17,
"prefill": [
{
"id": 1276,
"logprob": null,
"text": "What"
},
{
"id": 310,
"logprob": -1.5117188,
"text": " is"
},
{
"id": ... | text-generation-inference/integration-tests/models/__snapshots__/test_mpt/test_mpt.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mpt/test_mpt.json",
"repo_id": "text-generation-inference",
"token_count": 1691
} | 209 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_handle(launcher):
with launcher("huggingface/llama-7b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama(flash_llama_handle):
await flash_llama_handle.health(300)
return flash_llama_handle.cli... | text-generation-inference/integration-tests/models/test_flash_llama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama.py",
"repo_id": "text-generation-inference",
"token_count": 655
} | 210 |
[package]
name = "text-generation-client"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
futures = "^0.3"
grpc-metadata = { path = "../grpc-metadata" }
prost = "^0.12"
thiserror = "^1.0"
tokio = { version = "^1.32", features = ["sync"] }
tonic = "^0.... | text-generation-inference/router/client/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/client/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 180
} | 211 |
#!/bin/bash
if [[ -z "${HF_MODEL_ID}" ]]; then
echo "HF_MODEL_ID must be set"
exit 1
fi
export MODEL_ID="${HF_MODEL_ID}"
if [[ -n "${HF_MODEL_REVISION}" ]]; then
export REVISION="${HF_MODEL_REVISION}"
fi
if [[ -n "${SM_NUM_GPUS}" ]]; then
export NUM_SHARD="${SM_NUM_GPUS}"
fi
if [[ -n "${HF_MODEL_QUANTIZE}" ... | text-generation-inference/sagemaker-entrypoint.sh/0 | {
"file_path": "text-generation-inference/sagemaker-entrypoint.sh",
"repo_id": "text-generation-inference",
"token_count": 239
} | 212 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _column_remap_cuh
#define _column_remap_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
void column_remap_cuda
(
const half* x,
half* x_new,
const int x_height,
const int x_width,
const uint32_... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cuh",
"repo_id": "text-generation-inference",
"token_count": 152
} | 213 |
#ifndef _q_gemm_cuh
#define _q_gemm_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#include "q_matrix.cuh"
void gemm_half_q_half_cuda
(
cublasHandle_t cublas_handle,
const half* a,
QMatrix* b,
half* c,
int size_m,
i... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cuh",
"repo_id": "text-generation-inference",
"token_count": 293
} | 214 |
[tool.poetry]
name = "text-generation-server"
version = "1.4.0"
description = "Text Generation Inference Python gRPC Server"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
[tool.poetry.scripts]
text-generation-server = 'text_generation_server.cli:app'
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
protobuf... | text-generation-inference/server/pyproject.toml/0 | {
"file_path": "text-generation-inference/server/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 750
} | 215 |
import torch
from typing import Dict, Optional, TypeVar
from text_generation_server.models.types import Batch
B = TypeVar("B", bound=Batch)
class Cache:
def __init__(self):
self.cache: Dict[int, B] = {}
def pop(self, batch_id: int) -> Optional[B]:
return self.cache.pop(batch_id, None)
... | text-generation-inference/server/text_generation_server/cache.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/cache.py",
"repo_id": "text-generation-inference",
"token_count": 359
} | 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/idefics_config.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_config.py",
"repo_id": "text-generation-inference",
"token_count": 6204
} | 217 |
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_phi_modeling import (
FlashPhiForCausalLM,
PhiCo... | text-generation-inference/server/text_generation_server/models/flash_phi.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_phi.py",
"repo_id": "text-generation-inference",
"token_count": 1719
} | 218 |
from functools import lru_cache
@lru_cache(10)
def log_once(log, msg: str):
log(msg)
| text-generation-inference/server/text_generation_server/utils/log.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/log.py",
"repo_id": "text-generation-inference",
"token_count": 40
} | 219 |
exclude = ["node_modules/**/*.toml"]
# https://taplo.tamasfe.dev/configuration/formatter-options.html
[formatting]
align_entries = true
indent_tables = true
reorder_keys = true
| tokenizers/bindings/node/.taplo.toml/0 | {
"file_path": "tokenizers/bindings/node/.taplo.toml",
"repo_id": "tokenizers",
"token_count": 66
} | 220 |
import {
bpeDecoder,
byteFallbackDecoder,
ctcDecoder,
fuseDecoder,
metaspaceDecoder,
replaceDecoder,
sequenceDecoder,
stripDecoder,
wordPieceDecoder,
} from '../../'
describe('wordPieceDecoder', () => {
it('accepts `undefined` as first parameter', () => {
expect(wordPieceDecoder(undefined)).toB... | tokenizers/bindings/node/lib/bindings/decoders.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/decoders.test.ts",
"repo_id": "tokenizers",
"token_count": 1393
} | 221 |
# `tokenizers-freebsd-x64`
This is the **x86_64-unknown-freebsd** binary for `tokenizers`
| tokenizers/bindings/node/npm/freebsd-x64/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/freebsd-x64/README.md",
"repo_id": "tokenizers",
"token_count": 36
} | 222 |
# `tokenizers-win32-x64-msvc`
This is the **x86_64-pc-windows-msvc** binary for `tokenizers`
| tokenizers/bindings/node/npm/win32-x64-msvc/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-x64-msvc/README.md",
"repo_id": "tokenizers",
"token_count": 39
} | 223 |
use crate::models::Model;
use napi_derive::napi;
use std::sync::{Arc, RwLock};
use tokenizers as tk;
use tokenizers::models::TrainerWrapper;
#[napi]
pub struct Trainer {
trainer: Option<Arc<RwLock<TrainerWrapper>>>,
}
impl From<TrainerWrapper> for Trainer {
fn from(trainer: TrainerWrapper) -> Self {
Self {
... | tokenizers/bindings/node/src/trainers.rs/0 | {
"file_path": "tokenizers/bindings/node/src/trainers.rs",
"repo_id": "tokenizers",
"token_count": 641
} | 224 |
import argparse
import glob
from os.path import join
from tokenizers import ByteLevelBPETokenizer
parser = argparse.ArgumentParser()
parser.add_argument(
"--files",
default=None,
metavar="path",
type=str,
required=True,
help="The files to use as training; accept '**/*.txt' type of patterns \
... | tokenizers/bindings/python/examples/train_bytelevel_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/examples/train_bytelevel_bpe.py",
"repo_id": "tokenizers",
"token_count": 521
} | 225 |
from .. import normalizers
Normalizer = normalizers.Normalizer
BertNormalizer = normalizers.BertNormalizer
NFD = normalizers.NFD
NFKD = normalizers.NFKD
NFC = normalizers.NFC
NFKC = normalizers.NFKC
Sequence = normalizers.Sequence
Lowercase = normalizers.Lowercase
Prepend = normalizers.Prepend
Strip = normalizers.Str... | tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.py",
"repo_id": "tokenizers",
"token_count": 295
} | 226 |
[isort]
default_section = FIRSTPARTY
ensure_newline_before_comments = True
force_grid_wrap = 0
include_trailing_comma = True
known_first_party = transformers
known_third_party =
absl
conllu
datasets
elasticsearch
fairseq
faiss-cpu
fastprogress
fire
fugashi
git
h5py
matplo... | tokenizers/bindings/python/setup.cfg/0 | {
"file_path": "tokenizers/bindings/python/setup.cfg",
"repo_id": "tokenizers",
"token_count": 386
} | 227 |
use onig::Regex;
use pyo3::exceptions;
use pyo3::prelude::*;
/// Instantiate a new Regex with the given pattern
#[pyclass(module = "tokenizers", name = "Regex")]
pub struct PyRegex {
pub inner: Regex,
pub pattern: String,
}
#[pymethods]
impl PyRegex {
#[new]
#[pyo3(text_signature = "(self, pattern)")]... | tokenizers/bindings/python/src/utils/regex.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/regex.rs",
"repo_id": "tokenizers",
"token_count": 264
} | 228 |
import gzip
import os
import datasets
import pytest
from ..utils import data_dir, train_files
class TestTrainFromIterators:
@staticmethod
def get_tokenizer_trainer():
# START init_tokenizer_trainer
from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers, trainers
... | tokenizers/bindings/python/tests/documentation/test_tutorial_train_from_iterators.py/0 | {
"file_path": "tokenizers/bindings/python/tests/documentation/test_tutorial_train_from_iterators.py",
"repo_id": "tokenizers",
"token_count": 1587
} | 229 |
# Input Sequences
<tokenizerslangcontent>
<python>
These types represent all the different kinds of sequence that can be used as input of a Tokenizer.
Globally, any sequence can be either a string or a list of strings, according to the operating
mode of the tokenizer: `raw text` vs `pre-tokenized`.
## TextInputSequen... | tokenizers/docs/source-doc-builder/api/input-sequences.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/input-sequences.mdx",
"repo_id": "tokenizers",
"token_count": 402
} | 230 |
import re
from sphinx.directives.other import TocTree
class TocTreeTags(TocTree):
hasPat = re.compile("^\s*:(.+):(.+)$")
def filter_entries(self, entries):
filtered = []
for e in entries:
m = self.hasPat.match(e)
if m != None:
if self.env.app.tags.has(m... | tokenizers/docs/source/_ext/toctree_tags.py/0 | {
"file_path": "tokenizers/docs/source/_ext/toctree_tags.py",
"repo_id": "tokenizers",
"token_count": 345
} | 231 |
Installation
====================================================================================================
.. only:: python
.. include:: python.inc
.. only:: rust
.. include:: rust.inc
.. only:: node
.. include:: node.inc
| tokenizers/docs/source/installation/main.rst/0 | {
"file_path": "tokenizers/docs/source/installation/main.rst",
"repo_id": "tokenizers",
"token_count": 54
} | 232 |
#[macro_use]
extern crate criterion;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
use std::time::{Duration, Instant};
use criterion::black_box;
use criterion::Criterion;
use tokenizers::processors::template::TemplateProcessing;
use tokenizers::{EncodeInput, Encoding, PostProcessor, Token... | tokenizers/tokenizers/benches/layout_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/layout_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 1158
} | 233 |
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Hello wasm-pack!</title>
</head>
<body>
<noscript>This page contains webassembly and javascript content, please enable javascript in your browser.</noscript>
<script src="./bootstrap.js"></script>
</body>
</html>
| tokenizers/tokenizers/examples/unstable_wasm/www/index.html/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/index.html",
"repo_id": "tokenizers",
"token_count": 110
} | 234 |
//! [Byte Pair Encoding](https://www.aclweb.org/anthology/P16-1162/) model.
use std::{iter, mem};
mod model;
mod serialization;
pub mod trainer;
mod word;
type Pair = (u32, u32);
/// Errors that can be encountered while using or constructing a `BPE` model.
#[derive(thiserror::Error, Debug)]
pub enum Error {
/// ... | tokenizers/tokenizers/src/models/bpe/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/mod.rs",
"repo_id": "tokenizers",
"token_count": 891
} | 235 |
use super::{super::OrderedVocabIter, WordPiece, WordPieceBuilder};
use serde::{
de::{MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashSet;
impl Serialize for WordPiece {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Er... | tokenizers/tokenizers/src/models/wordpiece/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordpiece/serialization.rs",
"repo_id": "tokenizers",
"token_count": 2453
} | 236 |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
use unicode_categories::UnicodeCategories;
fn is_punc(x: char) -> bool {
char::is_ascii_punctuation(&x) || x.is_punctuation()
}
#[derive(Copy, Cl... | tokenizers/tokenizers/src/pre_tokenizers/punctuation.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/punctuation.rs",
"repo_id": "tokenizers",
"token_count": 1102
} | 237 |
use crate::utils::SysRegex;
use crate::{Offsets, Result};
use regex::Regex;
/// Pattern used to split a NormalizedString
pub trait Pattern {
/// Slice the given string in a list of pattern match positions, with
/// a boolean indicating whether this is a match or not.
///
/// This method *must* cover th... | tokenizers/tokenizers/src/tokenizer/pattern.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/pattern.rs",
"repo_id": "tokenizers",
"token_count": 3903
} | 238 |
#![cfg(feature = "http")]
use tokenizers::{FromPretrainedParameters, Result, Tokenizer};
#[test]
fn test_from_pretrained() -> Result<()> {
let tokenizer = Tokenizer::from_pretrained("bert-base-cased", None)?;
let encoding = tokenizer.encode("Hey there dear friend!", false)?;
assert_eq!(
encoding.ge... | tokenizers/tokenizers/tests/from_pretrained.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/from_pretrained.rs",
"repo_id": "tokenizers",
"token_count": 683
} | 239 |
ARG BASE_DOCKER_IMAGE
FROM $BASE_DOCKER_IMAGE
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands)
SHELL ["sh", "-lc"]
RUN apt update
RUN apt install -y git libsndfile1-dev tesseract-ocr espea... | transformers/docker/transformers-past-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-past-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 886
} | 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/de/accelerate.md/0 | {
"file_path": "transformers/docs/source/de/accelerate.md",
"repo_id": "transformers",
"token_count": 1929
} | 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 ... | transformers/docs/source/en/contributing.md/0 | {
"file_path": "transformers/docs/source/en/contributing.md",
"repo_id": "transformers",
"token_count": 5130
} | 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/internal/generation_utils.md/0 | {
"file_path": "transformers/docs/source/en/internal/generation_utils.md",
"repo_id": "transformers",
"token_count": 3136
} | 243 |
<!--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/main_classes/image_processor.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/image_processor.md",
"repo_id": "transformers",
"token_count": 343
} | 244 |
<!--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/audio-spectrogram-transformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/audio-spectrogram-transformer.md",
"repo_id": "transformers",
"token_count": 1220
} | 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/blenderbot-small.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/blenderbot-small.md",
"repo_id": "transformers",
"token_count": 1170
} | 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/encoder-decoder.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/encoder-decoder.md",
"repo_id": "transformers",
"token_count": 2640
} | 247 |
<!--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/luke.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/luke.md",
"repo_id": "transformers",
"token_count": 2521
} | 248 |
<!--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... | transformers/docs/source/en/model_doc/nllb-moe.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/nllb-moe.md",
"repo_id": "transformers",
"token_count": 2003
} | 249 |
<!--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... | transformers/docs/source/en/model_doc/phi.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/phi.md",
"repo_id": "transformers",
"token_count": 2611
} | 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/retribert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/retribert.md",
"repo_id": "transformers",
"token_count": 536
} | 251 |
<!--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/transfo-xl.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/transfo-xl.md",
"repo_id": "transformers",
"token_count": 1988
} | 252 |
<!--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/visual_bert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/visual_bert.md",
"repo_id": "transformers",
"token_count": 1676
} | 253 |
<!--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/xglm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/xglm.md",
"repo_id": "transformers",
"token_count": 1137
} | 254 |
<!--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/pipeline_tutorial.md/0 | {
"file_path": "transformers/docs/source/en/pipeline_tutorial.md",
"repo_id": "transformers",
"token_count": 4495
} | 255 |
<!--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... | transformers/docs/source/en/tasks/image_to_image.md/0 | {
"file_path": "transformers/docs/source/en/tasks/image_to_image.md",
"repo_id": "transformers",
"token_count": 1725
} | 256 |
<!--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... | transformers/docs/source/en/tasks/visual_question_answering.md/0 | {
"file_path": "transformers/docs/source/en/tasks/visual_question_answering.md",
"repo_id": "transformers",
"token_count": 4862
} | 257 |
<!--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/es/add_new_pipeline.md/0 | {
"file_path": "transformers/docs/source/es/add_new_pipeline.md",
"repo_id": "transformers",
"token_count": 4318
} | 258 |
<!--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/es/perplexity.md/0 | {
"file_path": "transformers/docs/source/es/perplexity.md",
"repo_id": "transformers",
"token_count": 3114
} | 259 |
# docstyle-ignore
INSTALL_CONTENT = """
# Installation de Transformers
! pip install transformers datasets
# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
noteboo... | transformers/docs/source/fr/_config.py/0 | {
"file_path": "transformers/docs/source/fr/_config.py",
"repo_id": "transformers",
"token_count": 173
} | 260 |
<!--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/it/perf_train_cpu_many.md/0 | {
"file_path": "transformers/docs/source/it/perf_train_cpu_many.md",
"repo_id": "transformers",
"token_count": 2562
} | 261 |
<!--
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 agree... | transformers/docs/source/ja/benchmarks.md/0 | {
"file_path": "transformers/docs/source/ja/benchmarks.md",
"repo_id": "transformers",
"token_count": 8797
} | 262 |
<!--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/ja/internal/generation_utils.md/0 | {
"file_path": "transformers/docs/source/ja/internal/generation_utils.md",
"repo_id": "transformers",
"token_count": 3735
} | 263 |
<!--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
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