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<!--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... | peft/docs/source/package_reference/vera.md/0 | {
"file_path": "peft/docs/source/package_reference/vera.md",
"repo_id": "peft",
"token_count": 807
} | 227 |
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
export DATASET_NAME="oftverse/control-celeba-hq"
export PROJECT_NAME="controlnet_${PEFT_TYPE}"
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export CONTROLNET_PATH=""
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# e... | peft/examples/boft_controlnet/train_controlnet.sh/0 | {
"file_path": "peft/examples/boft_controlnet/train_controlnet.sh",
"repo_id": "peft",
"token_count": 557
} | 228 |
import argparse
import os
import warnings
from typing import Optional
from huggingface_hub import HfFolder, whoami
from transformers import PretrainedConfig
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
... | peft/examples/boft_dreambooth/utils/args_loader.py/0 | {
"file_path": "peft/examples/boft_dreambooth/utils/args_loader.py",
"repo_id": "peft",
"token_count": 5745
} | 229 |
<jupyter_start><jupyter_text>Initializing weights with LoftQ by replacing LoRA weights in-place This notebook shows how to apply [LoftQ](https://huggingface.co/papers/2310.08659) initialization on our QLoRA model.In short, the idea behind LoftQ is the following. When we use QLoRA, i.e. we quantize the base model with b... | peft/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb/0 | {
"file_path": "peft/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb",
"repo_id": "peft",
"token_count": 2208
} | 230 |
<jupyter_start><jupyter_text>This notebook shows how to use the adapter merging methods from `peft` and apply them image generation models using `diffusers`. Turn `diffusers` LoRA checkpoints into `PeftModel`<jupyter_code>!pip install diffusers accelerate transformers -U -q
!pip install git+https://github.com/huggingf... | peft/examples/multi_adapter_examples/multi_adapter_weighted_inference_diffusers.ipynb/0 | {
"file_path": "peft/examples/multi_adapter_examples/multi_adapter_weighted_inference_diffusers.ipynb",
"repo_id": "peft",
"token_count": 1880
} | 231 |
# RoAd: 3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability
## Introduction
[RoAd](https://arxiv.org/pdf/2409.00119) is a novel method that adapts LLMs using simple 2D rotations. It is highly parameter-efficient,
achieving strong performance with less than 0.1% trainable param... | peft/examples/road_finetuning/README.md/0 | {
"file_path": "peft/examples/road_finetuning/README.md",
"repo_id": "peft",
"token_count": 1042
} | 232 |
# Copyright 2025-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/method_comparison/MetaMathQA/data.py/0 | {
"file_path": "peft/method_comparison/MetaMathQA/data.py",
"repo_id": "peft",
"token_count": 1625
} | 233 |
{
"auto_mapping": null,
"base_model_name_or_path": null,
"exclude_modules": null,
"inference_mode": false,
"modules_to_save": null,
"peft_type": "LN_TUNING",
"revision": null,
"target_modules": null,
"task_type": null
} | peft/method_comparison/MetaMathQA/experiments/ln_tuning/llama-3.2-3B-default/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/ln_tuning/llama-3.2-3B-default/adapter_config.json",
"repo_id": "peft",
"token_count": 100
} | 234 |
{
"auto_mapping": null,
"base_model_name_or_path": null,
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": false,
"init_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"modules_to_save": null,
"peft_type": "RANDLORA",
"projection_prng_key": 0,
"r": 32,
"randlora_a... | peft/method_comparison/MetaMathQA/experiments/randlora/llama-3.2-3B-default/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/randlora/llama-3.2-3B-default/adapter_config.json",
"repo_id": "peft",
"token_count": 215
} | 235 |
"""
Utility to clean cache files that exceed a specific time in days according to their
last access time recorded in the cache.
Exit code:
- 1 if no candidates are found
- 0 if candidates are found
Deletion can be enabled by passing `-d` parameter, otherwise it will only list the candidates.
"""
import sys
from date... | peft/scripts/ci_clean_cache.py/0 | {
"file_path": "peft/scripts/ci_clean_cache.py",
"repo_id": "peft",
"token_count": 807
} | 236 |
# Copyright 2025-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/optimizers/lorafa.py/0 | {
"file_path": "peft/src/peft/optimizers/lorafa.py",
"repo_id": "peft",
"token_count": 5476
} | 237 |
# 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/adaption_prompt/utils.py/0 | {
"file_path": "peft/src/peft/tuners/adaption_prompt/utils.py",
"repo_id": "peft",
"token_count": 2857
} | 238 |
# Copyright 2025-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/c3a/utils.py/0 | {
"file_path": "peft/src/peft/tuners/c3a/utils.py",
"repo_id": "peft",
"token_count": 747
} | 239 |
# 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/model.py/0 | {
"file_path": "peft/src/peft/tuners/ia3/model.py",
"repo_id": "peft",
"token_count": 9374
} | 240 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/lora/bnb.py/0 | {
"file_path": "peft/src/peft/tuners/lora/bnb.py",
"repo_id": "peft",
"token_count": 12187
} | 241 |
# Copyright 2025-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/miss/config.py/0 | {
"file_path": "peft/src/peft/tuners/miss/config.py",
"repo_id": "peft",
"token_count": 2708
} | 242 |
# 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/prompt_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/prompt_tuning/model.py",
"repo_id": "peft",
"token_count": 1486
} | 243 |
# 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/xlora/config.py/0 | {
"file_path": "peft/src/peft/tuners/xlora/config.py",
"repo_id": "peft",
"token_count": 1765
} | 244 |
# 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/conftest.py/0 | {
"file_path": "peft/tests/conftest.py",
"repo_id": "peft",
"token_count": 1038
} | 245 |
# Copyright 2024-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_helpers.py/0 | {
"file_path": "peft/tests/test_helpers.py",
"repo_id": "peft",
"token_count": 8301
} | 246 |
# Copyright 2025-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 ... | peft/tests/test_seq_classifier.py/0 | {
"file_path": "peft/tests/test_seq_classifier.py",
"repo_id": "peft",
"token_count": 4713
} | 247 |
"""
Convert weights from https://github.com/google-research/nested-transformer
NOTE: You'll need https://github.com/google/CommonLoopUtils, not included in requirements.txt
"""
import sys
import numpy as np
import torch
from clu import checkpoint
arch_depths = {
'nest_base': [2, 2, 20],
'nest_small': [2, 2... | pytorch-image-models/convert/convert_nest_flax.py/0 | {
"file_path": "pytorch-image-models/convert/convert_nest_flax.py",
"repo_id": "pytorch-image-models",
"token_count": 2670
} | 248 |
# DenseNet
**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each... | pytorch-image-models/hfdocs/source/models/densenet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/densenet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4191
} | 249 |
# Instagram ResNeXt WSL
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 transfo... | pytorch-image-models/hfdocs/source/models/ig-resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ig-resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3234
} | 250 |
# Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). 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... | pytorch-image-models/hfdocs/source/models/res2net.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/res2net.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3953
} | 251 |
# (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 unifor... | pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3327
} | 252 |
dependencies = ['torch']
import timm
globals().update(timm.models._registry._model_entrypoints)
| pytorch-image-models/hubconf.py/0 | {
"file_path": "pytorch-image-models/hubconf.py",
"repo_id": "pytorch-image-models",
"token_count": 32
} | 253 |
[dist_conda]
conda_name_differences = 'torch:pytorch'
channels = pytorch
noarch = True
[metadata]
url = "https://github.com/huggingface/pytorch-image-models" | pytorch-image-models/setup.cfg/0 | {
"file_path": "pytorch-image-models/setup.cfg",
"repo_id": "pytorch-image-models",
"token_count": 65
} | 254 |
""" Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | pytorch-image-models/timm/data/dataset.py/0 | {
"file_path": "pytorch-image-models/timm/data/dataset.py",
"repo_id": "pytorch-image-models",
"token_count": 2991
} | 255 |
from abc import abstractmethod
class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index, basename=basename, absolute=absolute)... | pytorch-image-models/timm/data/readers/reader.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader.py",
"repo_id": "pytorch-image-models",
"token_count": 171
} | 256 |
""" Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | pytorch-image-models/timm/layers/activations.py/0 | {
"file_path": "pytorch-image-models/timm/layers/activations.py",
"repo_id": "pytorch-image-models",
"token_count": 2008
} | 257 |
""" Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import Gl... | pytorch-image-models/timm/layers/create_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/create_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 1588
} | 258 |
""" Image to Patch Hybird Embedding Layer
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import math
from typing import List, Optional, Tuple, Union
import torch
from torch import nn as nn
import torch.nn.functional as F
from .format import Format, nchw_to
from .helpers import to_2tuple
from .p... | pytorch-image-models/timm/layers/hybrid_embed.py/0 | {
"file_path": "pytorch-image-models/timm/layers/hybrid_embed.py",
"repo_id": "pytorch-image-models",
"token_count": 5059
} | 259 |
import torch
def global_pool_nlc(
x: torch.Tensor,
pool_type: str = 'token',
num_prefix_tokens: int = 1,
reduce_include_prefix: bool = False,
):
if not pool_type:
return x
if pool_type == 'token':
x = x[:, 0] # class token
else:
x = x if reduce_inc... | pytorch-image-models/timm/layers/pool1d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pool1d.py",
"repo_id": "pytorch-image-models",
"token_count": 350
} | 260 |
from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
from .binary_cross_entropy import BinaryCrossEntropy
from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from .jsd import JsdCrossEntropy
| pytorch-image-models/timm/loss/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/loss/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 70
} | 261 |
import os
import pkgutil
from copy import deepcopy
from torch import nn as nn
from timm.layers import Conv2dSame, BatchNormAct2d, Linear
__all__ = ['extract_layer', 'set_layer', 'adapt_model_from_string', 'adapt_model_from_file']
def extract_layer(model, layer):
layer = layer.split('.')
module = model
... | pytorch-image-models/timm/models/_prune.py/0 | {
"file_path": "pytorch-image-models/timm/models/_prune.py",
"repo_id": "pytorch-image-models",
"token_count": 2096
} | 262 |
"""PyTorch CspNet
A PyTorch implementation of Cross Stage Partial Networks including:
* CSPResNet50
* CSPResNeXt50
* CSPDarkNet53
* and DarkNet53 for good measure
Based on paper `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
Reference impl via darknet cfg file... | pytorch-image-models/timm/models/cspnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/cspnet.py",
"repo_id": "pytorch-image-models",
"token_count": 20103
} | 263 |
# NOTE timm.models.layers is DEPRECATED, please use timm.layers, this is here to reduce breakages in transition
from timm.layers.activations import *
from timm.layers.adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from timm.layers.attention_p... | pytorch-image-models/timm/models/layers/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/models/layers/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 1220
} | 264 |
"""
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | pytorch-image-models/timm/models/pnasnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/pnasnet.py",
"repo_id": "pytorch-image-models",
"token_count": 7663
} | 265 |
""" Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... | pytorch-image-models/timm/models/sknet.py/0 | {
"file_path": "pytorch-image-models/timm/models/sknet.py",
"repo_id": "pytorch-image-models",
"token_count": 3811
} | 266 |
""" ViTamin
Paper: Designing Scalable Vison Models in the Vision-Language Era
A family of model weights on Huggingface: https://huggingface.co/collections/jienengchen/vitamin-family-661048126b72debdaca060bf
@inproceedings{chen2024vitamin,
title={ViTamin: Designing Scalable Vision Models in the Vision-language Era},... | pytorch-image-models/timm/models/vitamin.py/0 | {
"file_path": "pytorch-image-models/timm/models/vitamin.py",
"repo_id": "pytorch-image-models",
"token_count": 10085
} | 267 |
""" 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
"""
# Copyright 2022 Garena Online Private Limited
#
# Licensed under the Apache L... | pytorch-image-models/timm/optim/adan.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adan.py",
"repo_id": "pytorch-image-models",
"token_count": 5803
} | 268 |
"""
SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import ... | pytorch-image-models/timm/optim/sgdp.py/0 | {
"file_path": "pytorch-image-models/timm/optim/sgdp.py",
"repo_id": "pytorch-image-models",
"token_count": 1374
} | 269 |
import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
""" Dispatch to gradient clipping method
Args:
parameters (Iterable): model parameters to clip
value (float): clipping value/factor/norm, m... | pytorch-image-models/timm/utils/clip_grad.py/0 | {
"file_path": "pytorch-image-models/timm/utils/clip_grad.py",
"repo_id": "pytorch-image-models",
"token_count": 306
} | 270 |
- title: Get started
sections:
- local: index
title: Introduction
- local: installation
title: Installation options
- local: guided_tour
title: Guided tour
- title: Tutorials
sections:
- local: tutorials/building_good_agents
title: ✨ Building good agents
- local: tutorials/inspect_runs
... | smolagents/docs/source/en/_toctree.yml/0 | {
"file_path": "smolagents/docs/source/en/_toctree.yml",
"repo_id": "smolagents",
"token_count": 538
} | 271 |
# Tools
<Tip warning={true}>
Smolagents is an experimental API which is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.
</Tip>
To learn more about agents and tools make sure to read the [introductory guide](../index). This page
contains th... | smolagents/docs/source/en/reference/tools.md/0 | {
"file_path": "smolagents/docs/source/en/reference/tools.md",
"repo_id": "smolagents",
"token_count": 481
} | 272 |
# Tools
<Tip warning={true}>
Smolagents एक experimental API है जो किसी भी समय बदल सकता है। एजेंट्स द्वारा लौटाए गए परिणाम भिन्न हो सकते हैं क्योंकि APIs या underlying मॉडल बदलने की संभावना रखते हैं।
</Tip>
एजेंट्स और टूल्स के बारे में अधिक जानने के लिए [introductory guide](../index) पढ़ना सुनिश्चित करें।
यह पेज un... | smolagents/docs/source/hi/reference/tools.md/0 | {
"file_path": "smolagents/docs/source/hi/reference/tools.md",
"repo_id": "smolagents",
"token_count": 2093
} | 273 |
# Text-to-SQL
[[open-in-colab]]
在此教程中，我们将看到如何使用 `smolagents` 实现一个利用 SQL 的 agent。
> 让我们从经典问题开始：为什么不简单地使用标准的 text-to-SQL pipeline 呢？
标准的 text-to-SQL pipeline 很脆弱，因为生成的 SQL 查询可能会出错。更糟糕的是，查询可能出错却不引发错误警报，从而返回一些不正确或无用的结果。
👉 相反，agent 系统则可以检视输出结果并决定查询是否需要被更改，因此带来巨大的性能提升。
让我们来一起构建这个 agent! 💪
首先，我们构建一个 SQL 的环境：
```py
fr... | smolagents/docs/source/zh/examples/text_to_sql.md/0 | {
"file_path": "smolagents/docs/source/zh/examples/text_to_sql.md",
"repo_id": "smolagents",
"token_count": 2956
} | 274 |
from smolagents import Tool
from smolagents.models import Model
class TextInspectorTool(Tool):
name = "inspect_file_as_text"
description = """
You cannot load files yourself: instead call this tool to read a file as markdown text and ask questions about it.
This tool handles the following file extensions: [".... | smolagents/examples/open_deep_research/scripts/text_inspector_tool.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/text_inspector_tool.py",
"repo_id": "smolagents",
"token_count": 2346
} | 275 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | smolagents/src/smolagents/__init__.py/0 | {
"file_path": "smolagents/src/smolagents/__init__.py",
"repo_id": "smolagents",
"token_count": 311
} | 276 |
import ast
import builtins
from itertools import zip_longest
from .utils import BASE_BUILTIN_MODULES, get_source, is_valid_name
_BUILTIN_NAMES = set(vars(builtins))
class MethodChecker(ast.NodeVisitor):
"""
Checks that a method
- only uses defined names
- contains no local imports (e.g. numpy is ok... | smolagents/src/smolagents/tool_validation.py/0 | {
"file_path": "smolagents/src/smolagents/tool_validation.py",
"repo_id": "smolagents",
"token_count": 4921
} | 277 |
import os
import subprocess
import tempfile
def test_import_smolagents_without_extras(monkeypatch):
monkeypatch.delenv("VIRTUAL_ENV", raising=False)
with tempfile.TemporaryDirectory() as temp_dir:
# Create a virtual environment
venv_dir = os.path.join(temp_dir, "venv")
subprocess.run([... | smolagents/tests/test_import.py/0 | {
"file_path": "smolagents/tests/test_import.py",
"repo_id": "smolagents",
"token_count": 448
} | 278 |
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0
hooks:
- id: check-yaml
- id: end-of-file-fixer
exclude: crate-hashes.json
- id: trailing-whitespace
exclude: docs/source/reference/launcher.md
- repo: https://github.com/psf/black
rev: 24.2.0
... | text-generation-inference/.pre-commit-config.yaml/0 | {
"file_path": "text-generation-inference/.pre-commit-config.yaml",
"repo_id": "text-generation-inference",
"token_count": 314
} | 279 |
<div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a>
# Text Generation Inference
<a href="https://github.com/huggingface/text-generation-inference">
<... | text-generation-inference/README.md/0 | {
"file_path": "text-generation-inference/README.md",
"repo_id": "text-generation-inference",
"token_count": 4590
} | 280 |
# Examples of Docker Commands for Gaudi Backend
This page gives a list of examples of docker run commands for some of the most popular models.
> **Note:** The parameters are chosen for Gaudi2 hardware to maximize performance on this given hardware, please adjust the parameters based on your hardware. For example, if ... | text-generation-inference/backends/gaudi/examples/docker_commands/docker_commands.md/0 | {
"file_path": "text-generation-inference/backends/gaudi/examples/docker_commands/docker_commands.md",
"repo_id": "text-generation-inference",
"token_count": 1488
} | 281 |
# coding=utf-8
# Copyright 5 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen3_moe_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen3_moe_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 9436
} | 282 |
# Llamacpp backend
If all your dependencies are installed at the system level, running
cargo build should be sufficient. However, if you want to experiment
with different versions of llama.cpp, some additional setup is required.
## Install llama.cpp
LLAMACPP_PREFIX=$(pwd)/llama.cpp.out
git clone https://git... | text-generation-inference/backends/llamacpp/README.md/0 | {
"file_path": "text-generation-inference/backends/llamacpp/README.md",
"repo_id": "text-generation-inference",
"token_count": 297
} | 283 |
from typing import Any, Callable
import grpc
from google.rpc import code_pb2, status_pb2
from grpc_interceptor.server import AsyncServerInterceptor
from grpc_status import rpc_status
from loguru import logger
class ExceptionInterceptor(AsyncServerInterceptor):
async def intercept(
self,
method: C... | text-generation-inference/backends/neuron/server/text_generation_server/interceptor.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/interceptor.py",
"repo_id": "text-generation-inference",
"token_count": 399
} | 284 |
import os
import pytest
from tempfile import TemporaryDirectory
from optimum.neuron.models.inference.nxd.backend.config import NxDNeuronConfig
from optimum.neuron.utils import map_torch_dtype
from text_generation_server.tgi_env import (
get_neuron_config_for_model,
lookup_compatible_cached_model,
neuron_c... | text-generation-inference/backends/neuron/tests/test_entry_point.py/0 | {
"file_path": "text-generation-inference/backends/neuron/tests/test_entry_point.py",
"repo_id": "text-generation-inference",
"token_count": 1205
} | 285 |
from argparse import ArgumentParser
AWS_S3_CACHING_VARIABLES = {
"AWS_ACCESS_KEY_ID": "aws_access_key_id",
"AWS_SECRET_ACCESS_KEY": "aws_secret_access_key",
"AWS_SESSION_TOKEN": "aws_session_token",
"SCCACHE_REGION": "s3_region",
"SCCACHE_BUCKET": "s3_bucket_name",
}
ALL_CACHING_STORAGE_VARIABLES ... | text-generation-inference/backends/trtllm/scripts/setup_sccache.py/0 | {
"file_path": "text-generation-inference/backends/trtllm/scripts/setup_sccache.py",
"repo_id": "text-generation-inference",
"token_count": 663
} | 286 |
use crate::client::{
Batch, GrammarType, NextTokenChooserParameters, Request, StoppingCriteriaParameters,
};
use nohash_hasher::{BuildNoHashHasher, IntMap};
use std::cmp::min;
use std::collections::VecDeque;
use text_generation_router::infer::InferError;
use text_generation_router::infer::InferStreamResponse;
use t... | text-generation-inference/backends/v2/src/queue.rs/0 | {
"file_path": "text-generation-inference/backends/v2/src/queue.rs",
"repo_id": "text-generation-inference",
"token_count": 11054
} | 287 |
/// Inspired by https://github.com/orhun/rust-tui-template/blob/472aa515119d4c94903eac12d9784417281dc7f5/src/event.rs
use ratatui::crossterm::event;
use std::time::{Duration, Instant};
use tokio::sync::{broadcast, mpsc};
/// Events
#[derive(Debug)]
pub(crate) enum Event {
/// Terminal tick.
Tick,
/// Key p... | text-generation-inference/benchmark/src/event.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/event.rs",
"repo_id": "text-generation-inference",
"token_count": 917
} | 288 |
# 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 applicabl... | text-generation-inference/clients/python/text_generation/__init__.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 338
} | 289 |
# Serving Private & Gated Models
If the model you wish to serve is behind gated access or the model repository on Hugging Face Hub is private, and you have access to the model, you can provide your Hugging Face Hub access token. You can generate and copy a read token from [Hugging Face Hub tokens page](https://hugging... | text-generation-inference/docs/source/basic_tutorials/gated_model_access.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/gated_model_access.md",
"repo_id": "text-generation-inference",
"token_count": 290
} | 290 |
# Safetensors
Safetensors is a model serialization format for deep learning models. It is [faster](https://huggingface.co/docs/safetensors/speed) and safer compared to other serialization formats like pickle (which is used under the hood in many deep learning libraries).
TGI depends on safetensors format mainly to en... | text-generation-inference/docs/source/conceptual/safetensors.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/safetensors.md",
"repo_id": "text-generation-inference",
"token_count": 184
} | 291 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
},
{
"id": 49833,
"logprob": -10.5625,
"text": " dé... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_load.json",
"repo_id": "text-generation-inference",
"token_count": 7244
} | 292 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5267,
"logprob": -1.1464844,
"special": false,
"text": "?\n"
},
{
"id": 33464,
"logprob":... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 862
} | 293 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "Okay, let's analyze the image. \n\nThe image is a very plain, solid white square. That's it! \n\nIt's essentially a blank canvas. \n\nDo you want me to describe it in more detail, or ar... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgb_png.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgb_png.json",
"repo_id": "text-generation-inference",
"token_count": 324
} | 294 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 5229,
"logprob": -2.7988281,
"special": false,
"text": " failed"
},
{
"id": 29901,
"lo... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin_24/test_flash_llama_marlin.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin_24/test_flash_llama_marlin.json",
"repo_id": "text-generation-inference",
"token_count": 869
} | 295 |
{
"details": {
"finish_reason": "length",
"generated_tokens": 40,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -0.31347656,
"special": false,
"text": "\n"
},
{
"id": 13,
"logprob": -0.27441406,
"special": ... | text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_without_customer_support_adapter.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_without_customer_support_adapter.json",
"repo_id": "text-generation-inference",
"token_count": 3126
} | 296 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 29899,
"logprob": -1.4980469,
"special": false,
"text": "-"
},
{
"id": 1454,
"logprob": -... | text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 853
} | 297 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "I can't access real-time data, but I can provide you with current conditions and forecast for Paris, France:\n\nThe current conditions in Paris are mostly cloudy with a temperature of 6... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_tool_reply_response.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_tool_reply_response.json",
"repo_id": "text-generation-inference",
"token_count": 335
} | 298 |
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_w8an_handle(launcher):
with launcher(
"neuralmagic/Llama-3.2-1B-Instruct-FP8",
num_shard=2,
quantize="compressed-tensors",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def compressed_tenso... | text-generation-inference/integration-tests/models/test_compressed_tensors_w8an_fp.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_compressed_tensors_w8an_fp.py",
"repo_id": "text-generation-inference",
"token_count": 1000
} | 299 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_fp8_handle(launcher):
with launcher("meta-llama/Meta-Llama-3-8B", num_shard=2, quantize="fp8") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_fp8(flash_llama_fp8_handle):
await flash_llama_fp8_handle.health(... | text-generation-inference/integration-tests/models/test_flash_llama_fp8.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_fp8.py",
"repo_id": "text-generation-inference",
"token_count": 802
} | 300 |
import pytest
@pytest.fixture(scope="module")
def flash_phi_handle(launcher):
with launcher("microsoft/phi-2", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_phi(flash_phi_handle):
await flash_phi_handle.health(300)
return flash_phi_handle.client
@pytest.m... | text-generation-inference/integration-tests/models/test_flash_phi.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_phi.py",
"repo_id": "text-generation-inference",
"token_count": 749
} | 301 |
import pytest
@pytest.fixture(scope="module")
def flash_llava_next_handle(launcher):
with launcher(
"llava-hf/llava-v1.6-mistral-7b-hf",
num_shard=4,
max_input_length=4000,
max_total_tokens=4096,
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flas... | text-generation-inference/integration-tests/models/test_llava_next.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_llava_next.py",
"repo_id": "text-generation-inference",
"token_count": 961
} | 302 |
[project]
name = "text-generation-integration-tests"
version = "2.0.1"
description = "Text Generation Inference integration tests"
authors = ["Nicolas Patry <nicolas@huggingface.co>"]
requires-python = ">=3.10,<3.13"
dependencies = [
"pydantic>2,< 3",
"syrupy>=4.8.0",
"text-generation>=0.6.0",
"pytest>... | text-generation-inference/integration-tests/pyproject.toml/0 | {
"file_path": "text-generation-inference/integration-tests/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 245
} | 303 |
import json
import datasets
import tqdm
def main():
dataset = datasets.load_dataset("Open-Orca/OpenOrca", split="train")
# Select only the first 2k conversations that start with a human.
max = min(2000, len(dataset))
conversations = []
for item in tqdm.tqdm(dataset, total=max):
conversatio... | text-generation-inference/load_tests/orca.py/0 | {
"file_path": "text-generation-inference/load_tests/orca.py",
"repo_id": "text-generation-inference",
"token_count": 313
} | 304 |
// 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": 153
} | 305 |
#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": 294
} | 306 |
[project]
name = "text-generation-server"
version = "2.0.5-dev0"
description = "Text Generation Inference Python gRPC Server"
readme = "README.md"
requires-python = ">=3.9"
authors = [
{name = "Olivier Dehaene", email = "olivier@huggingface.co"},
{name = "Nicolas Patry", email = "nicolas@huggingface.co"},
]
depende... | text-generation-inference/server/pyproject.toml/0 | {
"file_path": "text-generation-inference/server/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 1325
} | 307 |
import torch
from text_generation_server.utils.tokens import (
StopSequenceCriteria,
StoppingCriteria,
FinishReason,
batch_top_tokens,
)
def test_stop_sequence_criteria():
criteria = StopSequenceCriteria("/test;")
assert not criteria("/")
assert not criteria("/test")
assert criteria("... | text-generation-inference/server/tests/utils/test_tokens.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_tokens.py",
"repo_id": "text-generation-inference",
"token_count": 1427
} | 308 |
from typing import Optional
from contextvars import ContextVar
from contextlib import contextmanager
import flashinfer
import torch
prefill_state: ContextVar[flashinfer.BatchPrefillWithRaggedKVCacheWrapper] = ContextVar(
"prefill_state"
)
prefill_with_paged_kv_state: ContextVar[
flashinfer.BatchPrefillWithPa... | text-generation-inference/server/text_generation_server/layers/attention/flashinfer.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/flashinfer.py",
"repo_id": "text-generation-inference",
"token_count": 2990
} | 309 |
from dataclasses import dataclass
import torch
from text_generation_server.utils.kernels import load_kernel
from text_generation_server.utils.weights import UnquantizedWeight
quantization_eetq = load_kernel(
module="quantization_eetq", repo_id="kernels-community/quantization-eetq"
)
@dataclass
class EETQWeight(... | text-generation-inference/server/text_generation_server/layers/eetq.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/eetq.py",
"repo_id": "text-generation-inference",
"token_count": 630
} | 310 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy
import torch
import torch.nn as nn
from loguru import logger
from text_generation_server.layers.marlin.util import (
_check_marlin_kernels,
marlin_zero_points,
permute_scales,
unpack_cols,
)
from text_generation_ser... | text-generation-inference/server/text_generation_server/layers/marlin/gptq.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/gptq.py",
"repo_id": "text-generation-inference",
"token_count": 7460
} | 311 |
# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of ... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_perceiver.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_perceiver.py",
"repo_id": "text-generation-inference",
"token_count": 5152
} | 312 |
import re
import torch
import torch.distributed
from transformers import (
PreTrainedTokenizerBase,
)
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import (
NextTokenChooser,
StoppingCriteria,
)
from t... | text-generation-inference/server/text_generation_server/models/galactica.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/galactica.py",
"repo_id": "text-generation-inference",
"token_count": 2499
} | 313 |
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
} | 314 |
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
} | 315 |
# `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
} | 316 |
# `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
} | 317 |
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
} | 318 |
import argparse
import glob
from tokenizers import BertWordPieceTokenizer
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_bert_wordpiece.py/0 | {
"file_path": "tokenizers/bindings/python/examples/train_bert_wordpiece.py",
"repo_id": "tokenizers",
"token_count": 472
} | 319 |
# Generated content DO NOT EDIT
class Model:
"""
Base class for all models
The model represents the actual tokenization algorithm. This is the part that
will contain and manage the learned vocabulary.
This class cannot be constructed directly. Please use one of the concrete models.
"""
def... | tokenizers/bindings/python/py_src/tokenizers/models/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/models/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 7626
} | 320 |
import tokenizers
from argparse import ArgumentParser
import sentencepiece as spm
from collections import Counter
import json
import os
import datetime
try:
from termcolor import colored
has_color = True
except Exception:
has_color = False
def main():
parser = ArgumentParser("SentencePiece parity ch... | tokenizers/bindings/python/scripts/spm_parity_check.py/0 | {
"file_path": "tokenizers/bindings/python/scripts/spm_parity_check.py",
"repo_id": "tokenizers",
"token_count": 4110
} | 321 |
use tokenizers as tk;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use super::{
DestroyPtr, PyNormalizedString, PyNormalizedStringRefMut, RefMutContainer, RefMutGuard,
};
use crate::encoding::PyEncoding;
use crate::error::ToPyResult;
use crate::token::PyToken;
use tk::{OffsetReferential, Offset... | tokenizers/bindings/python/src/utils/pretokenization.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/pretokenization.rs",
"repo_id": "tokenizers",
"token_count": 4958
} | 322 |
from tokenizers import Tokenizer
from ..utils import data_dir, doc_pipeline_bert_tokenizer, doc_wiki_tokenizer
disable_printing = True
original_print = print
def print(*args, **kwargs):
if not disable_printing:
original_print(*args, **kwargs)
class TestPipeline:
def test_pipeline(self, doc_wiki_to... | tokenizers/bindings/python/tests/documentation/test_pipeline.py/0 | {
"file_path": "tokenizers/bindings/python/tests/documentation/test_pipeline.py",
"repo_id": "tokenizers",
"token_count": 3351
} | 323 |
# Encode Inputs
<tokenizerslangcontent>
<python>
These types represent all the different kinds of input that a [`~tokenizers.Tokenizer`] accepts
when using [`~tokenizers.Tokenizer.encode_batch`].
## TextEncodeInput[[[[tokenizers.TextEncodeInput]]]]
<code>tokenizers.TextEncodeInput</code>
Represents a textual input ... | tokenizers/docs/source-doc-builder/api/encode-inputs.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/encode-inputs.mdx",
"repo_id": "tokenizers",
"token_count": 716
} | 324 |
from collections import defaultdict, abc
from typing import cast
from docutils import nodes
from docutils.parsers.rst import Directive
import sphinx
from sphinx.locale import _
from sphinx.util.docutils import SphinxDirective
from sphinx.errors import ExtensionError
from conf import languages as LANGUAGES
logger = ... | tokenizers/docs/source/_ext/entities.py/0 | {
"file_path": "tokenizers/docs/source/_ext/entities.py",
"repo_id": "tokenizers",
"token_count": 4032
} | 325 |
.. entities:: python
:global:
class
class
classmethod
class method
Tokenizer
:class:`~tokenizers.Tokenizer`
Tokenizer.train
:meth:`~tokenizers.Tokenizer.train`
Tokenizer.save
:meth:`~tokenizers.Tokenizer.save`
Tokenizer.from_file
:meth:`~toke... | tokenizers/docs/source/entities.inc/0 | {
"file_path": "tokenizers/docs/source/entities.inc",
"repo_id": "tokenizers",
"token_count": 2078
} | 326 |
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