text stringlengths 5 409k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 572 |
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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | open-r1/scripts/upload_details.py/0 | {
"file_path": "open-r1/scripts/upload_details.py",
"repo_id": "open-r1",
"token_count": 615
} | 178 |
# Copyright 2025 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... | open-r1/src/open_r1/sft.py/0 | {
"file_path": "open-r1/src/open_r1/sft.py",
"repo_id": "open-r1",
"token_count": 2556
} | 179 |
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SOURCEDIR = source
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" ... | peft/docs/Makefile/0 | {
"file_path": "peft/docs/Makefile",
"repo_id": "peft",
"token_count": 237
} | 180 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/developer_guides/model_merging.md/0 | {
"file_path": "peft/docs/source/developer_guides/model_merging.md",
"repo_id": "peft",
"token_count": 2547
} | 181 |
PEFT_TYPE="boft"
BLOCK_NUM=8
BLOCK_SIZE=0
N_BUTTERFLY_FACTOR=1
ITER_NUM=50000
export RUN_NAME="${PEFT_TYPE}_${BLOCK_NUM}${BLOCK_SIZE}${N_BUTTERFLY_FACTOR}"
export MODEL_NAME="stabilityai/stable-diffusion-2-1"
# export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATASET_NAME="oftverse/control-celeba-hq"
export... | peft/examples/boft_controlnet/test_controlnet.sh/0 | {
"file_path": "peft/examples/boft_controlnet/test_controlnet.sh",
"repo_id": "peft",
"token_count": 379
} | 182 |
IDX=$1
PROMPT_IDX=$((IDX % 25))
CLASS_IDX=$((IDX % 30))
# Define the UNIQUE_TOKEN, CLASS_TOKENs, and SUBJECT_NAMES
UNIQUE_TOKEN="qwe"
SUBJECT_NAMES=(
"backpack" "backpack_dog" "bear_plushie" "berry_bowl" "can"
"candle" "cat" "cat2" "clock" "colorful_sneaker"
"dog" "dog2" "dog3" "dog5" "dog6"
"dog7" "d... | peft/examples/boft_dreambooth/train_dreambooth.sh/0 | {
"file_path": "peft/examples/boft_dreambooth/train_dreambooth.sh",
"repo_id": "peft",
"token_count": 3412
} | 183 |
<jupyter_start><jupyter_code>from datasets import load_dataset
from transformers import set_seed, AutoModelForSeq2SeqLM, AutoTokenizer
from peft import get_peft_model, MultitaskPromptTuningConfig, TaskType, MultitaskPromptTuningInit
set_seed(42)
model_name = "google/flan-t5-base"
peft_config = MultitaskPromptTuningC... | peft/examples/conditional_generation/multitask_prompt_tuning.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/multitask_prompt_tuning.ipynb",
"repo_id": "peft",
"token_count": 3341
} | 184 |
<jupyter_start><jupyter_text>CPT Training and InferenceThis notebook demonstrates the training and evaluation process of Context-Aware Prompt Tuning (CPT) using the Hugging Face Trainer. For more details, refer to the [Paper](https://huggingface.co/papers/2410.17222). Sections Overview:1. **Setup**: Import libraries an... | peft/examples/cpt_finetuning/cpt_train_and_inference.ipynb/0 | {
"file_path": "peft/examples/cpt_finetuning/cpt_train_and_inference.ipynb",
"repo_id": "peft",
"token_count": 6570
} | 185 |
<jupyter_start><jupyter_text>Finetuning Whisper-large-V2 on Colab using PEFT-Lora + BNB INT8 training In this Colab, we present a step-by-step guide on how to fine-tune Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers and 🤗 PEFT. Using 🤗 PEFT and `bitsandbytes`, you can train the `whisper-l... | peft/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb/0 | {
"file_path": "peft/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb",
"repo_id": "peft",
"token_count": 7713
} | 186 |
<jupyter_start><jupyter_text>Using PEFT with custom models `peft` allows us to fine-tune models efficiently with LoRA. In this short notebook, we will demonstrate how to train a simple multilayer perceptron (MLP) using `peft`. Imports Make sure that you have the latest version of `peft` installed. To ensure that, run ... | peft/examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb/0 | {
"file_path": "peft/examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb",
"repo_id": "peft",
"token_count": 4094
} | 187 |
"""
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
} | 188 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/loha/model.py/0 | {
"file_path": "peft/src/peft/tuners/loha/model.py",
"repo_id": "peft",
"token_count": 1880
} | 189 |
# 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/layer.py/0 | {
"file_path": "peft/src/peft/tuners/lora/layer.py",
"repo_id": "peft",
"token_count": 37970
} | 190 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/p_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/p_tuning/model.py",
"repo_id": "peft",
"token_count": 2476
} | 191 |
# 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/src/peft/tuners/vblora/model.py/0 | {
"file_path": "peft/src/peft/tuners/vblora/model.py",
"repo_id": "peft",
"token_count": 8470
} | 192 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/utils/loftq_utils.py/0 | {
"file_path": "peft/src/peft/utils/loftq_utils.py",
"repo_id": "peft",
"token_count": 7250
} | 193 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_decoder_models.py/0 | {
"file_path": "peft/tests/test_decoder_models.py",
"repo_id": "peft",
"token_count": 12220
} | 194 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_other.py/0 | {
"file_path": "peft/tests/test_other.py",
"repo_id": "peft",
"token_count": 5229
} | 195 |
message: "If you use this software, please cite it as below."
title: "PyTorch Image Models"
version: "1.2.2"
doi: "10.5281/zenodo.4414861"
authors:
- family-names: Wightman
given-names: Ross
version: 1.0.11
year: "2019"
url: "https://github.com/huggingface/pytorch-image-models"
license: "Apache 2.0" | pytorch-image-models/CITATION.cff/0 | {
"file_path": "pytorch-image-models/CITATION.cff",
"repo_id": "pytorch-image-models",
"token_count": 122
} | 196 |
# Changelog
## Jan 19, 2025
* Fix loading of LeViT safetensor weights, remove conversion code which should have been deactivated
* Add 'SO150M' ViT weights trained with SBB recipes, decent results, but not optimal shape for ImageNet-12k/1k pretrain/ft
* `vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k_ft_in1k` - 86.7... | pytorch-image-models/hfdocs/source/changes.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/changes.mdx",
"repo_id": "pytorch-image-models",
"token_count": 50077
} | 197 |
# 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/hfdocs/source/models/efficientnet-pruned.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/efficientnet-pruned.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2777
} | 198 |
# (Tensorflow) MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
The weights from this model were ported from [Tenso... | pytorch-image-models/hfdocs/source/models/tf-mixnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-mixnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2361
} | 199 |
[build-system]
requires = ["pdm-backend"]
build-backend = "pdm.backend"
[project]
name = "timm"
authors = [
{name = "Ross Wightman", email = "ross@huggingface.co"},
]
description = "PyTorch Image Models"
readme = "README.md"
requires-python = ">=3.8"
keywords = ["pytorch", "image-classification"]
license = {text =... | pytorch-image-models/pyproject.toml/0 | {
"file_path": "pytorch-image-models/pyproject.toml",
"repo_id": "pytorch-image-models",
"token_count": 800
} | 200 |
""" Optimzier Tests
These tests were adapted from PyTorch' optimizer tests.
"""
import functools
import importlib
import os
from copy import deepcopy
import pytest
import torch
from torch.nn import Parameter
from torch.testing._internal.common_utils import TestCase
from timm.optim import create_optimizer_v2, list_o... | pytorch-image-models/tests/test_optim.py/0 | {
"file_path": "pytorch-image-models/tests/test_optim.py",
"repo_id": "pytorch-image-models",
"token_count": 9446
} | 201 |
import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | pytorch-image-models/timm/data/imagenet_info.py/0 | {
"file_path": "pytorch-image-models/timm/data/imagenet_info.py",
"repo_id": "pytorch-image-models",
"token_count": 1732
} | 202 |
from multiprocessing import Value
class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
self.shared_epoch.value = epoch
| pytorch-image-models/timm/data/readers/shared_count.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/shared_count.py",
"repo_id": "pytorch-image-models",
"token_count": 122
} | 203 |
""" PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | pytorch-image-models/timm/layers/cond_conv2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/cond_conv2d.py",
"repo_id": "pytorch-image-models",
"token_count": 2314
} | 204 |
""" Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
import torc... | pytorch-image-models/timm/layers/global_context.py/0 | {
"file_path": "pytorch-image-models/timm/layers/global_context.py",
"repo_id": "pytorch-image-models",
"token_count": 1169
} | 205 |
""" Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm im... | pytorch-image-models/timm/layers/norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/norm.py",
"repo_id": "pytorch-image-models",
"token_count": 4880
} | 206 |
""" Test Time Pooling (Average-Max Pool)
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from torch import nn
import torch.nn.functional as F
from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
_logger = logging.getLogger(__name__)
class TestTimePoolHead(nn.Module):
def __init__(sel... | pytorch-image-models/timm/layers/test_time_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/test_time_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 881
} | 207 |
""" Model creation / weight loading / state_dict helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from typing import Any, Callable, Dict, Optional, Union
import torch
try:
import safetensors.torch
_has_safetensors = True
except ImportError:
_has_safetensors = False
_... | pytorch-image-models/timm/models/_helpers.py/0 | {
"file_path": "pytorch-image-models/timm/models/_helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 2793
} | 208 |
""" ConViT Model
@article{d2021convit,
title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
journal={arXiv preprint arXiv:2103.10697},
year={2021}
}
P... | pytorch-image-models/timm/models/convit.py/0 | {
"file_path": "pytorch-image-models/timm/models/convit.py",
"repo_id": "pytorch-image-models",
"token_count": 7721
} | 209 |
""" EVA
EVA from https://github.com/baaivision/EVA , paper: https://arxiv.org/abs/2211.07636
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
Tiejun and ... | pytorch-image-models/timm/models/eva.py/0 | {
"file_path": "pytorch-image-models/timm/models/eva.py",
"repo_id": "pytorch-image-models",
"token_count": 25968
} | 210 |
""" Pytorch Inception-Resnet-V2 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functiona... | pytorch-image-models/timm/models/inception_resnet_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_resnet_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 6034
} | 211 |
""" Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | pytorch-image-models/timm/models/pit.py/0 | {
"file_path": "pytorch-image-models/timm/models/pit.py",
"repo_id": "pytorch-image-models",
"token_count": 7404
} | 212 |
""" 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": 3801
} | 213 |
""" VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | pytorch-image-models/timm/models/vovnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/vovnet.py",
"repo_id": "pytorch-image-models",
"token_count": 8067
} | 214 |
""" PyTorch Implementation of the Kron (PSGD) optimizer
This is a PSGD optimizer using a Kronecker-factored preconditioner.
This impl was adapted from https://github.com/evanatyourservice/kron_torch
by Evan Walters, licensed CC-BY-4.0.
Contributions to above also made by
* Lucas Nestler, added to his https://github.... | pytorch-image-models/timm/optim/kron.py/0 | {
"file_path": "pytorch-image-models/timm/optim/kron.py",
"repo_id": "pytorch-image-models",
"token_count": 10686
} | 215 |
""" Batch size decay and retry helpers.
Copyright 2022 Ross Wightman
"""
import math
def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
""" power of two batch-size decay with intra steps
Decay by stepping between powers of 2:
* determine power-of-2 floor of current batch size (base batch... | pytorch-image-models/timm/utils/decay_batch.py/0 | {
"file_path": "pytorch-image-models/timm/utils/decay_batch.py",
"repo_id": "pytorch-image-models",
"token_count": 656
} | 216 |
<!--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... | smolagents/docs/source/en/examples/text_to_sql.mdx/0 | {
"file_path": "smolagents/docs/source/en/examples/text_to_sql.mdx",
"repo_id": "smolagents",
"token_count": 2400
} | 217 |
<!--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... | smolagents/docs/source/hi/examples/multiagents.mdx/0 | {
"file_path": "smolagents/docs/source/hi/examples/multiagents.mdx",
"repo_id": "smolagents",
"token_count": 5840
} | 218 |
<!--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... | smolagents/docs/source/zh/examples/rag.mdx/0 | {
"file_path": "smolagents/docs/source/zh/examples/rag.mdx",
"repo_id": "smolagents",
"token_count": 4008
} | 219 |
from openinference.instrumentation.smolagents import SmolagentsInstrumentor
from phoenix.otel import register
register()
SmolagentsInstrumentor().instrument(skip_dep_check=True)
from smolagents import (
CodeAgent,
DuckDuckGoSearchTool,
HfApiModel,
ToolCallingAgent,
VisitWebpageTool,
)
# Then w... | smolagents/examples/inspect_multiagent_run.py/0 | {
"file_path": "smolagents/examples/inspect_multiagent_run.py",
"repo_id": "smolagents",
"token_count": 260
} | 220 |
# from huggingface_hub import login
# login()
import datasets
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.retrievers import BM25Retriever
knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
kno... | smolagents/examples/rag.py/0 | {
"file_path": "smolagents/examples/rag.py",
"repo_id": "smolagents",
"token_count": 805
} | 221 |
system_prompt: |-
You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
To solve the task, you must plan forward to pr... | smolagents/src/smolagents/prompts/code_agent.yaml/0 | {
"file_path": "smolagents/src/smolagents/prompts/code_agent.yaml",
"repo_id": "smolagents",
"token_count": 4632
} | 222 |
import subprocess
def test_import_smolagents_without_extras():
# Run the import statement in an isolated virtual environment
result = subprocess.run(
["uv", "run", "--isolated", "--no-editable", "-"], input="import smolagents", text=True, capture_output=True
)
# Check if the import was success... | smolagents/tests/test_import.py/0 | {
"file_path": "smolagents/tests/test_import.py",
"repo_id": "smolagents",
"token_count": 200
} | 223 |
[workspace]
members = [
"benchmark",
"backends/v2",
"backends/v3",
"backends/grpc-metadata",
"backends/trtllm",
"backends/llamacpp",
"launcher",
"router"
]
default-members = [
"benchmark",
"backends/v2",
"backends/v3",
"backends/grpc-metadata",
# "backends/trtllm",
... | text-generation-inference/Cargo.toml/0 | {
"file_path": "text-generation-inference/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 512
} | 224 |
use std::fs;
fn main() -> Result<(), Box<dyn std::error::Error>> {
println!("cargo:rerun-if-changed=../../proto/");
fs::create_dir_all("src/v2/pb").unwrap_or(());
let mut config = prost_build::Config::new();
config.protoc_arg("--experimental_allow_proto3_optional");
tonic_build::configure()
... | text-generation-inference/backends/client/build.rs/0 | {
"file_path": "text-generation-inference/backends/client/build.rs",
"repo_id": "text-generation-inference",
"token_count": 624
} | 225 |
[workspace]
members = [
"backends/v2",
"backends/grpc-metadata",
"launcher",
"router"
]
default-members = [
"backends/v2",
"backends/grpc-metadata",
"launcher",
"router"
]
resolver = "2"
[workspace.package]
version = "3.0.0"
edition = "2021"
authors = ["Olivier Dehaene"]
homepage = "https://github.com/... | text-generation-inference/backends/neuron/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/neuron/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 416
} | 226 |
use crate::client::{Batch, CachedBatch, ClientError, Generation, Health, ShardedClient};
/// Batching and inference logic
use crate::queue::{Entry, Queue};
use async_trait::async_trait;
use nohash_hasher::IntMap;
use std::sync::Arc;
use text_generation_router::infer::{Backend, GeneratedText, InferError, InferStreamResp... | text-generation-inference/backends/v2/src/backend.rs/0 | {
"file_path": "text-generation-inference/backends/v2/src/backend.rs",
"repo_id": "text-generation-inference",
"token_count": 9328
} | 227 |
use clap::{Parser, Subcommand};
use text_generation_router::{server, usage_stats};
use text_generation_router_v3::{connect_backend, V3Error};
use thiserror::Error;
/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
#[command(subcommand)]
command: Opt... | text-generation-inference/backends/v3/src/main.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 3407
} | 228 |
[tool.poetry]
name = "text-generation"
version = "0.7.0"
description = "Hugging Face Text Generation Python Client"
license = "Apache-2.0"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
maintainers = ["Olivier Dehaene <olivier@huggingface.co>"]
readme = "README.md"
homepage = "https://github.com/huggingface/tex... | text-generation-inference/clients/python/pyproject.toml/0 | {
"file_path": "text-generation-inference/clients/python/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 348
} | 229 |
# Text Generation Inference Architecture
This document aims at describing the architecture of Text Generation Inference (TGI), by describing the call flow between the separate components.
A high-level architecture diagram can be seen here:
![TGI architecture](https://huggingface.co/datasets/huggingface/documentation... | text-generation-inference/docs/source/architecture.md/0 | {
"file_path": "text-generation-inference/docs/source/architecture.md",
"repo_id": "text-generation-inference",
"token_count": 5207
} | 230 |
# Using TGI with Google TPUs
Check out this [guide](https://huggingface.co/docs/optimum-tpu) on how to serve models with TGI on TPUs.
| text-generation-inference/docs/source/installation_tpu.md/0 | {
"file_path": "text-generation-inference/docs/source/installation_tpu.md",
"repo_id": "text-generation-inference",
"token_count": 48
} | 231 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
},
{
"id": 49833,
"logprob": -10.5703125,
"text": " dég"
},
{
"... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m.json",
"repo_id": "text-generation-inference",
"token_count": 1548
} | 232 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 18682,
"logprob": -0.8769531,
"special": false,
"text": " Deep"
},
{
"id": 6975,
"logp... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an.json",
"repo_id": "text-generation-inference",
"token_count": 869
} | 233 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 185,
"logprob": -1.546875,
"special": false,
"text": "\n"
},
{
"id": 549,
"logprob": -... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2.json",
"repo_id": "text-generation-inference",
"token_count": 858
} | 234 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.0566406,
"special": false,
"text": "\n"
},
{
"id": 13,
"logprob": -1... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar.json",
"repo_id": "text-generation-inference",
"token_count": 866
} | 235 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 28747,
"logprob": 0.0,
"special": false,
"text": ":"
},
{
"id": 3169,
"logprob": -0.13073... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 856
} | 236 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 8,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 2502,
"logprob": -1.7890625,
"special": false,
"text": "image"
},
{
"id": 2196,
"log... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_pali_gemma/test_flash_pali_gemma_two_images.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_pali_gemma/test_flash_pali_gemma_two_images.json",
"repo_id": "text-generation-inference",
"token_count": 719
} | 237 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "The image shows a stylized scene set in what appears to be a diner or restaurant. In the foreground, there is a table with various food items, including a burger with lettuce and tomato... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2_vl/test_flash_qwen2_vl_inpaint.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2_vl/test_flash_qwen2_vl_inpaint.json",
"repo_id": "text-generation-inference",
"token_count": 422
} | 238 |
{
"details": {
"finish_reason": "eos_token",
"generated_tokens": 7,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 1,
"logprob": -0.49658203,
"special": true,
"text": "<s>"
},
{
"id": 28705,
"logprob": -0.0016384125,
"spec... | text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_dbpedia_adapter.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_dbpedia_adapter.json",
"repo_id": "text-generation-inference",
"token_count": 611
} | 239 |
import pytest
@pytest.fixture(scope="module")
def flash_gemma_handle(launcher):
with launcher("google/gemma-2b", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_gemma(flash_gemma_handle):
await flash_gemma_handle.health(300)
return flash_gemma_handle.client
... | text-generation-inference/integration-tests/models/test_flash_gemma.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_gemma.py",
"repo_id": "text-generation-inference",
"token_count": 678
} | 240 |
import pytest
@pytest.fixture(scope="module")
def flash_mixtral_handle(launcher):
with launcher("mistralai/Mixtral-8x7B-v0.1", num_shard=8) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_mixtral(flash_mixtral_handle):
await flash_mixtral_handle.health(300)
return flash_m... | text-generation-inference/integration-tests/models/test_flash_mixtral.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_mixtral.py",
"repo_id": "text-generation-inference",
"token_count": 926
} | 241 |
import pytest
@pytest.fixture(scope="module")
def flash_starcoder_gptq_handle(launcher):
with launcher("Narsil/starcoder-gptq", num_shard=2, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder_gptq(flash_starcoder_gptq_handle):
await flash_starcoder_gpt... | text-generation-inference/integration-tests/models/test_flash_starcoder_gptq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_starcoder_gptq.py",
"repo_id": "text-generation-inference",
"token_count": 802
} | 242 |
import pytest
@pytest.fixture(scope="module")
def t5_sharded_handle(launcher):
with launcher("google/flan-t5-xxl", num_shard=4) as handle:
yield handle
@pytest.fixture(scope="module")
async def t5_sharded(t5_sharded_handle):
await t5_sharded_handle.health(300)
return t5_sharded_handle.client
@... | text-generation-inference/integration-tests/models/test_t5_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_t5_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 443
} | 243 |
import json
def main():
with open("./ShareGPT_V3_unfiltered_cleaned_split.json", "r") as f:
data = json.load(f)
# Select only the first 2k conversations that start with a human.
max = 2000
conversations = []
for conversation in data:
conv = conversation.get("conversations")
... | text-generation-inference/load_tests/filter.py/0 | {
"file_path": "text-generation-inference/load_tests/filter.py",
"repo_id": "text-generation-inference",
"token_count": 307
} | 244 |
# Router
Also named `webserver` throughout the docs.
This router is handling most of the logic to handle the "batches" tell
when to pass new `prefill` requests and pausing `decode` requests, which ones etc...
It uses gRPC to communicate with the shards which can therefore be kept
much simpler and focus on having the... | text-generation-inference/router/README.md/0 | {
"file_path": "text-generation-inference/router/README.md",
"repo_id": "text-generation-inference",
"token_count": 1175
} | 245 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _cuda_compat_cuh
#define _cuda_compat_cuh
// atomicAdd for half types, to support CC < 7.x
__device__ __forceinline__ void atomicAdd_half(half* address, half val)
{
unsigned int * address_as_ui = (unsigned int *) ((char *)address - (... | text-generation-inference/server/exllama_kernels/exllama_kernels/cu_compat.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cu_compat.cuh",
"repo_id": "text-generation-inference",
"token_count": 692
} | 246 |
#ifndef _util_h
#define _util_h
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, __y) printf("%s, %s: %i, %i\n", #__x, #__y, __x, __y)
#define DBGI3(__x, __y, __z) printf("%s, %s, %s: %i, %i, %i\n", #__x, #__y, #__z, __x, __y, __z)
#define DBGF(__x) printf("%s: %... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cpp/util.h/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cpp/util.h",
"repo_id": "text-generation-inference",
"token_count": 296
} | 247 |
#ifndef _util_cuh
#define _util_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#define DIVIDE(x, size) (((x) + (size) - 1) / (size))
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, _... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/util.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/util.cuh",
"repo_id": "text-generation-inference",
"token_count": 1115
} | 248 |
import pytest
from unittest.mock import Mock
from text_generation_server.utils.adapter import (
get_attn_weights,
get_mlp_weights,
parse_lora_adapters,
AdapterInfo,
)
def test_parse_lora_adapters_empty():
assert parse_lora_adapters(None) == []
assert parse_lora_adapters("") == []
def test_pa... | text-generation-inference/server/tests/utils/test_adapter.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_adapter.py",
"repo_id": "text-generation-inference",
"token_count": 4022
} | 249 |
import os
from text_generation_server.utils.import_utils import SYSTEM
from .common import Seqlen
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
raise ImportError("`USE_FLASH_ATTENTION` is false.")
if SYSTEM == "cuda":
from .cuda import (
SUPPORTS_WINDOWING,
attention,
pa... | text-generation-inference/server/text_generation_server/layers/attention/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 404
} | 250 |
from typing import List, Optional, Union
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationType
from text_generation_server.layers.fp8 import (
Fp8Weight,
_load_scalar_or_matrix_scale,
requantize_with_max_scale,
normalize_e4m3fn_to_native_float8,
)
from text_genera... | text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8an_fp.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8an_fp.py",
"repo_id": "text-generation-inference",
"token_count": 3370
} | 251 |
import torch
from text_generation_server.utils.import_utils import SYSTEM
from torch.nn import functional as F
import os
if SYSTEM == "rocm":
ROCM_USE_SKINNY_GEMM = os.getenv("ROCM_USE_SKINNY_GEMM", "True").lower() in (
"true",
"1",
)
if ROCM_USE_SKINNY_GEMM:
try:
impor... | text-generation-inference/server/text_generation_server/layers/linear.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/linear.py",
"repo_id": "text-generation-inference",
"token_count": 1954
} | 252 |
import torch
from torch.nn import functional as F
from typing import Iterable, List
from text_generation_server.layers.linear import get_linear, FastLinear
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM == "ipex":
import intel_extension_for_pytorch as ipex
class LayerConcat(torch.nn.Modul... | text-generation-inference/server/text_generation_server/layers/tensor_parallel.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/tensor_parallel.py",
"repo_id": "text-generation-inference",
"token_count": 4175
} | 253 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 8969
} | 254 |
# coding=utf-8
# Copyright 2022 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... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_processing.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_processing.py",
"repo_id": "text-generation-inference",
"token_count": 8120
} | 255 |
import torch
import os
from loguru import logger
from typing import Dict, Optional
from text_generation_server.utils.log import log_master
REQUEST_LOGPROBS = os.getenv("REQUEST_LOGPROBS", "0").lower() in {"1", "true"}
ATTENTION = os.environ["ATTENTION"]
# default_prefix_caching = "1" if ATTENTION in {"flashinfer", "f... | text-generation-inference/server/text_generation_server/models/globals.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/globals.py",
"repo_id": "text-generation-inference",
"token_count": 900
} | 256 |
from typing import Iterable
from loguru import logger
from text_generation_server.pb import generate_pb2
def concat_text_chunks(chunks: Iterable[generate_pb2.InputChunk]) -> str:
"""
Concatenate text in text chunks. Non-text chunks are dropped.
"""
text = None
for chunk in chunks:
chunk_... | text-generation-inference/server/text_generation_server/utils/chunks.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/chunks.py",
"repo_id": "text-generation-inference",
"token_count": 332
} | 257 |
import re
from typing import List, Optional, Tuple, Set, Union
import torch
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason, GrammarType
from text_generation_server.utils.logits_process import (
FrequencyPenaltyLogitsProcessor,
GrammarLogitProc... | text-generation-inference/server/text_generation_server/utils/tokens.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/tokens.py",
"repo_id": "text-generation-inference",
"token_count": 11317
} | 258 |
import {
PaddingDirection,
WordPiece,
punctuationPreTokenizer,
sequencePreTokenizer,
whitespacePreTokenizer,
Encoding,
EncodeOptions,
Tokenizer,
} from '../../'
import { InputSequence } from '../../types'
const MOCKS_DIR = __dirname + '/__mocks__'
describe('Can modify pretokenizers on the fly', () => ... | tokenizers/bindings/node/lib/bindings/encoding.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/encoding.test.ts",
"repo_id": "tokenizers",
"token_count": 3021
} | 259 |
{
"name": "tokenizers-freebsd-x64",
"version": "0.13.4-rc1",
"os": [
"freebsd"
],
"cpu": [
"x64"
],
"main": "tokenizers.freebsd-x64.node",
"files": [
"tokenizers.freebsd-x64.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",
"N... | tokenizers/bindings/node/npm/freebsd-x64/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/freebsd-x64/package.json",
"repo_id": "tokenizers",
"token_count": 272
} | 260 |
{
"name": "tokenizers-win32-x64-msvc",
"version": "0.13.4-rc1",
"os": [
"win32"
],
"cpu": [
"x64"
],
"main": "tokenizers.win32-x64-msvc.node",
"files": [
"tokenizers.win32-x64-msvc.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",... | tokenizers/bindings/node/npm/win32-x64-msvc/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-x64-msvc/package.json",
"repo_id": "tokenizers",
"token_count": 277
} | 261 |
use napi::bindgen_prelude::*;
use napi_derive::napi;
use tokenizers as tk;
use tokenizers::Encoding;
use crate::encoding::JsEncoding;
#[napi]
pub fn slice(s: String, begin_index: Option<i32>, end_index: Option<i32>) -> Result<String> {
let len = s.chars().count();
let get_index = |x: i32| -> usize {
if x >= ... | tokenizers/bindings/node/src/utils.rs/0 | {
"file_path": "tokenizers/bindings/node/src/utils.rs",
"repo_id": "tokenizers",
"token_count": 503
} | 262 |
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
} | 263 |
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": 304
} | 264 |
[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
} | 265 |
use pyo3::exceptions;
use pyo3::prelude::*;
use tk::utils::SysRegex;
/// Instantiate a new Regex with the given pattern
#[pyclass(module = "tokenizers", name = "Regex")]
pub struct PyRegex {
pub inner: SysRegex,
pub pattern: String,
}
#[pymethods]
impl PyRegex {
#[new]
#[pyo3(text_signature = "(self, ... | tokenizers/bindings/python/src/utils/regex.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/regex.rs",
"repo_id": "tokenizers",
"token_count": 273
} | 266 |
from tokenizers import Tokenizer
from ..utils import data_dir, doc_wiki_tokenizer
disable_printing = True
original_print = print
def print(*args, **kwargs):
if not disable_printing:
original_print(*args, **kwargs)
class TestQuicktour:
# This method contains everything we don't want to run
@sta... | tokenizers/bindings/python/tests/documentation/test_quicktour.py/0 | {
"file_path": "tokenizers/bindings/python/tests/documentation/test_quicktour.py",
"repo_id": "tokenizers",
"token_count": 3290
} | 267 |
# Encoding
<tokenizerslangcontent>
<python>
## Encoding
[[autodoc]] tokenizers.Encoding
- all
- attention_mask
- ids
- n_sequences
- offsets
- overflowing
- sequence_ids
- special_tokens_mask
- tokens
- type_ids
- word_ids
- words
</python>
<rust>
The Rust API Reference... | tokenizers/docs/source-doc-builder/api/encoding.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/encoding.mdx",
"repo_id": "tokenizers",
"token_count": 190
} | 268 |
from docutils import nodes
import sphinx
from sphinx.locale import _
from conf import rust_version
logger = sphinx.util.logging.getLogger(__name__)
class RustRef:
def __call__(self, name, rawtext, text, lineno, inliner, options={}, content=[]):
doctype = name.split("_")[1]
parts = text.split(":... | tokenizers/docs/source/_ext/rust_doc.py/0 | {
"file_path": "tokenizers/docs/source/_ext/rust_doc.py",
"repo_id": "tokenizers",
"token_count": 1221
} | 269 |
Tokenizers
====================================================================================================
Fast State-of-the-art tokenizers, optimized for both research and production
`🤗 Tokenizers`_ provides an implementation of today's most used tokenizers, with
a focus on performance and versatility. These t... | tokenizers/docs/source/index.rst/0 | {
"file_path": "tokenizers/docs/source/index.rst",
"repo_id": "tokenizers",
"token_count": 404
} | 270 |
use std::time::{Duration, Instant};
use criterion::black_box;
use tokenizers::{
Decoder, EncodeInput, Model, Normalizer, PostProcessor, PreTokenizer, TokenizerImpl, Trainer,
};
pub fn iter_bench_encode<M, N, PT, PP, D>(
iters: u64,
tokenizer: &TokenizerImpl<M, N, PT, PP, D>,
lines: &[EncodeInput],
) ... | tokenizers/tokenizers/benches/common/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/common/mod.rs",
"repo_id": "tokenizers",
"token_count": 964
} | 271 |
use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize)]
/// The WordPiece decoder takes care of decoding a list of wordpiece tokens
/// back into a readable string.
#[serde(tag = "type")]
#[non_exhaustive]
pub struct WordPiece {
/// The prefix ... | tokenizers/tokenizers/src/decoders/wordpiece.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/wordpiece.rs",
"repo_id": "tokenizers",
"token_count": 1275
} | 272 |
use super::WordLevel;
use crate::utils::parallelism::*;
use crate::{AddedToken, Result, Trainer};
use serde::{Deserialize, Serialize};
use std::cmp::Ordering;
use std::collections::HashMap;
#[non_exhaustive]
#[derive(Debug, Clone, Builder, Serialize, Deserialize)]
pub struct WordLevelTrainer {
/// The minimum freq... | tokenizers/tokenizers/src/models/wordlevel/trainer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordlevel/trainer.rs",
"repo_id": "tokenizers",
"token_count": 2735
} | 273 |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
/// Pre tokenizes the numbers into single tokens. If individual_digits is set
/// to true, then all digits are ... | tokenizers/tokenizers/src/pre_tokenizers/digits.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/digits.rs",
"repo_id": "tokenizers",
"token_count": 1667
} | 274 |
use crate::parallelism::*;
use crate::tokenizer::{Offsets, Token};
use crate::utils::padding::PaddingDirection;
use crate::utils::truncation::TruncationDirection;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::ops::Range;
/// Represents the output of a `Tokenizer`.
#[derive(Default, Parti... | tokenizers/tokenizers/src/tokenizer/encoding.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/encoding.rs",
"repo_id": "tokenizers",
"token_count": 17192
} | 275 |
mod common;
use common::*;
use tokenizers::tokenizer::AddedToken;
#[test]
fn add_tokens() {
let mut tokenizer = get_empty();
assert_eq!(
tokenizer.add_special_tokens(&[
AddedToken::from("<cls>", true),
AddedToken::from("<sep>", true)
]),
2
);
assert_eq!... | tokenizers/tokenizers/tests/added_tokens.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/added_tokens.rs",
"repo_id": "tokenizers",
"token_count": 1770
} | 276 |
- local: index
title: 🤗 Transformers.js
- sections:
- local: installation
title: Installation
- local: pipelines
title: The pipeline API
- local: custom_usage
title: Custom usage
title: Get started
- sections:
- local: tutorials/vanilla-js
title: Building a Vanilla JS Application
- local:... | transformers.js/docs/source/_toctree.yml/0 | {
"file_path": "transformers.js/docs/source/_toctree.yml",
"repo_id": "transformers.js",
"token_count": 825
} | 277 |
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