text stringlengths 5 424k | id stringlengths 13 178 | metadata dict | __index_level_0__ int64 0 672 |
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# 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/__init__.py/0 | {
"file_path": "peft/src/peft/utils/__init__.py",
"repo_id": "peft",
"token_count": 2266
} | 259 |
# 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_adaption_prompt.py/0 | {
"file_path": "peft/tests/test_adaption_prompt.py",
"repo_id": "peft",
"token_count": 8196
} | 260 |
# 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_incremental_pca.py/0 | {
"file_path": "peft/tests/test_incremental_pca.py",
"repo_id": "peft",
"token_count": 2775
} | 261 |
# 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_stablediffusion.py/0 | {
"file_path": "peft/tests/test_stablediffusion.py",
"repo_id": "peft",
"token_count": 7695
} | 262 |
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
} | 263 |
# 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
} | 264 |
# 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": 2778
} | 265 |
# ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual block... | pytorch-image-models/hfdocs/source/models/resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 5077
} | 266 |
# (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": 2362
} | 267 |
[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
} | 268 |
""" 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": 9463
} | 269 |
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
} | 270 |
""" A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | pytorch-image-models/timm/data/readers/reader_image_folder.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_folder.py",
"repo_id": "pytorch-image-models",
"token_count": 1510
} | 271 |
from typing import List, Optional, Type, Union
import torch
from torch import nn as nn
from torch.nn import functional as F
from .config import use_fused_attn
from .create_conv2d import create_conv2d
from .helpers import to_2tuple
from .pool2d_same import create_pool2d
class MultiQueryAttentionV2(nn.Module):
""... | pytorch-image-models/timm/layers/attention2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention2d.py",
"repo_id": "pytorch-image-models",
"token_count": 6678
} | 272 |
""" DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl ins... | pytorch-image-models/timm/layers/drop.py/0 | {
"file_path": "pytorch-image-models/timm/layers/drop.py",
"repo_id": "pytorch-image-models",
"token_count": 3739
} | 273 |
import torch
from torch import nn
class LayerScale(nn.Module):
""" LayerScale on tensors with channels in last-dim.
"""
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
) -> None:
super().__init__()
self.inp... | pytorch-image-models/timm/layers/layer_scale.py/0 | {
"file_path": "pytorch-image-models/timm/layers/layer_scale.py",
"repo_id": "pytorch-image-models",
"token_count": 482
} | 274 |
""" Sin-cos, fourier, rotary position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import List, Tuple, Optional, Union
import torch
from torch import nn as nn
from ._fx import register_notrace_function
from .grid import ndgrid
from .trace_utils import ... | pytorch-image-models/timm/layers/pos_embed_sincos.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pos_embed_sincos.py",
"repo_id": "pytorch-image-models",
"token_count": 20095
} | 275 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .cross_entropy import LabelSmoothingCrossEntropy
class JsdCrossEntropy(nn.Module):
""" Jensen-Shannon Divergence + Cross-Entropy Loss
Based on impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
From paper: ... | pytorch-image-models/timm/loss/jsd.py/0 | {
"file_path": "pytorch-image-models/timm/loss/jsd.py",
"repo_id": "pytorch-image-models",
"token_count": 639
} | 276 |
""" Deep Layer Aggregation and DLA w/ Res2Net
DLA original adapted from Official Pytorch impl at: https://github.com/ucbdrive/dla
DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
Res2Net additions from: https://github.com/gasvn/Res2Net/
Res2Net Paper: `Res2Net: A New Multi-scale Backbone Architec... | pytorch-image-models/timm/models/dla.py/0 | {
"file_path": "pytorch-image-models/timm/models/dla.py",
"repo_id": "pytorch-image-models",
"token_count": 9154
} | 277 |
"""
An implementation of GhostNet & GhostNetV2 Models as defined in:
GhostNet: More Features from Cheap Operations. https://arxiv.org/abs/1911.11907
GhostNetV2: Enhance Cheap Operation with Long-Range Attention. https://proceedings.neurips.cc/paper_files/paper/2022/file/40b60852a4abdaa696b5a1a78da34635-Paper-Conference... | pytorch-image-models/timm/models/ghostnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/ghostnet.py",
"repo_id": "pytorch-image-models",
"token_count": 17881
} | 278 |
"""
Poolformer from MetaFormer is Actually What You Need for Vision https://arxiv.org/abs/2111.11418
IdentityFormer, RandFormer, PoolFormerV2, ConvFormer, and CAFormer
from MetaFormer Baselines for Vision https://arxiv.org/abs/2210.13452
All implemented models support feature extraction and variable input resolution.... | pytorch-image-models/timm/models/metaformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/metaformer.py",
"repo_id": "pytorch-image-models",
"token_count": 18677
} | 279 |
"""RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... | pytorch-image-models/timm/models/regnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/regnet.py",
"repo_id": "pytorch-image-models",
"token_count": 26296
} | 280 |
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... | pytorch-image-models/timm/models/swin_transformer_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 23221
} | 281 |
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partia... | pytorch-image-models/timm/models/xception_aligned.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception_aligned.py",
"repo_id": "pytorch-image-models",
"token_count": 7780
} | 282 |
""" PyTorch impl of LaProp optimizer
Code simplified from https://github.com/Z-T-WANG/LaProp-Optimizer, MIT License
Paper: LaProp: Separating Momentum and Adaptivity in Adam, https://arxiv.org/abs/2002.04839
@article{ziyin2020laprop,
title={LaProp: a Better Way to Combine Momentum with Adaptive Gradient},
author... | pytorch-image-models/timm/optim/laprop.py/0 | {
"file_path": "pytorch-image-models/timm/optim/laprop.py",
"repo_id": "pytorch-image-models",
"token_count": 2603
} | 283 |
""" Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise, k-decay.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class CosineLRSchedu... | pytorch-image-models/timm/scheduler/cosine_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/cosine_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 2070
} | 284 |
""" JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT executor. These ... | pytorch-image-models/timm/utils/jit.py/0 | {
"file_path": "pytorch-image-models/timm/utils/jit.py",
"repo_id": "pytorch-image-models",
"token_count": 1035
} | 285 |
# Agentic RAG
[[open-in-colab]]
## Introduction to Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) combines the power of large language models with external knowledge retrieval to produce more accurate, factual, and contextually relevant responses. At its core, RAG is about "using an LLM to... | smolagents/docs/source/en/examples/rag.md/0 | {
"file_path": "smolagents/docs/source/en/examples/rag.md",
"repo_id": "smolagents",
"token_count": 2523
} | 286 |
- title: Get started
sections:
- local: index
title: 소개
- local: installation
title: 설치 옵션
# - local: guided_tour
# title: 안내서
- title: 튜토리얼
sections:
- local: tutorials/building_good_agents
title: 좋은 에이전트 구축하기
# - local: tutorials/inspect_runs
# title: 📊 Inspect your agent runs using tel... | smolagents/docs/source/ko/_toctree.yml/0 | {
"file_path": "smolagents/docs/source/ko/_toctree.yml",
"repo_id": "smolagents",
"token_count": 796
} | 287 |
# Agents - 导览
[[open-in-colab]]
在本导览中，您将学习如何构建一个 agent（智能体），如何运行它，以及如何自定义它以使其更好地适应您的使用场景。
> [!TIP]
> 译者注：Agent 的业内术语是“智能体”。本译文将保留 agent，不作翻译，以带来更高效的阅读体验。(在中文为主的文章中，It's easier to 注意到英文。Attention Is All You Need!)
> [!TIP]
> 中文社区发布了关于 smolagents 的介绍和实践讲解视频(来源：[Issue#80](https://github.com/huggingface/smolagents/issu... | smolagents/docs/source/zh/guided_tour.md/0 | {
"file_path": "smolagents/docs/source/zh/guided_tour.md",
"repo_id": "smolagents",
"token_count": 10506
} | 288 |
from openinference.instrumentation.smolagents import SmolagentsInstrumentor
from phoenix.otel import register
register()
SmolagentsInstrumentor().instrument(skip_dep_check=True)
from smolagents import (
CodeAgent,
InferenceClientModel,
ToolCallingAgent,
VisitWebpageTool,
WebSearchTool,
)
# The... | smolagents/examples/inspect_multiagent_run.py/0 | {
"file_path": "smolagents/examples/inspect_multiagent_run.py",
"repo_id": "smolagents",
"token_count": 335
} | 289 |
import base64
import json
import mimetypes
import os
import uuid
from io import BytesIO
import PIL.Image
import requests
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
from smolagents import Tool, tool
load_dotenv(override=True)
def process_images_and_text(image_path, query, client):
... | smolagents/examples/open_deep_research/scripts/visual_qa.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/visual_qa.py",
"repo_id": "smolagents",
"token_count": 2558
} | 290 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | smolagents/src/smolagents/agent_types.py/0 | {
"file_path": "smolagents/src/smolagents/agent_types.py",
"repo_id": "smolagents",
"token_count": 3867
} | 291 |
#!/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/utils.py/0 | {
"file_path": "smolagents/src/smolagents/utils.py",
"repo_id": "smolagents",
"token_count": 6942
} | 292 |
import json
from textwrap import dedent
import pytest
from mcp import StdioServerParameters
from smolagents.mcp_client import MCPClient
@pytest.fixture
def echo_server_script():
return dedent(
'''
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Echo Server")
@mcp.tool()
... | smolagents/tests/test_mcp_client.py/0 | {
"file_path": "smolagents/tests/test_mcp_client.py",
"repo_id": "smolagents",
"token_count": 1956
} | 293 |
<!---
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 ... | text-generation-inference/CONTRIBUTING.md/0 | {
"file_path": "text-generation-inference/CONTRIBUTING.md",
"repo_id": "text-generation-inference",
"token_count": 1396
} | 294 |
{
"__inputs": [
{
"name": "DS_PROMETHEUS_EKS API INFERENCE PROD",
"label": "Prometheus EKS API Inference Prod",
"description": "",
"type": "datasource",
"pluginId": "prometheus",
"pluginName": "Prometheus"
}
],
"__elements": {},
"__requires": [
{
"type": "pa... | text-generation-inference/assets/tgi_grafana.json/0 | {
"file_path": "text-generation-inference/assets/tgi_grafana.json",
"repo_id": "text-generation-inference",
"token_count": 62818
} | 295 |
# Fork that adds only the correct stream to this kernel in order
# to make cuda graphs work.
awq_commit := bd1dc2d5254345cc76ab71894651fb821275bdd4
awq:
rm -rf llm-awq
git clone https://github.com/huggingface/llm-awq
build-awq: awq
cd llm-awq/ && git fetch && git checkout $(awq_commit)
cd llm-awq/awq/kernels && p... | text-generation-inference/backends/gaudi/server/Makefile-awq/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/Makefile-awq",
"repo_id": "text-generation-inference",
"token_count": 183
} | 296 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/lora.py
# License: Apache License Version 2.0, January 2004
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Type, Union
import torch
from peft impor... | text-generation-inference/backends/gaudi/server/text_generation_server/adapters/lora.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/adapters/lora.py",
"repo_id": "text-generation-inference",
"token_count": 8028
} | 297 |
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,
)
from text_generation_server.utils.weights import Weight... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/compressed_tensors/w8an_fp.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/compressed_tensors/w8an_fp.py",
"repo_id": "text-generation-inference",
"token_count": 4701
} | 298 |
from typing import Optional
import torch
import torch.nn as nn
from text_generation_server.utils.weights import UnquantizedWeight, Weights
from vllm_hpu_extension.ops import VllmMixtureOfExpertsOp
import habana_frameworks.torch as htorch
import torch.nn.functional as F
import os
class UnquantizedSparseMoELayer(nn.M... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py",
"repo_id": "text-generation-inference",
"token_count": 2816
} | 299 |
# 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/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 6328
} | 300 |
# coding=utf-8
# Copyright 2024 Starcoder2 AI 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
# t... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 9861
} | 301 |
# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
import asyncio
import os
import torch
import time
import signal
from grpc import aio
from loguru import logger
from grpc_reflection.v1alpha import reflection
from pathlib import Path
from typing import List, Optional
from text_generation_server.cache import C... | text-generation-inference/backends/gaudi/server/text_generation_server/server.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/server.py",
"repo_id": "text-generation-inference",
"token_count": 5307
} | 302 |
from typing import Optional
SUPPORT_CHUNKING: Optional[bool] = None
MAX_PREFILL_TOKENS: Optional[int] = None
def set_support_chunking(support_chunking: bool):
global SUPPORT_CHUNKING
SUPPORT_CHUNKING = support_chunking
def get_support_chunking() -> bool:
global SUPPORT_CHUNKING
return SUPPORT_CHUNK... | text-generation-inference/backends/gaudi/server/text_generation_server/utils/prefill_chunking.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/utils/prefill_chunking.py",
"repo_id": "text-generation-inference",
"token_count": 221
} | 303 |
use crate::llamacpp;
use async_trait::async_trait;
use std::ffi::CString;
use std::mem::replace;
use std::str::FromStr;
use std::sync::{mpsc, Once};
use text_generation_router::infer::{Backend, GeneratedText, InferError, InferStreamResponse};
use text_generation_router::validation::ValidGenerateRequest;
use text_gener... | text-generation-inference/backends/llamacpp/src/backend.rs/0 | {
"file_path": "text-generation-inference/backends/llamacpp/src/backend.rs",
"repo_id": "text-generation-inference",
"token_count": 13858
} | 304 |
#!/usr/bin/env python
import argparse
import logging
import os
import sys
from typing import Any, Dict, List, Optional
from optimum.neuron.modeling_decoder import get_available_cores
from optimum.neuron.cache import get_hub_cached_entries
from optimum.neuron.configuration_utils import NeuronConfig
from optimum.neuron... | text-generation-inference/backends/neuron/server/text_generation_server/tgi_env.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/tgi_env.py",
"repo_id": "text-generation-inference",
"token_count": 4375
} | 305 |
use async_trait::async_trait;
use cxx::UniquePtr;
use hashbrown::HashMap;
use std::hint;
use std::ops::Deref;
use std::path::Path;
use tokenizers::Tokenizer;
use tokio::sync::mpsc::{unbounded_channel, UnboundedReceiver, UnboundedSender};
use tokio::sync::TryAcquireError;
use tokio::task::spawn_blocking;
use tokio::time... | text-generation-inference/backends/trtllm/src/looper.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/looper.rs",
"repo_id": "text-generation-inference",
"token_count": 6376
} | 306 |
use std::fs;
fn main() -> Result<(), Box<dyn std::error::Error>> {
println!("cargo:rerun-if-changed=../../proto/");
fs::create_dir_all("src/client/pb").unwrap_or(());
let mut config = prost_build::Config::new();
config.protoc_arg("--experimental_allow_proto3_optional");
tonic_build::configure()
... | text-generation-inference/backends/v3/build.rs/0 | {
"file_path": "text-generation-inference/backends/v3/build.rs",
"repo_id": "text-generation-inference",
"token_count": 274
} | 307 |
/// Text Generation Inference benchmarking tool
///
/// Inspired by the great Oha app: https://github.com/hatoo/oha
/// and: https://github.com/orhun/rust-tui-template
use clap::Parser;
use std::path::Path;
use text_generation_client::v3::ShardedClient;
use tokenizers::{FromPretrainedParameters, Tokenizer};
use tracing... | text-generation-inference/benchmark/src/main.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 3164
} | 308 |
import os
import requests
from typing import Dict, Optional, List
from huggingface_hub.utils import build_hf_headers
from text_generation import Client, AsyncClient, __version__
from text_generation.types import DeployedModel
from text_generation.errors import NotSupportedError, parse_error
INFERENCE_ENDPOINT = os.e... | text-generation-inference/clients/python/text_generation/inference_api.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/inference_api.py",
"repo_id": "text-generation-inference",
"token_count": 2182
} | 309 |
# Tensor Parallelism
Tensor parallelism is a technique used to fit a large model in multiple GPUs. For example, when multiplying the input tensors with the first weight tensor, the matrix multiplication is equivalent to splitting the weight tensor column-wise, multiplying each column with the input separately, and the... | text-generation-inference/docs/source/conceptual/tensor_parallelism.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/tensor_parallelism.md",
"repo_id": "text-generation-inference",
"token_count": 272
} | 310 |
{
"nodes": {
"cachix": {
"inputs": {
"devenv": [
"crate2nix"
],
"flake-compat": [
"crate2nix"
],
"nixpkgs": "nixpkgs",
"pre-commit-hooks": [
"crate2nix"
]
},
"locked": {
"lastModified": 1709700175,
... | text-generation-inference/flake.lock/0 | {
"file_path": "text-generation-inference/flake.lock",
"repo_id": "text-generation-inference",
"token_count": 16562
} | 311 |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "As of your last question, the weather in Brooklyn, New York, is typically hot and humid throughout the year. The suburbs around New York City are jealously sheltered, and at least in ... | text-generation-inference/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json",
"repo_id": "text-generation-inference",
"token_count": 364
} | 312 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5380,
"logprob": -0.23840332,
"special": false,
"text": "?\n"
},
{
"id": 34564,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 853
} | 313 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 4,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 2143,
"logprob": -1.828125,
"special": false,
"text": " sent"
},
{
"id": 10081,
"logpro... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 424
} | 314 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "That's a fantastic question! However, the image doesn't show a dog. It shows a **Brown Swiss cow** standing on a beach. \n\nBrown Swiss cows are known for their beautiful reddish-brown ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow_dog.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow_dog.json",
"repo_id": "text-generation-inference",
"token_count": 340
} | 315 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 25,
"logprob": -2.9785156,
"special": false,
"text": ":"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_exl2/test_flash_llama_exl2_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_exl2/test_flash_llama_exl2_load.json",
"repo_id": "text-generation-inference",
"token_count": 4101
} | 316 |
[
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "Jeff Walker's Product Launch Formula is a comprehensive system",
"name": null,
"role": "assistant",
"tool_calls": null
},
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_prefix/test_flash_llama_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_prefix/test_flash_llama_load.json",
"repo_id": "text-generation-inference",
"token_count": 32395
} | 317 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -0.6953125,
"special": false,
"text": "\n"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json",
"repo_id": "text-generation-inference",
"token_count": 4066
} | 318 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 198,
"logprob": -2.9023438,
"special": false,
"text": "\n"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_load.json",
"repo_id": "text-generation-inference",
"token_count": 4044
} | 319 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 330,
"logprob": -0.09289551,
"special": false,
"text": " A"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json",
"repo_id": "text-generation-inference",
"token_count": 4039
} | 320 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 7,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": null,
"tokens": [
{
"id": 3,
"logprob": -0.7001953,
"specia... | text-generation-inference/integration-tests/models/__snapshots__/test_t5_sharded/test_t5_sharded.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_t5_sharded/test_t5_sharded.json",
"repo_id": "text-generation-inference",
"token_count": 680
} | 321 |
import pytest
import requests
@pytest.fixture(scope="module")
def llama_continue_final_message_handle(launcher):
with launcher("TinyLlama/TinyLlama-1.1B-Chat-v1.0") as handle:
yield handle
@pytest.fixture(scope="module")
async def llama_continue_final_message(llama_continue_final_message_handle):
aw... | text-generation-inference/integration-tests/models/test_continue_final_message.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_continue_final_message.py",
"repo_id": "text-generation-inference",
"token_count": 1102
} | 322 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_marlin_handle(launcher):
with launcher(
"neuralmagic/llama-2-7b-chat-marlin", num_shard=2, quantize="marlin"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_marlin(flash_llama_marlin_handle):
aw... | text-generation-inference/integration-tests/models/test_flash_llama_marlin.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_marlin.py",
"repo_id": "text-generation-inference",
"token_count": 748
} | 323 |
import pytest
@pytest.fixture(scope="module")
def flash_qwen2_5_vl_handle(launcher):
with launcher("Qwen/Qwen2.5-VL-3B-Instruct") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_qwen2_5(flash_qwen2_5_vl_handle):
await flash_qwen2_5_vl_handle.health(300)
return flash_qwen2... | text-generation-inference/integration-tests/models/test_flash_qwen2_5_vl.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_qwen2_5_vl.py",
"repo_id": "text-generation-inference",
"token_count": 2258
} | 324 |
import pytest
import asyncio
@pytest.fixture(scope="module")
def mllama_handle(launcher):
with launcher(
"unsloth/Llama-3.2-11B-Vision-Instruct",
num_shard=2,
) as handle:
yield handle
@pytest.fixture(scope="module")
async def mllama(mllama_handle):
await mllama_handle.health(300... | text-generation-inference/integration-tests/models/test_mllama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_mllama.py",
"repo_id": "text-generation-inference",
"token_count": 1584
} | 325 |
{
buildPythonPackage,
poetry-core,
aiohttp,
huggingface-hub,
pydantic,
}:
buildPythonPackage {
name = "text-generation";
src = ../clients/python;
pyproject = true;
build-system = [ poetry-core ];
dependencies = [
aiohttp
huggingface-hub
pydantic
];
}
| text-generation-inference/nix/client.nix/0 | {
"file_path": "text-generation-inference/nix/client.nix",
"repo_id": "text-generation-inference",
"token_count": 111
} | 326 |
use crate::infer::Infer;
use crate::{
default_parameters,
server::{generate_internal, ComputeType},
Deserialize, ErrorResponse, GenerateParameters, GenerateRequest, Serialize, ToSchema,
};
use axum::extract::{Extension, Path};
use axum::http::{HeaderMap, StatusCode};
use axum::response::IntoResponse;
use ax... | text-generation-inference/router/src/kserve.rs/0 | {
"file_path": "text-generation-inference/router/src/kserve.rs",
"repo_id": "text-generation-inference",
"token_count": 3533
} | 327 |
flash_att_v2_commit_cuda := v2.6.1
flash_att_v2_commit_rocm := 47bd46e0204a95762ae48712fd1a3978827c77fd
build-flash-attention-v2-cuda:
pip install -U packaging wheel
pip install flash-attn==$(flash_att_v2_commit_cuda)
install-flash-attention-v2-cuda: build-flash-attention-v2-cuda
echo "Flash v2 installed"
build-f... | text-generation-inference/server/Makefile-flash-att-v2/0 | {
"file_path": "text-generation-inference/server/Makefile-flash-att-v2",
"repo_id": "text-generation-inference",
"token_count": 397
} | 328 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include <ATen/cuda/CUDAContext.h>
#include "q4_matrix.cuh"
#include <vector>
#include "../util.cuh"
#include "../matrix.cuh"
using namespace std;
const int UNSHUF_BLOCKSIZE_X = 64;
const int RECONS_THREADS_X = 64; // Block size and thread... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cu",
"repo_id": "text-generation-inference",
"token_count": 2592
} | 329 |
#include "q_matrix.cuh"
#include "matrix_view.cuh"
#include "util.cuh"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define BLOCK_KN_SIZE 128
#define THREADS_X 32
#define THREADS_Y 32
// Shuffle quan... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu",
"repo_id": "text-generation-inference",
"token_count": 10524
} | 330 |
import os
from typing import Optional
import torch
from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers.attention import Seqlen
from text_generation_server.utils.log import log_master
from text_gene... | text-generation-inference/server/text_generation_server/layers/attention/rocm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/rocm.py",
"repo_id": "text-generation-inference",
"token_count": 5552
} | 331 |
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import torch
from loguru import logger
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.log import log_once
from text_generation_server.utils.weights import (
Weight,
Weights,
... | text-generation-inference/server/text_generation_server/layers/gptq/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/gptq/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 9078
} | 332 |
import torch
from torch import nn
from typing import Tuple, Optional
from text_generation_server.utils.speculate import get_speculate
from text_generation_server.layers.linear import FastLinear
from text_generation_server.layers.tensor_parallel import (
TensorParallelHead,
TensorParallelColumnLinear,
)
class ... | text-generation-inference/server/text_generation_server/layers/medusa.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/medusa.py",
"repo_id": "text-generation-inference",
"token_count": 2975
} | 333 |
# coding=utf-8
# Copyright 2024 Cohere 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 GPT-NeoX and OPT used by the M... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 8966
} | 334 |
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple
from text_generation_server.layers.attention import (
paged_attention,
attention,
Seqlen,
)
from text_generation_server.layers import (
TensorParallelMultiAda... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 7370
} | 335 |
# 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/LICENSE-2.0
#
# Unless r... | text-generation-inference/server/text_generation_server/models/custom_modeling/llava_next.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/llava_next.py",
"repo_id": "text-generation-inference",
"token_count": 5362
} | 336 |
import torch
import torch.distributed
from transformers import AutoTokenizer, PreTrainedTokenizerBase
from typing import Optional, Union
from text_generation_server.models.custom_modeling.mamba_modeling import (
MambaConfig,
)
from loguru import logger
from text_generation_server.pb import generate_pb2
from text_ge... | text-generation-inference/server/text_generation_server/models/mamba.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/mamba.py",
"repo_id": "text-generation-inference",
"token_count": 15065
} | 337 |
import os
import torch
from torch.distributed import ProcessGroup
from datetime import timedelta
from loguru import logger
from text_generation_server.utils.import_utils import SYSTEM
# Tensor Parallelism settings
RANK = int(os.getenv("RANK", "0"))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", "1"))
# CUDA memory fraction... | text-generation-inference/server/text_generation_server/utils/dist.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/dist.py",
"repo_id": "text-generation-inference",
"token_count": 1916
} | 338 |
.PHONY: style check-style test
DATA_DIR = data
dir_guard=@mkdir -p $(@D)
# Format source code automatically
style:
npm run lint
# Check the source code is formatted correctly
check-style:
npm run lint-check
TESTS_RESOURCES = $(DATA_DIR)/small.txt $(DATA_DIR)/roberta.json $(DATA_DIR)/tokenizer-wiki.json $(DATA_DI... | tokenizers/bindings/node/Makefile/0 | {
"file_path": "tokenizers/bindings/node/Makefile",
"repo_id": "tokenizers",
"token_count": 406
} | 339 |
import {
byteLevelPreTokenizer,
metaspacePreTokenizer,
punctuationPreTokenizer,
sequencePreTokenizer,
splitPreTokenizer,
whitespaceSplitPreTokenizer,
} from '../../'
describe('byteLevelPreTokenizer', () => {
it('instantiates correctly', () => {
const processor = byteLevelPreTokenizer()
expect(pro... | tokenizers/bindings/node/lib/bindings/pre-tokenizers.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/pre-tokenizers.test.ts",
"repo_id": "tokenizers",
"token_count": 728
} | 340 |
{
"name": "tokenizers-linux-arm64-gnu",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"arm64"
],
"main": "tokenizers.linux-arm64-gnu.node",
"files": [
"tokenizers.linux-arm64-gnu.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"N... | tokenizers/bindings/node/npm/linux-arm64-gnu/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-gnu/package.json",
"repo_id": "tokenizers",
"token_count": 289
} | 341 |
use crate::arc_rwlock_serde;
use serde::{Deserialize, Serialize};
extern crate tokenizers as tk;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use std::sync::{Arc, RwLock};
use tk::decoders::DecoderWrapper;
/// Decoder
#[derive(Clone, Serialize, Deserialize)]
#[napi]
pub struct Decoder {
#[serde(flatten, wi... | tokenizers/bindings/node/src/decoders.rs/0 | {
"file_path": "tokenizers/bindings/node/src/decoders.rs",
"repo_id": "tokenizers",
"token_count": 2037
} | 342 |
[target.x86_64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
"-C", "link-arg=-mmacosx-version-min=10.11",
]
[target.aarch64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
"-C", "link-arg=-mmacosx-version-min=10.11",
]
| tokenizers/bindings/python/.cargo/config.toml/0 | {
"file_path": "tokenizers/bindings/python/.cargo/config.toml",
"repo_id": "tokenizers",
"token_count": 146
} | 343 |
# Generated content DO NOT EDIT
class AddedToken:
"""
Represents a token that can be be added to a :class:`~tokenizers.Tokenizer`.
It can have special options that defines the way it should behave.
Args:
content (:obj:`str`): The content of the token
single_word (:obj:`bool`, defaults ... | tokenizers/bindings/python/py_src/tokenizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 19454
} | 344 |
# Generated content DO NOT EDIT
from .. import processors
PostProcessor = processors.PostProcessor
BertProcessing = processors.BertProcessing
ByteLevel = processors.ByteLevel
RobertaProcessing = processors.RobertaProcessing
Sequence = processors.Sequence
TemplateProcessing = processors.TemplateProcessing
| tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py",
"repo_id": "tokenizers",
"token_count": 74
} | 345 |
#![warn(clippy::all)]
#![allow(clippy::upper_case_acronyms)]
// Many false positives with pyo3 it seems &str, and &PyAny get flagged
#![allow(clippy::borrow_deref_ref)]
extern crate tokenizers as tk;
use once_cell::sync::Lazy;
use std::sync::Arc;
use tokio::runtime::Runtime;
// We create a global runtime that will b... | tokenizers/bindings/python/src/lib.rs/0 | {
"file_path": "tokenizers/bindings/python/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 1244
} | 346 |
from tokenizers import BertWordPieceTokenizer
from ..utils import bert_files, data_dir, multiprocessing_with_parallelism
class TestBertWordPieceTokenizer:
def test_basic_encode(self, bert_files):
tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"])
# Encode with special tokens by de... | tokenizers/bindings/python/tests/implementations/test_bert_wordpiece.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_bert_wordpiece.py",
"repo_id": "tokenizers",
"token_count": 914
} | 347 |
# Post-processors
<tokenizerslangcontent>
<python>
## BertProcessing
[[autodoc]] tokenizers.processors.BertProcessing
## ByteLevel
[[autodoc]] tokenizers.processors.ByteLevel
## RobertaProcessing
[[autodoc]] tokenizers.processors.RobertaProcessing
## TemplateProcessing
[[autodoc]] tokenizers.processors.Template... | tokenizers/docs/source-doc-builder/api/post-processors.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/post-processors.mdx",
"repo_id": "tokenizers",
"token_count": 174
} | 348 |
Crates.io
----------------------------------------------------------------------------------------------------
🤗 Tokenizers is available on `crates.io <https://crates.io/crates/tokenizers>`__.
You just need to add it to your :obj:`Cargo.toml`::
tokenizers = "0.10"
| tokenizers/docs/source/installation/rust.inc/0 | {
"file_path": "tokenizers/docs/source/installation/rust.inc",
"repo_id": "tokenizers",
"token_count": 74
} | 349 |
use tokenizers::Tokenizer;
fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
let tokenizer = Tokenizer::from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct", None)?;
let data = std::fs::read_to_string("data/big.txt")?;
let data: Vec<_> = data.lines().collect();
let add_special_tok... | tokenizers/tokenizers/examples/encode_batch.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/encode_batch.rs",
"repo_id": "tokenizers",
"token_count": 165
} | 350 |
import * as wasm from "unstable_wasm";
console.log(wasm.tokenize("ab"));
console.log(wasm.tokenize("abc"));
| tokenizers/tokenizers/examples/unstable_wasm/www/index.js/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/index.js",
"repo_id": "tokenizers",
"token_count": 43
} | 351 |
use super::{super::OrderedVocabIter, convert_merges_to_hashmap, BpeBuilder, Pair, BPE};
use ahash::AHashMap;
use serde::{
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
impl Serialize for BPE {
fn serialize<S>(&self, serializer: S) -> Result<... | tokenizers/tokenizers/src/models/bpe/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/serialization.rs",
"repo_id": "tokenizers",
"token_count": 4848
} | 352 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use serde::{Deserialize, Serialize};
use unicode_categories::UnicodeCategories;
/// Checks whether a character is whitespace
fn is_whitespace(c: char) -> bool {
// These are technically control characters but we count them as whitespace
match c {
... | tokenizers/tokenizers/src/normalizers/bert.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/bert.rs",
"repo_id": "tokenizers",
"token_count": 1856
} | 353 |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
use unicode_categories::UnicodeCategories;
fn is_punc(x: char) -> bool {
char::is_ascii_punctuation(&x) || x.is_punctuation()
}
#[derive(Copy, Cl... | tokenizers/tokenizers/src/pre_tokenizers/punctuation.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/punctuation.rs",
"repo_id": "tokenizers",
"token_count": 1103
} | 354 |
use crate::utils::SysRegex;
use crate::{Offsets, Result};
use regex::Regex;
/// Pattern used to split a NormalizedString
pub trait Pattern {
/// Slice the given string in a list of pattern match positions, with
/// a boolean indicating whether this is a match or not.
///
/// This method *must* cover th... | tokenizers/tokenizers/src/tokenizer/pattern.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/pattern.rs",
"repo_id": "tokenizers",
"token_count": 3902
} | 355 |
#![cfg(feature = "http")]
use tokenizers::{FromPretrainedParameters, Result, Tokenizer};
#[test]
fn test_from_pretrained() -> Result<()> {
let tokenizer = Tokenizer::from_pretrained("bert-base-cased", None)?;
let encoding = tokenizer.encode("Hey there dear friend!", false)?;
assert_eq!(
encoding.ge... | tokenizers/tokenizers/tests/from_pretrained.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/from_pretrained.rs",
"repo_id": "tokenizers",
"token_count": 683
} | 356 |
# Using quantized models (dtypes)
Before Transformers.js v3, we used the `quantized` option to specify whether to use a quantized (q8) or full-precision (fp32) variant of the model by setting `quantized` to `true` or `false`, respectively. Now, we've added the ability to select from a much larger list with the `dtype`... | transformers.js/docs/source/guides/dtypes.md/0 | {
"file_path": "transformers.js/docs/source/guides/dtypes.md",
"repo_id": "transformers.js",
"token_count": 1698
} | 357 |
{
"name": "adaptive-retrieval",
"version": "0.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "adaptive-retrieval",
"version": "0.0.0",
"dependencies": {
"@xenova/transformers": "^2.15.0",
"react": "^18.2.0",
"react-dom": "^18.2.0"
... | transformers.js/examples/adaptive-retrieval/package-lock.json/0 | {
"file_path": "transformers.js/examples/adaptive-retrieval/package-lock.json",
"repo_id": "transformers.js",
"token_count": 126980
} | 358 |
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