text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
|---|---|---|---|
DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 *... | pytorch-image-models/timm/data/constants.py/0 | {
"file_path": "pytorch-image-models/timm/data/constants.py",
"repo_id": "pytorch-image-models",
"token_count": 236
} | 237 |
from copy import deepcopy
__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use
_IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS) # set version, private, kept in sync
de... | pytorch-image-models/timm/data/readers/img_extensions.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/img_extensions.py",
"repo_id": "pytorch-image-models",
"token_count": 582
} | 238 |
from typing import Callable, Dict, List, Optional, Union, Tuple, Type
import torch
from torch import nn
try:
# NOTE we wrap torchvision fns to use timm leaf / no trace definitions
from torchvision.models.feature_extraction import create_feature_extractor as _create_feature_extractor
from torchvision.model... | pytorch-image-models/timm/layers/_fx.py/0 | {
"file_path": "pytorch-image-models/timm/layers/_fx.py",
"repo_id": "pytorch-image-models",
"token_count": 845
} | 239 |
""" Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Callable, Optional, Type, Union
from .activations import *
from .activations_me import *
from .config import is_exportable, is_scriptable
from .typing import LayerType
# PyTorch has an optimized, native 'silu' (aka 'swish'... | pytorch-image-models/timm/layers/create_act.py/0 | {
"file_path": "pytorch-image-models/timm/layers/create_act.py",
"repo_id": "pytorch-image-models",
"token_count": 1997
} | 240 |
""" Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple... | pytorch-image-models/timm/layers/helpers.py/0 | {
"file_path": "pytorch-image-models/timm/layers/helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 462
} | 241 |
""" Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on code in:
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision/tree/main/big_vision
Hacked together by / Copyright 2020 Ross Wightma... | pytorch-image-models/timm/layers/patch_embed.py/0 | {
"file_path": "pytorch-image-models/timm/layers/patch_embed.py",
"repo_id": "pytorch-image-models",
"token_count": 11216
} | 242 |
import torch
import math
import warnings
from torch import nn
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presen... | pytorch-image-models/timm/layers/weight_init.py/0 | {
"file_path": "pytorch-image-models/timm/layers/weight_init.py",
"repo_id": "pytorch-image-models",
"token_count": 2577
} | 243 |
import copy
from collections import deque, defaultdict
from dataclasses import dataclass, field, replace, asdict
from typing import Any, Deque, Dict, Tuple, Optional, Union
__all__ = ['PretrainedCfg', 'filter_pretrained_cfg', 'DefaultCfg']
@dataclass
class PretrainedCfg:
"""
"""
# weight source location... | pytorch-image-models/timm/models/_pretrained.py/0 | {
"file_path": "pytorch-image-models/timm/models/_pretrained.py",
"repo_id": "pytorch-image-models",
"token_count": 1341
} | 244 |
""" CrossViT Model
@inproceedings{
chen2021crossvit,
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
booktitle={International Conference on Computer Vision (ICCV)},
year={2021}
}
Paper l... | pytorch-image-models/timm/models/crossvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/crossvit.py",
"repo_id": "pytorch-image-models",
"token_count": 12479
} | 245 |
# FastViT for PyTorch
#
# Original implementation and weights from https://github.com/apple/ml-fastvit
#
# For licensing see accompanying LICENSE file at https://github.com/apple/ml-fastvit/tree/main
# Original work is copyright (C) 2023 Apple Inc. All Rights Reserved.
#
import os
from functools import partial
from typ... | pytorch-image-models/timm/models/fastvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/fastvit.py",
"repo_id": "pytorch-image-models",
"token_count": 29338
} | 246 |
""" Pytorch Inception-V4 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
from typing import List, Optional, Tuple, Union
import torch
impor... | pytorch-image-models/timm/models/inception_v4.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_v4.py",
"repo_id": "pytorch-image-models",
"token_count": 6625
} | 247 |
""" 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": 8538
} | 248 |
"""SHViT
SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design
Code: https://github.com/ysj9909/SHViT
Paper: https://arxiv.org/abs/2401.16456
@inproceedings{yun2024shvit,
author={Yun, Seokju and Ro, Youngmin},
title={SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design},
bo... | pytorch-image-models/timm/models/shvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/shvit.py",
"repo_id": "pytorch-image-models",
"token_count": 9449
} | 249 |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... | pytorch-image-models/timm/models/vision_transformer_sam.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer_sam.py",
"repo_id": "pytorch-image-models",
"token_count": 13996
} | 250 |
""" AdamW Optimizer
Impl copied from PyTorch master
References for added functionality:
Cautious Optimizers: https://arxiv.org/abs/2411.16085
Why Gradients Rapidly Increase Near the End of Training: https://arxiv.org/abs/2506.02285
NOTE: This impl has been deprecated in favour of torch.optim.AdamW and remains... | pytorch-image-models/timm/optim/adamw.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adamw.py",
"repo_id": "pytorch-image-models",
"token_count": 8144
} | 251 |
""" RMSProp modified to behave like Tensorflow impl
Originally cut & paste from PyTorch RMSProp
https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py
Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE
References for added functio... | pytorch-image-models/timm/optim/rmsprop_tf.py/0 | {
"file_path": "pytorch-image-models/timm/optim/rmsprop_tf.py",
"repo_id": "pytorch-image-models",
"token_count": 3755
} | 252 |
""" Checkpoint Saver
Track top-n training checkpoints and maintain recovery checkpoints on specified intervals.
Hacked together by / Copyright 2020 Ross Wightman
"""
import glob
import logging
import operator
import os
import shutil
import torch
from .model import unwrap_model, get_state_dict
_logger = logging.g... | pytorch-image-models/timm/utils/checkpoint_saver.py/0 | {
"file_path": "pytorch-image-models/timm/utils/checkpoint_saver.py",
"repo_id": "pytorch-image-models",
"token_count": 3258
} | 253 |
#!/usr/bin/env python3
""" ImageNet Validation Script
This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
canonical PyTorch, standard Python style, and good perform... | pytorch-image-models/validate.py/0 | {
"file_path": "pytorch-image-models/validate.py",
"repo_id": "pytorch-image-models",
"token_count": 10446
} | 254 |
# Models
<Tip warning={true}>
Smolagents is an experimental API which is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.
</Tip>
To learn more about agents and tools make sure to read the [introductory guide](../index). This page
contains t... | smolagents/docs/source/en/reference/models.md/0 | {
"file_path": "smolagents/docs/source/en/reference/models.md",
"repo_id": "smolagents",
"token_count": 3183
} | 255 |
# Agents
<Tip warning={true}>
Smolagents एक experimental API है जो किसी भी समय बदल सकता है। एजेंट्स द्वारा लौटाए गए परिणाम भिन्न हो सकते हैं क्योंकि APIs या underlying मॉडल बदलने की संभावना रखते हैं।
</Tip>
Agents और tools के बारे में अधिक जानने के लिए [introductory guide](../index) पढ़ना सुनिश्चित करें।
यह पेज un... | smolagents/docs/source/hi/reference/agents.md/0 | {
"file_path": "smolagents/docs/source/hi/reference/agents.md",
"repo_id": "smolagents",
"token_count": 4209
} | 256 |
# Agentic RAG
[[open-in-colab]]
Retrieval-Augmented-Generation (RAG) 是“使用大语言模型（LLM）来回答用户查询，但基于从知识库中检索的信息”。它比使用普通或微调的 LLM 具有许多优势：举几个例子，它允许将答案基于真实事实并减少虚构；它允许提供 LLM 领域特定的知识；并允许对知识库中的信息访问进行精细控制。
但是，普通的 RAG 存在一些局限性，以下两点尤为突出：
- 它只执行一次检索步骤：如果结果不好，生成的内容也会不好。
- 语义相似性是以用户查询为参考计算的，这可能不是最优的：例如，用户查询通常是一个问题，而包含真实答案的文档通常是肯定语态，因此其... | smolagents/docs/source/zh/examples/rag.md/0 | {
"file_path": "smolagents/docs/source/zh/examples/rag.md",
"repo_id": "smolagents",
"token_count": 3826
} | 257 |
"""
Async CodeAgent Example with Starlette
This example demonstrates how to use a CodeAgent in an async Starlette app,
running the agent in a background thread using anyio.to_thread.run_sync.
"""
import anyio.to_thread
from starlette.applications import Starlette
from starlette.requests import Request
from starlette.... | smolagents/examples/async_agent/main.py/0 | {
"file_path": "smolagents/examples/async_agent/main.py",
"repo_id": "smolagents",
"token_count": 484
} | 258 |
import json
import os
import shutil
import textwrap
from pathlib import Path
# import tqdm.asyncio
from smolagents.utils import AgentError
def serialize_agent_error(obj):
if isinstance(obj, AgentError):
return {"error_type": obj.__class__.__name__, "message": obj.message}
else:
return str(obj... | smolagents/examples/open_deep_research/scripts/run_agents.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/run_agents.py",
"repo_id": "smolagents",
"token_count": 1444
} | 259 |
[build-system]
requires = ["setuptools"]
build-backend = "setuptools.build_meta"
[project]
name = "smolagents"
version = "1.22.0.dev0"
description = "🤗 smolagents: a barebones library for agents. Agents write python code to call tools or orchestrate other agents."
authors = [
{ name="Aymeric Roucher", email="aymeri... | smolagents/pyproject.toml/0 | {
"file_path": "smolagents/pyproject.toml",
"repo_id": "smolagents",
"token_count": 1266
} | 260 |
#!/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/remote_executors.py/0 | {
"file_path": "smolagents/src/smolagents/remote_executors.py",
"repo_id": "smolagents",
"token_count": 13653
} | 261 |
# 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/tests/test_gradio_ui.py/0 | {
"file_path": "smolagents/tests/test_gradio_ui.py",
"repo_id": "smolagents",
"token_count": 7075
} | 262 |
install-server:
cd server && make install
install-server-cpu:
cd server && make install-server
install-router:
cargo install --path backends/v3/
install-launcher:
cargo install --path launcher/
install-benchmark:
cargo install --path benchmark/
install: install-server install-router install-launcher
install... | text-generation-inference/Makefile/0 | {
"file_path": "text-generation-inference/Makefile",
"repo_id": "text-generation-inference",
"token_count": 468
} | 263 |
# Text-generation-inference - Gaudi backend
## Description
This is the TGI backend for Intel Gaudi. This backend is composed of the tgi server optimized for Gaudi hardware.
## Build your own image
The simplest way to build TGI with the Gaudi backend is to use the provided `Makefile`:
Option 1: From the project roo... | text-generation-inference/backends/gaudi/README.md/0 | {
"file_path": "text-generation-inference/backends/gaudi/README.md",
"repo_id": "text-generation-inference",
"token_count": 1492
} | 264 |
from typing import Optional
import torch
import torch.nn as nn
try:
import habana_frameworks.torch.hpu # noqa: F401
convert_from_uint4 = torch.ops.hpu.convert_from_uint4
except Exception as e:
hpu_import_exception = e
def error_raiser_hpu(*args, **kwargs):
raise ValueError(
f"Try... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/awq/quantize/hpu.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/awq/quantize/hpu.py",
"repo_id": "text-generation-inference",
"token_count": 1871
} | 265 |
# 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 to in writing, software
# distributed under th... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen3_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen3_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 6026
} | 266 |
import inspect
import torch
from abc import ABC, abstractmethod
from typing import List, Tuple, Optional, TypeVar, Type, Dict
from collections import defaultdict
from transformers import PreTrainedTokenizerBase
from text_generation_server.models.types import Batch, Generation
from text_generation_server.models.global... | text-generation-inference/backends/gaudi/server/text_generation_server/models/model.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/model.py",
"repo_id": "text-generation-inference",
"token_count": 2095
} | 267 |
[package]
name = "text-generation-router-llamacpp"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[build-dependencies]
bindgen = "0.71.1"
pkg-config = "0.3.31"
[dependencies]
async-trait = "0.1.85"
clap = "4.5.27"
hf-hub.workspace = true
num_cpus = "1.16.0"
text-g... | text-generation-inference/backends/llamacpp/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/llamacpp/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 216
} | 268 |
import copy
import logging
import time
from abc import ABC
from enum import Enum
from typing import List, Optional, Tuple
import torch
from loguru import logger
from transformers import AutoTokenizer, PreTrainedTokenizerBase
from optimum.neuron.configuration_utils import NeuronConfig
from transformers.generation impor... | text-generation-inference/backends/neuron/server/text_generation_server/generator.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/generator.py",
"repo_id": "text-generation-inference",
"token_count": 12958
} | 269 |
from helpers import create_request
from text_generation_server.generator import NeuronGenerator
from text_generation_server.pb.generate_pb2 import Batch
def test_prefill(neuron_model_config):
"""Verify that a prefill for a single request generates the expected output."""
config_name = neuron_model_config["nam... | text-generation-inference/backends/neuron/tests/server/test_prefill.py/0 | {
"file_path": "text-generation-inference/backends/neuron/tests/server/test_prefill.py",
"repo_id": "text-generation-inference",
"token_count": 1606
} | 270 |
#!/bin/bash
set -ex
TRT_VER_BASE="10.8.0"
TRT_VER_FULL="${TRT_VER_BASE}.43"
CUDA_VER="12.8"
CUDNN_VER="9.7.0.66-1"
NCCL_VER="2.25.1-1+cuda${CUDA_VER}"
CUBLAS_VER="${CUDA_VER}.3.14-1"
NVRTC_VER="${CUDA_VER}.61-1"
for i in "$@"; do
case $i in
--TRT_VER=?*) TRT_VER="${i#*=}";;
--CUDA_VER=?*) CUDA_VE... | text-generation-inference/backends/trtllm/scripts/install_tensorrt.sh/0 | {
"file_path": "text-generation-inference/backends/trtllm/scripts/install_tensorrt.sh",
"repo_id": "text-generation-inference",
"token_count": 2083
} | 271 |
/// Inspired by https://github.com/hatoo/oha/blob/bb989ea3cd77727e7743e7daa60a19894bb5e901/src/monitor.rs
use crate::generation::{Decode, Message, Prefill};
use ratatui::crossterm::event::{KeyCode, KeyEvent, KeyModifiers};
use ratatui::layout::{Alignment, Constraint, Direction, Layout};
use ratatui::style::{Color, Modi... | text-generation-inference/benchmark/src/app.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/app.rs",
"repo_id": "text-generation-inference",
"token_count": 12188
} | 272 |
import pytest
from text_generation.types import Parameters, Request
from text_generation.errors import ValidationError
def test_parameters_validation():
# Test best_of
Parameters(best_of=1)
with pytest.raises(ValidationError):
Parameters(best_of=0)
with pytest.raises(ValidationError):
... | text-generation-inference/clients/python/tests/test_types.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_types.py",
"repo_id": "text-generation-inference",
"token_count": 984
} | 273 |
# Consuming Text Generation Inference
There are many ways to consume Text Generation Inference (TGI) server in your applications. After launching the server, you can use the [Messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api) `/v1/chat/completions` route and make a `POST` request to g... | text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md",
"repo_id": "text-generation-inference",
"token_count": 2308
} | 274 |
# Quantization
TGI offers many quantization schemes to run LLMs effectively and fast based on your use-case. TGI supports GPTQ, AWQ, bits-and-bytes, EETQ, Marlin, EXL2 and fp8 quantization.
To leverage GPTQ, AWQ, Marlin and EXL2 quants, you must provide pre-quantized weights. Whereas for bits-and-bytes, EETQ and fp8,... | text-generation-inference/docs/source/conceptual/quantization.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/quantization.md",
"repo_id": "text-generation-inference",
"token_count": 1442
} | 275 |
# Text-generation-launcher arguments
<!-- WRAP CODE BLOCKS -->
```shell
Text Generation Launcher
Usage: text-generation-launcher [OPTIONS]
Options:
```
## MODEL_ID
```shell
--model-id <MODEL_ID>
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `Open... | text-generation-inference/docs/source/reference/launcher.md/0 | {
"file_path": "text-generation-inference/docs/source/reference/launcher.md",
"repo_id": "text-generation-inference",
"token_count": 7909
} | 276 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 15,
"logprob": null,
"text": ","
},
{
"id": 1669,
"logprob": -5.4453125,
"text": " il"
},
{
"id": 1158... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1204
} | 277 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 76,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 18183,
"logprob": -1.5195312,
"special": false,
"text": " Deep"
},
{
"id": 6832,
"l... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int_dynamic_weight/test_compressed_tensors_w8a8_int_dynamic_weight.json",
"repo_id": "text-generation-inference",
"token_count": 5893
} | 278 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "Okay, let's analyze the image.\n\nThe image is a solid, bright white color. There is nothing else visible within it. \n\nIt's essentially a blank white square or rectangle.",
"n... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgb_jpg.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgb_jpg.json",
"repo_id": "text-generation-inference",
"token_count": 304
} | 279 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5351562,
"special": false,
"text": " for"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json",
"repo_id": "text-generation-inference",
"token_count": 4045
} | 280 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 25584,
"logprob": 0.0,
"special": false,
"text": "Grad"
},
{
"id": 993,
"logprob": 0.0,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 849
} | 281 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 60,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 2262,
"logprob": -0.045715332,
"special": false,
"text": "():"
},
{
"id": 284,
"logprob":... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_default_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_default_params.json",
"repo_id": "text-generation-inference",
"token_count": 4504
} | 282 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4911,
"logprob": -6.9765625,
"text": "User"
},
{
"id": 29... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics.json",
"repo_id": "text-generation-inference",
"token_count": 2062
} | 283 |
{
"details": {
"finish_reason": "length",
"generated_tokens": 40,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -1.0488281,
"special": false,
"text": "\n"
},
{
"id": 13,
"logprob": -1.0800781,
"special": fa... | text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_without_adapter.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_without_adapter.json",
"repo_id": "text-generation-inference",
"token_count": 3130
} | 284 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -2.3417969,
"special": false,
"text": "\n"
},
{
"id": 3057,
"logprob": ... | text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized.json",
"repo_id": "text-generation-inference",
"token_count": 867
} | 285 |
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_w8a8_int_dynamic_weight_handle(launcher):
with launcher(
"danieldk/Qwen2.5-1.5B-Instruct-w8a8-int-dynamic-weight",
num_shard=2,
quantize="compressed-tensors",
) as handle:
yield handle
@pytest.fixture(scope=... | text-generation-inference/integration-tests/models/test_compressed_tensors_w8a8_int_dynamic_weight.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_compressed_tensors_w8a8_int_dynamic_weight.py",
"repo_id": "text-generation-inference",
"token_count": 1234
} | 286 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_exl2_handle(launcher):
with launcher(
"turboderp/Llama-3-8B-Instruct-exl2",
revision="2.5bpw",
# Set max input length to avoid OOM due to extremely large
# scratch buffer.
max_input_length=1024,
num_shard... | text-generation-inference/integration-tests/models/test_flash_llama_exl2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_exl2.py",
"repo_id": "text-generation-inference",
"token_count": 886
} | 287 |
import pytest
@pytest.fixture(scope="module")
def flash_pali_gemma_handle(launcher):
with launcher(
"google/paligemma2-3b-pt-224",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_pali_gemma(flash_pali_gemma_handle):
await flash_pali_gemma_handle.health(300)
... | text-generation-inference/integration-tests/models/test_flash_pali_gemma2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_pali_gemma2.py",
"repo_id": "text-generation-inference",
"token_count": 356
} | 288 |
import pytest
import json
import requests
@pytest.fixture(scope="module")
def model_handle(launcher):
"""Fixture to provide the base URL for API calls."""
with launcher(
"google/gemma-3-4b-it",
num_shard=2,
disable_grammar_support=False,
) as handle:
yield handle
@pytest.... | text-generation-inference/integration-tests/models/test_json_schema_constrain.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_json_schema_constrain.py",
"repo_id": "text-generation-inference",
"token_count": 3156
} | 289 |
import os
import pytest
@pytest.fixture(scope="module", params=["hub-neuron", "hub", "local-neuron"])
async def tgi_service(request, neuron_launcher, neuron_model_config):
"""Expose a TGI service corresponding to a model configuration
For each model configuration, the service will be started using the follo... | text-generation-inference/integration-tests/neuron/test_implicit_env.py/0 | {
"file_path": "text-generation-inference/integration-tests/neuron/test_implicit_env.py",
"repo_id": "text-generation-inference",
"token_count": 827
} | 290 |
# https://www.gutenberg.org/cache/epub/103/pg103.txt
from openai import OpenAI
import os
import requests
if not os.path.exists("pg103.txt"):
response = requests.get("https://www.gutenberg.org/cache/epub/103/pg103.txt")
with open("pg103.txt", "w") as f:
f.write(response.text)
length = 130000
with open... | text-generation-inference/load_tests/long_prompt2.py/0 | {
"file_path": "text-generation-inference/load_tests/long_prompt2.py",
"repo_id": "text-generation-inference",
"token_count": 250
} | 291 |
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(tag = "model_type")]
#[serde(rename_all = "snake_case")]
pub struct LlavaNext {
pub(crate) text_config: TextConfig,
pub(crate) vision_config: VisionConfig,
pub(crate) image... | text-generation-inference/router/src/config.rs/0 | {
"file_path": "text-generation-inference/router/src/config.rs",
"repo_id": "text-generation-inference",
"token_count": 6233
} | 292 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "column_remap.cuh"
#include "../util.cuh"
const int SHUF_BLOCKSIZE_X = 256;
const int SHUF_BLOCKSIZE_Y = 16;
__global__ void column_remap_kernel
(
const half* __restrict__ x,
half* __restrict__ x_new,
const int x_width,
... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu",
"repo_id": "text-generation-inference",
"token_count": 696
} | 293 |
#include "q_gemm.cuh"
#include "util.cuh"
#include "matrix_view.cuh"
#include "../config.h"
#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 GPTQ_BLOCK_KN_SIZE 128
#define GPTQ_BLOCK_M_SIZE_MAX 8
#... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu",
"repo_id": "text-generation-inference",
"token_count": 3563
} | 294 |
[
{
"repo_id": "kernels-community/paged-attention",
"sha": "1e0a9708f0fe47009a3d292226c5492474353258",
"variants": {
"torch25-cxx11-cu118-x86_64-linux": {
"hash": "sha256-99710450ce815fdd0eeab3862ed0940c37a236c4f6cd49399e0112d66c9e40cb",
"hash_type": "git_lfs_concat"
},
"... | text-generation-inference/server/kernels.lock/0 | {
"file_path": "text-generation-inference/server/kernels.lock",
"repo_id": "text-generation-inference",
"token_count": 12258
} | 295 |
import torch
from text_generation_server.layers import (
TensorParallelEmbedding,
)
class ProcessGroup:
def __init__(self, rank: int, world_size: int):
self._rank = rank
self.world_size = world_size
def size(self) -> int:
return self.world_size
def rank(self) -> int:
... | text-generation-inference/server/tests/utils/test_layers.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_layers.py",
"repo_id": "text-generation-inference",
"token_count": 1146
} | 296 |
#!/usr/bin/env python
"""
Fused Attention
===============
This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao
(https://tridao.me/publications/flash2/flash2.pdf)
Credits: OpenAI kernel team, AMD ML Frameworks Triton team
Features supported:
1) Fwd with causal masking
2) Any sequence lengt... | text-generation-inference/server/text_generation_server/layers/attention/flash_attn_triton.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/flash_attn_triton.py",
"repo_id": "text-generation-inference",
"token_count": 14692
} | 297 |
from typing import Optional
import torch
import torch.nn as nn
from text_generation_server.layers.fp8 import fp8_quantize
from text_generation_server.layers.marlin.gptq import _check_valid_shape
from text_generation_server.layers.marlin.util import (
_check_marlin_kernels,
permute_scales,
)
from text_generatio... | text-generation-inference/server/text_generation_server/layers/marlin/fp8.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/fp8.py",
"repo_id": "text-generation-inference",
"token_count": 1856
} | 298 |
import torch
import time
import torch.distributed
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
AutoConfig,
AutoTokenizer,
AutoModelForCausalLM,
PreTrainedTokenizerBase,
)
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.ut... | text-generation-inference/server/text_generation_server/models/causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 16985
} | 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/server/text_generation_server/models/custom_modeling/idefics_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 28598
} | 300 |
from contextlib import nullcontext
import math
import os
import time
import torch
import torch.distributed
import numpy as np
from loguru import logger
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
PreTrainedTokenizerBase,
AutoConfig,
AutoTokenizer,
Gener... | text-generation-inference/server/text_generation_server/models/flash_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 51272
} | 301 |
from text_generation_server.utils.convert import convert_file, convert_files
from text_generation_server.utils.dist import initialize_torch_distributed
from text_generation_server.utils.weights import Weights
from text_generation_server.utils.peft import download_and_unload_peft
from text_generation_server.utils.hub im... | text-generation-inference/server/text_generation_server/utils/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 417
} | 302 |
target
.yarn | tokenizers/bindings/node/.prettierignore/0 | {
"file_path": "tokenizers/bindings/node/.prettierignore",
"repo_id": "tokenizers",
"token_count": 5
} | 303 |
{
"name": "tokenizers-win32-ia32-msvc",
"version": "0.13.4-rc1",
"os": [
"win32"
],
"cpu": [
"ia32"
],
"main": "tokenizers.win32-ia32-msvc.node",
"files": [
"tokenizers.win32-ia32-msvc.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NA... | tokenizers/bindings/node/npm/win32-ia32-msvc/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-ia32-msvc/package.json",
"repo_id": "tokenizers",
"token_count": 277
} | 304 |
use crate::decoders::Decoder;
use crate::encoding::{JsEncoding, JsTruncationDirection, JsTruncationStrategy};
use crate::models::Model;
use crate::normalizers::Normalizer;
use crate::pre_tokenizers::PreTokenizer;
use crate::processors::Processor;
use crate::tasks::tokenizer::{DecodeBatchTask, DecodeTask, EncodeBatchTas... | tokenizers/bindings/node/src/tokenizer.rs/0 | {
"file_path": "tokenizers/bindings/node/src/tokenizer.rs",
"repo_id": "tokenizers",
"token_count": 5695
} | 305 |
import argparse
import logging
import time
from tqdm import tqdm
from tokenizers import Tokenizer, decoders, pre_tokenizers
from tokenizers.models import BPE, WordPiece
from tokenizers.normalizers import BertNormalizer
from tokenizers.processors import BertProcessing
from transformers import BertTokenizer, GPT2Tokeni... | tokenizers/bindings/python/examples/example.py/0 | {
"file_path": "tokenizers/bindings/python/examples/example.py",
"repo_id": "tokenizers",
"token_count": 1770
} | 306 |
# Generated content DO NOT EDIT
from .. import models
Model = models.Model
BPE = models.BPE
Unigram = models.Unigram
WordLevel = models.WordLevel
WordPiece = models.WordPiece
| tokenizers/bindings/python/py_src/tokenizers/models/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/models/__init__.py",
"repo_id": "tokenizers",
"token_count": 56
} | 307 |
from argparse import ArgumentParser
from json import dump
from logging import basicConfig, getLogger
from os import linesep, remove
from os.path import exists
from tempfile import NamedTemporaryFile
from typing import Dict, List, Tuple
from requests import get
from sentencepiece import SentencePieceProcessor
from tqdm... | tokenizers/bindings/python/scripts/sentencepiece_extractor.py/0 | {
"file_path": "tokenizers/bindings/python/scripts/sentencepiece_extractor.py",
"repo_id": "tokenizers",
"token_count": 2231
} | 308 |
use super::regex::PyRegex;
use super::{DestroyPtr, RefMutContainer, RefMutGuard};
use crate::error::ToPyResult;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use tk::normalizer::{char_to_bytes, NormalizedString, Range, SplitDelimiterBehavior};
use tk::pattern::Pattern;
/// Represents a Pattern as use... | tokenizers/bindings/python/src/utils/normalization.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/normalization.rs",
"repo_id": "tokenizers",
"token_count": 8532
} | 309 |
# Decoders
<tokenizerslangcontent>
<python>
## BPEDecoder
[[autodoc]] tokenizers.decoders.BPEDecoder
## ByteLevel
[[autodoc]] tokenizers.decoders.ByteLevel
## CTC
[[autodoc]] tokenizers.decoders.CTC
## Metaspace
[[autodoc]] tokenizers.decoders.Metaspace
## WordPiece
[[autodoc]] tokenizers.decoders.WordPiece
<... | tokenizers/docs/source-doc-builder/api/decoders.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/decoders.mdx",
"repo_id": "tokenizers",
"token_count": 197
} | 310 |
# Training from memory
In the [Quicktour](quicktour), we saw how to build and train a
tokenizer using text files, but we can actually use any Python Iterator.
In this section we'll see a few different ways of training our
tokenizer.
For all the examples listed below, we'll use the same [`~tokenizers.Tokenizer`] and
[... | tokenizers/docs/source-doc-builder/training_from_memory.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/training_from_memory.mdx",
"repo_id": "tokenizers",
"token_count": 1199
} | 311 |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | tokenizers/docs/source/conf.py/0 | {
"file_path": "tokenizers/docs/source/conf.py",
"repo_id": "tokenizers",
"token_count": 781
} | 312 |
#[macro_use]
extern crate criterion;
mod common;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
use criterion::{Criterion, Throughput};
use tokenizers::models::wordpiece::{WordPiece, WordPieceTrainerBuilder};
use tokenizers::normalizers::{BertNormalizer, NormalizerWrapper};
use tokenizers... | tokenizers/tokenizers/benches/bert_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/bert_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 2072
} | 313 |
language: node_js
node_js: "10"
script:
- ./node_modules/.bin/webpack
| tokenizers/tokenizers/examples/unstable_wasm/www/.travis.yml/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/.travis.yml",
"repo_id": "tokenizers",
"token_count": 30
} | 314 |
use crate::decoders::DecoderWrapper;
use crate::tokenizer::{Decoder, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Sequence {
decoders: Vec<DecoderWrapper>,
}
impl Sequence {
pub fn new(decod... | tokenizers/tokenizers/src/decoders/sequence.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/sequence.rs",
"repo_id": "tokenizers",
"token_count": 689
} | 315 |
use super::OrderedVocabIter;
use crate::tokenizer::{Model, Result, Token};
use ahash::AHashMap;
use serde_json::Value;
use std::collections::HashMap;
use std::fs::File;
use std::io::{BufReader, Read, Write};
use std::path::{Path, PathBuf};
mod serialization;
mod trainer;
// Re-export
pub use trainer::*;
type Vocab =... | tokenizers/tokenizers/src/models/wordlevel/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordlevel/mod.rs",
"repo_id": "tokenizers",
"token_count": 3405
} | 316 |
use ahash::{AHashMap, AHashSet};
use std::sync::LazyLock;
use crate::utils::SysRegex;
use serde::{Deserialize, Serialize};
use crate::tokenizer::{
Decoder, Encoding, PostProcessor, PreTokenizedString, PreTokenizer, Result,
SplitDelimiterBehavior,
};
use crate::utils::macro_rules_attribute;
/// Converts bytes... | tokenizers/tokenizers/src/pre_tokenizers/byte_level.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/byte_level.rs",
"repo_id": "tokenizers",
"token_count": 10977
} | 317 |
use crate::processors::PostProcessorWrapper;
use crate::tokenizer::{Encoding, PostProcessor, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug, PartialEq, Eq)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Sequence {
processors: Vec<PostProcessorWr... | tokenizers/tokenizers/src/processors/sequence.rs/0 | {
"file_path": "tokenizers/tokenizers/src/processors/sequence.rs",
"repo_id": "tokenizers",
"token_count": 2674
} | 318 |
//!
//! This module defines helpers to allow optional Rayon usage.
//!
use rayon::iter::IterBridge;
use rayon::prelude::*;
use rayon_cond::CondIterator;
use std::sync::atomic::AtomicBool;
use std::sync::atomic::AtomicU8;
use std::sync::atomic::Ordering;
// Re-export rayon current_num_threads
pub use rayon::current_nu... | tokenizers/tokenizers/src/utils/parallelism.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/parallelism.rs",
"repo_id": "tokenizers",
"token_count": 3698
} | 319 |
To install via [NPM](https://www.npmjs.com/package/@huggingface/transformers), run:
```bash
npm i @huggingface/transformers
```
Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Gu... | transformers.js/docs/snippets/2_installation.snippet/0 | {
"file_path": "transformers.js/docs/snippets/2_installation.snippet",
"repo_id": "transformers.js",
"token_count": 176
} | 320 |
# Building an Electron application
*Full tutorial coming soon...* In the meantime, check out the example application: https://github.com/huggingface/transformers.js/tree/main/examples/electron
| transformers.js/docs/source/tutorials/electron.md/0 | {
"file_path": "transformers.js/docs/source/tutorials/electron.md",
"repo_id": "transformers.js",
"token_count": 51
} | 321 |
import Chart from 'chart.js/auto';
import Prism from 'prismjs';
// Import code and styles for supported languages
import 'prismjs/components/prism-javascript';
import 'prismjs/components/prism-python';
import 'prismjs/components/prism-markdown';
import 'prismjs/components/prism-clike';
import 'prismjs/themes/prism.c... | transformers.js/examples/demo-site/src/main.js/0 | {
"file_path": "transformers.js/examples/demo-site/src/main.js",
"repo_id": "transformers.js",
"token_count": 9224
} | 322 |
{
"name": "electron",
"productName": "electron",
"version": "1.0.0",
"description": "Transformers.js sample Electron application",
"main": "src/index.js",
"scripts": {
"start": "electron-forge start",
"package": "electron-forge package",
"make": "electron-forge make",
"publish": "electron-fo... | transformers.js/examples/electron/package.json/0 | {
"file_path": "transformers.js/examples/electron/package.json",
"repo_id": "transformers.js",
"token_count": 361
} | 323 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Transformers.js | Sample Browser Extension</title>
<!-- Load styles -->
<link rel="stylesheet" href... | transformers.js/examples/extension/src/popup.html/0 | {
"file_path": "transformers.js/examples/extension/src/popup.html",
"repo_id": "transformers.js",
"token_count": 246
} | 324 |
import { pipeline } from '@xenova/transformers';
import wavefile from 'wavefile';
// Load model
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
// Load audio data
let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let buffer = Buff... | transformers.js/examples/node-audio-processing/index.js/0 | {
"file_path": "transformers.js/examples/node-audio-processing/index.js",
"repo_id": "transformers.js",
"token_count": 479
} | 325 |
import { pipeline } from '@xenova/transformers';
/**
* This class uses the Singleton pattern to ensure that only one instance of the
* pipeline is loaded. This is because loading the pipeline is an expensive
* operation and we don't want to do it every time we want to translate a sentence.
*/
class MyTranslationP... | transformers.js/examples/react-translator/src/worker.js/0 | {
"file_path": "transformers.js/examples/react-translator/src/worker.js",
"repo_id": "transformers.js",
"token_count": 614
} | 326 |
# Semantic Image Search
This example shows you how to use Transformers.js to create a semantic image search engine. Check out the demo [here](https://huggingface.co/spaces/Xenova/semantic-image-search).
![Semantic Image Search Demo](https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/semantic-im... | transformers.js/examples/semantic-image-search/README.md/0 | {
"file_path": "transformers.js/examples/semantic-image-search/README.md",
"repo_id": "transformers.js",
"token_count": 1129
} | 327 |
'use client'
import { useState } from 'react'
import { Modal } from './components/Modal';
import { SearchBar } from './components/SearchBar';
import { ImageGrid } from './components/ImageGrid';
export default function Home() {
// Application state
const [images, setImages] = useState(null);
const [currentImage... | transformers.js/examples/semantic-image-search/src/app/page.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search/src/app/page.js",
"repo_id": "transformers.js",
"token_count": 345
} | 328 |
// Although not strictly necessary, we delegate the tokenization to a worker thread to avoid
// any potential issues with the tokenizer blocking the main thread (especially for large inputs).
import { env, AutoTokenizer } from '@xenova/transformers'
env.allowLocalModels = false;
// This is a map of all the tokenizer... | transformers.js/examples/tokenizer-playground/src/worker.js/0 | {
"file_path": "transformers.js/examples/tokenizer-playground/src/worker.js",
"repo_id": "transformers.js",
"token_count": 1112
} | 329 |
import './style.css';
import { env, AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';
env.backends.onnx.wasm.wasmPaths = 'https://cdn.jsdelivr.net/npm/onnxruntime-web@1.17.1/dist/';
env.backends.onnx.wasm.numThreads = 1;
// Reference the elements that we will need
const status = document.getElementBy... | transformers.js/examples/webgpu-video-background-removal/main.js/0 | {
"file_path": "transformers.js/examples/webgpu-video-background-removal/main.js",
"repo_id": "transformers.js",
"token_count": 1573
} | 330 |
{
"name": "@huggingface/transformers",
"version": "3.7.2",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "@huggingface/transformers",
"version": "3.7.2",
"license": "Apache-2.0",
"dependencies": {
"@huggingface/jinja": "^0.5.1",
"onnxruntime-no... | transformers.js/package-lock.json/0 | {
"file_path": "transformers.js/package-lock.json",
"repo_id": "transformers.js",
"token_count": 251206
} | 331 |
import onnx
from typing import Optional, Union
from pathlib import Path
import os
import logging
logger = logging.getLogger(__name__)
# https://github.com/onnx/onnx/pull/6556
MAXIMUM_PROTOBUF = 2147483648 # 2GiB
def strict_check_model(model_or_path: Union[onnx.ModelProto, str, Path]):
try:
onnx.checke... | transformers.js/scripts/utils.py/0 | {
"file_path": "transformers.js/scripts/utils.py",
"repo_id": "transformers.js",
"token_count": 1216
} | 332 |
import { GITHUB_ISSUE_URL, IMAGE_PROCESSOR_NAME } from '../../utils/constants.js';
import { getModelJSON } from '../../utils/hub.js';
import { ImageProcessor } from '../../base/image_processors_utils.js';
import * as AllImageProcessors from '../image_processors.js';
export class AutoImageProcessor {
/** @type {t... | transformers.js/src/models/auto/image_processing_auto.js/0 | {
"file_path": "transformers.js/src/models/auto/image_processing_auto.js",
"repo_id": "transformers.js",
"token_count": 475
} | 333 |
export * from './audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js';
export * from './encodec/feature_extraction_encodec.js';
export * from './clap/feature_extraction_clap.js';
export * from './dac/feature_extraction_dac.js';
export * from './gemma3n/feature_extraction_gemma3n.js';
expo... | transformers.js/src/models/feature_extractors.js/0 | {
"file_path": "transformers.js/src/models/feature_extractors.js",
"repo_id": "transformers.js",
"token_count": 336
} | 334 |
import { MaskFormerImageProcessor } from "../maskformer/image_processing_maskformer.js";
// NOTE: extends MaskFormerImageProcessor
export class Mask2FormerImageProcessor extends MaskFormerImageProcessor { }
| transformers.js/src/models/mask2former/image_processing_mask2former.js/0 | {
"file_path": "transformers.js/src/models/mask2former/image_processing_mask2former.js",
"repo_id": "transformers.js",
"token_count": 53
} | 335 |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
import { RawImage } from "../../utils/image.js";
const IMAGE_TOKEN = "<|image|>";
const IMAGE_TOKEN_PATTERN = /<\|image_\d+\|>/g;
expo... | transformers.js/src/models/phi3_v/processing_phi3_v.js/0 | {
"file_path": "transformers.js/src/models/phi3_v/processing_phi3_v.js",
"repo_id": "transformers.js",
"token_count": 827
} | 336 |
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