text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
|---|---|---|---|
# Models
[[autodoc]] timm.create_model
[[autodoc]] timm.list_models
| pytorch-image-models/hfdocs/source/reference/models.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/reference/models.mdx",
"repo_id": "pytorch-image-models",
"token_count": 29
} | 257 |
""" NaFlex (NaViT + FlexiViT) Transforms and Collation
Implements PyTorch versions of the transforms described in the NaViT and FlexiViT papers:
- NaViT: https://arxiv.org/abs/2307.14995
- FlexiViT: https://arxiv.org/abs/2212.08013
Enables variable resolution/aspect ratio image handling with efficient patching.
Hack... | pytorch-image-models/timm/data/naflex_transforms.py/0 | {
"file_path": "pytorch-image-models/timm/data/naflex_transforms.py",
"repo_id": "pytorch-image-models",
"token_count": 14531
} | 258 |
""" Tensorflow Preprocessing Adapter
Allows use of Tensorflow preprocessing pipeline in PyTorch Transform
Copyright of original Tensorflow code below.
Hacked together by / Copyright 2020 Ross Wightman
"""
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.... | pytorch-image-models/timm/data/tf_preprocessing.py/0 | {
"file_path": "pytorch-image-models/timm/data/tf_preprocessing.py",
"repo_id": "pytorch-image-models",
"token_count": 3775
} | 259 |
""" PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import torch
from torch import nn as nn
f... | pytorch-image-models/timm/layers/cond_conv2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/cond_conv2d.py",
"repo_id": "pytorch-image-models",
"token_count": 2327
} | 260 |
""" Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
import torc... | pytorch-image-models/timm/layers/global_context.py/0 | {
"file_path": "pytorch-image-models/timm/layers/global_context.py",
"repo_id": "pytorch-image-models",
"token_count": 1169
} | 261 |
""" Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm im... | pytorch-image-models/timm/layers/norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/norm.py",
"repo_id": "pytorch-image-models",
"token_count": 8998
} | 262 |
""" Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
Code:... | pytorch-image-models/timm/layers/std_conv.py/0 | {
"file_path": "pytorch-image-models/timm/layers/std_conv.py",
"repo_id": "pytorch-image-models",
"token_count": 2510
} | 263 |
""" PyTorch FX Based Feature Extraction Helpers
Using https://pytorch.org/vision/stable/feature_extraction.html
"""
from typing import Callable, Dict, List, Optional, Union, Tuple, Type
import torch
from torch import nn
from timm.layers import (
create_feature_extractor,
get_graph_node_names,
register_not... | pytorch-image-models/timm/models/_features_fx.py/0 | {
"file_path": "pytorch-image-models/timm/models/_features_fx.py",
"repo_id": "pytorch-image-models",
"token_count": 1325
} | 264 |
"""
CoaT architecture.
Paper: Co-Scale Conv-Attentional Image Transformers - https://arxiv.org/abs/2104.06399
Official CoaT code at: https://github.com/mlpc-ucsd/CoaT
Modified from timm/models/vision_transformer.py
"""
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.n... | pytorch-image-models/timm/models/coat.py/0 | {
"file_path": "pytorch-image-models/timm/models/coat.py",
"repo_id": "pytorch-image-models",
"token_count": 15596
} | 265 |
""" EfficientViT (by MSRA)
Paper: `EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention`
- https://arxiv.org/abs/2305.07027
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/EfficientViT
"""
__all__ = ['EfficientVitMsra']
import itertools
from collections impor... | pytorch-image-models/timm/models/efficientvit_msra.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientvit_msra.py",
"repo_id": "pytorch-image-models",
"token_count": 12924
} | 266 |
""" NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_classifier
from ._builder import... | pytorch-image-models/timm/models/nasnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/nasnet.py",
"repo_id": "pytorch-image-models",
"token_count": 13254
} | 267 |
""" ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... | pytorch-image-models/timm/models/rexnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/rexnet.py",
"repo_id": "pytorch-image-models",
"token_count": 9214
} | 268 |
""" Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... | pytorch-image-models/timm/models/visformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/visformer.py",
"repo_id": "pytorch-image-models",
"token_count": 10151
} | 269 |
""" Adafactor Optimizer
Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
Modified by Ross Wightman to fix some issues with factorization dims for non nn.Linear layers
Original header/copyright below.
"""
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is l... | pytorch-image-models/timm/optim/adafactor.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adafactor.py",
"repo_id": "pytorch-image-models",
"token_count": 4921
} | 270 |
""" NAdamW Optimizer
Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
Added multi-tensor (foreach) path.
References for added functionality:
Cautious Optimizers: https://arxiv.org/abs/2411.16085
Why Gradients Rapidly Increase Near the End of Trai... | pytorch-image-models/timm/optim/nadamw.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nadamw.py",
"repo_id": "pytorch-image-models",
"token_count": 7305
} | 271 |
""" TanH Scheduler
TanH schedule with warmup, cycle/restarts, noise.
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 TanhLRScheduler(Scheduler):
... | pytorch-image-models/timm/scheduler/tanh_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/tanh_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 2000
} | 272 |
import random
import numpy as np
import torch
def random_seed(seed=42, rank=0):
torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
random.seed(seed + rank)
| pytorch-image-models/timm/utils/random.py/0 | {
"file_path": "pytorch-image-models/timm/utils/random.py",
"repo_id": "pytorch-image-models",
"token_count": 68
} | 273 |
.PHONY: quality style test docs
check_dirs := examples src tests
# Check code quality of the source code
quality:
ruff check $(check_dirs)
ruff format --check $(check_dirs)
# Format source code automatically
style:
ruff check $(check_dirs) --fix
ruff format $(check_dirs)
# Run smolagents tests
test:
pytest ./... | smolagents/Makefile/0 | {
"file_path": "smolagents/Makefile",
"repo_id": "smolagents",
"token_count": 104
} | 274 |
# `smolagents`
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/license_to_call.png" style="max-width:700px"/>
</div>
## What is smolagents?
`smolagents` is an open-source Python library designed to make it extremely easy to buil... | smolagents/docs/source/en/index.md/0 | {
"file_path": "smolagents/docs/source/en/index.md",
"repo_id": "smolagents",
"token_count": 1993
} | 275 |
# एजेंटिक RAG
[[open-in-colab]]
रिट्रीवल-ऑगमेंटेड-जनरेशन (RAG) है "एक यूजर के प्रश्न का उत्तर देने के लिए LLM का उपयोग करना, लेकिन उत्तर को एक नॉलेज बेस से प्राप्त जानकारी पर आधारित करना"। इसमें वैनिला या फाइन-ट्यून्ड LLM का उपयोग करने की तुलना में कई फायदे हैं: कुछ नाम लेने के लिए, यह उत्तर को सत्य तथ्यों पर आधारित ... | smolagents/docs/source/hi/examples/rag.md/0 | {
"file_path": "smolagents/docs/source/hi/examples/rag.md",
"repo_id": "smolagents",
"token_count": 7604
} | 276 |
- title: 起步
sections:
- local: index
title: 🤗 Agents
- local: guided_tour
title: 导览
- title: Tutorials
sections:
- local: tutorials/building_good_agents
title: ✨ 构建好用的 agents
- local: tutorials/inspect_runs
title: 📊 监控 Agent 的运行
- local: tutorials/tools
title: 🛠️ 工具 - 深度指南
- local... | smolagents/docs/source/zh/_toctree.yml/0 | {
"file_path": "smolagents/docs/source/zh/_toctree.yml",
"repo_id": "smolagents",
"token_count": 555
} | 277 |
# 工具
[[open-in-colab]]
在这里，我们将学习高级工具的使用。
> [!TIP]
> 如果你是构建 agent 的新手，请确保先阅读 [agent 介绍](../conceptual_guides/intro_agents) 和 [smolagents 导览](../guided_tour)。
- [工具](#工具)
- [什么是工具，如何构建一个工具？](#什么是工具如何构建一个工具)
- [将你的工具分享到 Hub](#将你的工具分享到-hub)
- [将 Space 导入为工具](#将-space-导入为工具)
- [使用 LangChain 工具](#使用-langc... | smolagents/docs/source/zh/tutorials/tools.md/0 | {
"file_path": "smolagents/docs/source/zh/tutorials/tools.md",
"repo_id": "smolagents",
"token_count": 4839
} | 278 |
import argparse
import datetime
import json
import os
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
import datasets
import pandas as pd
from dotenv import load_dotenv
from tqdm import tqdm
from smolagents import (
AgentError,
CodeAgent,
... | smolagents/examples/smolagents_benchmark/run.py/0 | {
"file_path": "smolagents/examples/smolagents_benchmark/run.py",
"repo_id": "smolagents",
"token_count": 3660
} | 279 |
#!/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/monitoring.py/0 | {
"file_path": "smolagents/src/smolagents/monitoring.py",
"repo_id": "smolagents",
"token_count": 4314
} | 280 |
from unittest.mock import patch
import pytest
from smolagents.cli import load_model
from smolagents.local_python_executor import CodeOutput, LocalPythonExecutor
from smolagents.models import InferenceClientModel, LiteLLMModel, OpenAIServerModel, TransformersModel
@pytest.fixture
def set_env_vars(monkeypatch):
m... | smolagents/tests/test_cli.py/0 | {
"file_path": "smolagents/tests/test_cli.py",
"repo_id": "smolagents",
"token_count": 1975
} | 281 |
# 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_types.py/0 | {
"file_path": "smolagents/tests/test_types.py",
"repo_id": "smolagents",
"token_count": 1468
} | 282 |
ARG PLATFORM=xpu
FROM lukemathwalker/cargo-chef:latest-rust-1.85.1 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef AS planner
COPY Cargo.lock Cargo.lock
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router rout... | text-generation-inference/Dockerfile_intel/0 | {
"file_path": "text-generation-inference/Dockerfile_intel",
"repo_id": "text-generation-inference",
"token_count": 3628
} | 283 |
#!/bin/bash
git clone -b dill-0.3.7 https://github.com/uqfoundation/dill.git
pushd dill
cat <<EOF > dill-0.3.7.patch
diff --git a/dill/_dill.py b/dill/_dill.py
index d0cf543..f6eb662 100644
--- a/dill/_dill.py
+++ b/dill/_dill.py
@@ -69,7 +69,15 @@ TypeType = type # 'new-style' classes #XXX: unregistered
XRangeType = ... | text-generation-inference/backends/gaudi/server/dill-0.3.7-patch.sh/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/dill-0.3.7-patch.sh",
"repo_id": "text-generation-inference",
"token_count": 1641
} | 284 |
import torch
from text_generation_server.layers.attention import Seqlen, HPUPagedAttentionMetadata
from typing import Optional
from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
from vllm_hpu_extension import ops
from vllm_hpu_extension.utils import Matmul
from habana_frameworks.torch.hpex.k... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/attention/hpu.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/attention/hpu.py",
"repo_id": "text-generation-inference",
"token_count": 3794
} | 285 |
import torch
from torch import nn
from accelerate import init_empty_weights
# Monkey patching
@classmethod
def load_layer_norm(cls, prefix, weights, eps):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.bias")
with init_empty_weights():
ln = cls(weight.shape, e... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/layernorm.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/layernorm.py",
"repo_id": "text-generation-inference",
"token_count": 746
} | 286 |
# 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/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 8402
} | 287 |
# coding=utf-8
# Copyright 2024 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 requi... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 2030
} | 288 |
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,
GenerationConfig,
)
from typing import (... | text-generation-inference/backends/gaudi/server/text_generation_server/models/flash_causal_lm.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/flash_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 56151
} | 289 |
import torch
from abc import ABC, abstractmethod
from contextlib import contextmanager
from pathlib import Path
from typing import Dict, List, Optional, Union, Type
from safetensors import safe_open
from dataclasses import dataclass
class WeightsLoader(ABC):
"""
Instances of this type implement higher-level ... | text-generation-inference/backends/gaudi/server/text_generation_server/utils/weights.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/utils/weights.py",
"repo_id": "text-generation-inference",
"token_count": 6935
} | 290 |
# Initialize base variables
SHELL := /bin/bash
pkg_name := text_generation_server
BUILDDIR ?= $(CURDIR)/build
VERSION ?= 0.0.1
mkfile_path := $(abspath $(lastword $(MAKEFILE_LIST)))
mkfile_dir := $(dir $(mkfile_path))
pkg_dir := $(BUILDDIR)/$(pkg_name)
py_version := $(subst -,.,${VERSION})
pkg_dist := ${BUILDDIR}/dist/... | text-generation-inference/backends/neuron/server/Makefile/0 | {
"file_path": "text-generation-inference/backends/neuron/server/Makefile",
"repo_id": "text-generation-inference",
"token_count": 1003
} | 291 |
from helpers import create_request
from text_generation_server.generator import NeuronGenerator
from text_generation_server.pb.generate_pb2 import Batch
def test_continuous_batching_two_requests(neuron_model_config):
"""Verify that two requests added to the batch at different generation steps
generate the sam... | text-generation-inference/backends/neuron/tests/server/test_continuous_batching.py/0 | {
"file_path": "text-generation-inference/backends/neuron/tests/server/test_continuous_batching.py",
"repo_id": "text-generation-inference",
"token_count": 1271
} | 292 |
#include <ranges>
#include <nlohmann/json.hpp>
#include "backend.hpp"
#include "hardware.hpp"
namespace huggingface::tgi::backends::trtllm {
tle::ParallelConfig backend_workspace_t::parallel_config() const {
// Single engine (TP = PP = 1) -> using leader mode (no MPI involved)
const auto world_si... | text-generation-inference/backends/trtllm/csrc/backend.cpp/0 | {
"file_path": "text-generation-inference/backends/trtllm/csrc/backend.cpp",
"repo_id": "text-generation-inference",
"token_count": 1511
} | 293 |
use crate::block_allocator::{BlockAllocation, BlockAllocator};
use crate::client;
use crate::client::{
Batch, GrammarType, NextTokenChooserParameters, Request, StoppingCriteriaParameters,
};
use nohash_hasher::{BuildNoHashHasher, IntMap};
use std::cmp::max;
use std::collections::VecDeque;
use text_generation_router... | text-generation-inference/backends/v3/src/queue.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/queue.rs",
"repo_id": "text-generation-inference",
"token_count": 15467
} | 294 |
import pytest
from text_generation import __version__
from huggingface_hub.utils import build_hf_headers
@pytest.fixture
def flan_t5_xxl():
return "google/flan-t5-xxl"
@pytest.fixture
def llama_7b():
return "meta-llama/Llama-2-7b-chat-hf"
@pytest.fixture
def fake_model():
return "fake/model"
@pytes... | text-generation-inference/clients/python/tests/conftest.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 486
} | 295 |
# Gaudi Backend for Text Generation Inference
## Overview
Text Generation Inference (TGI) has been optimized to run on Gaudi hardware via the Gaudi backend for TGI.
## Supported Hardware
- **Gaudi1**: Available on [AWS EC2 DL1 instances](https://aws.amazon.com/ec2/instance-types/dl1/)
- **Gaudi2**: Available on [Inte... | text-generation-inference/docs/source/backends/gaudi.mdx/0 | {
"file_path": "text-generation-inference/docs/source/backends/gaudi.mdx",
"repo_id": "text-generation-inference",
"token_count": 5146
} | 296 |
# Using TGI with Google TPUs
Check out this [guide](https://huggingface.co/docs/optimum-tpu) on how to serve models with TGI on TPUs.
| text-generation-inference/docs/source/installation_tpu.md/0 | {
"file_path": "text-generation-inference/docs/source/installation_tpu.md",
"repo_id": "text-generation-inference",
"token_count": 48
} | 297 |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "Both an elephant and a mouse are mammals. However, the differences between elephants and mice are:\n\n1",
"role": "assistant"
}
}
],
"created": 1732541189,
"id... | text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt.json",
"repo_id": "text-generation-inference",
"token_count": 258
} | 298 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 688,
"logprob": -0.546875,
"special": false,
"text": "**"
},
{
"id": 103889,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma2/test_flash_gemma2.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma2/test_flash_gemma2.json",
"repo_id": "text-generation-inference",
"token_count": 877
} | 299 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 29896,
"logprob": -0.7709961,
"special": false,
"text": "1"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_load.json",
"repo_id": "text-generation-inference",
"token_count": 4039
} | 300 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 13,
"logprob": -2.2539062,
"special": false,
"text": "."
},
{
"id": 578,
"logprob": -0.15... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 859
} | 301 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 20910,
"logprob": -0.96484375,
"special": false,
"text": "Grad"
},
{
"id": 722,
"logpr... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mixtral/test_flash_mixtral.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mixtral/test_flash_mixtral.json",
"repo_id": "text-generation-inference",
"token_count": 868
} | 302 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -0.007621765,
"special": false,
"text": "\n"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_load.json",
"repo_id": "text-generation-inference",
"token_count": 4048
} | 303 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 42,
"logprob": -0.86279297,
"special": false,
"text": "I"
},
{
"id": 1353,
"logprob": ... | text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox.json",
"repo_id": "text-generation-inference",
"token_count": 853
} | 304 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_chat_handle(launcher):
with launcher(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0", num_shard=2, disable_grammar_support=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_chat(flash_llama_chat_handle):
... | text-generation-inference/integration-tests/models/test_chat_llama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_chat_llama.py",
"repo_id": "text-generation-inference",
"token_count": 594
} | 305 |
import pytest
@pytest.fixture(scope="module")
def flash_gemma_gptq_handle(launcher):
with launcher("TechxGenus/gemma-2b-GPTQ", num_shard=1, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_gemma_gptq(flash_gemma_gptq_handle):
await flash_gemma_gptq_handle.heal... | text-generation-inference/integration-tests/models/test_flash_gemma_gptq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_gemma_gptq.py",
"repo_id": "text-generation-inference",
"token_count": 804
} | 306 |
import pytest
@pytest.fixture(scope="module")
def flash_mixtral_gptq_handle(launcher):
with launcher(
"TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ",
revision="gptq-4bit-128g-actorder_True",
num_shard=2,
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_mi... | text-generation-inference/integration-tests/models/test_flash_mixtral_gptq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_mixtral_gptq.py",
"repo_id": "text-generation-inference",
"token_count": 950
} | 307 |
import pytest
import requests
from pydantic import BaseModel
from typing import List
@pytest.fixture(scope="module")
def llama_grammar_handle(launcher):
with launcher(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
num_shard=1,
disable_grammar_support=False,
use_flash_attention=False,
... | text-generation-inference/integration-tests/models/test_grammar_response_format_llama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_grammar_response_format_llama.py",
"repo_id": "text-generation-inference",
"token_count": 1857
} | 308 |
import pytest
from openai import OpenAI
from huggingface_hub import InferenceClient
from huggingface_hub.inference._generated.types.chat_completion import (
ChatCompletionOutputToolCall,
ChatCompletionOutputFunctionDefinition,
)
@pytest.fixture(scope="module")
def flash_llama_grammar_tools_handle(launcher):
... | text-generation-inference/integration-tests/models/test_tools_llama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_tools_llama.py",
"repo_id": "text-generation-inference",
"token_count": 8482
} | 309 |
import { check } from 'k6';
import { scenario } from 'k6/execution';
import http from 'k6/http';
import { Trend, Counter } from 'k6/metrics';
const host = __ENV.HOST;
const model_id = __ENV.MODEL_ID;
const timePerToken = new Trend('time_per_token', true);
const tokens = new Counter('tokens');
const new_tokens = new Co... | text-generation-inference/load_tests/common.js/0 | {
"file_path": "text-generation-inference/load_tests/common.js",
"repo_id": "text-generation-inference",
"token_count": 1530
} | 310 |
[package]
name = "text-generation-router"
description = "Text Generation Webserver"
build = "build.rs"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
anyhow = "1"
async-trait = "0.1.74"
async-stream = "0.3.5"
axum = { version = "0.7", features = ["js... | text-generation-inference/router/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 944
} | 311 |
[toolchain]
# Released on: 30 January, 2025
# https://releases.rs/docs/1.84.1/
channel = "1.85.1"
components = ["rustfmt", "clippy"]
| text-generation-inference/rust-toolchain.toml/0 | {
"file_path": "text-generation-inference/rust-toolchain.toml",
"repo_id": "text-generation-inference",
"token_count": 54
} | 312 |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
extra_compile_args = ["-std=c++17"]
setup(
name="custom_kernels",
ext_modules=[
CUDAExtension(
name="custom_kernels.fused_bloom_attention_cuda",
sources=["custom_kernels/fused_bloom... | text-generation-inference/server/custom_kernels/setup.py/0 | {
"file_path": "text-generation-inference/server/custom_kernels/setup.py",
"repo_id": "text-generation-inference",
"token_count": 309
} | 313 |
#ifndef _config_h
#define _config_h
#define MAX_Q_GEMM_ROWS 50
#define MAX_Q_GEMM_WEIGHTS 4 // must be <= MAX_Q_GEMM_ROWS
#define QMODE_2BIT 1
#define QMODE_3BIT 1
#define QMODE_4BIT 1
#define QMODE_5BIT 1
#define QMODE_6BIT 0
#define QMODE_8BIT 0
#endif
| text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h",
"repo_id": "text-generation-inference",
"token_count": 119
} | 314 |
#ifndef _qdq_util_cuh
#define _qdq_util_cuh
union half2_uint32
{
uint32_t as_uint32;
half2 as_half2;
__device__ half2_uint32(uint32_t val) : as_uint32(val) {}
__device__ half2_uint32(half2 val) : as_half2(val) {}
__device__ half2_uint32() : as_uint32(0) {}
};
union half_uint16
{
uint16_t as_ui... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh",
"repo_id": "text-generation-inference",
"token_count": 602
} | 315 |
import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.seq2seq_lm import Seq2SeqLM, Seq2SeqLMBatch
@pytest.fixture(scope="session")
def mt0_small_tokenizer():
tokenizer = AutoTokenizer.from_pr... | text-generation-inference/server/tests/models/test_seq2seq_lm.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_seq2seq_lm.py",
"repo_id": "text-generation-inference",
"token_count": 5528
} | 316 |
from typing import List, Optional, Union, TypeVar
from dataclasses import dataclass
from loguru import logger
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationType
from text_generation_server.layers.fp8 import _load_scalar_or_matrix_scale
from text_generation_server.utils.import_... | text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8a8_int.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8a8_int.py",
"repo_id": "text-generation-inference",
"token_count": 3986
} | 317 |
import torch
from torch import nn
from accelerate import init_empty_weights
from text_generation_server.utils.import_utils import (
SYSTEM,
)
# Monkey patching
@classmethod
def load_layer_norm(cls, prefix, weights, eps):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.... | text-generation-inference/server/text_generation_server/layers/layernorm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/layernorm.py",
"repo_id": "text-generation-inference",
"token_count": 3189
} | 318 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 6420
} | 319 |
from dataclasses import dataclass
import torch
from PIL import Image
from io import BytesIO
from opentelemetry import trace
from typing import Iterable, Optional, Tuple, List, Type, Dict
from transformers import PreTrainedTokenizerBase
from transformers.image_processing_utils import select_best_resolution
from text_g... | text-generation-inference/server/text_generation_server/models/vlm_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/vlm_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 21444
} | 320 |
import os
from typing import Union
from loguru import logger
import torch
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
def download_and_unload_peft(model_id, revision, trust_remote_code):
torch_dtype = torch.float16
logger.info("Trying to load a... | text-generation-inference/server/text_generation_server/utils/peft.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/peft.py",
"repo_id": "text-generation-inference",
"token_count": 981
} | 321 |
# EditorConfig helps developers define and maintain consistent
# coding styles between different editors or IDEs
# http://editorconfig.org
root = true
[*]
indent_style = space
indent_size = 2
end_of_line = lf
charset = utf-8
trim_trailing_whitespace = true
insert_final_newline = true
[*.md]
trim_trailing_whitespace =... | tokenizers/bindings/node/.editorconfig/0 | {
"file_path": "tokenizers/bindings/node/.editorconfig",
"repo_id": "tokenizers",
"token_count": 108
} | 322 |
/* tslint:disable */
/* eslint-disable */
/* prettier-ignore */
/* auto-generated by NAPI-RS */
const { existsSync, readFileSync } = require('fs')
const { join } = require('path')
const { platform, arch } = process
let nativeBinding = null
let localFileExisted = false
let loadError = null
function isMusl() {
// ... | tokenizers/bindings/node/index.js/0 | {
"file_path": "tokenizers/bindings/node/index.js",
"repo_id": "tokenizers",
"token_count": 5374
} | 323 |
{
"name": "tokenizers-linux-x64-musl",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"x64"
],
"main": "tokenizers.linux-x64-musl.node",
"files": [
"tokenizers.linux-x64-musl.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",... | tokenizers/bindings/node/npm/linux-x64-musl/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-x64-musl/package.json",
"repo_id": "tokenizers",
"token_count": 291
} | 324 |
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::processors::PostProcessorWrapper;
use tk::Encoding;
#[derive(Clone, Serialize, Deserialize)]
#[napi]
pub struct Processor {
#[se... | tokenizers/bindings/node/src/processors.rs/0 | {
"file_path": "tokenizers/bindings/node/src/processors.rs",
"repo_id": "tokenizers",
"token_count": 1336
} | 325 |
<p align="center">
<br>
<img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/>
<br>
<p>
<p align="center">
<a href="https://badge.fury.io/py/tokenizers">
<img alt="Build" src="https://badge.fury.io/py/tokenizers.svg">
</a>
<a href="https://github.c... | tokenizers/bindings/python/README.md/0 | {
"file_path": "tokenizers/bindings/python/README.md",
"repo_id": "tokenizers",
"token_count": 1621
} | 326 |
from typing import Dict, Iterator, List, Optional, Tuple, Union
from tokenizers import AddedToken, Tokenizer, decoders, pre_tokenizers, processors, trainers
from tokenizers.models import BPE
from tokenizers.normalizers import Lowercase, Sequence, unicode_normalizer_from_str
from .base_tokenizer import BaseTokenizer
... | tokenizers/bindings/python/py_src/tokenizers/implementations/byte_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/byte_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 1970
} | 327 |
# Generated content DO NOT EDIT
class Trainer:
"""
Base class for all trainers
This class is not supposed to be instantiated directly. Instead, any implementation of a
Trainer will return an instance of this class when instantiated.
"""
class BpeTrainer(Trainer):
"""
Trainer capable of tra... | tokenizers/bindings/python/py_src/tokenizers/trainers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/trainers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 2178
} | 328 |
use serde::Serialize;
use std::collections::{hash_map::DefaultHasher, HashMap};
use std::hash::{Hash, Hasher};
use numpy::{npyffi, PyArray1, PyArrayMethods};
use pyo3::class::basic::CompareOp;
use pyo3::exceptions;
use pyo3::intern;
use pyo3::prelude::*;
use pyo3::types::*;
use tk::models::bpe::BPE;
use tk::tokenizer:... | tokenizers/bindings/python/src/tokenizer.rs/0 | {
"file_path": "tokenizers/bindings/python/src/tokenizer.rs",
"repo_id": "tokenizers",
"token_count": 27922
} | 329 |
import json
import pickle
import pytest
from tokenizers.pre_tokenizers import (
BertPreTokenizer,
ByteLevel,
CharDelimiterSplit,
Digits,
FixedLength,
Metaspace,
PreTokenizer,
Punctuation,
Sequence,
Split,
UnicodeScripts,
Whitespace,
WhitespaceSplit,
)
class TestBy... | tokenizers/bindings/python/tests/bindings/test_pre_tokenizers.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_pre_tokenizers.py",
"repo_id": "tokenizers",
"token_count": 5762
} | 330 |
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for those with `?=`
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
BUILDDIR ?= build
SOURCEDIR = source
# Put it first so that "make" without argument is like "make html_all".
h... | tokenizers/docs/Makefile/0 | {
"file_path": "tokenizers/docs/Makefile",
"repo_id": "tokenizers",
"token_count": 393
} | 331 |
<!-- DISABLE-FRONTMATTER-SECTIONS -->
# Tokenizers
Fast State-of-the-art tokenizers, optimized for both research and
production
[🤗 Tokenizers](https://github.com/huggingface/tokenizers) provides an
implementation of today's most used tokenizers, with a focus on
performance and versatility. These tokenizers are also... | tokenizers/docs/source-doc-builder/index.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/index.mdx",
"repo_id": "tokenizers",
"token_count": 250
} | 332 |
Input sequences
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
These types represent all the different kinds of sequence that can be used as input of a Tokenizer.
Globally, any sequence can be either a string or a list of strings, according to the operating
mode of... | tokenizers/docs/source/api/python.inc/0 | {
"file_path": "tokenizers/docs/source/api/python.inc",
"repo_id": "tokenizers",
"token_count": 562
} | 333 |
pub fn set_panic_hook() {
// When the `console_error_panic_hook` feature is enabled, we can call the
// `set_panic_hook` function at least once during initialization, and then
// we will get better error messages if our code ever panics.
//
// For more details see
// https://github.com/rustwasm/... | tokenizers/tokenizers/examples/unstable_wasm/src/utils.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/src/utils.rs",
"repo_id": "tokenizers",
"token_count": 150
} | 334 |
use crate::tokenizer::{Decoder, Result};
use monostate::MustBe;
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize, Default)]
/// ByteFallback is a simple trick which converts tokens looking like `<0x61>`
/// to pure bytes, and attempts to make them into a string. If the tokens
/// can... | tokenizers/tokenizers/src/decoders/byte_fallback.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/byte_fallback.rs",
"repo_id": "tokenizers",
"token_count": 1851
} | 335 |
use super::{
lattice::Lattice,
trainer::UnigramTrainer,
trie::{Trie, TrieBuilder},
};
use crate::tokenizer::{Model, Result, Token};
use crate::utils::cache::{Cache, MAX_LENGTH};
use std::collections::HashMap;
use ahash::AHashMap;
use std::convert::TryInto;
use std::fs::read_to_string;
use std::path::{Path,... | tokenizers/tokenizers/src/models/unigram/model.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/model.rs",
"repo_id": "tokenizers",
"token_count": 11856
} | 336 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
use unicode_normalization_alignments::char::is_combining_mark;
#[derive(Copy, Clone, Debug, Deserialize, Serialize)]
#[serde(tag = "type")]
#[non_exhaustive]
pub struct Strip {
... | tokenizers/tokenizers/src/normalizers/strip.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/strip.rs",
"repo_id": "tokenizers",
"token_count": 2512
} | 337 |
use std::sync::LazyLock;
use regex::Regex;
use crate::tokenizer::{
pattern::Invert, PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior,
};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Whitespace;
impl Default ... | tokenizers/tokenizers/src/pre_tokenizers/whitespace.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/whitespace.rs",
"repo_id": "tokenizers",
"token_count": 1656
} | 338 |
//! This comes from the Rust libcore and is duplicated here because it is not exported
//! (cf <https://github.com/rust-lang/rust/blob/25091ed9b7739e12466fb2490baa1e8a2815121c/src/libcore/iter/adapters/mod.rs#L2664>)
//! We are now using the version from <https://stackoverflow.com/questions/44544323/how-to-unzip-a-sequ... | tokenizers/tokenizers/src/utils/iter.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/iter.rs",
"repo_id": "tokenizers",
"token_count": 1339
} | 339 |
import re
README_TEMPLATE = """
<p align="center">
<br/>
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/transformersjs-dark.svg" width="500" style="max-width: 100%;">
<source media="(prefers-color-scheme:... | transformers.js/docs/scripts/build_readme.py/0 | {
"file_path": "transformers.js/docs/scripts/build_readme.py",
"repo_id": "transformers.js",
"token_count": 1760
} | 340 |
# Transformers.js
<include>
{
"path": "../snippets/0_introduction.snippet"
}
</include>
## Quick tour
<include>
{
"path": "../snippets/1_quick-tour.snippet"
}
</include>
## Contents
The documentation is organized into 4 sections:
1. **GET STARTED** provides a quick tour of the library and installation ins... | transformers.js/docs/source/index.md/0 | {
"file_path": "transformers.js/docs/source/index.md",
"repo_id": "transformers.js",
"token_count": 495
} | 341 |
import { useState, useRef, useEffect, useCallback } from 'react'
import './App.css'
const PLACEHOLDER_TEXTS = [
"A panda is a large black-and-white bear native to China.",
"The typical life span of a panda is 20 years in the wild.",
"A panda's diet consists almost entirely of bamboo.",
"Ailuropoda melanoleuca ... | transformers.js/examples/adaptive-retrieval/src/App.jsx/0 | {
"file_path": "transformers.js/examples/adaptive-retrieval/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 2829
} | 342 |
@tailwind base;
@tailwind components;
@tailwind utilities;
:root {
font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
line-height: 1.5;
font-weight: 400;
color-scheme: light dark;
color: rgba(255, 255, 255, 0.87);
background-color: #242424;
font-synthesis: none;
text-rendering: opti... | transformers.js/examples/code-completion/src/index.css/0 | {
"file_path": "transformers.js/examples/code-completion/src/index.css",
"repo_id": "transformers.js",
"token_count": 514
} | 343 |
/** @type {import('tailwindcss').Config} */
export default {
content: [
"./index.html",
"./src/**/*.{js,ts,jsx,tsx}",
],
theme: {
extend: {},
},
plugins: [],
}
| transformers.js/examples/cross-encoder/tailwind.config.js/0 | {
"file_path": "transformers.js/examples/cross-encoder/tailwind.config.js",
"repo_id": "transformers.js",
"token_count": 82
} | 344 |
{
"manifest_version": 3,
"name": "extension",
"description": "Transformers.js | Sample browser extension",
"version": "0.0.1",
"permissions": [
"activeTab",
"scripting",
"contextMenus",
"storage",
"unlimitedStorage"
],
"background": {
"service_worker": "background.js",
"type": ... | transformers.js/examples/extension/public/manifest.json/0 | {
"file_path": "transformers.js/examples/extension/public/manifest.json",
"repo_id": "transformers.js",
"token_count": 421
} | 345 |
@tailwind base;
@tailwind components;
@tailwind utilities;
@layer utilities {
.scrollbar-thin::-webkit-scrollbar {
@apply w-2;
}
.scrollbar-thin::-webkit-scrollbar-track {
@apply rounded-full bg-gray-100 dark:bg-gray-700;
}
.scrollbar-thin::-webkit-scrollbar-thumb {
@apply rounded-full bg-gray-... | transformers.js/examples/florence2-webgpu/src/index.css/0 | {
"file_path": "transformers.js/examples/florence2-webgpu/src/index.css",
"repo_id": "transformers.js",
"token_count": 173
} | 346 |
import { pipeline } from "@huggingface/transformers";
// Use the Singleton pattern to enable lazy construction of the pipeline.
class PipelineSingleton {
static task = 'text-classification';
static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
static instance = null;
static async g... | transformers.js/examples/next-client/src/app/worker.js/0 | {
"file_path": "transformers.js/examples/next-client/src/app/worker.js",
"repo_id": "transformers.js",
"token_count": 369
} | 347 |
// The full list of languages in FLORES-200 is available here:
// https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200
const LANGUAGES = {
"Acehnese (Arabic script)": "ace_Arab",
"Acehnese (Latin script)": "ace_Latn",
"Afrikaans": "afr_Latn",
"Akan": "aka_Latn",
"... | transformers.js/examples/react-translator/src/components/LanguageSelector.jsx/0 | {
"file_path": "transformers.js/examples/react-translator/src/components/LanguageSelector.jsx",
"repo_id": "transformers.js",
"token_count": 3102
} | 348 |
// Reference the elements we will use
const statusLabel = document.getElementById('status');
const fileUpload = document.getElementById('upload');
const imageContainer = document.getElementById('container');
const example = document.getElementById('example');
const maskCanvas = document.getElementById('mask-output');
... | transformers.js/examples/segment-anything-client/index.js/0 | {
"file_path": "transformers.js/examples/segment-anything-client/index.js",
"repo_id": "transformers.js",
"token_count": 3452
} | 349 |
/** @type {import('next').NextConfig} */
const nextConfig = {
// (Optional) Export as a static site
// See https://nextjs.org/docs/pages/building-your-application/deploying/static-exports#configuration
output: 'export', // Feel free to modify/remove this option
// Override the default webpack configura... | transformers.js/examples/semantic-image-search-client/next.config.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/next.config.js",
"repo_id": "transformers.js",
"token_count": 269
} | 350 |
SUPABASE_URL=your-project-url
SUPABASE_ANON_KEY=your-anon-key
SUPABASE_SECRET_KEY=your-secret-key
| transformers.js/examples/semantic-image-search/.env.local.example/0 | {
"file_path": "transformers.js/examples/semantic-image-search/.env.local.example",
"repo_id": "transformers.js",
"token_count": 45
} | 351 |
export default function Progress({ text, percentage }) {
percentage ??= 0;
return (
<div className="relative text-black bg-white rounded-lg text-left overflow-hidden">
<div className='px-2 w-[1%] h-full bg-blue-500 whitespace-nowrap' style={{ width: `${percentage}%` }}>
{text} ({`${percentage.toF... | transformers.js/examples/text-to-speech-client/src/components/Progress.jsx/0 | {
"file_path": "transformers.js/examples/text-to-speech-client/src/components/Progress.jsx",
"repo_id": "transformers.js",
"token_count": 144
} | 352 |
import { useCallback, useEffect, useRef, useState } from 'react'
import { Token } from './components/Token'
import './App.css'
// Define list of tokenizers and their corresponding human-readable names
const TOKENIZER_OPTIONS = Object.freeze({
'Xenova/gpt-4': 'gpt-4 / gpt-3.5-turbo / text-embedding-ada-002',
'Xenov... | transformers.js/examples/tokenizer-playground/src/App.jsx/0 | {
"file_path": "transformers.js/examples/tokenizer-playground/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 3075
} | 353 |
* {
box-sizing: border-box;
padding: 0;
margin: 0;
font-family: sans-serif;
}
html,
body {
height: 100%;
}
body {
padding: 16px 32px;
}
body,
#container {
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
}
#controls {
display: flex;
padding: 1rem;
gap: 1... | transformers.js/examples/video-object-detection/style.css/0 | {
"file_path": "transformers.js/examples/video-object-detection/style.css",
"repo_id": "transformers.js",
"token_count": 445
} | 354 |
* {
box-sizing: border-box;
padding: 0;
margin: 0;
font-family: sans-serif;
}
html,
body {
height: 100%;
}
body {
padding: 16px 32px;
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
}
h1 {
text-align: center;
}
#status {
min-height: 16px;
margin: 8px 0;... | transformers.js/examples/webgpu-embedding-benchmark/style.css/0 | {
"file_path": "transformers.js/examples/webgpu-embedding-benchmark/style.css",
"repo_id": "transformers.js",
"token_count": 518
} | 355 |
import { useMemo } from "react";
const Chunk = ({ chunk, currentTime, onClick, ...props }) => {
const { text, timestamp } = chunk;
const [start, end] = timestamp;
const bolded = start <= currentTime && currentTime < end;
return (
<span {...props}>
{text.startsWith(' ') ? " " : ""}... | transformers.js/examples/whisper-word-timestamps/src/components/Transcript.jsx/0 | {
"file_path": "transformers.js/examples/whisper-word-timestamps/src/components/Transcript.jsx",
"repo_id": "transformers.js",
"token_count": 1253
} | 356 |
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