text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
|---|---|---|
# DenseNet
**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each... | pytorch-image-models/hfdocs/source/models/densenet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/densenet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4190
} |
# Instagram ResNeXt WSL
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transfo... | pytorch-image-models/hfdocs/source/models/ig-resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ig-resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3233
} |
# Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical... | pytorch-image-models/hfdocs/source/models/res2net.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/res2net.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3952
} |
# (Tensorflow) EfficientNet CondConv
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method unifor... | pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3326
} |
""" Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | pytorch-image-models/timm/data/dataset.py/0 | {
"file_path": "pytorch-image-models/timm/data/dataset.py",
"repo_id": "pytorch-image-models",
"token_count": 2991
} |
""" A dataset reader that reads tarfile based datasets
This reader can extract image samples from:
* a single tar of image files
* a folder of multiple tarfiles containing imagefiles
* a tar of tars containing image files
Labels are based on the combined folder and/or tar name structure.
Hacked together by / Copyrig... | pytorch-image-models/timm/data/readers/reader_image_in_tar.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_in_tar.py",
"repo_id": "pytorch-image-models",
"token_count": 4050
} |
"""
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
from functools import partial
from typing import Optional, Type
import torch
import torch.nn as nn
import torch.nn.functional a... | pytorch-image-models/timm/layers/blur_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/blur_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 1352
} |
""" 'Fast' Normalization Functions
For GroupNorm and LayerNorm these functions bypass typical AMP upcast to float32.
Additionally, for LayerNorm, the APEX fused LN is used if available (which also does not upcast)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import List, Optional
import torch
f... | pytorch-image-models/timm/layers/fast_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/fast_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 2881
} |
""" PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(num_chan, num_groups):
split = [nu... | pytorch-image-models/timm/layers/mixed_conv2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/mixed_conv2d.py",
"repo_id": "pytorch-image-models",
"token_count": 834
} |
""" Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
import torch
impor... | pytorch-image-models/timm/layers/split_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/split_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 1533
} |
""" EfficientNet, MobileNetV3, etc Builder
Assembles EfficieNet and related network feature blocks from string definitions.
Handles stride, dilation calculations, and selects feature extraction points.
Hacked together by / Copyright 2019, Ross Wightman
"""
from typing import Callable, Optional
import logging
import ... | pytorch-image-models/timm/models/_efficientnet_builder.py/0 | {
"file_path": "pytorch-image-models/timm/models/_efficientnet_builder.py",
"repo_id": "pytorch-image-models",
"token_count": 10990
} |
""" Bring-Your-Own-Attention Network
A flexible network w/ dataclass based config for stacking NN blocks including
self-attention (or similar) layers.
Currently used to implement experimental variants of:
* Bottleneck Transformers
* Lambda ResNets
* HaloNets
Consider all of the models definitions here as exper... | pytorch-image-models/timm/models/byoanet.py/0 | {
"file_path": "pytorch-image-models/timm/models/byoanet.py",
"repo_id": "pytorch-image-models",
"token_count": 9703
} |
""" EfficientFormer-V2
@article{
li2022rethinking,
title={Rethinking Vision Transformers for MobileNet Size and Speed},
author={Li, Yanyu and Hu, Ju and Wen, Yang and Evangelidis, Georgios and Salahi, Kamyar and Wang, Yanzhi and Tulyakov, Sergey and Ren, Jian},
journal={arXiv preprint arXiv:2212.08059}... | pytorch-image-models/timm/models/efficientformer_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 12757
} |
import math
from copy import deepcopy
from functools import partial
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit import Final
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import P... | pytorch-image-models/timm/models/hieradet_sam2.py/0 | {
"file_path": "pytorch-image-models/timm/models/hieradet_sam2.py",
"repo_id": "pytorch-image-models",
"token_count": 11828
} |
""" NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_co... | pytorch-image-models/timm/models/nasnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/nasnet.py",
"repo_id": "pytorch-image-models",
"token_count": 13276
} |
""" 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": 5803
} |
""" Relative Position Vision Transformer (ViT) in PyTorch
NOTE: these models are experimental / WIP, expect changes
Hacked together by / Copyright 2022, Ross Wightman
"""
import logging
import math
from functools import partial
from typing import List, Optional, Tuple, Type, Union
try:
from typing import Literal... | pytorch-image-models/timm/models/vision_transformer_relpos.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer_relpos.py",
"repo_id": "pytorch-image-models",
"token_count": 13346
} |
"""
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... | pytorch-image-models/timm/optim/adamp.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adamp.py",
"repo_id": "pytorch-image-models",
"token_count": 2028
} |
"""RAdam Optimizer.
Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
NOTE: This impl has been deprecated in favour of torch.optim.RAdam and remains as a reference
"""
import math
import torch
from to... | pytorch-image-models/timm/optim/radam.py/0 | {
"file_path": "pytorch-image-models/timm/optim/radam.py",
"repo_id": "pytorch-image-models",
"token_count": 2159
} |
import fnmatch
import re
from collections import OrderedDict
from typing import Union, Optional, List
import torch
class AttentionExtract(torch.nn.Module):
# defaults should cover a significant number of timm models with attention maps.
default_node_names = ['*attn.softmax']
default_module_names = ['*att... | pytorch-image-models/timm/utils/attention_extract.py/0 | {
"file_path": "pytorch-image-models/timm/utils/attention_extract.py",
"repo_id": "pytorch-image-models",
"token_count": 1467
} |
#!/usr/bin/env python3
""" ImageNet Training Script
This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
training results with some of the latest networks and training techniques. It favours canonical PyTorch
and standard Python style over trying to be able to 'do it al... | pytorch-image-models/train.py/0 | {
"file_path": "pytorch-image-models/train.py",
"repo_id": "pytorch-image-models",
"token_count": 26015
} |
- title: Get started
sections:
- local: index
title: 🤗 Agents
- local: guided_tour
title: Guided tour
- title: Tutorials
sections:
- local: tutorials/building_good_agents
title: ✨ Building good agents
- local: tutorials/inspect_runs
title: 📊 Inspect your agent runs using telemetry
- loca... | smolagents/docs/source/en/_toctree.yml/0 | {
"file_path": "smolagents/docs/source/en/_toctree.yml",
"repo_id": "smolagents",
"token_count": 395
} |
- 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/tools
title: 🛠️ 工具 - 深度指南
- local: tutorials/secure_code_execution
title: 🛡️ 使用 E2B 保护你的代... | smolagents/docs/source/zh/_toctree.yml/0 | {
"file_path": "smolagents/docs/source/zh/_toctree.yml",
"repo_id": "smolagents",
"token_count": 390
} |
<jupyter_start><jupyter_code>!pip install -e .. datasets sympy numpy matplotlib seaborn -q # Install dev version of smolagents + some packages<jupyter_output>[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.2.1[0m[39;49m -> [0m[32;49m24.3.1[0m
[1m[[0m[34;49mn... | smolagents/examples/benchmark.ipynb/0 | {
"file_path": "smolagents/examples/benchmark.ipynb",
"repo_id": "smolagents",
"token_count": 8351
} |
import base64
import json
import mimetypes
import os
import uuid
from io import BytesIO
from typing import Optional
import requests
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
from PIL import Image
from transformers import AutoProcessor
from smolagents import Tool, tool
load_dotenv(ov... | smolagents/examples/open_deep_research/scripts/visual_qa.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/visual_qa.py",
"repo_id": "smolagents",
"token_count": 2519
} |
#!/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/models.py/0 | {
"file_path": "smolagents/src/smolagents/models.py",
"repo_id": "smolagents",
"token_count": 14533
} |
# 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_function_type_hints_utils.py/0 | {
"file_path": "smolagents/tests/test_function_type_hints_utils.py",
"repo_id": "smolagents",
"token_count": 890
} |
[workspace]
members = [
"benchmark",
"backends/v2",
"backends/v3",
"backends/grpc-metadata",
"backends/trtllm",
"launcher",
"router"
]
default-members = [
"benchmark",
"backends/v2",
"backends/v3",
"backends/grpc-metadata",
# "backends/trtllm",
"launcher",
"router... | text-generation-inference/Cargo.toml/0 | {
"file_path": "text-generation-inference/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 500
} |
/// Single shard Client
use crate::v2::pb;
use crate::{ClientError, Result};
use crate::WARMUP_IMAGE_BASE64;
use grpc_metadata::InjectTelemetryContext;
use pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
use pb::generate::v2::*;
use std::cmp::min;
use std::time::Duration;
use tonic::tran... | text-generation-inference/backends/client/src/v2/client.rs/0 | {
"file_path": "text-generation-inference/backends/client/src/v2/client.rs",
"repo_id": "text-generation-inference",
"token_count": 4125
} |
#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
} |
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": 14884
} |
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": 479
} |
# TensorRT-LLM backend
The NVIDIA TensorRT-LLM (TRTLLM) backend is a high-performance backend for LLMs
that uses NVIDIA's TensorRT library for inference acceleration.
It makes use of specific optimizations for NVIDIA GPUs, such as custom kernels.
To use the TRTLLM backend **you need to compile** `engines` for the mod... | text-generation-inference/docs/source/backends/trtllm.md/0 | {
"file_path": "text-generation-inference/docs/source/backends/trtllm.md",
"repo_id": "text-generation-inference",
"token_count": 2127
} |
# HTTP API Reference
#### Table of Contents
- [Text Generation Inference custom API](#text-generation-inference-custom-api)
- [OpenAI Messages API](#openai-messages-api)
- [Making a Request](#making-a-request)
- [Streaming](#streaming)
- [Synchronous](#synchronous)
- [Hugging Face Inference Endpoints](#huggin... | text-generation-inference/docs/source/reference/api_reference.md/0 | {
"file_path": "text-generation-inference/docs/source/reference/api_reference.md",
"repo_id": "text-generation-inference",
"token_count": 1840
} |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "As of your last question, the weather in Brooklyn, New York, is typically hot and humid throughout the year. The suburbs around New York City are jealously sheltered, and at least in ... | text-generation-inference/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json",
"repo_id": "text-generation-inference",
"token_count": 364
} |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 23090,
"logprob": -1.828125,
"special": false,
"text": " Hello"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon_load.json",
"repo_id": "text-generation-inference",
"token_count": 3967
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "stop_sequence",
"generated_tokens": 5,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5229,
"logprob": -2.5839844,
"special": false,
"text": " failed"
},
{
"id": 29901,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 487
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5229,
"logprob": -1.2607422,
"special": false,
"text": " failed"
},
{
"id": 29901,
"logpr... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin/test_flash_llama_marlin_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin/test_flash_llama_marlin_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 855
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 60,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 2284,
"logprob": -0.31323242,
"special": false,
"text": "():"
},
{
"id": 303,
"logprob": ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2_default_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder2/test_flash_starcoder2_default_params.json",
"repo_id": "text-generation-inference",
"token_count": 4512
} |
{
"choices": [
{
"finish_reason": "eos_token",
"index": 0,
"logprobs": null,
"message": {
"content": null,
"name": null,
"role": "assistant",
"tool_calls": [
{
"function": {
"arguments": {
"format": "celsiu... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json",
"repo_id": "text-generation-inference",
"token_count": 495
} |
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_wna16_int_24_handle(launcher):
with launcher(
"danieldk/Llama-3.1-8B-w4a16-int-24",
num_shard=2,
quantize="compressed-tensors",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def compressed_... | text-generation-inference/integration-tests/models/test_compressed_tensors_wna16_int_24.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_compressed_tensors_wna16_int_24.py",
"repo_id": "text-generation-inference",
"token_count": 1080
} |
import pytest
@pytest.fixture(scope="module")
def flash_llama_marlin_handle(launcher):
with launcher(
"neuralmagic/llama-2-7b-chat-marlin", num_shard=2, quantize="marlin"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_marlin(flash_llama_marlin_handle):
aw... | text-generation-inference/integration-tests/models/test_flash_llama_marlin.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_marlin.py",
"repo_id": "text-generation-inference",
"token_count": 748
} |
import pytest
@pytest.fixture(scope="module")
def flash_qwen2_vl_handle(launcher):
with launcher("Qwen/Qwen2-VL-7B-Instruct") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_qwen2(flash_qwen2_vl_handle):
await flash_qwen2_vl_handle.health(300)
return flash_qwen2_vl_handle... | text-generation-inference/integration-tests/models/test_flash_qwen2_vl.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_qwen2_vl.py",
"repo_id": "text-generation-inference",
"token_count": 2158
} |
import pytest
@pytest.fixture(scope="module")
def mt0_base_handle(launcher):
with launcher("bigscience/mt0-base") as handle:
yield handle
@pytest.fixture(scope="module")
async def mt0_base(mt0_base_handle):
await mt0_base_handle.health(300)
return mt0_base_handle.client
@pytest.mark.release
@p... | text-generation-inference/integration-tests/models/test_mt0_base.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_mt0_base.py",
"repo_id": "text-generation-inference",
"token_count": 737
} |
syntax = "proto3";
package generate.v2;
service TextGenerationService {
/// Model Info
rpc Info (InfoRequest) returns (InfoResponse) {}
/// Service discovery
rpc ServiceDiscovery (ServiceDiscoveryRequest) returns (ServiceDiscoveryResponse) {}
/// Empties batch cache
rpc ClearCache (ClearCacheR... | text-generation-inference/proto/generate.proto/0 | {
"file_path": "text-generation-inference/proto/generate.proto",
"repo_id": "text-generation-inference",
"token_count": 2074
} |
use crate::infer::Infer;
use crate::server::{generate_internal, ComputeType};
use crate::{ChatRequest, ErrorResponse, GenerateParameters, GenerateRequest};
use axum::extract::Extension;
use axum::http::{HeaderMap, StatusCode};
use axum::response::{IntoResponse, Response};
use axum::Json;
use serde::{Deserialize, Serial... | text-generation-inference/router/src/vertex.rs/0 | {
"file_path": "text-generation-inference/router/src/vertex.rs",
"repo_id": "text-generation-inference",
"token_count": 2789
} |
#include <ATen/Dispatch.h>
#include <THC/THCAtomics.cuh>
#include <ATen/ATen.h>
#include <torch/torch.h>
#include <vector>
#include <optional>
/**
* Friendly reminder of how multithreading works in CUDA: https://developer.nvidia.com/blog/even-easier-introduction-cuda
* Check example at https://github.com/thomasw21/Li... | text-generation-inference/server/custom_kernels/custom_kernels/fused_attention_cuda.cu/0 | {
"file_path": "text-generation-inference/server/custom_kernels/custom_kernels/fused_attention_cuda.cu",
"repo_id": "text-generation-inference",
"token_count": 5265
} |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _util_cuh
#define _util_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#if defined(USE_ROCM)
#define cudaUnspecified hipErrorUnknown
#else
#define cudaUnspecified cudaErrorApiFailureBase
#endif
... | text-generation-inference/server/exllama_kernels/exllama_kernels/util.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/util.cuh",
"repo_id": "text-generation-inference",
"token_count": 283
} |
#ifndef _qdq_6_cuh
#define _qdq_6_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_6BIT == 1
// Not implemented
#else
__forceinline__ __device__ void shuffle_6bit_16
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_6bit_16
(
const uint32_t q_0,
const uint32_... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_6.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_6.cuh",
"repo_id": "text-generation-inference",
"token_count": 571
} |
import pytest
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch, CausalLM
@pytest.fixture(scope="session")
def default_santacoder():
return CausalLM.fallback(model_id="bigcode/santacoder")
@pytest.fixture
def default_pb_request(default_pb_param... | text-generation-inference/server/tests/models/test_santacoder.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_santacoder.py",
"repo_id": "text-generation-inference",
"token_count": 1480
} |
import torch
import grpc
from google.rpc import status_pb2, code_pb2
from grpc_status import rpc_status
from grpc_interceptor.server import AsyncServerInterceptor
from loguru import logger
from typing import Callable, Any
class ExceptionInterceptor(AsyncServerInterceptor):
def __init__(self, shutdown_callback):
... | text-generation-inference/server/text_generation_server/interceptor.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/interceptor.py",
"repo_id": "text-generation-inference",
"token_count": 545
} |
from typing import Any, Dict, List, Union
from compressed_tensors import QuantizationConfig, QuantizationStatus
from compressed_tensors.config import CompressionFormat
from compressed_tensors.quantization import (
QuantizationScheme,
QuantizationType,
find_name_or_class_matches,
)
from loguru import logger... | text-generation-inference/server/text_generation_server/layers/compressed_tensors/loader.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/compressed_tensors/loader.py",
"repo_id": "text-generation-inference",
"token_count": 3149
} |
import torch
# copied from https://github.com/openppl-public/ppq/blob/master/ppq/quantization/measure/norm.py
def torch_snr_error(
y_pred: torch.Tensor, y_real: torch.Tensor, reduction: str = "mean"
) -> torch.Tensor:
"""
Compute SNR between y_pred(tensor) and y_real(tensor)
SNR can be calcualted as ... | text-generation-inference/server/text_generation_server/layers/gptq/utils.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/gptq/utils.py",
"repo_id": "text-generation-inference",
"token_count": 742
} |
import os
import math
import torch
from torch import nn
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM == "cuda":
import rotary_emb
elif SYSTEM == "rocm":
import vllm._custom_ops as ops
elif SYSTEM == "ipex":
import intel_extension_for_pytorch as ipex
def _create_inv_freq(dim, bas... | text-generation-inference/server/text_generation_server/layers/rotary.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/rotary.py",
"repo_id": "text-generation-inference",
"token_count": 12738
} |
# 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": 6378
} |
# 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
} |
import re
import torch
import torch.distributed
from transformers import (
PreTrainedTokenizerBase,
)
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import (
NextTokenChooser,
StoppingCriteria,
)
from t... | text-generation-inference/server/text_generation_server/models/galactica.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/galactica.py",
"repo_id": "text-generation-inference",
"token_count": 2499
} |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/utils/adapter.py
# License: Apache License Version 2.0, January 2004
import warnings
import re
from dataclasses import dataclass
from functools import lru_cache
from typing import TYPE_CHECKING, Set, Tuple, Optional, List
from safet... | text-generation-inference/server/text_generation_server/utils/adapter.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/adapter.py",
"repo_id": "text-generation-inference",
"token_count": 4641
} |
import re
from typing import List, Optional, Tuple, Set, Union
import torch
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason, GrammarType
from text_generation_server.utils.logits_process import (
FrequencyPenaltyLogitsProcessor,
GrammarLogitProc... | text-generation-inference/server/text_generation_server/utils/tokens.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/tokens.py",
"repo_id": "text-generation-inference",
"token_count": 11317
} |
import {
bpeDecoder,
byteFallbackDecoder,
ctcDecoder,
fuseDecoder,
metaspaceDecoder,
replaceDecoder,
sequenceDecoder,
stripDecoder,
wordPieceDecoder,
} from '../../'
describe('wordPieceDecoder', () => {
it('accepts `undefined` as first parameter', () => {
expect(wordPieceDecoder(undefined)).toB... | tokenizers/bindings/node/lib/bindings/decoders.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/decoders.test.ts",
"repo_id": "tokenizers",
"token_count": 1393
} |
use crate::models::Model;
use napi_derive::napi;
use std::sync::{Arc, RwLock};
use tokenizers as tk;
use tokenizers::models::TrainerWrapper;
#[napi]
pub struct Trainer {
trainer: Option<Arc<RwLock<TrainerWrapper>>>,
}
impl From<TrainerWrapper> for Trainer {
fn from(trainer: TrainerWrapper) -> Self {
Self {
... | tokenizers/bindings/node/src/trainers.rs/0 | {
"file_path": "tokenizers/bindings/node/src/trainers.rs",
"repo_id": "tokenizers",
"token_count": 641
} |
import argparse
import glob
from tokenizers import BertWordPieceTokenizer
parser = argparse.ArgumentParser()
parser.add_argument(
"--files",
default=None,
metavar="path",
type=str,
required=True,
help="The files to use as training; accept '**/*.txt' type of patterns \
... | tokenizers/bindings/python/examples/train_bert_wordpiece.py/0 | {
"file_path": "tokenizers/bindings/python/examples/train_bert_wordpiece.py",
"repo_id": "tokenizers",
"token_count": 472
} |
# Generated content DO NOT EDIT
class Model:
"""
Base class for all models
The model represents the actual tokenization algorithm. This is the part that
will contain and manage the learned vocabulary.
This class cannot be constructed directly. Please use one of the concrete models.
"""
def... | tokenizers/bindings/python/py_src/tokenizers/models/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/models/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 7626
} |
import tokenizers
from argparse import ArgumentParser
import sentencepiece as spm
from collections import Counter
import json
import os
import datetime
try:
from termcolor import colored
has_color = True
except Exception:
has_color = False
def main():
parser = ArgumentParser("SentencePiece parity ch... | tokenizers/bindings/python/scripts/spm_parity_check.py/0 | {
"file_path": "tokenizers/bindings/python/scripts/spm_parity_check.py",
"repo_id": "tokenizers",
"token_count": 4110
} |
use tokenizers as tk;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use super::{
DestroyPtr, PyNormalizedString, PyNormalizedStringRefMut, RefMutContainer, RefMutGuard,
};
use crate::encoding::PyEncoding;
use crate::error::ToPyResult;
use crate::token::PyToken;
use tk::{OffsetReferential, Offset... | tokenizers/bindings/python/src/utils/pretokenization.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/pretokenization.rs",
"repo_id": "tokenizers",
"token_count": 4958
} |
from tokenizers import Tokenizer
from ..utils import data_dir, doc_pipeline_bert_tokenizer, doc_wiki_tokenizer
disable_printing = True
original_print = print
def print(*args, **kwargs):
if not disable_printing:
original_print(*args, **kwargs)
class TestPipeline:
def test_pipeline(self, doc_wiki_to... | tokenizers/bindings/python/tests/documentation/test_pipeline.py/0 | {
"file_path": "tokenizers/bindings/python/tests/documentation/test_pipeline.py",
"repo_id": "tokenizers",
"token_count": 3351
} |
# Encode Inputs
<tokenizerslangcontent>
<python>
These types represent all the different kinds of input that a [`~tokenizers.Tokenizer`] accepts
when using [`~tokenizers.Tokenizer.encode_batch`].
## TextEncodeInput[[[[tokenizers.TextEncodeInput]]]]
<code>tokenizers.TextEncodeInput</code>
Represents a textual input ... | tokenizers/docs/source-doc-builder/api/encode-inputs.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/encode-inputs.mdx",
"repo_id": "tokenizers",
"token_count": 716
} |
from collections import defaultdict, abc
from typing import cast
from docutils import nodes
from docutils.parsers.rst import Directive
import sphinx
from sphinx.locale import _
from sphinx.util.docutils import SphinxDirective
from sphinx.errors import ExtensionError
from conf import languages as LANGUAGES
logger = ... | tokenizers/docs/source/_ext/entities.py/0 | {
"file_path": "tokenizers/docs/source/_ext/entities.py",
"repo_id": "tokenizers",
"token_count": 4032
} |
.. entities:: python
:global:
class
class
classmethod
class method
Tokenizer
:class:`~tokenizers.Tokenizer`
Tokenizer.train
:meth:`~tokenizers.Tokenizer.train`
Tokenizer.save
:meth:`~tokenizers.Tokenizer.save`
Tokenizer.from_file
:meth:`~toke... | tokenizers/docs/source/entities.inc/0 | {
"file_path": "tokenizers/docs/source/entities.inc",
"repo_id": "tokenizers",
"token_count": 2078
} |
#[macro_use]
extern crate criterion;
mod common;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
use criterion::Criterion;
use tokenizers::models::bpe::{BpeTrainerBuilder, BPE};
use tokenizers::models::TrainerWrapper;
use tokenizers::pre_tokenizers::byte_level::ByteLevel;
use tokenizers::p... | tokenizers/tokenizers/benches/bpe_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/bpe_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 1631
} |
use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize, Default)]
/// Strip is a simple trick which converts tokens looking like `<0x61>`
/// to pure bytes, and attempts to make them into a string. If the tokens
/// cannot be decoded you will get � ... | tokenizers/tokenizers/src/decoders/strip.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/strip.rs",
"repo_id": "tokenizers",
"token_count": 1217
} |
use super::{super::OrderedVocabIter, WordLevel, WordLevelBuilder};
use serde::{
de::{MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashSet;
impl Serialize for WordLevel {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Er... | tokenizers/tokenizers/src/models/wordlevel/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordlevel/serialization.rs",
"repo_id": "tokenizers",
"token_count": 2084
} |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Copy, Clone, Debug, PartialEq, Eq)]
#[non_exhaustive]
#[macro_rules_attribute(impl_serde_type!)]
pub struct CharDelimiterSplit {
pub deli... | tokenizers/tokenizers/src/pre_tokenizers/delimiter.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/delimiter.rs",
"repo_id": "tokenizers",
"token_count": 296
} |
use super::{
normalizer::Range, Model, NormalizedString, Normalizer, Offsets, PreTokenizedString, Token,
};
use aho_corasick::{AhoCorasick, AhoCorasickBuilder, MatchKind};
use regex::Regex;
use serde::{ser::SerializeSeq, Deserialize, Serialize, Serializer};
use std::collections::{HashMap, HashSet};
/// Represent a... | tokenizers/tokenizers/src/tokenizer/added_vocabulary.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/added_vocabulary.rs",
"repo_id": "tokenizers",
"token_count": 17733
} |
use crate::tokenizer::{Encoding, Result};
use serde::{Deserialize, Serialize};
use std::cmp;
use std::mem;
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize, Eq, Default)]
pub enum TruncationDirection {
Left,
#[default]
Right,
}
impl std::convert::AsRef<str> for TruncationDirection {
fn a... | tokenizers/tokenizers/src/utils/truncation.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/truncation.rs",
"repo_id": "tokenizers",
"token_count": 5471
} |
By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.3.3/dist/), which should work out-of-the-box. You can customize this as follows:
### Settings
```javascript
impo... | transformers.js/docs/snippets/4_custom-usage.snippet/0 | {
"file_path": "transformers.js/docs/snippets/4_custom-usage.snippet",
"repo_id": "transformers.js",
"token_count": 588
} |
# Server-side Inference in Node.js
Although Transformers.js was originally designed to be used in the browser, it's also able to run inference on the server. In this tutorial, we will design a simple Node.js API that uses Transformers.js for sentiment analysis.
We'll also show you how to use the library in both Comm... | transformers.js/docs/source/tutorials/node.md/0 | {
"file_path": "transformers.js/docs/source/tutorials/node.md",
"repo_id": "transformers.js",
"token_count": 2271
} |
module.exports = {
env: { browser: true, es2020: true, 'node': true },
extends: [
'eslint:recommended',
'plugin:react/recommended',
'plugin:react/jsx-runtime',
'plugin:react-hooks/recommended',
],
parserOptions: { ecmaVersion: 'latest', sourceType: 'module' },
settings: { react: { version: '18... | transformers.js/examples/code-completion/.eslintrc.cjs/0 | {
"file_path": "transformers.js/examples/code-completion/.eslintrc.cjs",
"repo_id": "transformers.js",
"token_count": 179
} |
@charset "UTF-8";
/*!
* Start Bootstrap - Business Frontpage v5.0.7 (https://startbootstrap.com/template/business-frontpage)
* Copyright 2013-2021 Start Bootstrap
* Licensed under MIT (https://github.com/StartBootstrap/startbootstrap-business-frontpage/blob/master/LICENSE)
*/
/*!
* Bootstrap v5.1.3 (https://getbootst... | transformers.js/examples/demo-site/src/theme.css/0 | {
"file_path": "transformers.js/examples/demo-site/src/theme.css",
"repo_id": "transformers.js",
"token_count": 109692
} |
/* Styles go here */
* {
padding: 0;
margin: 0;
box-sizing: border-box;
font-family: 'Roboto', sans-serif;
}
h1 {
font-size: 54px;
text-align: center;
font-weight: 500;
}
h2 {
font-size: 24px;
text-align: center;
font-weight: 400;
margin-bottom: 16px;
}
.container {
w... | transformers.js/examples/electron/src/index.css/0 | {
"file_path": "transformers.js/examples/electron/src/index.css",
"repo_id": "transformers.js",
"token_count": 300
} |
import path from 'path';
import { fileURLToPath } from 'url';
import HtmlWebpackPlugin from 'html-webpack-plugin';
import CopyPlugin from 'copy-webpack-plugin';
const __dirname = path.dirname(fileURLToPath(import.meta.url));
const config = {
mode: 'development',
devtool: 'inline-source-map',
entry: {
... | transformers.js/examples/extension/webpack.config.js/0 | {
"file_path": "transformers.js/examples/extension/webpack.config.js",
"repo_id": "transformers.js",
"token_count": 536
} |
/** @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/next-client/next.config.js/0 | {
"file_path": "transformers.js/examples/next-client/next.config.js",
"repo_id": "transformers.js",
"token_count": 270
} |
const http = require('http');
const querystring = require('querystring');
const url = require('url');
class MyClassificationPipeline {
static task = 'text-classification';
static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
static instance = null;
static async getInstance(progress_callb... | transformers.js/examples/node/commonjs/app.js/0 | {
"file_path": "transformers.js/examples/node/commonjs/app.js",
"repo_id": "transformers.js",
"token_count": 583
} |
import Scatterplot from 'deepscatter';
import { getCachedJSON } from './utils';
// Start loading metadata and positions asynchronously as soon as possible.
let metadata = {};
getCachedJSON('https://huggingface.co/datasets/Xenova/MusicBenchEmbedded/resolve/main/metadata.json')
.then((data) => {
metadata = ... | transformers.js/examples/semantic-audio-search/index.js/0 | {
"file_path": "transformers.js/examples/semantic-audio-search/index.js",
"repo_id": "transformers.js",
"token_count": 1844
} |
'use client'
import Image from 'next/image'
import { blurHashToDataURL } from '../utils.js'
export function ImageGrid({ images, setCurrentImage }) {
return (
<div className="columns-2 gap-4 sm:columns-3 xl:columns-4 2xl:columns-5">
{images && images.map(({ id, url, ar, blur }) => (
... | transformers.js/examples/semantic-image-search-client/src/app/components/ImageGrid.jsx/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/src/app/components/ImageGrid.jsx",
"repo_id": "transformers.js",
"token_count": 791
} |
/** @type {import('next').NextConfig} */
const nextConfig = {
// (Optional) Export as a standalone site
// See https://nextjs.org/docs/pages/api-reference/next-config-js/output#automatically-copying-traced-files
output: 'standalone', // Feel free to modify/remove this option
// Indicate that these pack... | transformers.js/examples/semantic-image-search/next.config.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search/next.config.js",
"repo_id": "transformers.js",
"token_count": 207
} |
import { decode } from "blurhash"
const SIZE = 32;
export function blurHashToDataURL(hash) {
if (!hash) return undefined
const pixels = decode(hash, SIZE, SIZE)
const canvas = document.createElement("canvas");
canvas.width = SIZE;
canvas.height = SIZE;
const ctx = canvas.getContext("2d");
... | transformers.js/examples/semantic-image-search/src/app/utils.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search/src/app/utils.js",
"repo_id": "transformers.js",
"token_count": 500
} |
* {
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/webgpu-video-background-removal/style.css/0 | {
"file_path": "transformers.js/examples/webgpu-video-background-removal/style.css",
"repo_id": "transformers.js",
"token_count": 462
} |
@scope (.markdown) {
/* Code blocks */
pre {
margin: 0.5rem 0;
white-space: break-spaces;
}
code {
padding: 0.2em 0.4em;
border-radius: 4px;
font-family: Consolas, Monaco, 'Andale Mono', 'Ubuntu Mono', monospace;
font-size: 0.9em;
}
pre,
cod... | transformers.js/examples/webgpu-vlm/src/components/Chat.css/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/components/Chat.css",
"repo_id": "transformers.js",
"token_count": 947
} |
import json
import os
import shutil
from dataclasses import dataclass, field, asdict
from typing import Optional
from enum import Enum
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser
)
import onnxslim
from optimum.exporters.onnx import main_export, export_models
from optimum.onnx.g... | transformers.js/scripts/convert.py/0 | {
"file_path": "transformers.js/scripts/convert.py",
"repo_id": "transformers.js",
"token_count": 6945
} |
/**
* @file Processors are used to prepare inputs (e.g., text, image or audio) for a model.
*
* **Example:** Using a `WhisperProcessor` to prepare an audio input for a model.
* ```javascript
* import { AutoProcessor, read_audio } from '@huggingface/transformers';
*
* const processor = await AutoProcessor.from_... | transformers.js/src/base/processing_utils.js/0 | {
"file_path": "transformers.js/src/base/processing_utils.js",
"repo_id": "transformers.js",
"token_count": 2375
} |
export * from './beit/image_processing_beit.js'
export * from './bit/image_processing_bit.js'
export * from './chinese_clip/image_processing_chinese_clip.js'
export * from './clip/image_processing_clip.js'
export * from './convnext/image_processing_convnext.js'
export * from './deit/image_processing_deit.js'
export * ... | transformers.js/src/models/image_processors.js/0 | {
"file_path": "transformers.js/src/models/image_processors.js",
"repo_id": "transformers.js",
"token_count": 781
} |
import {
ImageProcessor,
post_process_semantic_segmentation,
} from "../../base/image_processors_utils.js";
export class SapiensImageProcessor extends ImageProcessor {
/** @type {typeof post_process_semantic_segmentation} */
post_process_semantic_segmentation(...args) {
return post_process_se... | transformers.js/src/models/sapiens/image_processing_sapiens.js/0 | {
"file_path": "transformers.js/src/models/sapiens/image_processing_sapiens.js",
"repo_id": "transformers.js",
"token_count": 145
} |
import { GenerationConfig } from "../../generation/configuration_utils.js";
export class WhisperGenerationConfig extends GenerationConfig {
/**
* Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`.
* @type {boolean}
*/
return_timestamps = null;
... | transformers.js/src/models/whisper/generation_whisper.js/0 | {
"file_path": "transformers.js/src/models/whisper/generation_whisper.js",
"repo_id": "transformers.js",
"token_count": 1043
} |
/**
* @file Helper module for mathematical processing.
*
* These functions and classes are only used internally,
* meaning an end-user shouldn't need to access anything here.
*
* @module utils/maths
*/
/**
* @typedef {Int8Array | Uint8Array | Uint8ClampedArray | Int16Array | Uint16Array | Int32Array | Uin... | transformers.js/src/utils/maths.js/0 | {
"file_path": "transformers.js/src/utils/maths.js",
"repo_id": "transformers.js",
"token_count": 16615
} |
import { BloomTokenizer, BloomForCausalLM } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
describe("BloomForCausalLM", () => {
const model_id = "hf-internal-testing/tiny-ran... | transformers.js/tests/models/bloom/test_modeling_bloom.js/0 | {
"file_path": "transformers.js/tests/models/bloom/test_modeling_bloom.js",
"repo_id": "transformers.js",
"token_count": 742
} |
import { EsmTokenizer } from "../../../src/tokenizers.js";
import { BASE_TEST_STRINGS, ESM_TEST_STRINGS } from "../test_strings.js";
export const TOKENIZER_CLASS = EsmTokenizer;
export const TEST_CONFIG = {
"Xenova/nucleotide-transformer-500m-human-ref": {
SIMPLE: {
text: BASE_TEST_STRINGS.SIMPLE,
//... | transformers.js/tests/models/esm/test_tokenization_esm.js/0 | {
"file_path": "transformers.js/tests/models/esm/test_tokenization_esm.js",
"repo_id": "transformers.js",
"token_count": 6890
} |
import { GroundingDinoProcessor, GroundingDinoForObjectDetection, RawImage } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
const text = "a cat."; // NB: text query needs to be l... | transformers.js/tests/models/grounding_dino/test_modeling_grounding_dino.js/0 | {
"file_path": "transformers.js/tests/models/grounding_dino/test_modeling_grounding_dino.js",
"repo_id": "transformers.js",
"token_count": 681
} |
import { AutoImageProcessor, MobileViTFeatureExtractor, MobileViTImageProcessor } from "../../../src/transformers.js";
import { load_cached_image } from "../../asset_cache.js";
import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
// MobileViTFeatureExtractor
des... | transformers.js/tests/models/mobilevit/test_image_processing_mobilevit.js/0 | {
"file_path": "transformers.js/tests/models/mobilevit/test_image_processing_mobilevit.js",
"repo_id": "transformers.js",
"token_count": 1322
} |
import { PatchTSTModel, PatchTSTForPrediction, Tensor } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
const dims = [64, 512, 7];
const prod = dims.reduce((a, b) => a * b, 1);
... | transformers.js/tests/models/patchtst/test_modeling_patchtst.js/0 | {
"file_path": "transformers.js/tests/models/patchtst/test_modeling_patchtst.js",
"repo_id": "transformers.js",
"token_count": 870
} |
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