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# Res2NeXt Res2NeXt is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. 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 residual-li...	pytorch-image-models/hfdocs/source/models/res2next.mdx/0	{ "file_path": "pytorch-image-models/hfdocs/source/models/res2next.mdx", "repo_id": "pytorch-image-models", "token_count": 1711 }	186
# (Tensorflow) EfficientNet Lite EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly...	pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx/0	{ "file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx", "repo_id": "pytorch-image-models", "token_count": 3373 }	187
#!/usr/bin/env python3 """PyTorch Inference Script An example inference script that outputs top-k class ids for images in a folder into a csv. Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman) """ import argparse import json import logging import os import time from contextlib import su...	pytorch-image-models/inference.py/0	{ "file_path": "pytorch-image-models/inference.py", "repo_id": "pytorch-image-models", "token_count": 6803 }	188
import math import torch from torch.utils.data import Sampler import torch.distributed as dist class OrderedDistributedSampler(Sampler): """Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`. In suc...	pytorch-image-models/timm/data/distributed_sampler.py/0	{ "file_path": "pytorch-image-models/timm/data/distributed_sampler.py", "repo_id": "pytorch-image-models", "token_count": 2276 }	189
""" Dataset reader for webdataset Hacked together by / Copyright 2022 Ross Wightman """ import io import json import logging import math import os import random import sys from dataclasses import dataclass from functools import partial from itertools import islice from typing import Any, Callable, Dict, List, Optional...	pytorch-image-models/timm/data/readers/reader_wds.py/0	{ "file_path": "pytorch-image-models/timm/data/readers/reader_wds.py", "repo_id": "pytorch-image-models", "token_count": 7878 }	190
""" Classifier head and layer factory Hacked together by / Copyright 2020 Ross Wightman """ from collections import OrderedDict from functools import partial from typing import Optional, Union, Callable import torch import torch.nn as nn from torch.nn import functional as F from .adaptive_avgmax_pool import SelectAd...	pytorch-image-models/timm/layers/classifier.py/0	{ "file_path": "pytorch-image-models/timm/layers/classifier.py", "repo_id": "pytorch-image-models", "token_count": 3585 }	191
""" Gather-Excite Attention Block Paper: `Gather-Excite: Exploiting Feature Context in CNNs` - https://arxiv.org/abs/1810.12348 Official code here, but it's only partial impl in Caffe: https://github.com/hujie-frank/GENet I've tried to support all of the extent both w/ and w/o params. I don't believe I've seen anoth...	pytorch-image-models/timm/layers/gather_excite.py/0	{ "file_path": "pytorch-image-models/timm/layers/gather_excite.py", "repo_id": "pytorch-image-models", "token_count": 1956 }	192
""" Normalization + Activation Layers Provides Norm+Act fns for standard PyTorch norm layers such as * BatchNorm * GroupNorm * LayerNorm This allows swapping with alternative layers that are natively both norm + act such as * EvoNorm (evo_norm.py) * FilterResponseNorm (filter_response_norm.py) * InplaceABN (inplace_a...	pytorch-image-models/timm/layers/norm_act.py/0	{ "file_path": "pytorch-image-models/timm/layers/norm_act.py", "repo_id": "pytorch-image-models", "token_count": 8051 }	193
try: from torch import _assert except ImportError: def _assert(condition: bool, message: str): assert condition, message def _float_to_int(x: float) -> int: """ Symbolic tracing helper to substitute for inbuilt `int`. Hint: Inbuilt `int` can't accept an argument of type `Proxy` """ ...	pytorch-image-models/timm/layers/trace_utils.py/0	{ "file_path": "pytorch-image-models/timm/layers/trace_utils.py", "repo_id": "pytorch-image-models", "token_count": 119 }	194
import hashlib import json import logging import os from functools import partial from pathlib import Path from tempfile import TemporaryDirectory from typing import Iterable, Optional, Union import torch from torch.hub import HASH_REGEX, download_url_to_file, urlparse try: from torch.hub import get_dir except Im...	pytorch-image-models/timm/models/_hub.py/0	{ "file_path": "pytorch-image-models/timm/models/_hub.py", "repo_id": "pytorch-image-models", "token_count": 6737 }	195
""" ConvMixer """ import torch import torch.nn as nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from timm.layers import SelectAdaptivePool2d from ._registry import register_model, generate_default_cfgs from ._builder import build_model_with_cfg from ._manipulate import checkpoint_seq __all__ =...	pytorch-image-models/timm/models/convmixer.py/0	{ "file_path": "pytorch-image-models/timm/models/convmixer.py", "repo_id": "pytorch-image-models", "token_count": 2228 }	196
# NOTE timm.models.layers is DEPRECATED, please use timm.layers, this is here to reduce breakages in transition from timm.layers.activations import * from timm.layers.adaptive_avgmax_pool import \ adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d from timm.layers.attention_p...	pytorch-image-models/timm/models/layers/__init__.py/0	{ "file_path": "pytorch-image-models/timm/models/layers/__init__.py", "repo_id": "pytorch-image-models", "token_count": 1240 }	197
"""RegNet X, Y, Z, and more Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678 Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877 Original Impl: None Based on original PyTorch...	pytorch-image-models/timm/models/regnet.py/0	{ "file_path": "pytorch-image-models/timm/models/regnet.py", "repo_id": "pytorch-image-models", "token_count": 21400 }	198
""" Transformer in Transformer (TNT) in PyTorch A PyTorch implement of TNT as described in 'Transformer in Transformer' - https://arxiv.org/abs/2103.00112 The official mindspore code is released and available at https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT """ import math import torch ...	pytorch-image-models/timm/models/tnt.py/0	{ "file_path": "pytorch-image-models/timm/models/tnt.py", "repo_id": "pytorch-image-models", "token_count": 6715 }	199
""" Adafactor Optimizer Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py Original header/copyright below. """ # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source...	pytorch-image-models/timm/optim/adafactor.py/0	{ "file_path": "pytorch-image-models/timm/optim/adafactor.py", "repo_id": "pytorch-image-models", "token_count": 3656 }	200
""" SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.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 """ import ...	pytorch-image-models/timm/optim/sgdp.py/0	{ "file_path": "pytorch-image-models/timm/optim/sgdp.py", "repo_id": "pytorch-image-models", "token_count": 1186 }	201
""" Batch size decay and retry helpers. Copyright 2022 Ross Wightman """ import math def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False): """ power of two batch-size decay with intra steps Decay by stepping between powers of 2: * determine power-of-2 floor of current batch size (base batch...	pytorch-image-models/timm/utils/decay_batch.py/0	{ "file_path": "pytorch-image-models/timm/utils/decay_batch.py", "repo_id": "pytorch-image-models", "token_count": 656 }	202
Hugging Face Optimized Inference License 1.0 (HFOILv1.0) This License Agreement governs the use of the Software and its Modifications. It is a binding agreement between the Licensor and You. This License Agreement shall be referred to as Hugging Face Optimized Inference License 1.0 or HFOILv1.0. We may publish revis...	text-generation-inference/LICENSE/0	{ "file_path": "text-generation-inference/LICENSE", "repo_id": "text-generation-inference", "token_count": 2207 }	203
# Text Generation The Hugging Face Text Generation Python library provides a convenient way of interfacing with a `text-generation-inference` instance running on [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) or on the Hugging Face Hub. ## Get Started ### Install ```shell pip install...	text-generation-inference/clients/python/README.md/0	{ "file_path": "text-generation-inference/clients/python/README.md", "repo_id": "text-generation-inference", "token_count": 2193 }	204
# Consuming Text Generation Inference There are many ways you can consume Text Generation Inference server in your applications. After launching, you can use the `/generate` route and make a `POST` request to get results from the server. You can also use the `/generate_stream` route if you want TGI to return a stream ...	text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md/0	{ "file_path": "text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md", "repo_id": "text-generation-inference", "token_count": 2262 }	205
# Messages API Text Generation Inference (TGI) now supports the Messages API, which is fully compatible with the OpenAI Chat Completion API. This feature is available starting from version 1.4.0. You can use OpenAI's client libraries or third-party libraries expecting OpenAI schema to interact with TGI's Messages API....	text-generation-inference/docs/source/messages_api.md/0	{ "file_path": "text-generation-inference/docs/source/messages_api.md", "repo_id": "text-generation-inference", "token_count": 1731 }	206
[ { "details": { "best_of_sequences": null, "finish_reason": "length", "generated_tokens": 10, "prefill": [ { "id": 50, "logprob": null, "text": "G" }, { "id": 330, "logprob": -5.96875, "text": "ir" ...	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": 21427 }	207
[ { "details": { "best_of_sequences": null, "finish_reason": "length", "generated_tokens": 10, "prefill": [ { "id": 1, "logprob": null, "text": "<s>" }, { "id": 1724, "logprob": -10.734375, "text": "What" ...	text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_load.json/0	{ "file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_load.json", "repo_id": "text-generation-inference", "token_count": 5726 }	208
[ { "details": { "best_of_sequences": null, "finish_reason": "length", "generated_tokens": 10, "prefill": [ { "id": 563, "logprob": null, "text": "def" }, { "id": 942, "logprob": -5.1367188, "text": " print...	text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder_load.json/0	{ "file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder_load.json", "repo_id": "text-generation-inference", "token_count": 5188 }	209
{ "details": { "best_of_sequences": null, "finish_reason": "length", "generated_tokens": 17, "prefill": [ { "id": 1276, "logprob": null, "text": "What" }, { "id": 310, "logprob": -1.5117188, "text": " is" }, { "id": ...	text-generation-inference/integration-tests/models/__snapshots__/test_mpt/test_mpt.json/0	{ "file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mpt/test_mpt.json", "repo_id": "text-generation-inference", "token_count": 1691 }	210
import pytest @pytest.fixture(scope="module") def bloom_560_handle(launcher): with launcher("bigscience/bloom-560m") as handle: yield handle @pytest.fixture(scope="module") async def bloom_560(bloom_560_handle): await bloom_560_handle.health(240) return bloom_560_handle.client @pytest.mark.asy...	text-generation-inference/integration-tests/models/test_bloom_560m.py/0	{ "file_path": "text-generation-inference/integration-tests/models/test_bloom_560m.py", "repo_id": "text-generation-inference", "token_count": 752 }	211
import pytest @pytest.fixture(scope="module") def flash_starcoder_handle(launcher): with launcher("bigcode/starcoder", num_shard=2) as handle: yield handle @pytest.fixture(scope="module") async def flash_starcoder(flash_starcoder_handle): await flash_starcoder_handle.health(300) return flash_sta...	text-generation-inference/integration-tests/models/test_flash_starcoder.py/0	{ "file_path": "text-generation-inference/integration-tests/models/test_flash_starcoder.py", "repo_id": "text-generation-inference", "token_count": 578 }	212
[package] name = "text-generation-launcher" description = "Text Generation Launcher" version.workspace = true edition.workspace = true authors.workspace = true homepage.workspace = true [dependencies] clap = { version = "4.4.5", features = ["derive", "env"] } ctrlc = { version = "3.4.1", features = ["termination"] } n...	text-generation-inference/launcher/Cargo.toml/0	{ "file_path": "text-generation-inference/launcher/Cargo.toml", "repo_id": "text-generation-inference", "token_count": 287 }	213
eetq_commit := 71adb5e191bb8290069a580abff0355d7b2dd5c9 eetq: # Clone eetq pip install packaging git clone https://github.com/NetEase-FuXi/EETQ.git eetq build-eetq: eetq cd eetq && git fetch && git checkout $(eetq_commit) && git submodule update --init --recursive cd eetq && python setup.py build install-eet...	text-generation-inference/server/Makefile-eetq/0	{ "file_path": "text-generation-inference/server/Makefile-eetq", "repo_id": "text-generation-inference", "token_count": 155 }	214
// Adapted from turboderp exllama: https://github.com/turboderp/exllama #include <ATen/cuda/CUDAContext.h> #include "q4_matrix.cuh" #include <vector> #include "../util.cuh" #include "../matrix.cuh" using namespace std; const int UNSHUF_BLOCKSIZE_X = 64; const int RECONS_THREADS_X = 64; // Block size and thread...	text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cu/0	{ "file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cu", "repo_id": "text-generation-inference", "token_count": 2592 }	215
#include "q_matrix.cuh" #include "matrix_view.cuh" #include "util.cuh" #include "quant/qdq_2.cuh" #include "quant/qdq_3.cuh" #include "quant/qdq_4.cuh" #include "quant/qdq_5.cuh" #include "quant/qdq_6.cuh" #include "quant/qdq_8.cuh" #define BLOCK_KN_SIZE 128 #define THREADS_X 32 #define THREADS_Y 32 // Shuffle quan...	text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu/0	{ "file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu", "repo_id": "text-generation-inference", "token_count": 10524 }	216
import torch from loguru import logger from transformers.configuration_utils import PretrainedConfig from transformers.models.auto import modeling_auto from huggingface_hub import hf_hub_download from typing import Optional from pathlib import Path from text_generation_server.utils.speculate import get_speculate, set...	text-generation-inference/server/text_generation_server/models/__init__.py/0	{ "file_path": "text-generation-inference/server/text_generation_server/models/__init__.py", "repo_id": "text-generation-inference", "token_count": 10041 }	217
import torch from typing import Optional from text_generation_server.models.flash_mistral import BaseFlashMistral from text_generation_server.models.custom_modeling.flash_mixtral_modeling import ( MixtralConfig, FlashMixtralForCausalLM, ) class FlashMixtral(BaseFlashMistral): def __init__( self,...	text-generation-inference/server/text_generation_server/models/flash_mixtral.py/0	{ "file_path": "text-generation-inference/server/text_generation_server/models/flash_mixtral.py", "repo_id": "text-generation-inference", "token_count": 421 }	218
import torch import torch.distributed from transformers import AutoConfig, AutoTokenizer from typing import Optional, List, Tuple from text_generation_server.models import CausalLM from text_generation_server.models.custom_modeling.phi_modeling import ( PhiConfig, PhiForCausalLM, ) from text_generation_server...	text-generation-inference/server/text_generation_server/models/phi.py/0	{ "file_path": "text-generation-inference/server/text_generation_server/models/phi.py", "repo_id": "text-generation-inference", "token_count": 1000 }	219
import torch from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params # Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension none_tensor = torch.empty((1, 1), device="meta") def ext_make_q4(qweight, qzeros, scales, g_idx, device): """Construct Q4...	text-generation-inference/server/text_generation_server/utils/gptq/exllama.py/0	{ "file_path": "text-generation-inference/server/text_generation_server/utils/gptq/exllama.py", "repo_id": "text-generation-inference", "token_count": 1833 }	220
<p align="center"> <br> <img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/> <br> <p> <p align="center"> <img alt="Build" src="https://github.com/huggingface/tokenizers/workflows/Rust/badge.svg"> <a href="https://github.com/huggingface/tokenizers/blob/main/LI...	tokenizers/README.md/0	{ "file_path": "tokenizers/README.md", "repo_id": "tokenizers", "token_count": 945 }	221
/* eslint-disable */ var globRequire = require; describe("pipelineExample", () => { // This is a hack to let us require using path similar to what the user has to use function require(mod: string) { if (mod.startsWith("tokenizers")) { // let path = mod.slice("tokenizers".length); ...	tokenizers/bindings/node/examples/documentation/pipeline.test.ts/0	{ "file_path": "tokenizers/bindings/node/examples/documentation/pipeline.test.ts", "repo_id": "tokenizers", "token_count": 2710 }	222
# `tokenizers-android-arm-eabi` This is the armv7-linux-androideabi binary for `tokenizers`	tokenizers/bindings/node/npm/android-arm-eabi/README.md/0	{ "file_path": "tokenizers/bindings/node/npm/android-arm-eabi/README.md", "repo_id": "tokenizers", "token_count": 35 }	223
# `tokenizers-linux-x64-gnu` This is the x86_64-unknown-linux-gnu binary for `tokenizers`	tokenizers/bindings/node/npm/linux-x64-gnu/README.md/0	{ "file_path": "tokenizers/bindings/node/npm/linux-x64-gnu/README.md", "repo_id": "tokenizers", "token_count": 36 }	224
use crate::arc_rwlock_serde; use crate::tasks::models::{BPEFromFilesTask, WordLevelFromFilesTask, WordPieceFromFilesTask}; use crate::trainers::Trainer; use napi::bindgen_prelude::*; use napi_derive::napi; use serde::{Deserialize, Serialize}; use std::collections::HashMap; use std::path::{Path, PathBuf}; use std::sync:...	tokenizers/bindings/node/src/models.rs/0	{ "file_path": "tokenizers/bindings/node/src/models.rs", "repo_id": "tokenizers", "token_count": 3681 }	225