text stringlengths 7 324k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 463 |
|---|---|---|---|
accelerate launch --config_file "configs/deepspeed_config.yaml" train.py \
--seed 100 \
--model_name_or_path "meta-llama/Llama-2-70b-hf" \
--dataset_name "smangrul/ultrachat-10k-chatml" \
--chat_template_format "chatml" \
--add_special_tokens False \
--append_concat_token False \
--splits "train,test" \
--max_seq_len ... | peft/examples/sft/run_peft_deepspeed.sh/0 | {
"file_path": "peft/examples/sft/run_peft_deepspeed.sh",
"repo_id": "peft",
"token_count": 454
} | 166 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/adalora/layer.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/layer.py",
"repo_id": "peft",
"token_count": 6986
} | 167 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/prefix_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/prefix_tuning/config.py",
"repo_id": "peft",
"token_count": 447
} | 168 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/regression/test_regression.py/0 | {
"file_path": "peft/tests/regression/test_regression.py",
"repo_id": "peft",
"token_count": 9655
} | 169 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_other.py/0 | {
"file_path": "peft/tests/test_other.py",
"repo_id": "peft",
"token_count": 892
} | 170 |
# PyTorch Image Models
- [What's New](#whats-new)
- [Introduction](#introduction)
- [Models](#models)
- [Features](#features)
- [Results](#results)
- [Getting Started (Documentation)](#getting-started-documentation)
- [Train, Validation, Inference Scripts](#train-validation-inference-scripts)
- [Awesome PyTorch Resourc... | pytorch-image-models/README.md/0 | {
"file_path": "pytorch-image-models/README.md",
"repo_id": "pytorch-image-models",
"token_count": 13284
} | 171 |
"""
Run this script to generate the model-index files in `models` from the templates in `.templates/models`.
"""
import argparse
from pathlib import Path
from jinja2 import Environment, FileSystemLoader
import modelindex
def generate_readmes(templates_path: Path, dest_path: Path):
"""Add the code snippet templ... | pytorch-image-models/docs/models/.templates/generate_readmes.py/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/generate_readmes.py",
"repo_id": "pytorch-image-models",
"token_count": 725
} | 172 |
# (Gluon) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswit... | pytorch-image-models/docs/models/.templates/models/gloun-inception-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-inception-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1073
} | 173 |
# MobileNet v2
**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expa... | pytorch-image-models/docs/models/.templates/models/mobilenet-v2.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/mobilenet-v2.md",
"repo_id": "pytorch-image-models",
"token_count": 2583
} | 174 |
# SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
{% include 'code_snippets.md'... | pytorch-image-models/docs/models/.templates/models/seresnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/seresnext.md",
"repo_id": "pytorch-image-models",
"token_count": 1929
} | 175 |
# Wide ResNet
**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).
{% include 'code_snippet... | pytorch-image-models/docs/models/.templates/models/wide-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/wide-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1220
} | 176 |
# FBNet
**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and... | pytorch-image-models/docs/models/fbnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/fbnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1702
} | 177 |
# MnasNet
**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and late... | pytorch-image-models/docs/models/mnasnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/mnasnet.md",
"repo_id": "pytorch-image-models",
"token_count": 2098
} | 178 |
# SelecSLS
**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
## How do I use this model on an image?
To load a pretrained model:
```python
import timm
model = timm.create_model('selecsls42b'... | pytorch-image-models/docs/models/selecsls.md/0 | {
"file_path": "pytorch-image-models/docs/models/selecsls.md",
"repo_id": "pytorch-image-models",
"token_count": 2417
} | 179 |
# CSP-DarkNet
**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The u... | pytorch-image-models/hfdocs/source/models/csp-darknet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/csp-darknet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1756
} | 180 |
# (Gluon) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this... | pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2538
} | 181 |
# PNASNet
**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to comple... | pytorch-image-models/hfdocs/source/models/pnasnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/pnasnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1622
} | 182 |
# SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual b... | pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2425
} | 183 |
# Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in `timm`.
## Schedulers
### Factory functions
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2
### Scheduler Classes
[[... | pytorch-image-models/hfdocs/source/reference/schedulers.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/reference/schedulers.mdx",
"repo_id": "pytorch-image-models",
"token_count": 242
} | 184 |
DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 *... | pytorch-image-models/timm/data/constants.py/0 | {
"file_path": "pytorch-image-models/timm/data/constants.py",
"repo_id": "pytorch-image-models",
"token_count": 236
} | 185 |
""" A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | pytorch-image-models/timm/data/readers/reader_image_folder.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_folder.py",
"repo_id": "pytorch-image-models",
"token_count": 1510
} | 186 |
""" Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... | pytorch-image-models/timm/layers/attention_pool2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool2d.py",
"repo_id": "pytorch-image-models",
"token_count": 2301
} | 187 |
""" EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato ... | pytorch-image-models/timm/layers/evo_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/evo_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 6684
} | 188 |
from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identi... | pytorch-image-models/timm/layers/ml_decoder.py/0 | {
"file_path": "pytorch-image-models/timm/layers/ml_decoder.py",
"repo_id": "pytorch-image-models",
"token_count": 3177
} | 189 |
""" Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
Thi... | pytorch-image-models/timm/layers/split_batchnorm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/split_batchnorm.py",
"repo_id": "pytorch-image-models",
"token_count": 1394
} | 190 |
import os
from typing import Any, Dict, Optional, Union
from urllib.parse import urlsplit
from timm.layers import set_layer_config
from ._helpers import load_checkpoint
from ._hub import load_model_config_from_hf
from ._pretrained import PretrainedCfg
from ._registry import is_model, model_entrypoint, split_model_name... | pytorch-image-models/timm/models/_factory.py/0 | {
"file_path": "pytorch-image-models/timm/models/_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 1944
} | 191 |
""" Bring-Your-Own-Blocks Network
A flexible network w/ dataclass based config for stacking those NN blocks.
This model is currently used to implement the following networks:
GPU Efficient (ResNets) - gernet_l/m/s (original versions called genet, but this was already used (by SENet author)).
Paper: `Neural Architect... | pytorch-image-models/timm/models/byobnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/byobnet.py",
"repo_id": "pytorch-image-models",
"token_count": 42793
} | 192 |
""" The EfficientNet Family in PyTorch
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* EfficientNet-V2
- `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/A... | pytorch-image-models/timm/models/efficientnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientnet.py",
"repo_id": "pytorch-image-models",
"token_count": 47473
} | 193 |
"""
InceptionNeXt paper: https://arxiv.org/abs/2303.16900
Original implementation & weights from: https://github.com/sail-sg/inceptionnext
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, Drop... | pytorch-image-models/timm/models/inception_next.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_next.py",
"repo_id": "pytorch-image-models",
"token_count": 7709
} | 194 |
""" Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | pytorch-image-models/timm/models/pit.py/0 | {
"file_path": "pytorch-image-models/timm/models/pit.py",
"repo_id": "pytorch-image-models",
"token_count": 7347
} | 195 |
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | pytorch-image-models/timm/models/swin_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 16908
} | 196 |
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partia... | pytorch-image-models/timm/models/xception_aligned.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception_aligned.py",
"repo_id": "pytorch-image-models",
"token_count": 7719
} | 197 |
""" Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... | pytorch-image-models/timm/optim/nvnovograd.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nvnovograd.py",
"repo_id": "pytorch-image-models",
"token_count": 2415
} | 198 |
""" Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021... | pytorch-image-models/timm/utils/agc.py/0 | {
"file_path": "pytorch-image-models/timm/utils/agc.py",
"repo_id": "pytorch-image-models",
"token_count": 661
} | 199 |
#!/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": 24460
} | 200 |
use crate::app::Data;
use tabled::settings::Merge;
use tabled::{builder::Builder, settings::Style, Table};
#[allow(clippy::too_many_arguments)]
pub(crate) fn parameters_table(
tokenizer_name: String,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: us... | text-generation-inference/benchmark/src/table.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/table.rs",
"repo_id": "text-generation-inference",
"token_count": 2298
} | 201 |
from enum import Enum
from pydantic import BaseModel, validator
from typing import Optional, List, Union, Any
from text_generation.errors import ValidationError
# enum for grammar type
class GrammarType(str, Enum):
Json = "json"
Regex = "regex"
# Grammar type and value
class Grammar(BaseModel):
# Gramm... | text-generation-inference/clients/python/text_generation/types.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/types.py",
"repo_id": "text-generation-inference",
"token_count": 4249
} | 202 |
# Streaming
## What is Streaming?
Token streaming is the mode in which the server returns the tokens one by one as the model generates them. This enables showing progressive generations to the user rather than waiting for the whole generation. Streaming is an essential aspect of the end-user experience as it reduces ... | text-generation-inference/docs/source/conceptual/streaming.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/streaming.md",
"repo_id": "text-generation-inference",
"token_count": 1972
} | 203 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 1724,
"logprob": -7.6914062,
"text": "What"
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq_sharded/test_flash_llama_awq_load_sharded.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq_sharded/test_flash_llama_awq_load_sharded.json",
"repo_id": "text-generation-inference",
"token_count": 5776
} | 204 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.7890625,
"text": "Test"
},
{
"id": 20... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq.json",
"repo_id": "text-generation-inference",
"token_count": 1046
} | 205 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2271,
"logprob": null,
"text": "Test"
},
{
"id": 1681,
"logprob": -8.8515625,
"text": " request"
}
],
"seed"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2.json",
"repo_id": "text-generation-inference",
"token_count": 988
} | 206 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4911,
"logprob": -6.9804688,
"text": "User"
... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics_load.json",
"repo_id": "text-generation-inference",
"token_count": 9573
} | 207 |
{
"choices": [
{
"finish_reason": "eos_token",
"index": 0,
"logprobs": null,
"message": {
"content": null,
"name": null,
"role": "assistant",
"tool_calls": {
"function": {
"description": null,
"name": "tools",
"p... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json",
"repo_id": "text-generation-inference",
"token_count": 469
} | 208 |
import pytest
@pytest.fixture(scope="module")
def flash_neox_sharded_handle(launcher):
with launcher("OpenAssistant/oasst-sft-1-pythia-12b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_neox_sharded(flash_neox_sharded_handle):
await flash_neox_sharded_handle.h... | text-generation-inference/integration-tests/models/test_flash_neox_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_neox_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 491
} | 209 |
[package]
name = "text-generation-client"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
futures = "^0.3"
grpc-metadata = { path = "../grpc-metadata" }
prost = "^0.12"
thiserror = "^1.0"
tokio = { version = "^1.32", features = ["sync"] }
tonic = "^0.... | text-generation-inference/router/client/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/client/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 180
} | 210 |
#!/bin/bash
if [[ -z "${HF_MODEL_ID}" ]]; then
echo "HF_MODEL_ID must be set"
exit 1
fi
export MODEL_ID="${HF_MODEL_ID}"
if [[ -n "${HF_MODEL_REVISION}" ]]; then
export REVISION="${HF_MODEL_REVISION}"
fi
if [[ -n "${SM_NUM_GPUS}" ]]; then
export NUM_SHARD="${SM_NUM_GPUS}"
fi
if [[ -n "${HF_MODEL_QUANTIZE}" ... | text-generation-inference/sagemaker-entrypoint.sh/0 | {
"file_path": "text-generation-inference/sagemaker-entrypoint.sh",
"repo_id": "text-generation-inference",
"token_count": 239
} | 211 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "column_remap.cuh"
#include "../util.cuh"
const int SHUF_BLOCKSIZE_X = 256;
const int SHUF_BLOCKSIZE_Y = 16;
__global__ void column_remap_kernel
(
const half* __restrict__ x,
half* __restrict__ x_new,
const int x_width,
... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu",
"repo_id": "text-generation-inference",
"token_count": 696
} | 212 |
#include "q_gemm.cuh"
#include "util.cuh"
#include "matrix_view.cuh"
#include "../config.h"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define GPTQ_BLOCK_KN_SIZE 128
#define GPTQ_BLOCK_M_SIZE_MAX 8
#... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu",
"repo_id": "text-generation-inference",
"token_count": 3563
} | 213 |
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers import (
TensorParallelRowLinear,
TensorParallelColumnLi... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 6592
} | 214 |
import math
import os
import time
import itertools
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
from typing import Optional, Tuple, List, Type, Dict
from text_gener... | text-generation-inference/server/text_generation_server/models/flash_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 23791
} | 215 |
import torch
import torch.distributed
from transformers import AutoTokenizer, PreTrainedTokenizerBase
from typing import Optional
import os
from text_generation_server.models.custom_modeling.mamba_modeling import (
MambaConfig,
)
from loguru import logger
from text_generation_server.pb import generate_pb2
from text... | text-generation-inference/server/text_generation_server/models/mamba.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/mamba.py",
"repo_id": "text-generation-inference",
"token_count": 14236
} | 216 |
import datetime
import torch
import os
from loguru import logger
from pathlib import Path
from safetensors.torch import save_file, load_file, _find_shared_tensors, _is_complete
from typing import List, Dict
from collections import defaultdict
def _remove_duplicate_names(
state_dict: Dict[str, torch.Tensor],
... | text-generation-inference/server/text_generation_server/utils/convert.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/convert.py",
"repo_id": "text-generation-inference",
"token_count": 1769
} | 217 |
import re
from typing import List, Optional, Tuple
import math
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": 10984
} | 218 |
MIT License
Copyright (c) 2020 N-API for Rust
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, di... | tokenizers/bindings/node/LICENSE/0 | {
"file_path": "tokenizers/bindings/node/LICENSE",
"repo_id": "tokenizers",
"token_count": 276
} | 219 |
/* eslint-disable @typescript-eslint/no-explicit-any */
import { bertProcessing, byteLevelProcessing, robertaProcessing, sequenceProcessing, templateProcessing } from '../../'
describe('bertProcessing', () => {
it('instantiates correctly with only two parameters', () => {
const processor = bertProcessing(['sep'... | tokenizers/bindings/node/lib/bindings/post-processors.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/post-processors.test.ts",
"repo_id": "tokenizers",
"token_count": 1022
} | 220 |
# `tokenizers-linux-arm64-gnu`
This is the **aarch64-unknown-linux-gnu** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-arm64-gnu/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-gnu/README.md",
"repo_id": "tokenizers",
"token_count": 35
} | 221 |
use serde::de::Deserializer;
use serde::ser::Serializer;
use serde::{Deserialize, Serialize};
use std::sync::{Arc, RwLock};
pub fn serialize<S, T>(val: &Option<Arc<RwLock<T>>>, s: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
T: Serialize,
{
T::serialize(&*(val.clone().unwrap()).read().unwrap(), s)
}
pub f... | tokenizers/bindings/node/src/arc_rwlock_serde.rs/0 | {
"file_path": "tokenizers/bindings/node/src/arc_rwlock_serde.rs",
"repo_id": "tokenizers",
"token_count": 220
} | 222 |
# Generated content DO NOT EDIT
class AddedToken:
"""
Represents a token that can be be added to a :class:`~tokenizers.Tokenizer`.
It can have special options that defines the way it should behave.
Args:
content (:obj:`str`): The content of the token
single_word (:obj:`bool`, defaults ... | tokenizers/bindings/python/py_src/tokenizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 16502
} | 223 |
# Generated content DO NOT EDIT
from .. import processors
PostProcessor = processors.PostProcessor
BertProcessing = processors.BertProcessing
ByteLevel = processors.ByteLevel
RobertaProcessing = processors.RobertaProcessing
Sequence = processors.Sequence
TemplateProcessing = processors.TemplateProcessing
| tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py",
"repo_id": "tokenizers",
"token_count": 74
} | 224 |
#![warn(clippy::all)]
#![allow(clippy::upper_case_acronyms)]
// Many false positives with pyo3 it seems &str, and &PyAny get flagged
#![allow(clippy::borrow_deref_ref)]
extern crate tokenizers as tk;
mod decoders;
mod encoding;
mod error;
mod models;
mod normalizers;
mod pre_tokenizers;
mod processors;
mod token;
mod... | tokenizers/bindings/python/src/lib.rs/0 | {
"file_path": "tokenizers/bindings/python/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 1086
} | 225 |
from tokenizers import ByteLevelBPETokenizer
from ..utils import data_dir, multiprocessing_with_parallelism, roberta_files
class TestByteLevelBPE:
def test_basic_encode(self, roberta_files):
tokenizer = ByteLevelBPETokenizer.from_file(roberta_files["vocab"], roberta_files["merges"])
output = toke... | tokenizers/bindings/python/tests/implementations/test_byte_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_byte_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 1653
} | 226 |
# Pre-tokenizers
<tokenizerslangcontent>
<python>
## BertPreTokenizer
[[autodoc]] tokenizers.pre_tokenizers.BertPreTokenizer
## ByteLevel
[[autodoc]] tokenizers.pre_tokenizers.ByteLevel
## CharDelimiterSplit
[[autodoc]] tokenizers.pre_tokenizers.CharDelimiterSplit
## Digits
[[autodoc]] tokenizers.pre_tokenizers... | tokenizers/docs/source-doc-builder/api/pre-tokenizers.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/pre-tokenizers.mdx",
"repo_id": "tokenizers",
"token_count": 371
} | 227 |
The tokenization pipeline
====================================================================================================
When calling :entity:`Tokenizer.encode` or :entity:`Tokenizer.encode_batch`, the input text(s) go
through the following pipeline:
- :ref:`normalization`
- :ref:`pre-tokenization`
- :ref:`mode... | tokenizers/docs/source/pipeline.rst/0 | {
"file_path": "tokenizers/docs/source/pipeline.rst",
"repo_id": "tokenizers",
"token_count": 6323
} | 228 |
[package]
name = "unstable_wasm"
version = "0.1.0"
authors = ["Nicolas Patry"]
edition = "2018"
[lib]
crate-type = ["cdylib", "rlib"]
[features]
default = ["console_error_panic_hook"]
[dependencies]
wasm-bindgen = "0.2.63"
# The `console_error_panic_hook` crate provides better debugging of panics by
# logging them ... | tokenizers/tokenizers/examples/unstable_wasm/Cargo.toml/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/Cargo.toml",
"repo_id": "tokenizers",
"token_count": 364
} | 229 |
const CopyWebpackPlugin = require("copy-webpack-plugin");
const path = require('path');
module.exports = {
entry: "./bootstrap.js",
output: {
path: path.resolve(__dirname, "dist"),
filename: "bootstrap.js",
},
mode: "development",
plugins: [
new CopyWebpackPlugin(['index.html'])
],
};
| tokenizers/tokenizers/examples/unstable_wasm/www/webpack.config.js/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/webpack.config.js",
"repo_id": "tokenizers",
"token_count": 114
} | 230 |
//! Popular tokenizer models.
pub mod bpe;
pub mod unigram;
pub mod wordlevel;
pub mod wordpiece;
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use serde::{Deserialize, Serialize, Serializer};
use crate::models::bpe::{BpeTrainer, BPE};
use crate::models::unigram::{Unigram, UnigramTrainer};
use crat... | tokenizers/tokenizers/src/models/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/mod.rs",
"repo_id": "tokenizers",
"token_count": 3660
} | 231 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug, Deserialize, Serialize)]
#[serde(tag = "type")]
pub struct Prepend {
pub prepend: String,
}
impl Prepend {
pub fn new(prepend: String) -> Self {
Self { prepend }
}
}
impl Norm... | tokenizers/tokenizers/src/normalizers/prepend.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/prepend.rs",
"repo_id": "tokenizers",
"token_count": 856
} | 232 |
// Generated by modified Perl script at https://github.com/google/sentencepiece/blob/master/data/gen_unicode_scripts_code.pl
// Unicode scripts : https://gist.github.com/Narsil/07556f26dc84a6baeff4d499e68d3cd2
// Rust adaptation : https://gist.github.com/Narsil/1df9fbbf5296a8d4d62de55dcb2fe700
#[derive(PartialEq, Debu... | tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/scripts.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/scripts.rs",
"repo_id": "tokenizers",
"token_count": 46440
} | 233 |
use crate::Result;
use hf_hub::{api::sync::ApiBuilder, Repo, RepoType};
use std::collections::HashMap;
use std::path::PathBuf;
/// Defines the aditional parameters available for the `from_pretrained` function
#[derive(Debug, Clone)]
pub struct FromPretrainedParameters {
pub revision: String,
pub user_agent: Ha... | tokenizers/tokenizers/src/utils/from_pretrained.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/from_pretrained.rs",
"repo_id": "tokenizers",
"token_count": 913
} | 234 |
# Troubleshooting
This is a document explaining how to deal with various issues on Circle-CI. The entries may include actual solutions or pointers to Issues that cover those.
## Circle CI
* pytest worker runs out of resident RAM and gets killed by `cgroups`: https://github.com/huggingface/transformers/issues/11408
| transformers/.circleci/TROUBLESHOOT.md/0 | {
"file_path": "transformers/.circleci/TROUBLESHOOT.md",
"repo_id": "transformers",
"token_count": 80
} | 235 |
FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
LABEL maintainer="Hugging Face"
LABEL repository="transformers"
RUN apt update && \
apt install -y bash \
build-essential \
git \
curl \
ca-certificates \
python3 \
... | transformers/docker/transformers-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 397
} | 236 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/de/quicktour.md/0 | {
"file_path": "transformers/docs/source/de/quicktour.md",
"repo_id": "transformers",
"token_count": 7330
} | 237 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/big_models.md/0 | {
"file_path": "transformers/docs/source/en/big_models.md",
"repo_id": "transformers",
"token_count": 1722
} | 238 |
<!---
Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or ... | transformers/docs/source/en/installation.md/0 | {
"file_path": "transformers/docs/source/en/installation.md",
"repo_id": "transformers",
"token_count": 2901
} | 239 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/main_classes/data_collator.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/data_collator.md",
"repo_id": "transformers",
"token_count": 681
} | 240 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/albert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/albert.md",
"repo_id": "transformers",
"token_count": 3405
} | 241 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/clip.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/clip.md",
"repo_id": "transformers",
"token_count": 2696
} | 242 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/decision_transformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/decision_transformer.md",
"repo_id": "transformers",
"token_count": 639
} | 243 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/efficientnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/efficientnet.md",
"repo_id": "transformers",
"token_count": 725
} | 244 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/funnel.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/funnel.md",
"repo_id": "transformers",
"token_count": 1879
} | 245 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/hubert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/hubert.md",
"repo_id": "transformers",
"token_count": 930
} | 246 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/mt5.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mt5.md",
"repo_id": "transformers",
"token_count": 1400
} | 247 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/rag.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/rag.md",
"repo_id": "transformers",
"token_count": 1273
} | 248 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/seggpt.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/seggpt.md",
"repo_id": "transformers",
"token_count": 1204
} | 249 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/unispeech-sat.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/unispeech-sat.md",
"repo_id": "transformers",
"token_count": 1045
} | 250 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/model_doc/xlnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/xlnet.md",
"repo_id": "transformers",
"token_count": 2042
} | 251 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/run_scripts.md/0 | {
"file_path": "transformers/docs/source/en/run_scripts.md",
"repo_id": "transformers",
"token_count": 5916
} | 252 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/tasks/monocular_depth_estimation.md/0 | {
"file_path": "transformers/docs/source/en/tasks/monocular_depth_estimation.md",
"repo_id": "transformers",
"token_count": 1553
} | 253 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/testing.md/0 | {
"file_path": "transformers/docs/source/en/testing.md",
"repo_id": "transformers",
"token_count": 13408
} | 254 |
<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or ... | transformers/docs/source/es/pr_checks.md/0 | {
"file_path": "transformers/docs/source/es/pr_checks.md",
"repo_id": "transformers",
"token_count": 2659
} | 255 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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the License. You may obtain a copy of the License at
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