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<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Deploy 🤗 Transformers for inference Welcome to this getting started guide, we will use the new Hugging Face Inference DLCs and Amazon SageMaker Python SDK to deploy a transformer model for inference. In this example we deploy a trained Hugging Face Transformer m... | notebooks/sagemaker/10_deploy_model_from_s3/deploy_transformer_model_from_s3.ipynb/0 | {
"file_path": "notebooks/sagemaker/10_deploy_model_from_s3/deploy_transformer_model_from_s3.ipynb",
"repo_id": "notebooks",
"token_count": 1178
} | 160 |
<jupyter_start><jupyter_text>Train LLMs using QLoRA on Amazon SageMakerIn this sagemaker example, we are going to learn how to apply [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314) to fine-tune Falcon 40B. QLoRA is an efficient finetuning technique that quantizes a pretrained language ... | notebooks/sagemaker/28_train_llms_with_qlora/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/28_train_llms_with_qlora/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3913
} | 161 |
<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# DeepSpeed
[DeepSpeed](https://www.deepspeed.ai/) is a library designed for speed and scale for distributed training of large models with billions ... | peft/docs/source/accelerate/deepspeed.md/0 | {
"file_path": "peft/docs/source/accelerate/deepspeed.md",
"repo_id": "peft",
"token_count": 7491
} | 162 |
<jupyter_start><jupyter_code>from datasets import load_dataset
from transformers import set_seed, AutoModelForSeq2SeqLM, AutoTokenizer
from peft import get_peft_model, MultitaskPromptTuningConfig, TaskType, MultitaskPromptTuningInit
set_seed(42)
model_name = "google/flan-t5-base"
peft_config = MultitaskPromptTuningC... | peft/examples/conditional_generation/multitask_prompt_tuning.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/multitask_prompt_tuning.ipynb",
"repo_id": "peft",
"token_count": 3341
} | 163 |
<jupyter_start><jupyter_text>IntroductionIn this notebook, we will learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune an image classification model by ONLY using **0.77%** of the original trainable parameters of the model. LoRA adds low-rank "update matrices" to certain blocks in the un... | peft/examples/image_classification/image_classification_peft_lora.ipynb/0 | {
"file_path": "peft/examples/image_classification/image_classification_peft_lora.ipynb",
"repo_id": "peft",
"token_count": 6369
} | 164 |
import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from contextlib import nullcontext
from pathlib import Path
from typing import Optional
import datasets
import diffusers
import numpy as np
import psutil
import torch
import torch.nn.function... | peft/examples/lora_dreambooth/train_dreambooth.py/0 | {
"file_path": "peft/examples/lora_dreambooth/train_dreambooth.py",
"repo_id": "peft",
"token_count": 20153
} | 165 |
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from transformers import HfArgumentParser, TrainingArguments, set_seed
from trl import SFTTrainer
from utils import create_and_prepare_model, create_datasets
# Define and parse arguments.
@dataclass
class ModelArguments:
""... | peft/examples/sft/train.py/0 | {
"file_path": "peft/examples/sft/train.py",
"repo_id": "peft",
"token_count": 2380
} | 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/import_utils.py/0 | {
"file_path": "peft/src/peft/import_utils.py",
"repo_id": "peft",
"token_count": 907
} | 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/adaption_prompt/utils.py/0 | {
"file_path": "peft/src/peft/tuners/adaption_prompt/utils.py",
"repo_id": "peft",
"token_count": 2179
} | 168 |
# Copyright 2024-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/lora/awq.py/0 | {
"file_path": "peft/src/peft/tuners/lora/awq.py",
"repo_id": "peft",
"token_count": 1532
} | 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/src/peft/tuners/oft/model.py/0 | {
"file_path": "peft/src/peft/tuners/oft/model.py",
"repo_id": "peft",
"token_count": 1600
} | 170 |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not u... | peft/src/peft/utils/__init__.py/0 | {
"file_path": "peft/src/peft/utils/__init__.py",
"repo_id": "peft",
"token_count": 703
} | 171 |
# 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_decoder_models.py/0 | {
"file_path": "peft/tests/test_decoder_models.py",
"repo_id": "peft",
"token_count": 7349
} | 172 |
"""
Convert weights from https://github.com/google-research/nested-transformer
NOTE: You'll need https://github.com/google/CommonLoopUtils, not included in requirements.txt
"""
import sys
import numpy as np
import torch
from clu import checkpoint
arch_depths = {
'nest_base': [2, 2, 20],
'nest_small': [2, 2... | pytorch-image-models/convert/convert_nest_flax.py/0 | {
"file_path": "pytorch-image-models/convert/convert_nest_flax.py",
"repo_id": "pytorch-image-models",
"token_count": 2670
} | 173 |
# CSP-ResNeXt
**CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use o... | pytorch-image-models/docs/models/.templates/models/csp-resnext.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/csp-resnext.md",
"repo_id": "pytorch-image-models",
"token_count": 916
} | 174 |
# HRNet
**HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradual... | pytorch-image-models/docs/models/.templates/models/hrnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/hrnet.md",
"repo_id": "pytorch-image-models",
"token_count": 4240
} | 175 |
# SWSL 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 ... | pytorch-image-models/docs/models/.templates/models/swsl-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/swsl-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1630
} | 176 |
# Hugging Face Timm Docs
## Getting Started
```
pip install git+https://github.com/huggingface/doc-builder.git@main#egg=hf-doc-builder
pip install watchdog black
```
## Preview the Docs Locally
```
doc-builder preview timm hfdocs/source
```
| pytorch-image-models/hfdocs/README.md/0 | {
"file_path": "pytorch-image-models/hfdocs/README.md",
"repo_id": "pytorch-image-models",
"token_count": 88
} | 177 |
# ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https:/... | pytorch-image-models/hfdocs/source/models/ecaresnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ecaresnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3641
} | 178 |
# ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the [1×1 convolution](https://paperswithcode.co... | pytorch-image-models/hfdocs/source/models/resnet-d.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/resnet-d.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3932
} | 179 |
Import:
- ./docs/models/*.md
Library:
Name: PyTorch Image Models
Headline: PyTorch image models, scripts, pretrained weights
Website: https://rwightman.github.io/pytorch-image-models/
Repository: https://github.com/rwightman/pytorch-image-models
Docs: https://rwightman.github.io/pytorch-image-models/
README... | pytorch-image-models/model-index.yml/0 | {
"file_path": "pytorch-image-models/model-index.yml",
"repo_id": "pytorch-image-models",
"token_count": 253
} | 180 |
"""Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a ... | pytorch-image-models/tests/test_models.py/0 | {
"file_path": "pytorch-image-models/tests/test_models.py",
"repo_id": "pytorch-image-models",
"token_count": 9191
} | 181 |
""" Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from... | pytorch-image-models/timm/data/loader.py/0 | {
"file_path": "pytorch-image-models/timm/data/loader.py",
"repo_id": "pytorch-image-models",
"token_count": 6793
} | 182 |
""" Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabels... | pytorch-image-models/timm/data/real_labels.py/0 | {
"file_path": "pytorch-image-models/timm/data/real_labels.py",
"repo_id": "pytorch-image-models",
"token_count": 854
} | 183 |
""" Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to... | pytorch-image-models/timm/layers/config.py/0 | {
"file_path": "pytorch-image-models/timm/layers/config.py",
"repo_id": "pytorch-image-models",
"token_count": 1787
} | 184 |
from typing import Tuple
import torch
def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
"""generate N-D grid in dimension order.
The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
That is, the statement
[X1,X2,X3] = ndgrid(x1,x2,x3)
produc... | pytorch-image-models/timm/layers/grid.py/0 | {
"file_path": "pytorch-image-models/timm/layers/grid.py",
"repo_id": "pytorch-image-models",
"token_count": 565
} | 185 |
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
num_prefix_tokens: int = 1,
... | pytorch-image-models/timm/layers/patch_dropout.py/0 | {
"file_path": "pytorch-image-models/timm/layers/patch_dropout.py",
"repo_id": "pytorch-image-models",
"token_count": 842
} | 186 |
import torch
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_no... | pytorch-image-models/timm/layers/weight_init.py/0 | {
"file_path": "pytorch-image-models/timm/layers/weight_init.py",
"repo_id": "pytorch-image-models",
"token_count": 1838
} | 187 |
import copy
from collections import deque, defaultdict
from dataclasses import dataclass, field, replace, asdict
from typing import Any, Deque, Dict, Tuple, Optional, Union
__all__ = ['PretrainedCfg', 'filter_pretrained_cfg', 'DefaultCfg']
@dataclass
class PretrainedCfg:
"""
"""
# weight source location... | pytorch-image-models/timm/models/_pretrained.py/0 | {
"file_path": "pytorch-image-models/timm/models/_pretrained.py",
"repo_id": "pytorch-image-models",
"token_count": 1341
} | 188 |
""" CrossViT Model
@inproceedings{
chen2021crossvit,
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
booktitle={International Conference on Computer Vision (ICCV)},
year={2021}
}
Paper l... | pytorch-image-models/timm/models/crossvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/crossvit.py",
"repo_id": "pytorch-image-models",
"token_count": 12463
} | 189 |
""" MaxVit and CoAtNet Vision Transformer - CNN Hybrids in PyTorch
This is a from-scratch implementation of both CoAtNet and MaxVit in PyTorch.
99% of the implementation was done from papers, however last minute some adjustments were made
based on the (as yet unfinished?) public code release https://github.com/google... | pytorch-image-models/timm/models/maxxvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/maxxvit.py",
"repo_id": "pytorch-image-models",
"token_count": 42620
} | 190 |
""" RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | pytorch-image-models/timm/models/repvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/repvit.py",
"repo_id": "pytorch-image-models",
"token_count": 8357
} | 191 |
""" Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... | pytorch-image-models/timm/models/twins.py/0 | {
"file_path": "pytorch-image-models/timm/models/twins.py",
"repo_id": "pytorch-image-models",
"token_count": 9685
} | 192 |
"""
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": 1863
} | 193 |
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
from .scheduler_factory import create_scheduler, create_scheduler_v2, schedul... | pytorch-image-models/timm/scheduler/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 112
} | 194 |
""" JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT exectutor. These... | pytorch-image-models/timm/utils/jit.py/0 | {
"file_path": "pytorch-image-models/timm/utils/jit.py",
"repo_id": "pytorch-image-models",
"token_count": 1036
} | 195 |
<div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a>
# Text Generation Inference
<a href="https://github.com/huggingface/text-generation-infer... | text-generation-inference/README.md/0 | {
"file_path": "text-generation-inference/README.md",
"repo_id": "text-generation-inference",
"token_count": 3371
} | 196 |
[tool.poetry]
name = "text-generation"
version = "0.6.1"
description = "Hugging Face Text Generation Python Client"
license = "Apache-2.0"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
maintainers = ["Olivier Dehaene <olivier@huggingface.co>"]
readme = "README.md"
homepage = "https://github.com/huggingface/tex... | text-generation-inference/clients/python/pyproject.toml/0 | {
"file_path": "text-generation-inference/clients/python/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 336
} | 197 |
# Text-generation-launcher arguments
<!-- WRAP CODE BLOCKS -->
```shell
Text Generation Launcher
Usage: text-generation-launcher [OPTIONS]
Options:
```
## MODEL_ID
```shell
--model-id <MODEL_ID>
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `Open... | text-generation-inference/docs/source/basic_tutorials/launcher.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/launcher.md",
"repo_id": "text-generation-inference",
"token_count": 6114
} | 198 |
# Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
## Supported Models
The following models are optimized and can be served with TGI, which uses custom CUDA k... | text-generation-inference/docs/source/supported_models.md/0 | {
"file_path": "text-generation-inference/docs/source/supported_models.md",
"repo_id": "text-generation-inference",
"token_count": 1170
} | 199 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -10.0,
"text": "Test"
},
{
"id": 3853,... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1031
} | 200 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 3735,
"logprob": -12.9140625,
"text": "Test"
},
{
"id": 2... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral.json",
"repo_id": "text-generation-inference",
"token_count": 1050
} | 201 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 60,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6328125,
"text": " print"
},
{
"id"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_default_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder_default_params.json",
"repo_id": "text-generation-inference",
"token_count": 4734
} | 202 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 5,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": 0,
"tokens": [
{
"id": 926,
"logprob": -4.3554688,
"special... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base.json",
"repo_id": "text-generation-inference",
"token_count": 532
} | 203 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle(launcher):
with launcher(
"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
num_shard=1,
quantize="awq",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_awq(... | text-generation-inference/integration-tests/models/test_flash_awq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_awq.py",
"repo_id": "text-generation-inference",
"token_count": 842
} | 204 |
import pytest
@pytest.fixture(scope="module")
def flash_starcoder_gptq_handle(launcher):
with launcher("Narsil/starcoder-gptq", num_shard=2, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder_gptq(flash_starcoder_gptq_handle):
await flash_starcoder_gpt... | text-generation-inference/integration-tests/models/test_flash_starcoder_gptq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_starcoder_gptq.py",
"repo_id": "text-generation-inference",
"token_count": 710
} | 205 |
use std::fmt;
use std::process::Command;
pub(crate) struct Env {
cargo_target: &'static str,
cargo_version: &'static str,
git_sha: &'static str,
docker_label: &'static str,
nvidia_env: String,
}
impl Env {
pub fn new() -> Self {
let nvidia_env = nvidia_smi();
Self {
... | text-generation-inference/launcher/src/env_runtime.rs/0 | {
"file_path": "text-generation-inference/launcher/src/env_runtime.rs",
"repo_id": "text-generation-inference",
"token_count": 650
} | 206 |
[package]
name = "grpc-metadata"
version = "0.1.0"
edition = "2021"
[dependencies]
opentelemetry = "^0.20"
tonic = "^0.10"
tracing = "^0.1"
tracing-opentelemetry = "^0.21"
| text-generation-inference/router/grpc-metadata/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/grpc-metadata/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 83
} | 207 |
flash_att_v2_commit_cuda := 02ac572f3ffc4f402e4183aaa6824b45859d3ed3
flash_att_v2_commit_rocm := 8736558c287ff2ef28b24878e42828c595ac3e69
flash-attention-v2-cuda:
# Clone flash attention
pip install -U packaging ninja --no-cache-dir
git clone https://github.com/HazyResearch/flash-attention.git flash-attention-v2... | text-generation-inference/server/Makefile-flash-att-v2/0 | {
"file_path": "text-generation-inference/server/Makefile-flash-att-v2",
"repo_id": "text-generation-inference",
"token_count": 496
} | 208 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include <torch/extension.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include "util.cuh"
#include "tuning.h"
#include "cuda_buffers.cu... | text-generation-inference/server/exllama_kernels/exllama_kernels/exllama_ext.cpp/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/exllama_ext.cpp",
"repo_id": "text-generation-inference",
"token_count": 3279
} | 209 |
#ifndef _qdq_2_cuh
#define _qdq_2_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_2BIT == 1
// Permutation:
//
// ffddbb99 77553311 eeccaa88 66442200
__forceinline__ __device__ void shuffle_2bit_16
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unrol... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh",
"repo_id": "text-generation-inference",
"token_count": 1589
} | 210 |
import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.utils import weight_hub_files, download_weights
from text_generation_server.models.bl... | text-generation-inference/server/tests/models/test_bloom.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_bloom.py",
"repo_id": "text-generation-inference",
"token_count": 5296
} | 211 |
import math
import torch
from typing import Optional, List, Tuple
BLOCK_SIZE: int = 16
# Will be set in warmup
CACHE_MANAGER: Optional["CacheManager"] = None
class CacheManager:
def __init__(
self,
num_blocks: int,
num_layers: int,
num_heads: int,
head_size: int,
... | text-generation-inference/server/text_generation_server/models/cache_manager.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/cache_manager.py",
"repo_id": "text-generation-inference",
"token_count": 2033
} | 212 |
# 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": 28490
} | 213 |
import torch
import torch.distributed
from opentelemetry import trace
from transformers import AutoConfig, AutoTokenizer
from typing import Optional
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_phi_modeling import (
FlashPhiForCausalLM,
PhiCo... | text-generation-inference/server/text_generation_server/models/flash_phi.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_phi.py",
"repo_id": "text-generation-inference",
"token_count": 1738
} | 214 |
import torch
import torch.distributed
from typing import Optional, List
from transformers import AutoTokenizer, AutoModelForCausalLM
from text_generation_server.models import CausalLM
FIM_PREFIX = "<fim-prefix>"
FIM_MIDDLE = "<fim-middle>"
FIM_SUFFIX = "<fim-suffix>"
FIM_PAD = "<fim-pad>"
EOD = "<|endoftext|>"
cla... | text-generation-inference/server/text_generation_server/models/santacoder.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/santacoder.py",
"repo_id": "text-generation-inference",
"token_count": 1196
} | 215 |
import math
import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd
try:
import triton
import triton.language as tl
from . import custom_autotune
# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
configs=[
... | text-generation-inference/server/text_generation_server/utils/gptq/quant_linear.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/gptq/quant_linear.py",
"repo_id": "text-generation-inference",
"token_count": 7008
} | 216 |
[target.aarch64-unknown-linux-musl]
linker = "aarch64-linux-musl-gcc"
rustflags = ["-C", "target-feature=-crt-static"]
| tokenizers/bindings/node/.cargo/config.toml/0 | {
"file_path": "tokenizers/bindings/node/.cargo/config.toml",
"repo_id": "tokenizers",
"token_count": 50
} | 217 |
/* tslint:disable */
/* eslint-disable */
/* auto-generated by NAPI-RS */
export function bpeDecoder(suffix?: string | undefined | null): Decoder
export function byteFallbackDecoder(): Decoder
export function ctcDecoder(
padToken?: string = '<pad>',
wordDelimiterToken?: string | undefined | null,
cleanup?: bool... | tokenizers/bindings/node/index.d.ts/0 | {
"file_path": "tokenizers/bindings/node/index.d.ts",
"repo_id": "tokenizers",
"token_count": 2717
} | 218 |
# `tokenizers-android-arm64`
This is the **aarch64-linux-android** binary for `tokenizers`
| tokenizers/bindings/node/npm/android-arm64/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/android-arm64/README.md",
"repo_id": "tokenizers",
"token_count": 31
} | 219 |
# `tokenizers-linux-x64-musl`
This is the **x86_64-unknown-linux-musl** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-x64-musl/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-x64-musl/README.md",
"repo_id": "tokenizers",
"token_count": 38
} | 220 |
use crate::arc_rwlock_serde;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use serde::{Deserialize, Serialize};
use std::sync::{Arc, RwLock};
use tk::pre_tokenizers::PreTokenizerWrapper;
use tk::PreTokenizedString;
use tk::SplitDelimiterBehavior;
use tokenizers as tk;
#[napi(string_enum)]
pub enum JsSplitDelimi... | tokenizers/bindings/node/src/pre_tokenizers.rs/0 | {
"file_path": "tokenizers/bindings/node/src/pre_tokenizers.rs",
"repo_id": "tokenizers",
"token_count": 2935
} | 221 |
.PHONY: style check-style test
DATA_DIR = data
dir_guard=@mkdir -p $(@D)
check_dirs := examples py_src/tokenizers tests
# Format source code automatically
style:
python stub.py
ruff check $(check_dirs) --fix
ruff format $(check_dirs)t
# Check the source code is formatted correctly
check-style:
python stub.py... | tokenizers/bindings/python/Makefile/0 | {
"file_path": "tokenizers/bindings/python/Makefile",
"repo_id": "tokenizers",
"token_count": 357
} | 222 |
from typing import Dict, Iterator, List, Optional, Tuple, Union
from tokenizers import AddedToken, Tokenizer, decoders, pre_tokenizers, processors, trainers
from tokenizers.models import BPE
from tokenizers.normalizers import Lowercase, Sequence, unicode_normalizer_from_str
from .base_tokenizer import BaseTokenizer
... | tokenizers/bindings/python/py_src/tokenizers/implementations/byte_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/byte_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 1978
} | 223 |
# Generated content DO NOT EDIT
class Trainer:
"""
Base class for all trainers
This class is not supposed to be instantiated directly. Instead, any implementation of a
Trainer will return an instance of this class when instantiated.
"""
class BpeTrainer(Trainer):
"""
Trainer capable of tra... | tokenizers/bindings/python/py_src/tokenizers/trainers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/trainers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 2178
} | 224 |
use std::collections::{hash_map::DefaultHasher, HashMap};
use std::hash::{Hash, Hasher};
use numpy::{npyffi, PyArray1};
use pyo3::class::basic::CompareOp;
use pyo3::exceptions;
use pyo3::intern;
use pyo3::prelude::*;
use pyo3::types::*;
use tk::models::bpe::BPE;
use tk::tokenizer::{
Model, PaddingDirection, Paddin... | tokenizers/bindings/python/src/tokenizer.rs/0 | {
"file_path": "tokenizers/bindings/python/src/tokenizer.rs",
"repo_id": "tokenizers",
"token_count": 26008
} | 225 |
import json
import pickle
import pytest
from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.pre_tokenizers import ByteLevel as ByteLevelPreTokenizer
from tokenizers.processors import (
BertProcessing,
ByteLevel,
PostProcessor,
RobertaProcessing,
Sequence,
Templat... | tokenizers/bindings/python/tests/bindings/test_processors.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_processors.py",
"repo_id": "tokenizers",
"token_count": 4124
} | 226 |
## Requirements
In order to generate the documentation, it is necessary to have a Python environment with the
following:
```python
pip install sphinx sphinx_rtd_theme setuptools_rust
```
It is also necessary to have the `tokenizers` library in this same environment, for Sphinx to
generate all the API Reference and li... | tokenizers/docs/README.md/0 | {
"file_path": "tokenizers/docs/README.md",
"repo_id": "tokenizers",
"token_count": 266
} | 227 |
# Installation
<tokenizerslangcontent>
<python>
🤗 Tokenizers is tested on Python 3.5+.
You should install 🤗 Tokenizers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're
unfamiliar with Python virtual environments, check out the [user
guide](https://packaging.python.org/guides/instal... | tokenizers/docs/source-doc-builder/installation.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/installation.mdx",
"repo_id": "tokenizers",
"token_count": 554
} | 228 |
.. only:: python
.. include:: python.inc
.. only:: rust
.. include:: rust.inc
.. only:: node
.. include:: node.inc
| tokenizers/docs/source/api/reference.rst/0 | {
"file_path": "tokenizers/docs/source/api/reference.rst",
"repo_id": "tokenizers",
"token_count": 47
} | 229 |
DATA_DIR = data
BENCHMARK_DIR = benches
TESTS_DIR = tests
dir_guard=@mkdir -p $(@D)
SHARED_RESOURCES = $(DATA_DIR)/gpt2-vocab.json $(DATA_DIR)/gpt2-merges.txt $(DATA_DIR)/bert-base-uncased-vocab.txt $(DATA_DIR)/big.txt $(DATA_DIR)/small.txt $(DATA_DIR)/albert-base-v1-tokenizer.json
BENCHMARK_RESOURCES = $(SHARED_RES... | tokenizers/tokenizers/Makefile/0 | {
"file_path": "tokenizers/tokenizers/Makefile",
"repo_id": "tokenizers",
"token_count": 939
} | 230 |
pub mod bpe;
pub mod byte_fallback;
pub mod ctc;
pub mod fuse;
pub mod sequence;
pub mod strip;
pub mod wordpiece;
// Re-export these as decoders
pub use super::pre_tokenizers::byte_level;
pub use super::pre_tokenizers::metaspace;
use serde::{Deserialize, Serialize};
use crate::decoders::bpe::BPEDecoder;
use crate::... | tokenizers/tokenizers/src/decoders/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/mod.rs",
"repo_id": "tokenizers",
"token_count": 1434
} | 231 |
use std::collections::HashMap;
use std::hash::Hash;
#[derive(Default)]
pub struct TrieBuilder<Label> {
trie: Trie<Label>,
}
impl<Label: Eq + Hash + Copy> TrieBuilder<Label> {
pub fn push(&mut self, element: &[Label]) {
self.trie.push(element);
}
pub fn build(self) -> Trie<Label> {
sel... | tokenizers/tokenizers/src/models/unigram/trie.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/trie.rs",
"repo_id": "tokenizers",
"token_count": 944
} | 232 |
use std::collections::{HashMap, HashSet};
use crate::utils::SysRegex;
use serde::{Deserialize, Serialize};
use crate::tokenizer::{
Decoder, Encoding, PostProcessor, PreTokenizedString, PreTokenizer, Result,
SplitDelimiterBehavior,
};
use crate::utils::macro_rules_attribute;
/// Converts bytes to unicode char... | tokenizers/tokenizers/src/pre_tokenizers/byte_level.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/byte_level.rs",
"repo_id": "tokenizers",
"token_count": 10930
} | 233 |
//! # Template Processing
//!
//! Provides a way to specify templates in order to add the special tokens to each
//! input sequence as relevant.
//!
//! ## Example
//!
//! Let's take `BERT` tokenizer as an example. It uses two special tokens, used to
//! delimitate each sequence. `[CLS]` is always used at the beginning... | tokenizers/tokenizers/src/processors/template.rs/0 | {
"file_path": "tokenizers/tokenizers/src/processors/template.rs",
"repo_id": "tokenizers",
"token_count": 21195
} | 234 |
#[cfg(feature = "progressbar")]
pub(crate) use indicatif::{ProgressBar, ProgressStyle};
#[cfg(not(feature = "progressbar"))]
mod progressbar {
use std::borrow::Cow;
pub struct ProgressBar;
impl ProgressBar {
pub fn new(_length: u64) -> Self {
Self {}
}
pub fn set_length... | tokenizers/tokenizers/src/utils/progress.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/progress.rs",
"repo_id": "tokenizers",
"token_count": 403
} | 235 |
FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
RUN apt update
RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
RUN python3 -m pip install --no-cache-dir --upgrade pip
ARG REF=main
RUN git clone https://githu... | transformers/docker/transformers-pytorch-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-pytorch-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 611
} | 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/autoclass_tutorial.md/0 | {
"file_path": "transformers/docs/source/de/autoclass_tutorial.md",
"repo_id": "transformers",
"token_count": 2644
} | 237 |
# Optimizing inference
perf_infer_gpu_many: perf_infer_gpu_one
| transformers/docs/source/en/_redirects.yml/0 | {
"file_path": "transformers/docs/source/en/_redirects.yml",
"repo_id": "transformers",
"token_count": 25
} | 238 |
<!--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/custom_tools.md/0 | {
"file_path": "transformers/docs/source/en/custom_tools.md",
"repo_id": "transformers",
"token_count": 8736
} | 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/model.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/model.md",
"repo_id": "transformers",
"token_count": 2010
} | 240 |
<!--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/bark.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bark.md",
"repo_id": "transformers",
"token_count": 2760
} | 241 |
<!--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/blip.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/blip.md",
"repo_id": "transformers",
"token_count": 1242
} | 242 |
<!--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/detr.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/detr.md",
"repo_id": "transformers",
"token_count": 4104
} | 243 |
<!--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/esm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/esm.md",
"repo_id": "transformers",
"token_count": 1906
} | 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/gpt2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/gpt2.md",
"repo_id": "transformers",
"token_count": 2619
} | 245 |
<!--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/jukebox.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/jukebox.md",
"repo_id": "transformers",
"token_count": 1219
} | 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/m2m_100.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/m2m_100.md",
"repo_id": "transformers",
"token_count": 1685
} | 247 |
<!--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/mluke.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mluke.md",
"repo_id": "transformers",
"token_count": 825
} | 248 |
<!--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/phobert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/phobert.md",
"repo_id": "transformers",
"token_count": 776
} | 249 |
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