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<jupyter_start><jupyter_text>Going Production: Auto-scale Hugging Face Transformer Endpoints with Amazon SageMaker 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 real-time inference. In this example we are going to... | notebooks/sagemaker/13_deploy_and_autoscaling_transformers/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/13_deploy_and_autoscaling_transformers/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 2793
} | 155 |
import os
from transformers import AutoConfig, AutoTokenizer
import torch
import torch.neuron
# To use one neuron core per worker
os.environ["NEURON_RT_NUM_CORES"] = "1"
# saved weights name
AWS_NEURON_TRACED_WEIGHTS_NAME = "neuron_model.pt"
def model_fn(model_dir):
# load tokenizer and neuron model from model_... | notebooks/sagemaker/18_inferentia_inference/code/inference.py/0 | {
"file_path": "notebooks/sagemaker/18_inferentia_inference/code/inference.py",
"repo_id": "notebooks",
"token_count": 519
} | 156 |
import os
import argparse
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
set_seed,
default_data_collator,
)
from datasets import load_from_disk
import torch
from transformers import Trainer, TrainingArguments
import torch.distributed as dist
def safe_save_model_for_hf_trainer(trainer:... | notebooks/sagemaker/25_pytorch_fsdp_model_parallelism/scripts/run_clm.py/0 | {
"file_path": "notebooks/sagemaker/25_pytorch_fsdp_model_parallelism/scripts/run_clm.py",
"repo_id": "notebooks",
"token_count": 1807
} | 157 |
- title: Get started
sections:
- local: index
title: 🤗 PEFT
- local: quicktour
title: Quicktour
- local: install
title: Installation
- title: Tutorial
sections:
- local: tutorial/peft_model_config
title: Configurations and models
- local: tutorial/peft_integrations
title: Integration... | peft/docs/source/_toctree.yml/0 | {
"file_path": "peft/docs/source/_toctree.yml",
"repo_id": "peft",
"token_count": 1139
} | 158 |
<jupyter_start><jupyter_code>from transformers import AutoModelForCausalLM
from peft import get_peft_config, get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType, PeftType
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
fr... | peft/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb",
"repo_id": "peft",
"token_count": 4787
} | 159 |
import argparse
import os
from typing import Dict
import torch
from diffusers import UNet2DConditionModel
from safetensors.torch import save_file
from transformers import CLIPTextModel
from peft import PeftModel, get_peft_model_state_dict
# Default kohya_ss LoRA replacement modules
# https://github.com/kohya-ss/sd-... | peft/examples/lora_dreambooth/convert_peft_sd_lora_to_kohya_ss.py/0 | {
"file_path": "peft/examples/lora_dreambooth/convert_peft_sd_lora_to_kohya_ss.py",
"repo_id": "peft",
"token_count": 1639
} | 160 |
import argparse
import evaluate
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_li... | peft/examples/sequence_classification/peft_no_lora_accelerate.py/0 | {
"file_path": "peft/examples/sequence_classification/peft_no_lora_accelerate.py",
"repo_id": "peft",
"token_count": 3361
} | 161 |
import inspect
from copy import deepcopy
from functools import update_wrapper
from types import MethodType
from .peft_model import PeftModel
def update_forward_signature(model: PeftModel) -> None:
"""
Args:
Updates the forward signature of the PeftModel to include parents class signature
model (`... | peft/src/peft/helpers.py/0 | {
"file_path": "peft/src/peft/helpers.py",
"repo_id": "peft",
"token_count": 1690
} | 162 |
# coding=utf-8
# 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 ap... | peft/src/peft/tuners/adaption_prompt/model.py/0 | {
"file_path": "peft/src/peft/tuners/adaption_prompt/model.py",
"repo_id": "peft",
"token_count": 2820
} | 163 |
# coding=utf-8
# 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 ap... | peft/src/peft/tuners/lora/bnb.py/0 | {
"file_path": "peft/src/peft/tuners/lora/bnb.py",
"repo_id": "peft",
"token_count": 8138
} | 164 |
# coding=utf-8
# 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 ap... | peft/src/peft/utils/constants.py/0 | {
"file_path": "peft/src/peft/utils/constants.py",
"repo_id": "peft",
"token_count": 2630
} | 165 |
# coding=utf-8
# 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 ap... | peft/tests/test_gpu_examples.py/0 | {
"file_path": "peft/tests/test_gpu_examples.py",
"repo_id": "peft",
"token_count": 26064
} | 166 |
title: Model Pages | pytorch-image-models/docs/models/.pages/0 | {
"file_path": "pytorch-image-models/docs/models/.pages",
"repo_id": "pytorch-image-models",
"token_count": 4
} | 167 |
# ESE-VoVNet
**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel.
Read about [one-shot aggregatio... | pytorch-image-models/docs/models/.templates/models/ese-vovnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ese-vovnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1127
} | 168 |
# MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
{% include 'code_snippets.md' %}
## How do I train this model?
... | pytorch-image-models/docs/models/.templates/models/mixnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/mixnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1878
} | 169 |
# SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) 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/se-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/se-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1371
} | 170 |
# TResNet
A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-down... | pytorch-image-models/docs/models/.templates/models/tresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/tresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 3391
} | 171 |
# # Ensemble Adversarial Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception arch... | pytorch-image-models/docs/models/ensemble-adversarial.md/0 | {
"file_path": "pytorch-image-models/docs/models/ensemble-adversarial.md",
"repo_id": "pytorch-image-models",
"token_count": 2206
} | 172 |
# RexNet
**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.paperswithcode.com/method/relu6).
## How do... | pytorch-image-models/docs/models/rexnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/rexnet.md",
"repo_id": "pytorch-image-models",
"token_count": 3081
} | 173 |
# AdvProp (EfficientNet)
**AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.
The w... | pytorch-image-models/hfdocs/source/models/advprop.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/advprop.mdx",
"repo_id": "pytorch-image-models",
"token_count": 6032
} | 174 |
# (Gluon) ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformatio... | pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2709
} | 175 |
# NASNet
**NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('nasnetalarge', pretrained=T... | pytorch-image-models/hfdocs/source/models/nasnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/nasnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1536
} | 176 |
# SK-ResNeXt
**SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK ... | pytorch-image-models/hfdocs/source/models/skresnext.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/skresnext.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1643
} | 177 |
# Models
[[autodoc]] timm.create_model
[[autodoc]] timm.list_models
| pytorch-image-models/hfdocs/source/reference/models.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/reference/models.mdx",
"repo_id": "pytorch-image-models",
"token_count": 29
} | 178 |
from abc import abstractmethod
class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index, basename=basename, absolute=absolute)... | pytorch-image-models/timm/data/readers/reader.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader.py",
"repo_id": "pytorch-image-models",
"token_count": 171
} | 179 |
""" Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | pytorch-image-models/timm/layers/activations_jit.py/0 | {
"file_path": "pytorch-image-models/timm/layers/activations_jit.py",
"repo_id": "pytorch-image-models",
"token_count": 1008
} | 180 |
""" Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from torchvision.ops.misc import Fr... | pytorch-image-models/timm/layers/create_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/create_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 630
} | 181 |
""" Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
@misc{2102.08602,
Author = {Irwan Bello},
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
Year = {2021},
}
Status:
This impl is a WIP. Code snippets in the... | pytorch-image-models/timm/layers/lambda_layer.py/0 | {
"file_path": "pytorch-image-models/timm/layers/lambda_layer.py",
"repo_id": "pytorch-image-models",
"token_count": 2611
} | 182 |
""" Selective Kernel Convolution/Attention
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv_bn_act import ConvNormActAa
from .helpers import make_divisible
from .trace_utils import _assert
de... | pytorch-image-models/timm/layers/selective_kernel.py/0 | {
"file_path": "pytorch-image-models/timm/layers/selective_kernel.py",
"repo_id": "pytorch-image-models",
"token_count": 2318
} | 183 |
from .beit import *
from .byoanet import *
from .byobnet import *
from .cait import *
from .coat import *
from .convit import *
from .convmixer import *
from .convnext import *
from .crossvit import *
from .cspnet import *
from .davit import *
from .deit import *
from .densenet import *
from .dla import *
from .dpn imp... | pytorch-image-models/timm/models/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/models/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 1061
} | 184 |
""" PyTorch implementation of DualPathNetworks
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.
This implementation is compatible with the pretrained weights from cypw's MXNet implementation.
Hacked together b... | pytorch-image-models/timm/models/dpn.py/0 | {
"file_path": "pytorch-image-models/timm/models/dpn.py",
"repo_id": "pytorch-image-models",
"token_count": 6985
} | 185 |
from ._builder import *
from ._helpers import *
from ._manipulate import *
from ._prune import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)
| pytorch-image-models/timm/models/helpers.py/0 | {
"file_path": "pytorch-image-models/timm/models/helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 64
} | 186 |
""" Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... | pytorch-image-models/timm/models/nest.py/0 | {
"file_path": "pytorch-image-models/timm/models/nest.py",
"repo_id": "pytorch-image-models",
"token_count": 10075
} | 187 |
""" Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | pytorch-image-models/timm/models/sequencer.py/0 | {
"file_path": "pytorch-image-models/timm/models/sequencer.py",
"repo_id": "pytorch-image-models",
"token_count": 9227
} | 188 |
""" VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | pytorch-image-models/timm/models/vovnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/vovnet.py",
"repo_id": "pytorch-image-models",
"token_count": 7769
} | 189 |
import math
import torch
from torch.optim.optimizer import Optimizer
class Nadam(Optimizer):
"""Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).
It has been proposed in `Incorporating Nesterov Momentum into Adam`__.
Arguments:
params (iterable): iterable of parameters ... | pytorch-image-models/timm/optim/nadam.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nadam.py",
"repo_id": "pytorch-image-models",
"token_count": 1921
} | 190 |
""" TanH Scheduler
TanH schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
"""
Hyberbolic-Tan... | pytorch-image-models/timm/scheduler/tanh_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/tanh_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1960
} | 191 |
""" Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | pytorch-image-models/timm/utils/summary.py/0 | {
"file_path": "pytorch-image-models/timm/utils/summary.py",
"repo_id": "pytorch-image-models",
"token_count": 633
} | 192 |
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.71 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef as planner
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN ca... | text-generation-inference/Dockerfile_amd/0 | {
"file_path": "text-generation-inference/Dockerfile_amd",
"repo_id": "text-generation-inference",
"token_count": 2127
} | 193 |
unit-tests:
python -m pytest --cov=text_generation tests
install:
pip install pip --upgrade
pip install -e . | text-generation-inference/clients/python/Makefile/0 | {
"file_path": "text-generation-inference/clients/python/Makefile",
"repo_id": "text-generation-inference",
"token_count": 40
} | 194 |
- sections:
- local: index
title: Text Generation Inference
- local: quicktour
title: Quick Tour
- local: installation
title: Installation
- local: supported_models
title: Supported Models and Hardware
- local: messages_api
title: Messages API
title: Getting started
- sections:
- local... | text-generation-inference/docs/source/_toctree.yml/0 | {
"file_path": "text-generation-inference/docs/source/_toctree.yml",
"repo_id": "text-generation-inference",
"token_count": 384
} | 195 |
# Quick Tour
The easiest way of getting started is using the official Docker container. Install Docker following [their installation instructions](https://docs.docker.com/get-docker/).
Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) model with TGI. Here is an exampl... | text-generation-inference/docs/source/quicktour.md/0 | {
"file_path": "text-generation-inference/docs/source/quicktour.md",
"repo_id": "text-generation-inference",
"token_count": 1205
} | 196 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama.json",
"repo_id": "text-generation-inference",
"token_count": 1050
} | 197 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 14402,
"logprob": null,
"text": "Test"
},
{
"id": 2581,
"logprob": -11.6171875,
"text": " request"
}
],
"see... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi.json",
"repo_id": "text-generation-inference",
"token_count": 1003
} | 198 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 9,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": 0,
"tokens": [
{
"id": 16017,
"logprob": -0.30908203,
"spec... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 831
} | 199 |
import pytest
@pytest.fixture(scope="module")
def flash_mistral_handle(launcher):
with launcher("mistralai/Mistral-7B-Instruct-v0.1") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_mistral(flash_mistral_handle):
await flash_mistral_handle.health(300)
return flash_mistral... | text-generation-inference/integration-tests/models/test_flash_mistral.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_mistral.py",
"repo_id": "text-generation-inference",
"token_count": 738
} | 200 |
//! Text Generation gRPC client library
mod client;
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod sharded_client;
pub use client::Client;
pub use pb::generate::v2::HealthResponse;
pub use pb::generate::v2::InfoResponse as ShardInfo;
pub use pb::generate::v2::{
Batch, CachedBatch, FinishReason, Genera... | text-generation-inference/router/client/src/lib.rs/0 | {
"file_path": "text-generation-inference/router/client/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 460
} | 201 |
awq_commit := f084f40bd996f3cf3a0633c1ad7d9d476c318aaa
awq:
rm -rf llm-awq
git clone https://github.com/mit-han-lab/llm-awq
build-awq: awq
cd llm-awq/ && git fetch && git checkout $(awq_commit)
cd llm-awq/awq/kernels && python setup.py build
install-awq: build-awq
pip uninstall awq_inference_engine -y || true
... | text-generation-inference/server/Makefile-awq/0 | {
"file_path": "text-generation-inference/server/Makefile-awq",
"repo_id": "text-generation-inference",
"token_count": 165
} | 202 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#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 count along columns in out, each t... | 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": 2516
} | 203 |
#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": 10391
} | 204 |
import torch
from loguru import logger
from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto import modeling_auto
from typing import Optional
from text_generation_server.utils.speculate import get_speculate, set_speculate
from text_generation_server.models.model import Model
from... | 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": 7094
} | 205 |
# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of ... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_perceiver.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_perceiver.py",
"repo_id": "text-generation-inference",
"token_count": 5171
} | 206 |
import re
import torch
import torch.distributed
from typing import List, Optional, Type
from transformers import (
AutoTokenizer,
AutoConfig,
PreTrainedTokenizerBase,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation... | text-generation-inference/server/text_generation_server/models/galactica.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/galactica.py",
"repo_id": "text-generation-inference",
"token_count": 3723
} | 207 |
from text_generation_server.utils.convert import convert_file, convert_files
from text_generation_server.utils.dist import initialize_torch_distributed
from text_generation_server.utils.weights import Weights
from text_generation_server.utils.peft import download_and_unload_peft
from text_generation_server.utils.hub im... | text-generation-inference/server/text_generation_server/utils/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 417
} | 208 |
import torch
# vllm imports
from vllm import cache_ops
from vllm import attention_ops
_PARTITION_SIZE = 512
def reshape_and_cache(
key: torch.Tensor,
value: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
slots: torch.Tensor,
):
cache_ops.reshape_and_cache(key, value, key_c... | text-generation-inference/server/text_generation_server/utils/paged_attention.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/paged_attention.py",
"repo_id": "text-generation-inference",
"token_count": 1485
} | 209 |
[package]
authors = ["Nicolas Patry <nicolas@huggingface.co>"]
edition = "2021"
name = "node"
version = "0.15.2-dev.0"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[lib]
crate-type = ["cdylib"]
[dependencies]
napi = "2"
napi-derive = "2"
serde = { v... | tokenizers/bindings/node/Cargo.toml/0 | {
"file_path": "tokenizers/bindings/node/Cargo.toml",
"repo_id": "tokenizers",
"token_count": 200
} | 210 |
import { prependNormalizer, stripAccentsNormalizer, stripNormalizer } from '../../'
describe('stripNormalizer', () => {
it('instantiates with no parameters', () => {
const normalizer = stripNormalizer()
expect(normalizer.constructor.name).toEqual('Normalizer')
})
it('accepts `undefined` as first paramet... | tokenizers/bindings/node/lib/bindings/normalizers.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/normalizers.test.ts",
"repo_id": "tokenizers",
"token_count": 468
} | 211 |
{
"name": "tokenizers-linux-arm-gnueabihf",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"arm"
],
"main": "tokenizers.linux-arm-gnueabihf.node",
"files": [
"tokenizers.linux-arm-gnueabihf.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-r... | tokenizers/bindings/node/npm/linux-arm-gnueabihf/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm-gnueabihf/package.json",
"repo_id": "tokenizers",
"token_count": 278
} | 212 |
tab_spaces = 2
| tokenizers/bindings/node/rustfmt.toml/0 | {
"file_path": "tokenizers/bindings/node/rustfmt.toml",
"repo_id": "tokenizers",
"token_count": 7
} | 213 |
export type TextInputSequence = string
export type PreTokenizedInputSequence = string[]
export type InputSequence = TextInputSequence | PreTokenizedInputSequence
export type TextEncodeInput = TextInputSequence | [TextInputSequence, TextInputSequence]
export type PreTokenizedEncodeInput = PreTokenizedInputSequence | [P... | tokenizers/bindings/node/types.ts/0 | {
"file_path": "tokenizers/bindings/node/types.ts",
"repo_id": "tokenizers",
"token_count": 114
} | 214 |
from enum import Enum
from typing import List, Tuple, Union
Offsets = Tuple[int, int]
TextInputSequence = str
"""A :obj:`str` that represents an input sequence """
PreTokenizedInputSequence = Union[List[str], Tuple[str]]
"""A pre-tokenized input sequence. Can be one of:
- A :obj:`List` of :obj:`str`
- A :o... | tokenizers/bindings/python/py_src/tokenizers/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/__init__.py",
"repo_id": "tokenizers",
"token_count": 984
} | 215 |
# Generated content DO NOT EDIT
class PreTokenizer:
"""
Base class for all pre-tokenizers
This class is not supposed to be instantiated directly. Instead, any implementation of a
PreTokenizer will return an instance of this class when instantiated.
"""
def pre_tokenize(self, pretok):
"... | tokenizers/bindings/python/py_src/tokenizers/pre_tokenizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/pre_tokenizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 9461
} | 216 |
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::type_object::PyTypeInfo;
use std::fmt::{Display, Formatter, Result as FmtResult};
use tokenizers::tokenizer::Result;
#[derive(Debug)]
pub struct PyError(pub String);
impl PyError {
#[allow(dead_code)]
pub fn from(s: &str) -> Self {
PyError(String::f... | tokenizers/bindings/python/src/error.rs/0 | {
"file_path": "tokenizers/bindings/python/src/error.rs",
"repo_id": "tokenizers",
"token_count": 531
} | 217 |
import pytest
from tokenizers import BertWordPieceTokenizer
from ..utils import bert_files, data_dir, multiprocessing_with_parallelism
class TestBertWordPieceTokenizer:
def test_basic_encode(self, bert_files):
tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"])
# Encode with speci... | tokenizers/bindings/python/tests/implementations/test_bert_wordpiece.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_bert_wordpiece.py",
"repo_id": "tokenizers",
"token_count": 919
} | 218 |
# Post-processors
<tokenizerslangcontent>
<python>
## BertProcessing
[[autodoc]] tokenizers.processors.BertProcessing
## ByteLevel
[[autodoc]] tokenizers.processors.ByteLevel
## RobertaProcessing
[[autodoc]] tokenizers.processors.RobertaProcessing
## TemplateProcessing
[[autodoc]] tokenizers.processors.Template... | tokenizers/docs/source-doc-builder/api/post-processors.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/post-processors.mdx",
"repo_id": "tokenizers",
"token_count": 174
} | 219 |
Crates.io
----------------------------------------------------------------------------------------------------
🤗 Tokenizers is available on `crates.io <https://crates.io/crates/tokenizers>`__.
You just need to add it to your :obj:`Cargo.toml`::
tokenizers = "0.10"
| tokenizers/docs/source/installation/rust.inc/0 | {
"file_path": "tokenizers/docs/source/installation/rust.inc",
"repo_id": "tokenizers",
"token_count": 74
} | 220 |
{
"name": "create-wasm-app",
"version": "0.1.0",
"description": "create an app to consume rust-generated wasm packages",
"main": "index.js",
"bin": {
"create-wasm-app": ".bin/create-wasm-app.js"
},
"scripts": {
"build": "webpack --config webpack.config.js",
"start": "... | tokenizers/tokenizers/examples/unstable_wasm/www/package.json/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/package.json",
"repo_id": "tokenizers",
"token_count": 516
} | 221 |
#![allow(clippy::map_entry)]
use super::{Pair, WithFirstLastIterator, Word, BPE};
use crate::parallelism::*;
use crate::tokenizer::{AddedToken, Result, Trainer};
use crate::utils::progress::{ProgressBar, ProgressStyle};
use serde::{Deserialize, Serialize};
use std::cmp::Ordering;
use std::collections::{BinaryHeap, Has... | tokenizers/tokenizers/src/models/bpe/trainer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/trainer.rs",
"repo_id": "tokenizers",
"token_count": 15117
} | 222 |
pub mod bert;
pub mod precompiled;
pub mod prepend;
pub mod replace;
pub mod strip;
pub mod unicode;
pub mod utils;
pub use crate::normalizers::bert::BertNormalizer;
pub use crate::normalizers::precompiled::Precompiled;
pub use crate::normalizers::prepend::Prepend;
pub use crate::normalizers::replace::Replace;
pub use... | tokenizers/tokenizers/src/normalizers/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/mod.rs",
"repo_id": "tokenizers",
"token_count": 1090
} | 223 |
mod pre_tokenizer;
mod scripts;
// Re-export the PreTokenizer
pub use pre_tokenizer::UnicodeScripts;
| tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/mod.rs",
"repo_id": "tokenizers",
"token_count": 35
} | 224 |
use std::borrow::Borrow;
use std::collections::HashMap;
use std::hash::Hash;
use std::sync::RwLock;
/// The default capacity for a `BPE`'s internal cache.
pub static DEFAULT_CACHE_CAPACITY: usize = 10_000;
/// Provides a simple multithread cache to speed up BPE tokenization that will try to read values
/// concurrent... | tokenizers/tokenizers/src/utils/cache.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/cache.rs",
"repo_id": "tokenizers",
"token_count": 1436
} | 225 |
use tokenizers::models::bpe::BPE;
use tokenizers::pre_tokenizers::whitespace::Whitespace;
use tokenizers::{DecoderWrapper, NormalizerWrapper, PostProcessorWrapper, PreTokenizerWrapper};
use tokenizers::{Model, Tokenizer, TokenizerBuilder};
#[test]
fn bpe_values_after_training() {
let mut tokenizer = TokenizerBuild... | tokenizers/tokenizers/tests/training.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/training.rs",
"repo_id": "tokenizers",
"token_count": 851
} | 226 |
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11
FROM nvcr.io/nvidia/pytorch:23.11-py3
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
ARG PYTORCH='2.1.0'
# Example: `cu102`, `cu113`, etc.
ARG CUDA='cu121'
RUN apt -y update
RUN apt install -y libaio-... | transformers/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 893
} | 227 |
<!--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/add_tensorflow_model.md/0 | {
"file_path": "transformers/docs/source/de/add_tensorflow_model.md",
"repo_id": "transformers",
"token_count": 9906
} | 228 |
# 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
} | 229 |
<!--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": 8660
} | 230 |
<!--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
} | 231 |
<!--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
} | 232 |
<!--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
} | 233 |
<!--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
} | 234 |
<!--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
} | 235 |
<!--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/madlad-400.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/madlad-400.md",
"repo_id": "transformers",
"token_count": 930
} | 236 |
<!--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/mobilebert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mobilebert.md",
"repo_id": "transformers",
"token_count": 1548
} | 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/model_doc/nystromformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/nystromformer.md",
"repo_id": "transformers",
"token_count": 907
} | 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 agreed... | transformers/docs/source/en/model_doc/plbart.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/plbart.md",
"repo_id": "transformers",
"token_count": 1586
} | 239 |
<!--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/roc_bert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/roc_bert.md",
"repo_id": "transformers",
"token_count": 999
} | 240 |
<!--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/vit_mae.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/vit_mae.md",
"repo_id": "transformers",
"token_count": 1492
} | 241 |
<!--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/xlm-roberta.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/xlm-roberta.md",
"repo_id": "transformers",
"token_count": 3907
} | 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 ... | transformers/docs/source/en/perf_hardware.md/0 | {
"file_path": "transformers/docs/source/en/perf_hardware.md",
"repo_id": "transformers",
"token_count": 2301
} | 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/preprocessing.md/0 | {
"file_path": "transformers/docs/source/en/preprocessing.md",
"repo_id": "transformers",
"token_count": 8685
} | 244 |
<!--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_explained.md/0 | {
"file_path": "transformers/docs/source/en/tasks_explained.md",
"repo_id": "transformers",
"token_count": 6963
} | 245 |
<!---
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
} | 246 |
<!--⚠️ 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.
-->
# Traduction en cours. | transformers/docs/source/fr/in_translation.md/0 | {
"file_path": "transformers/docs/source/fr/in_translation.md",
"repo_id": "transformers",
"token_count": 54
} | 247 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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