text stringlengths 7 1.24M | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 519 |
|---|---|---|---|
import hydra
from omegaconf import DictConfig
def make_robot(cfg: DictConfig):
robot = hydra.utils.instantiate(cfg)
return robot
| lerobot/lerobot/common/robot_devices/robots/factory.py/0 | {
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"repo_id": "lerobot",
"token_count": 49
} | 168 |
# @package _global_
# Use `act_real.yaml` to train on real-world Aloha/Aloha2 datasets.
# Compared to `act.yaml`, it contains 4 cameras (i.e. cam_right_wrist, cam_left_wrist, images,
# cam_low) instead of 1 camera (i.e. top). Also, `training.eval_freq` is set to -1. This config is used
# to evaluate checkpoints at a c... | lerobot/lerobot/configs/policy/act_real.yaml/0 | {
"file_path": "lerobot/lerobot/configs/policy/act_real.yaml",
"repo_id": "lerobot",
"token_count": 1466
} | 169 |
#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/lerobot/scripts/visualize_image_transforms.py/0 | {
"file_path": "lerobot/lerobot/scripts/visualize_image_transforms.py",
"repo_id": "lerobot",
"token_count": 2530
} | 170 |
#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/tests/conftest.py/0 | {
"file_path": "lerobot/tests/conftest.py",
"repo_id": "lerobot",
"token_count": 432
} | 171 |
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#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/tests/scripts/save_image_transforms_to_safetensors.py/0 | {
"file_path": "lerobot/tests/scripts/save_image_transforms_to_safetensors.py",
"repo_id": "lerobot",
"token_count": 1312
} | 183 |
#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/tests/test_visualize_dataset.py/0 | {
"file_path": "lerobot/tests/test_visualize_dataset.py",
"repo_id": "lerobot",
"token_count": 399
} | 184 |
check_dirs := .
quality:
black --check $(check_dirs)
ruff $(check_dirs)
style:
black $(check_dirs)
ruff $(check_dirs) --fix
| parler-tts/Makefile/0 | {
"file_path": "parler-tts/Makefile",
"repo_id": "parler-tts",
"token_count": 55
} | 185 |
from transformers import PretrainedConfig
class DACConfig(PretrainedConfig):
model_type = "dac"
def __init__(
self,
num_codebooks: int = 9,
model_bitrate: int = 8, # kbps
codebook_size: int = 1024,
latent_dim: int = 1024,
frame_rate: int = 86,
samplin... | parler-tts/parler_tts/dac_wrapper/configuration_dac.py/0 | {
"file_path": "parler-tts/parler_tts/dac_wrapper/configuration_dac.py",
"repo_id": "parler-tts",
"token_count": 300
} | 186 |
# Builds GPU docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.8
# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/acc... | peft/docker/peft-gpu/Dockerfile/0 | {
"file_path": "peft/docker/peft-gpu/Dockerfile",
"repo_id": "peft",
"token_count": 1085
} | 187 |
<!--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... | peft/docs/source/developer_guides/mixed_models.md/0 | {
"file_path": "peft/docs/source/developer_guides/mixed_models.md",
"repo_id": "peft",
"token_count": 770
} | 188 |
<!--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... | peft/docs/source/quicktour.md/0 | {
"file_path": "peft/docs/source/quicktour.md",
"repo_id": "peft",
"token_count": 2384
} | 189 |
import argparse
import os
from typing import Optional
from huggingface_hub import HfFolder, whoami
from transformers import PretrainedConfig
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organi... | peft/examples/boft_controlnet/utils/args_loader.py/0 | {
"file_path": "peft/examples/boft_controlnet/utils/args_loader.py",
"repo_id": "peft",
"token_count": 7255
} | 190 |
import gc
import threading
import psutil
import torch
# Converting Bytes to Megabytes
def b2mb(x):
return int(x / 2**20)
# This context manager is used to track the peak memory usage of the process
class TorchTracemalloc:
def __enter__(self):
gc.collect()
torch.cuda.empty_cache()
to... | peft/examples/boft_dreambooth/utils/tracemalloc.py/0 | {
"file_path": "peft/examples/boft_dreambooth/utils/tracemalloc.py",
"repo_id": "peft",
"token_count": 786
} | 191 |
import os
import torch
from accelerate import Accelerator
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import LoraConfig, TaskType, get_pef... | peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py/0 | {
"file_path": "peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py",
"repo_id": "peft",
"token_count": 2543
} | 192 |
accelerate launch --config_file config.yaml peft_adalora_whisper_large_training.py \
--model_name_or_path "openai/whisper-large-v2" \
--language "Marathi" \
--language_abbr "mr" \
--task "transcribe" \
--dataset_name "mozilla-foundation/common_voice_11_0" \
--push_to_hub \
--preprocessing_nu... | peft/examples/int8_training/run_adalora_whisper_int8.sh/0 | {
"file_path": "peft/examples/int8_training/run_adalora_whisper_int8.sh",
"repo_id": "peft",
"token_count": 509
} | 193 |
<jupyter_start><jupyter_text>Dreambooth with OFTThis Notebook assumes that you already ran the train_dreambooth.py script to create your own adapter.<jupyter_code>from diffusers import DiffusionPipeline
from diffusers.utils import check_min_version, get_logger
from peft import PeftModel
# Will error if the minimal ver... | peft/examples/oft_dreambooth/oft_dreambooth_inference.ipynb/0 | {
"file_path": "peft/examples/oft_dreambooth/oft_dreambooth_inference.ipynb",
"repo_id": "peft",
"token_count": 376
} | 194 |
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
} | 195 |
# 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/auto.py/0 | {
"file_path": "peft/src/peft/auto.py",
"repo_id": "peft",
"token_count": 2741
} | 196 |
# 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": 7167
} | 197 |
# 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/fourierft/layer.py/0 | {
"file_path": "peft/src/peft/tuners/fourierft/layer.py",
"repo_id": "peft",
"token_count": 3638
} | 198 |
# 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/lora/layer.py/0 | {
"file_path": "peft/src/peft/tuners/lora/layer.py",
"repo_id": "peft",
"token_count": 24562
} | 199 |
# 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": 10756
} | 200 |
# 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_mixed.py/0 | {
"file_path": "peft/tests/test_mixed.py",
"repo_id": "peft",
"token_count": 17543
} | 201 |
# Feature Extraction
All of the models in `timm` have consistent mechanisms for obtaining various types of features from the model for tasks besides classification.
## Penultimate Layer Features (Pre-Classifier Features)
The features from the penultimate model layer can be obtained in several ways without requiring ... | pytorch-image-models/hfdocs/source/feature_extraction.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/feature_extraction.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3391
} | 202 |
# EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network wi... | pytorch-image-models/hfdocs/source/models/efficientnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/efficientnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4915
} | 203 |
# (Tensorflow) MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-bloc... | pytorch-image-models/hfdocs/source/models/tf-mobilenet-v3.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-mobilenet-v3.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4781
} | 204 |
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops.misc import FrozenBatchNorm2d
import timm
from timm.utils.model import freeze, unfreeze
def test_freeze_unfreeze():
model = timm.create_model('resnet18')
# Freeze all
freeze(model)
# Check top level module
assert model.fc.we... | pytorch-image-models/tests/test_utils.py/0 | {
"file_path": "pytorch-image-models/tests/test_utils.py",
"repo_id": "pytorch-image-models",
"token_count": 776
} | 205 |
""" Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | pytorch-image-models/timm/data/random_erasing.py/0 | {
"file_path": "pytorch-image-models/timm/data/random_erasing.py",
"repo_id": "pytorch-image-models",
"token_count": 2258
} | 206 |
import math
import numbers
import random
import warnings
from typing import List, Sequence, Tuple, Union
import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
try:
from torchvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
exce... | pytorch-image-models/timm/data/transforms.py/0 | {
"file_path": "pytorch-image-models/timm/data/transforms.py",
"repo_id": "pytorch-image-models",
"token_count": 9216
} | 207 |
""" Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Any, Dict, Optional, Type
from torch import nn as nn
from .typing import LayerType, PadType
from .blur_pool import create_aa
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ... | pytorch-image-models/timm/layers/conv_bn_act.py/0 | {
"file_path": "pytorch-image-models/timm/layers/conv_bn_act.py",
"repo_id": "pytorch-image-models",
"token_count": 1426
} | 208 |
""" Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon Shlens},
Title = {Scaling Local Self... | pytorch-image-models/timm/layers/halo_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/halo_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 4601
} | 209 |
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794 and https://arxiv.org/pdf/2208.07220
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
... | 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": 858
} | 210 |
import torch
import math
import warnings
from torch import nn
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/presen... | 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": 2579
} | 211 |
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
} | 212 |
""" 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": 12490
} | 213 |
# NOTE timm.models.layers is DEPRECATED, please use timm.layers, this is here to reduce breakages in transition
from timm.layers.activations import *
from timm.layers.adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from timm.layers.attention_p... | pytorch-image-models/timm/models/layers/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/models/layers/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 1222
} | 214 |
""" TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import itertools
from functools import partial
from typing import Dict, Option... | pytorch-image-models/timm/models/tiny_vit.py/0 | {
"file_path": "pytorch-image-models/timm/models/tiny_vit.py",
"repo_id": "pytorch-image-models",
"token_count": 12466
} | 215 |
from .adabelief import AdaBelief
from .adafactor import Adafactor
from .adahessian import Adahessian
from .adamp import AdamP
from .adamw import AdamW
from .adan import Adan
from .lamb import Lamb
from .lars import Lars
from .lookahead import Lookahead
from .madgrad import MADGRAD
from .nadam import Nadam
from .nvnovog... | pytorch-image-models/timm/optim/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/optim/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 170
} | 216 |
"""RAdam Optimizer.
Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
def __init__(self, params, ... | pytorch-image-models/timm/optim/radam.py/0 | {
"file_path": "pytorch-image-models/timm/optim/radam.py",
"repo_id": "pytorch-image-models",
"token_count": 1967
} | 217 |
""" Checkpoint Saver
Track top-n training checkpoints and maintain recovery checkpoints on specified intervals.
Hacked together by / Copyright 2020 Ross Wightman
"""
import glob
import operator
import os
import logging
import torch
from .model import unwrap_model, get_state_dict
_logger = logging.getLogger(__nam... | pytorch-image-models/timm/utils/checkpoint_saver.py/0 | {
"file_path": "pytorch-image-models/timm/utils/checkpoint_saver.py",
"repo_id": "pytorch-image-models",
"token_count": 2818
} | 218 |
#!/usr/bin/env python3
""" ImageNet Validation Script
This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
canonical PyTorch, standard Python style, and good perform... | pytorch-image-models/validate.py/0 | {
"file_path": "pytorch-image-models/validate.py",
"repo_id": "pytorch-image-models",
"token_count": 9310
} | 219 |
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0
hooks:
- id: check-yaml
- id: end-of-file-fixer
- id: trailing-whitespace
exclude: docs/source/reference/launcher.md
- repo: https://github.com/psf/black
rev: 24.2.0
hooks:
- id: black
- repo:... | text-generation-inference/.pre-commit-config.yaml/0 | {
"file_path": "text-generation-inference/.pre-commit-config.yaml",
"repo_id": "text-generation-inference",
"token_count": 298
} | 220 |
{
"__inputs": [
{
"name": "DS_PROMETHEUS_EKS API INFERENCE PROD",
"label": "Prometheus EKS API Inference Prod",
"description": "",
"type": "datasource",
"pluginId": "prometheus",
"pluginName": "Prometheus"
}
],
"__elements": {},
"__requires": [
{
"type": "pa... | text-generation-inference/assets/tgi_grafana.json/0 | {
"file_path": "text-generation-inference/assets/tgi_grafana.json",
"repo_id": "text-generation-inference",
"token_count": 62818
} | 221 |
use cxx_build::CFG;
use pkg_config;
use std::env;
use std::env::consts::ARCH;
use std::path::{absolute, PathBuf};
const ADDITIONAL_BACKEND_LINK_LIBRARIES: [&str; 2] = ["spdlog", "fmt"];
const CUDA_ARCH_LIST: Option<&str> = option_env!("CUDA_ARCH_LIST");
const CUDA_REQUIRED_VERSION: &str = "12.5";
const MPI_REQUIRED_VE... | text-generation-inference/backends/trtllm/build.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/build.rs",
"repo_id": "text-generation-inference",
"token_count": 2548
} | 222 |
//
// Created by mfuntowicz on 7/2/24.
//
#include <catch2/catch_all.hpp>
#include <spdlog/spdlog.h>
#include "../include/backend.h"
TEST_CASE("Load TRTLLM Engine on the TGI Backend", "[trtllm][engine][load]") {
const auto engines = std::filesystem::path("/home/mfuntowicz/.cache/huggingface/assets/trtllm/0.11.0.de... | text-generation-inference/backends/trtllm/tests/infer_test.cpp/0 | {
"file_path": "text-generation-inference/backends/trtllm/tests/infer_test.cpp",
"repo_id": "text-generation-inference",
"token_count": 306
} | 223 |
/// Inspired by https://github.com/orhun/rust-tui-template/blob/472aa515119d4c94903eac12d9784417281dc7f5/src/event.rs
use crossterm::event;
use std::time::{Duration, Instant};
use tokio::sync::{broadcast, mpsc};
/// Events
#[derive(Debug)]
pub(crate) enum Event {
/// Terminal tick.
Tick,
/// Key press.
... | text-generation-inference/benchmark/src/event.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/event.rs",
"repo_id": "text-generation-inference",
"token_count": 913
} | 224 |
# 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 applicabl... | text-generation-inference/clients/python/text_generation/__init__.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 338
} | 225 |
# Train Medusa
This tutorial will show you how to train a Medusa model on a dataset of your choice. Please check out the [speculation documentation](../conceptual/speculation) for more information on how Medusa works and speculation in general.
## What are the benefits of training a Medusa model?
Training Medusa hea... | text-generation-inference/docs/source/basic_tutorials/train_medusa.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/train_medusa.md",
"repo_id": "text-generation-inference",
"token_count": 3478
} | 226 |
# Using TGI with Intel Gaudi
Check out this [repository](https://github.com/huggingface/tgi-gaudi) to serve models with TGI on Gaudi and Gaudi2 with [Optimum Habana](https://huggingface.co/docs/optimum/habana/index).
| text-generation-inference/docs/source/installation_gaudi.md/0 | {
"file_path": "text-generation-inference/docs/source/installation_gaudi.md",
"repo_id": "text-generation-inference",
"token_count": 75
} | 227 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 15,
"logprob": null,
"text": ","
},
{
"id": 1669,
"logprob": -5.4453125,
"text": " il"
},
{
"id": 1158... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1204
} | 228 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50,
"logprob": null,
"text": "G"
},
{
"id": 330,
"logprob": -5.96875,
"text": "ir"
},
{
"id": 1622,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json",
"repo_id": "text-generation-inference",
"token_count": 4604
} | 229 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 806,
"logprob": -11.890625,
"text": "Wh"
},
{
"id": 1446,... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_regex.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_regex.json",
"repo_id": "text-generation-inference",
"token_count": 1292
} | 230 |
{
"details": {
"finish_reason": "length",
"generated_tokens": 40,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -0.31347656,
"special": false,
"text": "\n"
},
{
"id": 13,
"logprob": -0.27441406,
"special": ... | text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_without_customer_support_adapter.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_without_customer_support_adapter.json",
"repo_id": "text-generation-inference",
"token_count": 3126
} | 231 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 7,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": null,
"tokens": [
{
"id": 3,
"logprob": -0.7001953,
"specia... | text-generation-inference/integration-tests/models/__snapshots__/test_t5_sharded/test_t5_sharded.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_t5_sharded/test_t5_sharded.json",
"repo_id": "text-generation-inference",
"token_count": 680
} | 232 |
import pytest
@pytest.fixture(scope="module")
def flash_gemma2_handle(launcher):
with launcher("google/gemma-2-9b-it", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_gemma2(flash_gemma2_handle):
await flash_gemma2_handle.health(300)
return flash_gemma2_handl... | text-generation-inference/integration-tests/models/test_flash_gemma2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_gemma2.py",
"repo_id": "text-generation-inference",
"token_count": 602
} | 233 |
import pytest
@pytest.fixture(scope="module")
def flash_qwen2_handle(launcher):
with launcher("Qwen/Qwen1.5-0.5B") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_qwen2(flash_qwen2_handle):
await flash_qwen2_handle.health(300)
return flash_qwen2_handle.client
@pytest.ma... | text-generation-inference/integration-tests/models/test_flash_qwen2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_qwen2.py",
"repo_id": "text-generation-inference",
"token_count": 747
} | 234 |
import pytest
@pytest.fixture(scope="module")
def opt_sharded_handle(launcher):
with launcher("facebook/opt-6.7b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def opt_sharded(opt_sharded_handle):
await opt_sharded_handle.health(300)
return opt_sharded_handle.client... | text-generation-inference/integration-tests/models/test_opt.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_opt.py",
"repo_id": "text-generation-inference",
"token_count": 160
} | 235 |
{
nix-filter,
buildPythonPackage,
poetry-core,
mypy-protobuf,
awq-inference-engine,
causal-conv1d,
eetq,
einops,
exllamav2,
fbgemm-gpu,
flashinfer,
flash-attn,
flash-attn-layer-norm,
flash-attn-rotary,
grpc-interceptor,
grpcio-reflection,
grpcio-status,
grpcio-tools,
hf-transfer,
... | text-generation-inference/nix/server.nix/0 | {
"file_path": "text-generation-inference/nix/server.nix",
"repo_id": "text-generation-inference",
"token_count": 980
} | 236 |
use axum::http::HeaderValue;
use clap::Parser;
use clap::Subcommand;
use hf_hub::api::tokio::{Api, ApiBuilder, ApiRepo};
use hf_hub::{Cache, Repo, RepoType};
use opentelemetry::sdk::propagation::TraceContextPropagator;
use opentelemetry::sdk::trace;
use opentelemetry::sdk::trace::Sampler;
use opentelemetry::sdk::Resour... | text-generation-inference/router/src/main.rs.back/0 | {
"file_path": "text-generation-inference/router/src/main.rs.back",
"repo_id": "text-generation-inference",
"token_count": 12333
} | 237 |
selective_scan_commit := 2a3704fd47ba817b415627b06fd796b971fdc137
causal-conv1d:
rm -rf causal-conv1d
git clone https://github.com/Dao-AILab/causal-conv1d.git
build-causal-conv1d: causal-conv1d
cd causal-conv1d/ && git checkout v1.1.1 # known latest working version tag
cd causal-conv1d/ && CAUSAL_CONV1D_FORCE_BUI... | text-generation-inference/server/Makefile-selective-scan/0 | {
"file_path": "text-generation-inference/server/Makefile-selective-scan",
"repo_id": "text-generation-inference",
"token_count": 351
} | 238 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _hip_compat_cuh
#define _hip_compat_cuh
// Workaround for a bug in hipamd, backported from upstream, this is fixed in ROCm 5.6.
__device__ __forceinline__ __half __compat_hrcp(__half x) {
return __half_raw{
static_cast<_Float1... | text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh",
"repo_id": "text-generation-inference",
"token_count": 1710
} | 239 |
#ifndef _qdq_3_cuh
#define _qdq_3_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_3BIT == 1
// Permutation:
//
// v9997775 55333111 u8886664 44222000 (u, v lsb)
// vjjjhhhf ffdddbbb uiiiggge eecccaaa
// vtttrrrp ppnnnlll usssqqqo oommmkkk
__forceinline__ __device__ void shuffle_3bit_32
(
uin... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh",
"repo_id": "text-generation-inference",
"token_count": 3335
} | 240 |
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 CausalLM, CausalLMBatch
@pytest.fixture(scope="session")
def default_causal_lm():
return CausalLM.fallback("gpt2")
@pyt... | text-generation-inference/server/tests/models/test_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 5387
} | 241 |
import torch
from typing import Dict, Optional, TypeVar
from text_generation_server.models.types import Batch
B = TypeVar("B", bound=Batch)
class Cache:
def __init__(self):
self.cache: Dict[int, B] = {}
def pop(self, batch_id: int) -> Optional[B]:
return self.cache.pop(batch_id, None)
... | text-generation-inference/server/text_generation_server/cache.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/cache.py",
"repo_id": "text-generation-inference",
"token_count": 359
} | 242 |
from dataclasses import dataclass
from typing import List, Union
import torch
from text_generation_server.utils.weights import Weight, Weights, WeightsLoader
@dataclass
class Exl2Weight(Weight):
"""
Exllama2 exl2 quantized weights.
"""
q_weight: torch.Tensor
q_scale: torch.Tensor
q_invperm: ... | text-generation-inference/server/text_generation_server/layers/exl2.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/exl2.py",
"repo_id": "text-generation-inference",
"token_count": 1050
} | 243 |
import functools
from typing import List, Tuple
import numpy
import torch
from text_generation_server.utils.import_utils import SYSTEM
try:
import marlin_kernels
except ImportError:
marlin_kernels = None
try:
major, _minor = torch.cuda.get_device_capability()
has_sm_8_0 = major >= 8
except Exception:... | text-generation-inference/server/text_generation_server/layers/marlin/util.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/util.py",
"repo_id": "text-generation-inference",
"token_count": 1782
} | 244 |
# 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": 28596
} | 245 |
import torch
import torch.distributed
from typing import Optional
from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
from text_generation_server.models.custom_modeling.idefics_processing import (
IdeficsProcessor,
)
from transformers import LlamaTokenizerFast
from text_generat... | text-generation-inference/server/text_generation_server/models/idefics.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/idefics.py",
"repo_id": "text-generation-inference",
"token_count": 1649
} | 246 |
import time
import os
from datetime import timedelta
from loguru import logger
from pathlib import Path
from typing import Optional, List
from huggingface_hub import file_download, hf_api, HfApi, hf_hub_download
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub.utils import (
LocalE... | text-generation-inference/server/text_generation_server/utils/hub.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/hub.py",
"repo_id": "text-generation-inference",
"token_count": 3950
} | 247 |
<p align="center">
<br>
<img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/>
<br>
<p>
<p align="center">
<img alt="Build" src="https://github.com/huggingface/tokenizers/workflows/Rust/badge.svg">
<a href="https://github.com/huggingface/tokenizers/blob/main/LI... | tokenizers/README.md/0 | {
"file_path": "tokenizers/README.md",
"repo_id": "tokenizers",
"token_count": 1056
} | 248 |
/* eslint-disable */
var globRequire = require;
describe("pipelineExample", () => {
// This is a hack to let us require using path similar to what the user has to use
function require(mod: string) {
if (mod.startsWith("tokenizers")) {
// let path = mod.slice("tokenizers".length);
... | tokenizers/bindings/node/examples/documentation/pipeline.test.ts/0 | {
"file_path": "tokenizers/bindings/node/examples/documentation/pipeline.test.ts",
"repo_id": "tokenizers",
"token_count": 2710
} | 249 |
# `tokenizers-android-arm-eabi`
This is the **armv7-linux-androideabi** binary for `tokenizers`
| tokenizers/bindings/node/npm/android-arm-eabi/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/android-arm-eabi/README.md",
"repo_id": "tokenizers",
"token_count": 35
} | 250 |
# `tokenizers-linux-x64-gnu`
This is the **x86_64-unknown-linux-gnu** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-x64-gnu/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-x64-gnu/README.md",
"repo_id": "tokenizers",
"token_count": 36
} | 251 |
use crate::arc_rwlock_serde;
use crate::tasks::models::{BPEFromFilesTask, WordLevelFromFilesTask, WordPieceFromFilesTask};
use crate::trainers::Trainer;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::sync:... | tokenizers/bindings/node/src/models.rs/0 | {
"file_path": "tokenizers/bindings/node/src/models.rs",
"repo_id": "tokenizers",
"token_count": 3681
} | 252 |
[package]
name = "tokenizers-python"
version = "0.20.0-dev.0"
authors = ["Anthony MOI <m.anthony.moi@gmail.com>"]
edition = "2021"
[lib]
name = "tokenizers"
crate-type = ["cdylib"]
[dependencies]
rayon = "1.10"
serde = { version = "1.0", features = [ "rc", "derive" ]}
serde_json = "1.0"
libc = "0.2"
env_logger = "0.1... | tokenizers/bindings/python/Cargo.toml/0 | {
"file_path": "tokenizers/bindings/python/Cargo.toml",
"repo_id": "tokenizers",
"token_count": 266
} | 253 |
from .base_tokenizer import BaseTokenizer
from .bert_wordpiece import BertWordPieceTokenizer
from .byte_level_bpe import ByteLevelBPETokenizer
from .char_level_bpe import CharBPETokenizer
from .sentencepiece_bpe import SentencePieceBPETokenizer
from .sentencepiece_unigram import SentencePieceUnigramTokenizer
| tokenizers/bindings/python/py_src/tokenizers/implementations/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/__init__.py",
"repo_id": "tokenizers",
"token_count": 94
} | 254 |
.tokenized-text {
width:100%;
padding:2rem;
max-height: 400px;
overflow-y: auto;
box-sizing:border-box;
line-height:4rem; /* Lots of space between lines */
font-family: "Roboto Light", "Ubuntu Light", "Ubuntu", monospace;
box-shadow: 2px 2px 2px rgba(0,0,0,0.2);
background-color: rgb... | tokenizers/bindings/python/py_src/tokenizers/tools/visualizer-styles.css/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/tools/visualizer-styles.css",
"repo_id": "tokenizers",
"token_count": 1806
} | 255 |
use std::sync::{Arc, RwLock};
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::ser::SerializeStruct;
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use tk::normalizer::SplitDelimiterBehavior;
use tk::pre_tokenizers::bert::BertPreTokenizer;
use tk::pre_tokenizers::byte_level::... | tokenizers/bindings/python/src/pre_tokenizers.rs/0 | {
"file_path": "tokenizers/bindings/python/src/pre_tokenizers.rs",
"repo_id": "tokenizers",
"token_count": 13648
} | 256 |
import pytest
from tokenizers import BertWordPieceTokenizer
from ..utils import bert_files, data_dir
class TestEncoding:
@pytest.fixture(scope="class")
def encodings(self, bert_files):
tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"])
single_encoding = tokenizer.encode("I lov... | tokenizers/bindings/python/tests/bindings/test_encoding.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_encoding.py",
"repo_id": "tokenizers",
"token_count": 1991
} | 257 |
import pytest
from tokenizers import SentencePieceBPETokenizer, SentencePieceUnigramTokenizer
class TestSentencePieceBPE:
def test_train_from_iterator(self):
text = ["A first sentence", "Another sentence", "And a last one"]
tokenizer = SentencePieceBPETokenizer()
tokenizer.train_from_iter... | tokenizers/bindings/python/tests/implementations/test_sentencepiece.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_sentencepiece.py",
"repo_id": "tokenizers",
"token_count": 1118
} | 258 |
# Trainers
<tokenizerslangcontent>
<python>
## BpeTrainer
[[autodoc]] tokenizers.trainers.BpeTrainer
## UnigramTrainer
[[autodoc]] tokenizers.trainers.UnigramTrainer
## WordLevelTrainer
[[autodoc]] tokenizers.trainers.WordLevelTrainer
## WordPieceTrainer
[[autodoc]] tokenizers.trainers.WordPieceTrainer
</python... | tokenizers/docs/source-doc-builder/api/trainers.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/trainers.mdx",
"repo_id": "tokenizers",
"token_count": 183
} | 259 |
/* Our DOM objects */
/* Version control */
.selectors {
margin-bottom: 10px;
}
.dropdown-button {
display: inline-block;
width: 50%;
background-color: #6670FF;
color: white;
border: none;
padding: 5px;
font-size: 15px;
cursor: pointer;
}
.dropdown-button:hover, .dropdown-button:... | tokenizers/docs/source/_static/css/huggingface.css/0 | {
"file_path": "tokenizers/docs/source/_static/css/huggingface.css",
"repo_id": "tokenizers",
"token_count": 2708
} | 260 |
Training from memory
----------------------------------------------------------------------------------------------------
In the `Quicktour <quicktour>`__, we saw how to build and train a tokenizer using text files,
but we can actually use any Python Iterator. In this section we'll see a few different ways of
training... | tokenizers/docs/source/tutorials/python/training_from_memory.rst/0 | {
"file_path": "tokenizers/docs/source/tutorials/python/training_from_memory.rst",
"repo_id": "tokenizers",
"token_count": 1149
} | 261 |
[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
} | 262 |
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
} | 263 |
//! 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, Deserializer, Serialize, Serializer};
use crate::models::bpe::{BpeTrainer, BPE};
use crate::models::unigram::{Unigram, UnigramTrai... | tokenizers/tokenizers/src/models/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/mod.rs",
"repo_id": "tokenizers",
"token_count": 6101
} | 264 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
pub use spm_precompiled::Precompiled;
use std::cmp::Ordering;
use unicode_segmentation::UnicodeSegmentation;
fn replace(transformations: &mut Vec<(char, isize)>, old_part: &str, new_part: &str) {
let old_count = old_part.chars().count() as isize;
le... | tokenizers/tokenizers/src/normalizers/precompiled.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/precompiled.rs",
"repo_id": "tokenizers",
"token_count": 1432
} | 265 |
use crate::pre_tokenizers::unicode_scripts::scripts::{get_script, Script};
use crate::tokenizer::{normalizer::Range, PreTokenizedString, PreTokenizer, Result};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct UnicodeScripts;
impl Uni... | tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/pre_tokenizer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/pre_tokenizer.rs",
"repo_id": "tokenizers",
"token_count": 2584
} | 266 |
use crate::tokenizer::pattern::Pattern;
use crate::Offsets;
use fancy_regex::Regex;
use std::error::Error;
#[derive(Debug)]
pub struct SysRegex {
regex: Regex,
}
impl SysRegex {
pub fn find_iter<'r, 't>(&'r self, inside: &'t str) -> Matches<'r, 't> {
Matches(self.regex.find_iter(inside))
}
pu... | tokenizers/tokenizers/src/utils/fancy.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/fancy.rs",
"repo_id": "tokenizers",
"token_count": 831
} | 267 |
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