text stringlengths 7 1.24M | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 519 |
|---|---|---|---|
# Copyright 2024 Katherine Crowson, The HuggingFace Team and hlky. 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
#
# ... | diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py",
"repo_id": "diffusers",
"token_count": 9739
} | 166 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/src/diffusers/schedulers/scheduling_utils_flax.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_utils_flax.py",
"repo_id": "diffusers",
"token_count": 4948
} | 167 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class StableDiffusionKDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "k_diffusion"]
def __init__(self, *args, **kwargs):
requires_backends(se... | diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py",
"repo_id": "diffusers",
"token_count": 451
} | 168 |
import functools
import importlib
import inspect
import io
import logging
import multiprocessing
import os
import random
import re
import struct
import sys
import tempfile
import time
import unittest
import urllib.parse
from contextlib import contextmanager
from io import BytesIO, StringIO
from pathlib import Path
from... | diffusers/src/diffusers/utils/testing_utils.py/0 | {
"file_path": "diffusers/src/diffusers/utils/testing_utils.py",
"repo_id": "diffusers",
"token_count": 14935
} | 169 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/models/autoencoders/test_models_vae.py/0 | {
"file_path": "diffusers/tests/models/autoencoders/test_models_vae.py",
"repo_id": "diffusers",
"token_count": 21124
} | 170 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/models/transformers/test_models_transformer_latte.py/0 | {
"file_path": "diffusers/tests/models/transformers/test_models_transformer_latte.py",
"repo_id": "diffusers",
"token_count": 1169
} | 171 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/tests/others/test_check_dummies.py/0 | {
"file_path": "diffusers/tests/others/test_check_dummies.py",
"repo_id": "diffusers",
"token_count": 1872
} | 172 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AnimateDiffPipeline,
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
LCMScheduler,
MotionAdapter,
StableDiffus... | diffusers/tests/pipelines/animatediff/test_animatediff.py/0 | {
"file_path": "diffusers/tests/pipelines/animatediff/test_animatediff.py",
"repo_id": "diffusers",
"token_count": 10689
} | 173 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNet2DModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
nightly,
require_torch_2,
... | diffusers/tests/pipelines/consistency_models/test_consistency_models.py/0 | {
"file_path": "diffusers/tests/pipelines/consistency_models/test_consistency_models.py",
"repo_id": "diffusers",
"token_count": 5052
} | 174 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc and The InstantX 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 b... | diffusers/tests/pipelines/controlnet_sd3/test_controlnet_sd3.py/0 | {
"file_path": "diffusers/tests/pipelines/controlnet_sd3/test_controlnet_sd3.py",
"repo_id": "diffusers",
"token_count": 5922
} | 175 |
import gc
import inspect
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
LatentConsistencyModelPipeline,
LCMScheduler,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
en... | diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models.py/0 | {
"file_path": "diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models.py",
"repo_id": "diffusers",
"token_count": 4773
} | 176 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/musicldm/test_musicldm.py/0 | {
"file_path": "diffusers/tests/pipelines/musicldm/test_musicldm.py",
"repo_id": "diffusers",
"token_count": 8048
} | 177 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py",
"repo_id": "diffusers",
"token_count": 11083
} | 178 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py",
"repo_id": "diffusers",
"token_count": 12911
} | 179 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class OnnxPipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,
equivalence of ... | diffusers/tests/pipelines/test_pipelines_onnx_common.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipelines_onnx_common.py",
"repo_id": "diffusers",
"token_count": 118
} | 180 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class CMStochasticIterativeSchedulerTest(SchedulerCommonTest):
scheduler_classes = (CMStochasticIterativeScheduler,)
num_inference_steps = 10
def get_scheduler_config(self, **kwargs):
... | diffusers/tests/schedulers/test_scheduler_consistency_model.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_consistency_model.py",
"repo_id": "diffusers",
"token_count": 3029
} | 181 |
import torch
from diffusers import HeunDiscreteScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class HeunDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (HeunDiscreteScheduler,)
num_inference_steps = 10
def get_scheduler_... | diffusers/tests/schedulers/test_scheduler_heun.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_heun.py",
"repo_id": "diffusers",
"token_count": 3945
} | 182 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/single_file/test_model_controlnet_single_file.py/0 | {
"file_path": "diffusers/tests/single_file/test_model_controlnet_single_file.py",
"repo_id": "diffusers",
"token_count": 991
} | 183 |
# coding=utf-8
# Copyright 2024 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... | diffusers/utils/check_config_docstrings.py/0 | {
"file_path": "diffusers/utils/check_config_docstrings.py",
"repo_id": "diffusers",
"token_count": 1113
} | 184 |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2024 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
#
# Unles... | diffusers/utils/print_env.py/0 | {
"file_path": "diffusers/utils/print_env.py",
"repo_id": "diffusers",
"token_count": 424
} | 185 |
# Video benchmark
## Questions
What is the optimal trade-off between:
- maximizing loading time with random access,
- minimizing memory space on disk,
- maximizing success rate of policies,
- compatibility across devices/platforms for decoding videos (e.g. video players, web browsers).
How to encode videos?
- Which ... | lerobot/benchmarks/video/README.md/0 | {
"file_path": "lerobot/benchmarks/video/README.md",
"repo_id": "lerobot",
"token_count": 6168
} | 186 |
#!/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/__init__.py/0 | {
"file_path": "lerobot/lerobot/__init__.py",
"repo_id": "lerobot",
"token_count": 3408
} | 187 |
#!/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/common/datasets/push_dataset_to_hub/_diffusion_policy_replay_buffer.py/0 | {
"file_path": "lerobot/lerobot/common/datasets/push_dataset_to_hub/_diffusion_policy_replay_buffer.py",
"repo_id": "lerobot",
"token_count": 10692
} | 188 |
#!/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/common/datasets/sampler.py/0 | {
"file_path": "lerobot/lerobot/common/datasets/sampler.py",
"repo_id": "lerobot",
"token_count": 952
} | 189 |
#!/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/common/policies/utils.py/0 | {
"file_path": "lerobot/lerobot/common/policies/utils.py",
"repo_id": "lerobot",
"token_count": 590
} | 190 |
defaults:
- _self_
- env: pusht
- policy: diffusion
hydra:
run:
# Set `dir` to where you would like to save all of the run outputs. If you run another training session
# with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
dir: outputs/train/${now:%Y-%m-%d... | lerobot/lerobot/configs/default.yaml/0 | {
"file_path": "lerobot/lerobot/configs/default.yaml",
"repo_id": "lerobot",
"token_count": 1759
} | 191 |
_target_: lerobot.common.robot_devices.robots.koch.KochRobot
calibration_path: .cache/calibration/koch_bimanual.pkl
leader_arms:
left:
_target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
port: /dev/tty.usbmodem585A0085511
motors:
# name: (index, model)
shoulder_pan: [1, ... | lerobot/lerobot/configs/robot/koch_bimanual.yaml/0 | {
"file_path": "lerobot/lerobot/configs/robot/koch_bimanual.yaml",
"repo_id": "lerobot",
"token_count": 1135
} | 192 |
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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/test_image_transforms.py/0 | {
"file_path": "lerobot/tests/test_image_transforms.py",
"repo_id": "lerobot",
"token_count": 4575
} | 206 |
from transformers import AutoFeatureExtractor, AutoTokenizer
from parler_tts import ParlerTTSForConditionalGeneration
path = "TODO"
repo_id = "parler_tts_600M"
AutoFeatureExtractor.from_pretrained("ylacombe/dac_44khZ_8kbps").push_to_hub(repo_id)
AutoTokenizer.from_pretrained("google/t5-v1_1-base").push_to_hub(repo... | parler-tts/helpers/push_to_hub_scripts/push_trained_parler_tts_to_hub.py/0 | {
"file_path": "parler-tts/helpers/push_to_hub_scripts/push_trained_parler_tts_to_hub.py",
"repo_id": "parler-tts",
"token_count": 158
} | 207 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import Seq2SeqTrainingArguments
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"hel... | parler-tts/training/arguments.py/0 | {
"file_path": "parler-tts/training/arguments.py",
"repo_id": "parler-tts",
"token_count": 5930
} | 208 |
<!--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/conceptual_guides/ia3.md/0 | {
"file_path": "peft/docs/source/conceptual_guides/ia3.md",
"repo_id": "peft",
"token_count": 1030
} | 209 |
<!--⚠️ 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.
-->
# Models
[`PeftModel`] is the base model class for specifying the base Transformer model and configuration to apply a PEFT method to. The base `Peft... | peft/docs/source/package_reference/peft_model.md/0 | {
"file_path": "peft/docs/source/package_reference/peft_model.md",
"repo_id": "peft",
"token_count": 564
} | 210 |
# 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/examples/boft_controlnet/eval.py/0 | {
"file_path": "peft/examples/boft_controlnet/eval.py",
"repo_id": "peft",
"token_count": 3390
} | 211 |
<!--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/examples/boft_dreambooth/boft_dreambooth.md/0 | {
"file_path": "peft/examples/boft_dreambooth/boft_dreambooth.md",
"repo_id": "peft",
"token_count": 2634
} | 212 |
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorWithPadding,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
def train_model(
... | peft/examples/dora_finetuning/dora_finetuning.py/0 | {
"file_path": "peft/examples/dora_finetuning/dora_finetuning.py",
"repo_id": "peft",
"token_count": 3104
} | 213 |
# Fine-tuning for image classification using LoRA and 🤗 PEFT
## Vision Transformer model from transformers
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_peft_lora.ipyn... | peft/examples/image_classification/README.md/0 | {
"file_path": "peft/examples/image_classification/README.md",
"repo_id": "peft",
"token_count": 457
} | 214 |
<jupyter_start><jupyter_code>import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from pathlib import Path
from typing import Optional
import psutil
import json
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from tor... | peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb/0 | {
"file_path": "peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb",
"repo_id": "peft",
"token_count": 2282
} | 215 |
from mistralrs import ChatCompletionRequest, Runner, Which
runner = Runner(
which=Which.XLora(
tok_model_id=None, # Automatically determine from ordering file
model_id=..., # Model ID of the base model (local path of HF model ID)
xlora_model_id=..., # X-LoRA Model ID of the base model (... | peft/examples/xlora/xlora_inference_mistralrs.py/0 | {
"file_path": "peft/examples/xlora/xlora_inference_mistralrs.py",
"repo_id": "peft",
"token_count": 356
} | 216 |
# 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/peft_model.py/0 | {
"file_path": "peft/src/peft/peft_model.py",
"repo_id": "peft",
"token_count": 60834
} | 217 |
# 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/boft/config.py/0 | {
"file_path": "peft/src/peft/tuners/boft/config.py",
"repo_id": "peft",
"token_count": 2537
} | 218 |
# 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/ia3/config.py/0 | {
"file_path": "peft/src/peft/tuners/ia3/config.py",
"repo_id": "peft",
"token_count": 1842
} | 219 |
# 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/aqlm.py/0 | {
"file_path": "peft/src/peft/tuners/lora/aqlm.py",
"repo_id": "peft",
"token_count": 1399
} | 220 |
# 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/multitask_prompt_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/multitask_prompt_tuning/model.py",
"repo_id": "peft",
"token_count": 2251
} | 221 |
# 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/utils/loftq_utils.py/0 | {
"file_path": "peft/src/peft/utils/loftq_utils.py",
"repo_id": "peft",
"token_count": 7269
} | 222 |
# 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_feature_extraction_models.py/0 | {
"file_path": "peft/tests/test_feature_extraction_models.py",
"repo_id": "peft",
"token_count": 3721
} | 223 |
# 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/tests/test_vision_models.py/0 | {
"file_path": "peft/tests/test_vision_models.py",
"repo_id": "peft",
"token_count": 2448
} | 224 |
#!/usr/bin/env python3
""" Bulk Model Script Runner
Run validation or benchmark script in separate process for each model
Benchmark all 'vit*' models:
python bulk_runner.py --model-list 'vit*' --results-file vit_bench.csv benchmark.py --amp -b 512
Validate all models:
python bulk_runner.py --model-list all --resul... | pytorch-image-models/bulk_runner.py/0 | {
"file_path": "pytorch-image-models/bulk_runner.py",
"repo_id": "pytorch-image-models",
"token_count": 3627
} | 225 |
# CSP-DarkNet
**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The u... | pytorch-image-models/hfdocs/source/models/csp-darknet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/csp-darknet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1756
} | 226 |
# PNASNet
**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to comple... | pytorch-image-models/hfdocs/source/models/pnasnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/pnasnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1622
} | 227 |
# SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual b... | pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2425
} | 228 |
# Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in `timm`.
## Schedulers
### Factory functions
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2
### Scheduler Classes
[[... | pytorch-image-models/hfdocs/source/reference/schedulers.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/reference/schedulers.mdx",
"repo_id": "pytorch-image-models",
"token_count": 242
} | 229 |
DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 *... | pytorch-image-models/timm/data/constants.py/0 | {
"file_path": "pytorch-image-models/timm/data/constants.py",
"repo_id": "pytorch-image-models",
"token_count": 236
} | 230 |
""" A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | pytorch-image-models/timm/data/readers/reader_image_folder.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_folder.py",
"repo_id": "pytorch-image-models",
"token_count": 1510
} | 231 |
""" Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... | pytorch-image-models/timm/layers/attention_pool2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool2d.py",
"repo_id": "pytorch-image-models",
"token_count": 5737
} | 232 |
""" EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato ... | pytorch-image-models/timm/layers/evo_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/evo_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 6684
} | 233 |
""" Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kern... | pytorch-image-models/timm/layers/median_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/median_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 883
} | 234 |
import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
bs: torch.jit.Final[int]
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, s... | pytorch-image-models/timm/layers/space_to_depth.py/0 | {
"file_path": "pytorch-image-models/timm/layers/space_to_depth.py",
"repo_id": "pytorch-image-models",
"token_count": 568
} | 235 |
""" EfficientNet, MobileNetV3, etc Blocks
Hacked together by / Copyright 2019, Ross Wightman
"""
from typing import Callable, Dict, Optional, Type
import torch
import torch.nn as nn
from torch.nn import functional as F
from timm.layers import create_conv2d, DropPath, make_divisible, create_act_layer, create_aa, to_2... | pytorch-image-models/timm/models/_efficientnet_blocks.py/0 | {
"file_path": "pytorch-image-models/timm/models/_efficientnet_blocks.py",
"repo_id": "pytorch-image-models",
"token_count": 13538
} | 236 |
""" BEiT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
Model from official source: https://github.com/microsoft/unilm/tree/master/beit
@inproceedings{beit,
title={{BEiT}: {BERT} Pre-Training of Image Transformers},
author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
booktitle=... | pytorch-image-models/timm/models/beit.py/0 | {
"file_path": "pytorch-image-models/timm/models/beit.py",
"repo_id": "pytorch-image-models",
"token_count": 14385
} | 237 |
""" EfficientFormer
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov,
Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={20... | pytorch-image-models/timm/models/efficientformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientformer.py",
"repo_id": "pytorch-image-models",
"token_count": 10905
} | 238 |
""" An PyTorch implementation of Hiera
Adapted for timm from originals at https://github.com/facebookresearch/hiera
"""
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#... | pytorch-image-models/timm/models/hiera.py/0 | {
"file_path": "pytorch-image-models/timm/models/hiera.py",
"repo_id": "pytorch-image-models",
"token_count": 18107
} | 239 |
""" NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_co... | pytorch-image-models/timm/models/nasnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/nasnet.py",
"repo_id": "pytorch-image-models",
"token_count": 13276
} | 240 |
""" ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... | pytorch-image-models/timm/models/rexnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/rexnet.py",
"repo_id": "pytorch-image-models",
"token_count": 5803
} | 241 |
""" Relative Position Vision Transformer (ViT) in PyTorch
NOTE: these models are experimental / WIP, expect changes
Hacked together by / Copyright 2022, Ross Wightman
"""
import logging
import math
from functools import partial
from typing import List, Optional, Tuple, Type, Union
try:
from typing import Literal... | pytorch-image-models/timm/models/vision_transformer_relpos.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer_relpos.py",
"repo_id": "pytorch-image-models",
"token_count": 13354
} | 242 |
""" PyTorch LARS / LARC Optimizer
An implementation of LARS (SGD) + LARC in PyTorch
Based on:
* PyTorch SGD: https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
* NVIDIA APEX LARC: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
Additional cleanup and modifications to properly su... | pytorch-image-models/timm/optim/lars.py/0 | {
"file_path": "pytorch-image-models/timm/optim/lars.py",
"repo_id": "pytorch-image-models",
"token_count": 2571
} | 243 |
""" Polynomial Scheduler
Polynomial LR schedule with warmup, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
from typing import List
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class PolyLRScheduler(Scheduler):
""" Polynomial LR... | pytorch-image-models/timm/scheduler/poly_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/poly_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1979
} | 244 |
""" Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, nam... | pytorch-image-models/timm/utils/misc.py/0 | {
"file_path": "pytorch-image-models/timm/utils/misc.py",
"repo_id": "pytorch-image-models",
"token_count": 451
} | 245 |
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.80 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef AS planner
COPY Cargo.lock Cargo.lock
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
CO... | text-generation-inference/Dockerfile_amd/0 | {
"file_path": "text-generation-inference/Dockerfile_amd",
"repo_id": "text-generation-inference",
"token_count": 2713
} | 246 |
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod client;
mod sharded_client;
pub use client::Client;
pub use pb::generate::v3::{
input_chunk::Chunk, Batch, CachedBatch, FinishReason, GeneratedText, Generation, GrammarType,
HealthResponse, Image, InfoResponse, Input, InputChunk, NextTokenChooserParam... | text-generation-inference/backends/client/src/v3/mod.rs/0 | {
"file_path": "text-generation-inference/backends/client/src/v3/mod.rs",
"repo_id": "text-generation-inference",
"token_count": 142
} | 247 |
//
// Created by mfuntowicz on 7/23/24.
//
#ifndef TGI_TRTLLM_BACKEND_HARDWARE_H
#define TGI_TRTLLM_BACKEND_HARDWARE_H
#include <cstdint>
#include <limits>
#include <fmt/base.h>
#include <spdlog/spdlog.h>
#include <nvml.h>
namespace huggingface::hardware::cuda {
#define AMPERE_SM_MAJOR 8
#define HOPPER_SM_MAJOR 8
... | text-generation-inference/backends/trtllm/include/hardware.h/0 | {
"file_path": "text-generation-inference/backends/trtllm/include/hardware.h",
"repo_id": "text-generation-inference",
"token_count": 774
} | 248 |
use crate::client::{ClientError, Result};
/// Multi shard Client
use crate::client::{Health, ShardInfo};
use crate::client::grpc_client::{DecodeTimings, PrefillTimings};
use crate::client::{
Batch, CachedBatch, Client, Generation, GrammarType, HealthResponse,
NextTokenChooserParameters, Request, StoppingCriter... | text-generation-inference/backends/v3/src/client/sharded_client.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/client/sharded_client.rs",
"repo_id": "text-generation-inference",
"token_count": 4198
} | 249 |
# Legacy warning ⚠️
The inference clients from [huggingface_hub](https://huggingface.co/docs/huggingface_hub/guides/inference) are recommended over `text_generation`.
# Text Generation
The Hugging Face Text Generation Python library provides a convenient way of interfacing with a
`text-generation-inference` instance ... | text-generation-inference/clients/python/README.md/0 | {
"file_path": "text-generation-inference/clients/python/README.md",
"repo_id": "text-generation-inference",
"token_count": 2491
} | 250 |
- sections:
- local: index
title: Text Generation Inference
- local: quicktour
title: Quick Tour
- local: installation_nvidia
title: Using TGI with Nvidia GPUs
- local: installation_amd
title: Using TGI with AMD GPUs
- local: installation_gaudi
title: Using TGI with Intel Gaudi
- 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": 747
} | 251 |
# Quantization
TGI offers many quantization schemes to run LLMs effectively and fast based on your use-case. TGI supports GPTQ, AWQ, bits-and-bytes, EETQ, Marlin, EXL2 and fp8 quantization.
To leverage GPTQ, AWQ, Marlin and EXL2 quants, you must provide pre-quantized weights. Whereas for bits-and-bytes, EETQ and fp8,... | text-generation-inference/docs/source/conceptual/quantization.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/quantization.md",
"repo_id": "text-generation-inference",
"token_count": 1430
} | 252 |
# Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models (VLMs & LLMs) are supported.
## Supported Models
- [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
- [Idefics 2](https:/... | 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": 1141
} | 253 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 1724,
"logprob": -7.703125,
"text": "What"
},
{
"id": 338... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq/test_flash_llama_awq.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq/test_flash_llama_awq.json",
"repo_id": "text-generation-inference",
"token_count": 1236
} | 254 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -9.640625,
"text": "Test"
},
{
"id": 3... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq.json",
"repo_id": "text-generation-inference",
"token_count": 1053
} | 255 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 128000,
"logprob": null,
"text": "<|begin_of_text|>"
},
{
"id": 2323,
"logprob": -9.421875,
"text": "Test"
},
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8.json",
"repo_id": "text-generation-inference",
"token_count": 1053
} | 256 |
{
"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_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mistral/test_flash_mistral_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1041
} | 257 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 589,
"logprob": null,
"text": "def"
},
{
"id": 1459,
"logprob": -5.6289062,
"text": " print"
},
{
"id"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder/test_flash_starcoder.json",
"repo_id": "text-generation-inference",
"token_count": 1111
} | 258 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 330,
"logprob": -0.08660889,
"special": false,
"text": " A"
},
{
"id": 13088,
"logprob... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_simple.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_simple.json",
"repo_id": "text-generation-inference",
"token_count": 868
} | 259 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 6,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": null,
"tokens": [
{
"id": 259,
... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json",
"repo_id": "text-generation-inference",
"token_count": 2874
} | 260 |
import pytest
@pytest.fixture(scope="module")
def bloom_560m_sharded_handle(launcher):
with launcher("bigscience/bloom-560m", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def bloom_560m_sharded(bloom_560m_sharded_handle):
await bloom_560m_sharded_handle.health(240)
... | text-generation-inference/integration-tests/models/test_bloom_560m_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_bloom_560m_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 527
} | 261 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_marlin_handle(launcher):
with launcher(
"neuralmagic/llama-2-7b-chat-marlin", num_shard=2, quantize="marlin"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_marlin(flash_llama_marlin_handle):
aw... | text-generation-inference/integration-tests/models/test_flash_llama_marlin.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_marlin.py",
"repo_id": "text-generation-inference",
"token_count": 748
} | 262 |
import pytest
import base64
# TODO fix the server parsser to count inline image tokens correctly
def get_chicken():
with open("integration-tests/images/chicken_on_money.png", "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
return f"data:image/png;base64,{encoded_string.decode... | text-generation-inference/integration-tests/models/test_idefics2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_idefics2.py",
"repo_id": "text-generation-inference",
"token_count": 1397
} | 263 |
use std::error::Error;
use vergen::EmitBuilder;
fn main() -> Result<(), Box<dyn Error>> {
// Emit cargo and rustc compile time values
EmitBuilder::builder().all_cargo().all_rustc().emit()?;
// Try to get the git sha from the local git repository
if EmitBuilder::builder()
.fail_on_error()
... | text-generation-inference/launcher/build.rs/0 | {
"file_path": "text-generation-inference/launcher/build.rs",
"repo_id": "text-generation-inference",
"token_count": 363
} | 264 |
// pub(crate) mod v2;
mod chat_template;
pub mod tool_grammar;
use crate::validation::{ValidGenerateRequest, Validation, ValidationError};
use crate::Tool;
use crate::{
ChatTemplateVersions, FinishReason, GenerateRequest, HubProcessorConfig, HubTokenizerConfig,
Message, PrefillToken, Token,
};
use async_trait:... | text-generation-inference/router/src/infer/mod.rs/0 | {
"file_path": "text-generation-inference/router/src/infer/mod.rs",
"repo_id": "text-generation-inference",
"token_count": 5577
} | 265 |
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