text stringlengths 5 409k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 572 |
|---|---|---|---|
# 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/pag/test_pag_hunyuan_dit.py/0 | {
"file_path": "diffusers/tests/pipelines/pag/test_pag_hunyuan_dit.py",
"repo_id": "diffusers",
"token_count": 6488
} | 141 |
# These are canonical sets of parameters for different types of pipelines.
# They are set on subclasses of `PipelineTesterMixin` as `params` and
# `batch_params`.
#
# If a pipeline's set of arguments has minor changes from one of the common sets
# of arguments, do not make modifications to the existing common sets of a... | diffusers/tests/pipelines/pipeline_params.py/0 | {
"file_path": "diffusers/tests/pipelines/pipeline_params.py",
"repo_id": "diffusers",
"token_count": 1584
} | 142 |
# 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": 8236
} | 143 |
# 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": 12835
} | 144 |
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
} | 145 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils.testing_utils import require_torchsde, torch_device
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class DPMSolverSDESchedulerTest(SchedulerCommonTest):
scheduler_classes = (DPMSolverSDEScheduler,)
num_inferenc... | diffusers/tests/schedulers/test_scheduler_dpm_sde.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_dpm_sde.py",
"repo_id": "diffusers",
"token_count": 3136
} | 146 |
import torch
from diffusers import TCDScheduler
from .test_schedulers import SchedulerCommonTest
class TCDSchedulerTest(SchedulerCommonTest):
scheduler_classes = (TCDScheduler,)
forward_default_kwargs = (("num_inference_steps", 10),)
def get_scheduler_config(self, **kwargs):
config = {
... | diffusers/tests/schedulers/test_scheduler_tcd.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_tcd.py",
"repo_id": "diffusers",
"token_count": 3098
} | 147 |
import gc
import tempfile
import unittest
import torch
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
backend_empty_cache,
... | diffusers/tests/single_file/test_stable_diffusion_controlnet_single_file.py/0 | {
"file_path": "diffusers/tests/single_file/test_stable_diffusion_controlnet_single_file.py",
"repo_id": "diffusers",
"token_count": 3350
} | 148 |
# 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
#
"""
Utility that checks that mo... | diffusers/utils/check_support_list.py/0 | {
"file_path": "diffusers/utils/check_support_list.py",
"repo_id": "diffusers",
"token_count": 2088
} | 149 |
#!/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/benchmarks/video/capture_camera_feed.py/0 | {
"file_path": "lerobot/benchmarks/video/capture_camera_feed.py",
"repo_id": "lerobot",
"token_count": 1061
} | 150 |
import shutil
from pathlib import Path
import numpy as np
import torch
from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, LeRobotDataset
from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface.... | lerobot/examples/port_datasets/pusht_zarr.py/0 | {
"file_path": "lerobot/examples/port_datasets/pusht_zarr.py",
"repo_id": "lerobot",
"token_count": 3177
} | 151 |
#!/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
} | 152 |
#!/usr/bin/env python
# Copyright 2024 Tony Z. Zhao and 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... | lerobot/lerobot/common/policies/act/modeling_act.py/0 | {
"file_path": "lerobot/lerobot/common/policies/act/modeling_act.py",
"repo_id": "lerobot",
"token_count": 15973
} | 153 |
#!/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": 791
} | 154 |
"""Logic to calibrate a robot arm built with feetech motors"""
# TODO(rcadene, aliberts): move this logic into the robot code when refactoring
import time
import numpy as np
from lerobot.common.robot_devices.motors.feetech import (
CalibrationMode,
TorqueMode,
convert_degrees_to_steps,
)
from lerobot.com... | lerobot/lerobot/common/robot_devices/robots/feetech_calibration.py/0 | {
"file_path": "lerobot/lerobot/common/robot_devices/robots/feetech_calibration.py",
"repo_id": "lerobot",
"token_count": 7917
} | 155 |
import inspect
import sys
from argparse import ArgumentError
from functools import wraps
from pathlib import Path
from typing import Sequence
import draccus
from lerobot.common.utils.utils import has_method
PATH_KEY = "path"
draccus.set_config_type("json")
def get_cli_overrides(field_name: str, args: Sequence[str]... | lerobot/lerobot/configs/parser.py/0 | {
"file_path": "lerobot/lerobot/configs/parser.py",
"repo_id": "lerobot",
"token_count": 2017
} | 156 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Interactive Video Background Page</title>
<script src="https://cdn.tailwindcss.com"></script>
<script defer src="https://cdn.jsdelivr.net/npm/alpinejs@3.x.x/di... | lerobot/lerobot/templates/visualize_dataset_homepage.html/0 | {
"file_path": "lerobot/lerobot/templates/visualize_dataset_homepage.html",
"repo_id": "lerobot",
"token_count": 1560
} | 157 |
version https://git-lfs.github.com/spec/v1
oid sha256:aee60956925da9687546aafa770d5e6a04f99576f903b08d0bd5f8003a7f4f3e
size 111338
| lerobot/tests/data/save_dataset_to_safetensors/lerobot/pusht/frame_159.safetensors/0 | {
"file_path": "lerobot/tests/data/save_dataset_to_safetensors/lerobot/pusht/frame_159.safetensors",
"repo_id": "lerobot",
"token_count": 68
} | 158 |
version https://git-lfs.github.com/spec/v1
oid sha256:a5ec46abc5a3c85675a5ee4a1bb362eecb3ff4c546082ff309c89fc7821f38bd
size 515400
| lerobot/tests/data/save_policy_to_safetensors/aloha_act_1000_steps/actions.safetensors/0 | {
"file_path": "lerobot/tests/data/save_policy_to_safetensors/aloha_act_1000_steps/actions.safetensors",
"repo_id": "lerobot",
"token_count": 68
} | 159 |
version https://git-lfs.github.com/spec/v1
oid sha256:6cdb181ba6acc4aa1209a9ea5dd783f077ff87760257de1026c33f8e2fb2b2b1
size 472
| lerobot/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/actions.safetensors/0 | {
"file_path": "lerobot/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/actions.safetensors",
"repo_id": "lerobot",
"token_count": 66
} | 160 |
#!/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_policy_to_safetensors.py/0 | {
"file_path": "lerobot/tests/scripts/save_policy_to_safetensors.py",
"repo_id": "lerobot",
"token_count": 2416
} | 161 |
"""
This file contains generic tests to ensure that nothing breaks if we modify the push_dataset_to_hub API.
Also, this file contains backward compatibility tests. Because they are slow and require to download the raw datasets,
we skip them for now in our CI.
Example to run backward compatiblity tests locally:
```
pyt... | lerobot/tests/test_push_dataset_to_hub.py/0 | {
"file_path": "lerobot/tests/test_push_dataset_to_hub.py",
"repo_id": "lerobot",
"token_count": 6380
} | 162 |
# Model arguments
model_name_or_path: Qwen/Qwen2.5-Math-7B
model_revision: main
torch_dtype: bfloat16
attn_implementation: flash_attention_2
# Data training arguments
dataset_name: DigitalLearningGmbH/MATH-lighteval
dataset_configs:
- train
system_prompt: "You are a helpful AI Assistant, designed to provided well-reas... | open-r1/recipes/Qwen2.5-Math-7B/grpo/config_simple_rl.yaml/0 | {
"file_path": "open-r1/recipes/Qwen2.5-Math-7B/grpo/config_simple_rl.yaml",
"repo_id": "open-r1",
"token_count": 505
} | 163 |
#!/bin/bash
#SBATCH --job-name=open-r1-sft
#SBATCH --ntasks-per-node=1
#SBATCH --exclusive
#SBATCH --gres=gpu:8
#SBATCH --partition=hopper-prod # Adjust this for your cluster
#SBATCH --output=./logs/%x-%j.out
#SBATCH --err=./logs/%x-%j.err
#SBATCH --requeue
# Specific configuration optimized for the Hugging Face Comp... | open-r1/slurm/train.slurm/0 | {
"file_path": "open-r1/slurm/train.slurm",
"repo_id": "open-r1",
"token_count": 1241
} | 164 |
import unittest
from open_r1.rewards import (
accuracy_reward,
format_reward,
get_code_format_reward,
get_cosine_scaled_reward,
get_repetition_penalty_reward,
len_reward,
reasoning_steps_reward,
)
class TestRewards(unittest.TestCase):
def test_accuracy_reward_correct_answer(self):
... | open-r1/tests/test_rewards.py/0 | {
"file_path": "open-r1/tests/test_rewards.py",
"repo_id": "open-r1",
"token_count": 7598
} | 165 |
<!---
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 ... | peft/README.md/0 | {
"file_path": "peft/README.md",
"repo_id": "peft",
"token_count": 3408
} | 166 |
<!--⚠️ 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.
-->
# Soft prompts
Training large pretrained language models is very time-consuming and compute-intensive. As they continue to grow in size, there is in... | peft/docs/source/conceptual_guides/prompting.md/0 | {
"file_path": "peft/docs/source/conceptual_guides/prompting.md",
"repo_id": "peft",
"token_count": 2195
} | 167 |
<!--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/tutorial/peft_model_config.md/0 | {
"file_path": "peft/docs/source/tutorial/peft_model_config.md",
"repo_id": "peft",
"token_count": 2576
} | 168 |
# 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... | peft/examples/boft_controlnet/utils/unet_2d_condition.py/0 | {
"file_path": "peft/examples/boft_controlnet/utils/unet_2d_condition.py",
"repo_id": "peft",
"token_count": 5908
} | 169 |
<jupyter_start><jupyter_code>from transformers import AutoModelForCausalLM
from peft import PeftModel, PeftConfig
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_wi... | peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb",
"repo_id": "peft",
"token_count": 2945
} | 170 |
<jupyter_start><jupyter_code>import os
import torch
from transformers import (
AutoTokenizer,
default_data_collator,
AutoModelForSeq2SeqLM,
Seq2SeqTrainingArguments,
Seq2SeqTrainer,
GenerationConfig,
)
from peft import get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType
from datasets... | peft/examples/conditional_generation/peft_prompt_tuning_seq2seq_with_generate.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_prompt_tuning_seq2seq_with_generate.ipynb",
"repo_id": "peft",
"token_count": 2021
} | 171 |
# 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/examples/eva_finetuning/utils.py/0 | {
"file_path": "peft/examples/eva_finetuning/utils.py",
"repo_id": "peft",
"token_count": 1382
} | 172 |
# 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
} | 173 |
<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
} | 174 |
<jupyter_start><jupyter_text>Named Entity Recognition with Peft Model 🤗 In this notebook, we will learn how to perform Named Entity Recognition(NER) on the CoNLL-2003 dataset using the Trainer class This notebook has been adapted from the main NLP course here - https://huggingface.co/learn/nlp-course/chapter7/2?fw=ptf... | peft/examples/token_classification/peft_lora_ner.ipynb/0 | {
"file_path": "peft/examples/token_classification/peft_lora_ner.ipynb",
"repo_id": "peft",
"token_count": 2233
} | 175 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/import_utils.py/0 | {
"file_path": "peft/src/peft/import_utils.py",
"repo_id": "peft",
"token_count": 2391
} | 176 |
# 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/config.py/0 | {
"file_path": "peft/src/peft/tuners/lora/config.py",
"repo_id": "peft",
"token_count": 13800
} | 177 |
# 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
} | 178 |
# 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/xlora/layer.py/0 | {
"file_path": "peft/src/peft/tuners/xlora/layer.py",
"repo_id": "peft",
"token_count": 4097
} | 179 |
# 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_adaption_prompt.py/0 | {
"file_path": "peft/tests/test_adaption_prompt.py",
"repo_id": "peft",
"token_count": 16292
} | 180 |
# 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_integrations.py/0 | {
"file_path": "peft/tests/test_integrations.py",
"repo_id": "peft",
"token_count": 1367
} | 181 |
# 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/testing_common.py/0 | {
"file_path": "peft/tests/testing_common.py",
"repo_id": "peft",
"token_count": 34640
} | 182 |
#!/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": 3951
} | 183 |
# 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": 1758
} | 184 |
# 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": 1624
} | 185 |
# 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": 2427
} | 186 |
# 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
} | 187 |
""" AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/au... | pytorch-image-models/timm/data/auto_augment.py/0 | {
"file_path": "pytorch-image-models/timm/data/auto_augment.py",
"repo_id": "pytorch-image-models",
"token_count": 15926
} | 188 |
""" Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | pytorch-image-models/timm/data/readers/reader_hfds.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_hfds.py",
"repo_id": "pytorch-image-models",
"token_count": 1197
} | 189 |
from typing import List, Optional, Type, Union
import torch
from torch import nn as nn
from torch.nn import functional as F
from .config import use_fused_attn
from .create_conv2d import create_conv2d
from .helpers import to_2tuple
from .pool2d_same import create_pool2d
class MultiQueryAttentionV2(nn.Module):
""... | pytorch-image-models/timm/layers/attention2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention2d.py",
"repo_id": "pytorch-image-models",
"token_count": 6678
} | 190 |
""" DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl ins... | pytorch-image-models/timm/layers/drop.py/0 | {
"file_path": "pytorch-image-models/timm/layers/drop.py",
"repo_id": "pytorch-image-models",
"token_count": 3016
} | 191 |
import torch
from torch import nn
class LayerScale(nn.Module):
""" LayerScale on tensors with channels in last-dim.
"""
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
) -> None:
super().__init__()
self.inp... | pytorch-image-models/timm/layers/layer_scale.py/0 | {
"file_path": "pytorch-image-models/timm/layers/layer_scale.py",
"repo_id": "pytorch-image-models",
"token_count": 482
} | 192 |
""" 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 ConvNormAct
from .helpers import make_divisible
from .trace_utils import _assert
def ... | 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": 2314
} | 193 |
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": 1762
} | 194 |
""" 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": 7004
} | 195 |
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", FutureWarning)
| pytorch-image-models/timm/models/helpers.py/0 | {
"file_path": "pytorch-image-models/timm/models/helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 62
} | 196 |
""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import to... | pytorch-image-models/timm/models/mobilenetv3.py/0 | {
"file_path": "pytorch-image-models/timm/models/mobilenetv3.py",
"repo_id": "pytorch-image-models",
"token_count": 31009
} | 197 |
""" ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
from torch import nn
from timm.data... | pytorch-image-models/timm/models/resnest.py/0 | {
"file_path": "pytorch-image-models/timm/models/resnest.py",
"repo_id": "pytorch-image-models",
"token_count": 4439
} | 198 |
""" Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... | pytorch-image-models/timm/models/visformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/visformer.py",
"repo_id": "pytorch-image-models",
"token_count": 10151
} | 199 |
""" Adafactor Optimizer
Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
Modified by Ross Wightman to fix some issues with factorization dims for non nn.Linear layers
Original header/copyright below.
"""
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is l... | pytorch-image-models/timm/optim/adafactor.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adafactor.py",
"repo_id": "pytorch-image-models",
"token_count": 4921
} | 200 |
""" NAdamW Optimizer
Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
Added multi-tensor (foreach) path.
"""
import math
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from ._types import ParamsT
# Modified from github.... | pytorch-image-models/timm/optim/nadamw.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nadamw.py",
"repo_id": "pytorch-image-models",
"token_count": 6905
} | 201 |
""" 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 typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
... | 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": 2000
} | 202 |
import random
import numpy as np
import torch
def random_seed(seed=42, rank=0):
torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
random.seed(seed + rank)
| pytorch-image-models/timm/utils/random.py/0 | {
"file_path": "pytorch-image-models/timm/utils/random.py",
"repo_id": "pytorch-image-models",
"token_count": 68
} | 203 |
<!---
Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or ... | smolagents/docs/README.md/0 | {
"file_path": "smolagents/docs/README.md",
"repo_id": "smolagents",
"token_count": 3202
} | 204 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | smolagents/docs/source/en/tutorials/memory.mdx/0 | {
"file_path": "smolagents/docs/source/en/tutorials/memory.mdx",
"repo_id": "smolagents",
"token_count": 1685
} | 205 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | smolagents/docs/source/hi/tutorials/secure_code_execution.mdx/0 | {
"file_path": "smolagents/docs/source/hi/tutorials/secure_code_execution.mdx",
"repo_id": "smolagents",
"token_count": 5833
} | 206 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | smolagents/docs/source/zh/tutorials/tools.mdx/0 | {
"file_path": "smolagents/docs/source/zh/tutorials/tools.mdx",
"repo_id": "smolagents",
"token_count": 5019
} | 207 |
# This is copied from Magentic-one's great repo: https://github.com/microsoft/autogen/blob/v0.4.4/python/packages/autogen-magentic-one/src/autogen_magentic_one/markdown_browser/mdconvert.py
# Thanks to Microsoft researchers for open-sourcing this!
# type: ignore
import base64
import copy
import html
import json
import ... | smolagents/examples/open_deep_research/scripts/mdconvert.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/mdconvert.py",
"repo_id": "smolagents",
"token_count": 16974
} | 208 |
#!/usr/bin/env python
# coding=utf-8
# 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/L... | smolagents/src/smolagents/default_tools.py/0 | {
"file_path": "smolagents/src/smolagents/default_tools.py",
"repo_id": "smolagents",
"token_count": 4715
} | 209 |
# 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... | smolagents/tests/test_agents.py/0 | {
"file_path": "smolagents/tests/test_agents.py",
"repo_id": "smolagents",
"token_count": 18176
} | 210 |
# 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... | smolagents/tests/test_utils.py/0 | {
"file_path": "smolagents/tests/test_utils.py",
"repo_id": "smolagents",
"token_count": 4205
} | 211 |
install-server:
cd server && make install
install-server-cpu:
cd server && make install-server
install-router:
cargo install --path backends/v3/
install-launcher:
cargo install --path launcher/
install-benchmark:
cargo install --path benchmark/
install: install-server install-router install-launcher
install... | text-generation-inference/Makefile/0 | {
"file_path": "text-generation-inference/Makefile",
"repo_id": "text-generation-inference",
"token_count": 440
} | 212 |
//! A crate to extract and inject a OpenTelemetry context from and to a gRPC request.
//! Inspired by: https://github.com/open-telemetry/opentelemetry-rust gRPC examples
use opentelemetry::global;
use opentelemetry::propagation::Injector;
use tracing_opentelemetry::OpenTelemetrySpanExt;
/// Inject context in the meta... | text-generation-inference/backends/grpc-metadata/src/lib.rs/0 | {
"file_path": "text-generation-inference/backends/grpc-metadata/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 543
} | 213 |
from typing import Any, Callable
import grpc
from google.rpc import code_pb2, status_pb2
from grpc_interceptor.server import AsyncServerInterceptor
from grpc_status import rpc_status
from loguru import logger
class ExceptionInterceptor(AsyncServerInterceptor):
async def intercept(
self,
method: C... | text-generation-inference/backends/neuron/server/text_generation_server/interceptor.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/interceptor.py",
"repo_id": "text-generation-inference",
"token_count": 362
} | 214 |
use async_trait::async_trait;
use cxx::UniquePtr;
use hashbrown::HashMap;
use std::hint;
use std::ops::Deref;
use std::path::Path;
use tokenizers::Tokenizer;
use tokio::sync::mpsc::{unbounded_channel, UnboundedReceiver, UnboundedSender};
use tokio::sync::TryAcquireError;
use tokio::task::spawn_blocking;
use tokio::time... | text-generation-inference/backends/trtllm/src/looper.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/looper.rs",
"repo_id": "text-generation-inference",
"token_count": 6376
} | 215 |
/// Text Generation Inference benchmarking tool
///
/// Inspired by the great Oha app: https://github.com/hatoo/oha
/// and: https://github.com/orhun/rust-tui-template
use clap::Parser;
use std::path::Path;
use text_generation_client::v3::ShardedClient;
use tokenizers::{FromPretrainedParameters, Tokenizer};
use tracing... | text-generation-inference/benchmark/src/main.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 3164
} | 216 |
import os
import requests
from typing import Dict, Optional, List
from huggingface_hub.utils import build_hf_headers
from text_generation import Client, AsyncClient, __version__
from text_generation.types import DeployedModel
from text_generation.errors import NotSupportedError, parse_error
INFERENCE_ENDPOINT = os.e... | text-generation-inference/clients/python/text_generation/inference_api.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/inference_api.py",
"repo_id": "text-generation-inference",
"token_count": 2182
} | 217 |
# Model safety.
[Pytorch uses pickle](https://pytorch.org/docs/master/generated/torch.load.html) by default meaning that for quite a long while
*Every* model using that format is potentially executing unintended code while purely loading the model.
There is a big red warning on Python's page for pickle [link](https:/... | text-generation-inference/docs/source/basic_tutorials/safety.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/safety.md",
"repo_id": "text-generation-inference",
"token_count": 465
} | 218 |
# Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
![Text Generation Inference](https://hugging... | text-generation-inference/docs/source/index.md/0 | {
"file_path": "text-generation-inference/docs/source/index.md",
"repo_id": "text-generation-inference",
"token_count": 562
} | 219 |
{
inputs = {
crate2nix = {
url = "github:nix-community/crate2nix";
inputs.nixpkgs.follows = "tgi-nix/nixpkgs";
};
nix-filter.url = "github:numtide/nix-filter";
tgi-nix.url = "github:huggingface/text-generation-inference-nix/hub-rotary";
nixpkgs.follows = "tgi-nix/nixpkgs";
flake-ut... | text-generation-inference/flake.nix/0 | {
"file_path": "text-generation-inference/flake.nix",
"repo_id": "text-generation-inference",
"token_count": 2858
} | 220 |
[
{
"choices": [
{
"delta": {
"content": "**",
"role": "assistant",
"tool_calls": null
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1726656043,
"id": "",
"model": "meta-llama/Meta-Llama-3.1-... | text-generation-inference/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_stream_usage.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_stream_usage.json",
"repo_id": "text-generation-inference",
"token_count": 2511
} | 221 |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "Both an elephant and a mouse are mammals. However, the differences between elephants and mice are:\n\n1",
"role": "assistant"
}
}
],
"created": 1732541189,
"id... | text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt.json",
"repo_id": "text-generation-inference",
"token_count": 258
} | 222 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 688,
"logprob": -0.546875,
"special": false,
"text": "**"
},
{
"id": 103889,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma2/test_flash_gemma2.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma2/test_flash_gemma2.json",
"repo_id": "text-generation-inference",
"token_count": 877
} | 223 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 13,
"logprob": -1.9980469,
"special": false,
"text": "."
},
{
"id": 578,
"logprob": -0.15... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_exl2/test_flash_llama_exl2_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_exl2/test_flash_llama_exl2_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 856
} | 224 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 311,
"logprob": -1.4277344,
"special": false,
"text": " to"
},
{
"id": 279,
"logprob": -0... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 876
} | 225 |
{
"choices": [
{
"finish_reason": "eos_token",
"index": 0,
"logprobs": null,
"message": {
"content": null,
"name": null,
"role": "assistant",
"tool_calls": [
{
"function": {
"arguments": {
"format": "celsiu... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json",
"repo_id": "text-generation-inference",
"token_count": 495
} | 226 |
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_wna16_handle(launcher):
with launcher(
"neuralmagic/gemma-2-2b-it-quantized.w4a16",
num_shard=2,
quantize="compressed-tensors",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def compressed_... | text-generation-inference/integration-tests/models/test_compressed_tensors_wna16_int.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_compressed_tensors_wna16_int.py",
"repo_id": "text-generation-inference",
"token_count": 1007
} | 227 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_gptq_handle(launcher):
with launcher(
"astronomer/Llama-3-8B-Instruct-GPTQ-4-Bit", num_shard=2, quantize="gptq"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_gptq(flash_llama_gptq_handle):
awa... | text-generation-inference/integration-tests/models/test_flash_llama_gptq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_gptq.py",
"repo_id": "text-generation-inference",
"token_count": 769
} | 228 |
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
} | 229 |
import pytest
import asyncio
@pytest.fixture(scope="module")
def mllama_handle(launcher):
with launcher(
"meta-llama/Llama-3.2-11B-Vision-Instruct",
num_shard=2,
) as handle:
yield handle
@pytest.fixture(scope="module")
async def mllama(mllama_handle):
await mllama_handle.health(... | text-generation-inference/integration-tests/models/test_mllama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_mllama.py",
"repo_id": "text-generation-inference",
"token_count": 1585
} | 230 |
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
} | 231 |
{
dockerTools,
cacert,
text-generation-inference,
stream ? false,
}:
let
build = if stream then dockerTools.streamLayeredImage else dockerTools.buildLayeredImage;
in
build {
name = "tgi-docker";
tag = "latest";
config = {
EntryPoint = [ "${text-generation-inference}/bin/text-generation-inference" ]... | text-generation-inference/nix/docker.nix/0 | {
"file_path": "text-generation-inference/nix/docker.nix",
"repo_id": "text-generation-inference",
"token_count": 160
} | 232 |
use crate::infer::Infer;
use crate::server::{chat_completions, compat_generate, completions, ComputeType};
use crate::{
ChatCompletion, ChatCompletionChunk, ChatRequest, Chunk, CompatGenerateRequest,
CompletionFinal, CompletionRequest, ErrorResponse, GenerateResponse, Info, StreamResponse,
};
use axum::extract:... | text-generation-inference/router/src/sagemaker.rs/0 | {
"file_path": "text-generation-inference/router/src/sagemaker.rs",
"repo_id": "text-generation-inference",
"token_count": 962
} | 233 |
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
} | 234 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include <torch/extension.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include "util.cuh"
#include "tuning.h"
#include "cuda_buffers.cu... | text-generation-inference/server/exllama_kernels/exllama_kernels/exllama_ext.cpp/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/exllama_ext.cpp",
"repo_id": "text-generation-inference",
"token_count": 3279
} | 235 |
#ifndef _qdq_2_cuh
#define _qdq_2_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_2BIT == 1
// Permutation:
//
// ffddbb99 77553311 eeccaa88 66442200
__forceinline__ __device__ void shuffle_2bit_16
(
uint32_t* q,
int stride
)
{
uint32_t qa = q[0];
uint32_t qb = 0;
#pragma unrol... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_2.cuh",
"repo_id": "text-generation-inference",
"token_count": 1589
} | 236 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/config.py
# License: Apache License Version 2.0, January 2004
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Dict, Set, Tuple
import torch
from text_generation_server.adapters.weig... | text-generation-inference/server/text_generation_server/adapters/config.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/adapters/config.py",
"repo_id": "text-generation-inference",
"token_count": 275
} | 237 |
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM == "ipex":
from .ipex import WQLinear
elif SYSTEM == "cuda":
from .cuda import WQLinear
__all__ = ["WQLinear"]
| text-generation-inference/server/text_generation_server/layers/awq/quantize/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/awq/quantize/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 71
} | 238 |
from text_generation_server.layers.gptq import GPTQWeight
import torch
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
none_tensor = torch.empty((1, 1), device="meta")
def ext_make_q4(qw... | text-generation-inference/server/text_generation_server/layers/gptq/exllama.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/gptq/exllama.py",
"repo_id": "text-generation-inference",
"token_count": 1888
} | 239 |
from typing import Optional, Protocol, runtime_checkable
import torch
import torch.nn as nn
from loguru import logger
from transformers.activations import ACT2FN
from text_generation_server.layers import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
)
from text_generation_server.layers.fp8 import Hyb... | text-generation-inference/server/text_generation_server/layers/moe/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/moe/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 4641
} | 240 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.