text stringlengths 5 424k | id stringlengths 13 178 | metadata dict | __index_level_0__ int64 0 672 |
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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/src/lerobot/datasets/image_writer.py/0 | {
"file_path": "lerobot/src/lerobot/datasets/image_writer.py",
"repo_id": "lerobot",
"token_count": 2712
} | 200 |
# Copyright 2025 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
#
# Unless required by appl... | lerobot/src/lerobot/model/kinematics.py/0 | {
"file_path": "lerobot/src/lerobot/model/kinematics.py",
"repo_id": "lerobot",
"token_count": 1957
} | 201 |
# 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
#
# Unless required by appl... | lerobot/src/lerobot/policies/pi0/paligemma_with_expert.py/0 | {
"file_path": "lerobot/src/lerobot/policies/pi0/paligemma_with_expert.py",
"repo_id": "lerobot",
"token_count": 8263
} | 202 |
# Copyright 2025 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
#
# Unless required by appl... | lerobot/src/lerobot/policies/smolvla/smolvlm_with_expert.py/0 | {
"file_path": "lerobot/src/lerobot/policies/smolvla/smolvlm_with_expert.py",
"repo_id": "lerobot",
"token_count": 11239
} | 203 |
#!/usr/bin/env python
# Copyright 2025 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/src/lerobot/processor/device_processor.py/0 | {
"file_path": "lerobot/src/lerobot/processor/device_processor.py",
"repo_id": "lerobot",
"token_count": 2879
} | 204 |
# !/usr/bin/env python
# Copyright 2025 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/src/lerobot/rl/gym_manipulator.py/0 | {
"file_path": "lerobot/src/lerobot/rl/gym_manipulator.py",
"repo_id": "lerobot",
"token_count": 12026
} | 205 |
#!/usr/bin/env python
# Copyright 2025 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/src/lerobot/robots/so100_follower/robot_kinematic_processor.py/0 | {
"file_path": "lerobot/src/lerobot/robots/so100_follower/robot_kinematic_processor.py",
"repo_id": "lerobot",
"token_count": 10367
} | 206 |
# 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
#
# Unless required by appl... | lerobot/src/lerobot/scripts/lerobot_calibrate.py/0 | {
"file_path": "lerobot/src/lerobot/scripts/lerobot_calibrate.py",
"repo_id": "lerobot",
"token_count": 849
} | 207 |
#!/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/src/lerobot/teleoperators/koch_leader/koch_leader.py/0 | {
"file_path": "lerobot/src/lerobot/teleoperators/koch_leader/koch_leader.py",
"repo_id": "lerobot",
"token_count": 3269
} | 208 |
#!/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/src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py/0 | {
"file_path": "lerobot/src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py",
"repo_id": "lerobot",
"token_count": 1387
} | 209 |
#!/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/src/lerobot/utils/io_utils.py/0 | {
"file_path": "lerobot/src/lerobot/utils/io_utils.py",
"repo_id": "lerobot",
"token_count": 1707
} | 210 |
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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/cameras/test_realsense.py/0 | {
"file_path": "lerobot/tests/cameras/test_realsense.py",
"repo_id": "lerobot",
"token_count": 2355
} | 214 |
# 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
#
# Unless required by appl... | lerobot/tests/fixtures/dataset_factories.py/0 | {
"file_path": "lerobot/tests/fixtures/dataset_factories.py",
"repo_id": "lerobot",
"token_count": 10224
} | 215 |
# !/usr/bin/env python
# Copyright 2025 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/policies/hilserl/test_modeling_classifier.py/0 | {
"file_path": "lerobot/tests/policies/hilserl/test_modeling_classifier.py",
"repo_id": "lerobot",
"token_count": 1770
} | 216 |
#!/usr/bin/env python
# Copyright 2025 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/processor/test_pipeline_from_pretrained_helpers.py/0 | {
"file_path": "lerobot/tests/processor/test_pipeline_from_pretrained_helpers.py",
"repo_id": "lerobot",
"token_count": 3208
} | 217 |
#!/usr/bin/env python
# Copyright 2025 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_control_robot.py/0 | {
"file_path": "lerobot/tests/test_control_robot.py",
"repo_id": "lerobot",
"token_count": 1660
} | 218 |
import argparse
import asyncio
import hashlib
import json
import os
import random
from asyncio import Lock
from typing import Set
from datasets import load_dataset
from tqdm.asyncio import tqdm
import aiofiles
import aiohttp
import uvloop
file_lock = Lock()
async def generate_completion(session, prompt, args):
... | open-r1/scripts/generate_reasoning.py/0 | {
"file_path": "open-r1/scripts/generate_reasoning.py",
"repo_id": "open-r1",
"token_count": 2800
} | 219 |
#!/bin/bash
#SBATCH --partition=hopper-cpu
#SBATCH --mem=16g
#SBATCH --cpus-per-task=16
#SBATCH --output=/fsx/open-r1/logs/morph_router/%x-%j.out
#SBATCH --err=/fsx/open-r1/logs/morph_router/%x-%j.err
#SBATCH --requeue
#SBATCH --time=7-00:00:00
echo "Starting job"
set -x -e
source ~/.bashrc
source openr1/bin/activa... | open-r1/slurm/morph_router.slurm/0 | {
"file_path": "open-r1/slurm/morph_router.slurm",
"repo_id": "open-r1",
"token_count": 175
} | 220 |
from .cf_scoring import score_submission
from .code_patcher import patch_code
from .ioi_scoring import SubtaskResult, score_subtask, score_subtasks
from .ioi_utils import add_includes
from .morph_client import get_morph_client_from_env
from .piston_client import get_piston_client_from_env, get_slurm_piston_endpoints
... | open-r1/src/open_r1/utils/competitive_programming/__init__.py/0 | {
"file_path": "open-r1/src/open_r1/utils/competitive_programming/__init__.py",
"repo_id": "open-r1",
"token_count": 216
} | 221 |
<!--Copyright 2025 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/package_reference/trainable_tokens.md/0 | {
"file_path": "peft/docs/source/package_reference/trainable_tokens.md",
"repo_id": "peft",
"token_count": 745
} | 222 |
<!--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_controlnet/boft_controlnet.md/0 | {
"file_path": "peft/examples/boft_controlnet/boft_controlnet.md",
"repo_id": "peft",
"token_count": 2427
} | 223 |
<jupyter_start><jupyter_code>from transformers import AutoModelForCausalLM
from peft import get_peft_config, get_peft_model, PrefixTuningConfig, TaskType, PeftType
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers im... | peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb/0 | {
"file_path": "peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb",
"repo_id": "peft",
"token_count": 4824
} | 224 |
# 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/corda_finetuning/datautils.py/0 | {
"file_path": "peft/examples/corda_finetuning/datautils.py",
"repo_id": "peft",
"token_count": 4106
} | 225 |
<jupyter_start><jupyter_text>Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.The fine-tuning method will rely on a recent me... | peft/examples/int8_training/Finetune_opt_bnb_peft.ipynb/0 | {
"file_path": "peft/examples/int8_training/Finetune_opt_bnb_peft.ipynb",
"repo_id": "peft",
"token_count": 2830
} | 226 |
# 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/pissa_finetuning/preprocess.py/0 | {
"file_path": "peft/examples/pissa_finetuning/preprocess.py",
"repo_id": "peft",
"token_count": 938
} | 227 |
<jupyter_start><jupyter_text>IntroductionIn this notebook, we are going to fine-tune the LayoutLM model by Microsoft Research on the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset, which is a collection of annotated form documents. The goal of our model is to learn the annotations of a number of labels ("ques... | peft/examples/token_classification/peft_lora_token_cls.ipynb/0 | {
"file_path": "peft/examples/token_classification/peft_lora_token_cls.ipynb",
"repo_id": "peft",
"token_count": 12369
} | 228 |
{
"optimizer_kwargs": {
"lr": 3e-1,
"weight_decay": 1e-5
}
}
| peft/method_comparison/MetaMathQA/experiments/c3a/llama-3.2-3B-default/training_params.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/c3a/llama-3.2-3B-default/training_params.json",
"repo_id": "peft",
"token_count": 43
} | 229 |
{
"optimizer_kwargs": {
"lr": 0.2
}
}
| peft/method_comparison/MetaMathQA/experiments/trainable_tokens/llama-3.2-3B-sos+eos/training_params.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/trainable_tokens/llama-3.2-3B-sos+eos/training_params.json",
"repo_id": "peft",
"token_count": 27
} | 230 |
## Base Model Inference Caching
The benchmarking suite uses a separate script, `run_base.py`, to measure base model inference times and save results for reuse. This should be run once per model configuration to avoid redundant computations and ensure consistent baseline metrics for all PEFT experiments.
**Usage:**
``... | peft/method_comparison/text_generation_benchmark/README.md/0 | {
"file_path": "peft/method_comparison/text_generation_benchmark/README.md",
"repo_id": "peft",
"token_count": 1797
} | 231 |
# 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": 73421
} | 232 |
# 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": 3160
} | 233 |
# 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/cpt/config.py/0 | {
"file_path": "peft/src/peft/tuners/cpt/config.py",
"repo_id": "peft",
"token_count": 1663
} | 234 |
# 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/ln_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/ln_tuning/config.py",
"repo_id": "peft",
"token_count": 1153
} | 235 |
# 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": 17729
} | 236 |
# 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/miss/layer.py/0 | {
"file_path": "peft/src/peft/tuners/miss/layer.py",
"repo_id": "peft",
"token_count": 8150
} | 237 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/oft/layer.py/0 | {
"file_path": "peft/src/peft/tuners/oft/layer.py",
"repo_id": "peft",
"token_count": 17462
} | 238 |
# Copyright 2025-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/trainable_tokens/config.py/0 | {
"file_path": "peft/src/peft/tuners/trainable_tokens/config.py",
"repo_id": "peft",
"token_count": 1520
} | 239 |
# 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
} | 240 |
# 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_helpers.py/0 | {
"file_path": "peft/tests/test_helpers.py",
"repo_id": "peft",
"token_count": 8301
} | 241 |
# Copyright 2025-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 ... | peft/tests/test_seq_classifier.py/0 | {
"file_path": "peft/tests/test_seq_classifier.py",
"repo_id": "peft",
"token_count": 4713
} | 242 |
# Hugging Face Timm Docs
## Getting Started
```
pip install git+https://github.com/huggingface/doc-builder.git@main#egg=hf-doc-builder
pip install watchdog black
```
## Preview the Docs Locally
```
doc-builder preview timm hfdocs/source
```
| pytorch-image-models/hfdocs/README.md/0 | {
"file_path": "pytorch-image-models/hfdocs/README.md",
"repo_id": "pytorch-image-models",
"token_count": 88
} | 243 |
# Dual Path Network (DPN)
A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while DenseNet enables new feature exploration, and both are importa... | pytorch-image-models/hfdocs/source/models/dpn.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/dpn.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3695
} | 244 |
# ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: \\( V = \text{Concat} \{ V^{1},V^{2},\cdo... | pytorch-image-models/hfdocs/source/models/resnest.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/resnest.mdx",
"repo_id": "pytorch-image-models",
"token_count": 5469
} | 245 |
# (Tensorflow) EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scale... | pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 8002
} | 246 |
""" ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | pytorch-image-models/onnx_export.py/0 | {
"file_path": "pytorch-image-models/onnx_export.py",
"repo_id": "pytorch-image-models",
"token_count": 2010
} | 247 |
import pytest
import torch
import torch.nn as nn
from timm.layers import create_act_layer, set_layer_config, get_act_layer, get_act_fn, Attention2d, MultiQueryAttentionV2
import importlib
import os
torch_backend = os.environ.get('TORCH_BACKEND')
if torch_backend is not None:
importlib.import_module(torch_backend... | pytorch-image-models/tests/test_layers.py/0 | {
"file_path": "pytorch-image-models/tests/test_layers.py",
"repo_id": "pytorch-image-models",
"token_count": 1935
} | 248 |
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractmethod
def label_descriptions(sel... | pytorch-image-models/timm/data/dataset_info.py/0 | {
"file_path": "pytorch-image-models/timm/data/dataset_info.py",
"repo_id": "pytorch-image-models",
"token_count": 941
} | 249 |
""" 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": 1441
} | 250 |
""" PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | pytorch-image-models/timm/layers/adaptive_avgmax_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/adaptive_avgmax_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 3039
} | 251 |
""" Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import (
GroupNorm,
GroupNorm1,
LayerNorm,
LayerNorm2d,
LayerNormFp32,
La... | pytorch-image-models/timm/layers/create_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/create_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 902
} | 252 |
""" Interpolation helpers for timm layers
RegularGridInterpolator from https://github.com/sbarratt/torch_interpolations
Copyright Shane Barratt, Apache 2.0 license
"""
import torch
from itertools import product
class RegularGridInterpolator:
""" Interpolate data defined on a rectilinear grid with even or uneven ... | pytorch-image-models/timm/layers/interpolate.py/0 | {
"file_path": "pytorch-image-models/timm/layers/interpolate.py",
"repo_id": "pytorch-image-models",
"token_count": 1121
} | 253 |
""" Position Embedding Utilities
Hacked together by / Copyright 2022 Ross Wightman
"""
import logging
import math
from typing import List, Tuple, Optional, Union
import torch
import torch.nn.functional as F
from ._fx import register_notrace_function
_logger = logging.getLogger(__name__)
@torch.fx.wrap
@register_n... | pytorch-image-models/timm/layers/pos_embed.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pos_embed.py",
"repo_id": "pytorch-image-models",
"token_count": 1160
} | 254 |
""" Binary Cross Entropy w/ a few extras
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryCrossEntropy(nn.Module):
""" BCE with optional one-hot from dense targets, label smoothing, thresholdin... | pytorch-image-models/timm/loss/binary_cross_entropy.py/0 | {
"file_path": "pytorch-image-models/timm/loss/binary_cross_entropy.py",
"repo_id": "pytorch-image-models",
"token_count": 1082
} | 255 |
""" DeiT - Data-efficient Image Transformers
DeiT model defs and weights from https://github.com/facebookresearch/deit, original copyright below
paper: `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
paper: `DeiT III: Revenge of the ViT` - https://arxiv.org/abs/2204.07118
Modifications ... | pytorch-image-models/timm/models/deit.py/0 | {
"file_path": "pytorch-image-models/timm/models/deit.py",
"repo_id": "pytorch-image-models",
"token_count": 8370
} | 256 |
"""
MambaOut models for image classification.
Some implementations are modified from:
timm (https://github.com/rwightman/pytorch-image-models),
MetaFormer (https://github.com/sail-sg/metaformer),
InceptionNeXt (https://github.com/sail-sg/inceptionnext)
"""
from collections import OrderedDict
from typing import List, Op... | pytorch-image-models/timm/models/mambaout.py/0 | {
"file_path": "pytorch-image-models/timm/models/mambaout.py",
"repo_id": "pytorch-image-models",
"token_count": 11680
} | 257 |
"""
RDNet
Copyright (c) 2024-present NAVER Cloud Corp.
Apache-2.0
"""
from functools import partial
from typing import List, Optional, Tuple, Union, Callable
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, calculate_drop_path_rate... | pytorch-image-models/timm/models/rdnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/rdnet.py",
"repo_id": "pytorch-image-models",
"token_count": 9542
} | 258 |
"""SwiftFormer
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Code: https://github.com/Amshaker/SwiftFormer
Paper: https://arxiv.org/pdf/2303.15446
@InProceedings{Shaker_2023_ICCV,
author = {Shaker, Abdelrahman and Maaz, Muhammad and Rasheed, Hanoona and Kha... | pytorch-image-models/timm/models/swiftformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/swiftformer.py",
"repo_id": "pytorch-image-models",
"token_count": 10987
} | 259 |
""" VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | pytorch-image-models/timm/models/vovnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/vovnet.py",
"repo_id": "pytorch-image-models",
"token_count": 9086
} | 260 |
""" PyTorch Implementation of the Kron (PSGD) optimizer
This is a PSGD optimizer using a Kronecker-factored preconditioner.
This impl was adapted from https://github.com/evanatyourservice/kron_torch
by Evan Walters, licensed CC-BY-4.0.
Contributions to above also made by
* Lucas Nestler, added to his https://github.... | pytorch-image-models/timm/optim/kron.py/0 | {
"file_path": "pytorch-image-models/timm/optim/kron.py",
"repo_id": "pytorch-image-models",
"token_count": 11053
} | 261 |
""" Batch size decay and retry helpers.
Copyright 2022 Ross Wightman
"""
import math
def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
""" power of two batch-size decay with intra steps
Decay by stepping between powers of 2:
* determine power-of-2 floor of current batch size (base batch... | pytorch-image-models/timm/utils/decay_batch.py/0 | {
"file_path": "pytorch-image-models/timm/utils/decay_batch.py",
"repo_id": "pytorch-image-models",
"token_count": 656
} | 262 |
# Orchestrate a multi-agent system 🤖🤝🤖
[[open-in-colab]]
In this notebook we will make a **multi-agent web browser: an agentic system with several agents collaborating to solve problems using the web!**
It will be a simple hierarchy:
```
+----------------+
| Manager agent |
... | smolagents/docs/source/en/examples/multiagents.md/0 | {
"file_path": "smolagents/docs/source/en/examples/multiagents.md",
"repo_id": "smolagents",
"token_count": 2107
} | 263 |
# Secure code execution
[[open-in-colab]]
> [!TIP]
> If you're new to building agents, make sure to first read the [intro to agents](../conceptual_guides/intro_agents) and the [guided tour of smolagents](../guided_tour).
### Code agents
[Multiple](https://huggingface.co/papers/2402.01030) [research](https://hugging... | smolagents/docs/source/en/tutorials/secure_code_execution.md/0 | {
"file_path": "smolagents/docs/source/en/tutorials/secure_code_execution.md",
"repo_id": "smolagents",
"token_count": 5877
} | 264 |
# Tools
[[open-in-colab]]
यहाँ, हम एडवांस्ड tools उपयोग देखेंगे।
> [!TIP]
> यदि आप एजेंट्स बनाने में नए हैं, तो सबसे पहले [एजेंट्स का परिचय](../conceptual_guides/intro_agents) और [smolagents की गाइडेड टूर](../guided_tour) पढ़ना सुनिश्चित करें।
- [Tools](#tools)
- [टूल क्या है, और इसे कैसे बनाएं?](#टूल-क्या-है-औ... | smolagents/docs/source/hi/tutorials/tools.md/0 | {
"file_path": "smolagents/docs/source/hi/tutorials/tools.md",
"repo_id": "smolagents",
"token_count": 10673
} | 265 |
# Text-to-SQL
[[open-in-colab]]
在此教程中，我们将看到如何使用 `smolagents` 实现一个利用 SQL 的 agent。
> 让我们从经典问题开始：为什么不简单地使用标准的 text-to-SQL pipeline 呢？
标准的 text-to-SQL pipeline 很脆弱，因为生成的 SQL 查询可能会出错。更糟糕的是，查询可能出错却不引发错误警报，从而返回一些不正确或无用的结果。
👉 相反，agent 系统则可以检视输出结果并决定查询是否需要被更改，因此带来巨大的性能提升。
让我们来一起构建这个 agent! 💪
首先，我们构建一个 SQL 的环境：
```py
fr... | smolagents/docs/source/zh/examples/text_to_sql.md/0 | {
"file_path": "smolagents/docs/source/zh/examples/text_to_sql.md",
"repo_id": "smolagents",
"token_count": 2956
} | 266 |
from smolagents import Tool
from smolagents.models import Model
class TextInspectorTool(Tool):
name = "inspect_file_as_text"
description = """
You cannot load files yourself: instead call this tool to read a file as markdown text and ask questions about it.
This tool handles the following file extensions: [".... | smolagents/examples/open_deep_research/scripts/text_inspector_tool.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/text_inspector_tool.py",
"repo_id": "smolagents",
"token_count": 2346
} | 267 |
#!/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/__init__.py/0 | {
"file_path": "smolagents/src/smolagents/__init__.py",
"repo_id": "smolagents",
"token_count": 311
} | 268 |
import ast
import builtins
from itertools import zip_longest
from .utils import BASE_BUILTIN_MODULES, get_source, is_valid_name
_BUILTIN_NAMES = set(vars(builtins))
class MethodChecker(ast.NodeVisitor):
"""
Checks that a method
- only uses defined names
- contains no local imports (e.g. numpy is ok... | smolagents/src/smolagents/tool_validation.py/0 | {
"file_path": "smolagents/src/smolagents/tool_validation.py",
"repo_id": "smolagents",
"token_count": 4921
} | 269 |
import os
import subprocess
import tempfile
def test_import_smolagents_without_extras(monkeypatch):
monkeypatch.delenv("VIRTUAL_ENV", raising=False)
with tempfile.TemporaryDirectory() as temp_dir:
# Create a virtual environment
venv_dir = os.path.join(temp_dir, "venv")
subprocess.run([... | smolagents/tests/test_import.py/0 | {
"file_path": "smolagents/tests/test_import.py",
"repo_id": "smolagents",
"token_count": 448
} | 270 |
# This file instructs Redocly's linter to ignore the rules contained for specific parts of your API.
# See https://redoc.ly/docs/cli/ for more information.
docs/openapi.json:
no-empty-servers:
- '#/openapi'
spec:
- >-
#/components/schemas/GenerateParameters/properties/best_of/exclusiveMinimum
- >-... | text-generation-inference/.redocly.lint-ignore.yaml/0 | {
"file_path": "text-generation-inference/.redocly.lint-ignore.yaml",
"repo_id": "text-generation-inference",
"token_count": 1750
} | 271 |
from typing import Optional
import torch
import torch.nn as nn
import os
from text_generation_server.utils.weights import Weights
from text_generation_server.layers.fp8 import (
Fp8Weight,
fp8_quantize,
quant_dtype,
normalize_e4m3fn_to_native_float8,
dynamic_quant,
dequant_block_fp8_weight_nai... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/fp8.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/fp8.py",
"repo_id": "text-generation-inference",
"token_count": 4907
} | 272 |
from typing import List, Optional, Tuple
import torch
import torch.distributed
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
from text_generation_server.layers import (
SpeculativeHead,
TensorParallelColumnLinear,
... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 11295
} | 273 |
use bindgen::callbacks::{ItemInfo, ParseCallbacks};
use std::env;
use std::path::PathBuf;
#[derive(Debug)]
struct PrefixStripper;
impl ParseCallbacks for PrefixStripper {
fn generated_name_override(&self, item_info: ItemInfo<'_>) -> Option<String> {
item_info.name.strip_prefix("llama_").map(str::to_string... | text-generation-inference/backends/llamacpp/build.rs/0 | {
"file_path": "text-generation-inference/backends/llamacpp/build.rs",
"repo_id": "text-generation-inference",
"token_count": 766
} | 274 |
import os
import shutil
import time
from typing import Optional
from huggingface_hub import snapshot_download
from huggingface_hub.constants import HF_HUB_CACHE
from loguru import logger
from optimum.neuron.cache import get_hub_cached_entries
from optimum.neuron.configuration_utils import NeuronConfig
from .tgi_env... | text-generation-inference/backends/neuron/server/text_generation_server/model.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/model.py",
"repo_id": "text-generation-inference",
"token_count": 1909
} | 275 |
#!/bin/bash
set -e -o pipefail -u
export ENV_FILEPATH=$(mktemp)
trap "rm -f ${ENV_FILEPATH}" EXIT
touch $ENV_FILEPATH
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
${SCRIPT_DIR}/tgi_entry_point.py $@
source $ENV_FILEPATH
exec text-generation-launcher $@
| text-generation-inference/backends/neuron/tgi-entrypoint.sh/0 | {
"file_path": "text-generation-inference/backends/neuron/tgi-entrypoint.sh",
"repo_id": "text-generation-inference",
"token_count": 130
} | 276 |
use std::path::PathBuf;
use thiserror::Error;
use text_generation_router::server;
#[derive(Debug, Error)]
pub enum TensorRtLlmBackendError {
#[error("Provided engine folder {0} doesn't exist")]
EngineFolderDoesntExists(PathBuf),
#[error("Provided executorWorker binary path {0} doesn't exist")]
Executo... | text-generation-inference/backends/trtllm/src/errors.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/errors.rs",
"repo_id": "text-generation-inference",
"token_count": 285
} | 277 |
use std::time::{Duration, Instant};
use text_generation_client::v3::{
Batch, CachedBatch, NextTokenChooserParameters, Request, ShardedClient,
StoppingCriteriaParameters,
};
use text_generation_client::{Chunk, ClientError, Input};
use tokenizers::{Tokenizer, TruncationDirection};
use tokio::sync::{broadcast, mps... | text-generation-inference/benchmark/src/generation.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/generation.rs",
"repo_id": "text-generation-inference",
"token_count": 3420
} | 278 |
import json
import requests
import warnings
from aiohttp import ClientSession, ClientTimeout
from pydantic import ValidationError
from typing import Dict, Optional, List, AsyncIterator, Iterator, Union
from text_generation import DEPRECATION_WARNING
from text_generation.types import (
StreamResponse,
Response... | text-generation-inference/clients/python/text_generation/client.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/client.py",
"repo_id": "text-generation-inference",
"token_count": 19243
} | 279 |
# Monitoring TGI server with Prometheus and Grafana dashboard
TGI server deployment can easily be monitored through a Grafana dashboard, consuming a Prometheus data collection. Example of inspectable metrics are statistics on the effective batch sizes used by TGI, prefill/decode latencies, number of generated tokens, ... | text-generation-inference/docs/source/basic_tutorials/monitoring.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/monitoring.md",
"repo_id": "text-generation-inference",
"token_count": 1376
} | 280 |
## Speculation
Speculative decoding, assisted generation, Medusa, and others are a few different names for the same idea.
The idea is to generate tokens *before* the large model actually runs, and only *check* if those tokens where valid.
So you are making *more* computations on your LLM, but if you are correct you ... | text-generation-inference/docs/source/conceptual/speculation.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/speculation.md",
"repo_id": "text-generation-inference",
"token_count": 706
} | 281 |
# Supported Models
Text Generation Inference enables serving optimized models. The following sections list which models (VLMs & LLMs) are supported.
- [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
- [Deepseek V3](https://huggingface.co/deepseek-ai/DeepSeek-V3)
- [Idefics 2](https://huggingface.co/Hug... | 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": 1536
} | 282 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "Okay, let's analyze the image. \n\nThe transparent image reveals a stylized depiction of **a human head**. It's a minimalist, geometric representation, showing the basic shapes of the s... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgba.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_base64_rgba.json",
"repo_id": "text-generation-inference",
"token_count": 330
} | 283 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5229,
"logprob": -0.6645508,
"special": false,
"text": " failed"
},
{
"id": 29901,
"logpr... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin_24/test_flash_llama_marlin24_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin_24/test_flash_llama_marlin24_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 857
} | 284 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 13,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 450,
"logprob": -0.2602539,
"special": false,
"text": " The"
},
{
"id": 21282,
"log... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics_two_images.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics/test_idefics_two_images.json",
"repo_id": "text-generation-inference",
"token_count": 1102
} | 285 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 100,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 2721,
"logprob": -0.21582031,
"special": false,
"text": " people"
},
{
"id": 21807,
"... | text-generation-inference/integration-tests/models/__snapshots__/test_transformers_llama4/test_flash_llama4.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_transformers_llama4/test_flash_llama4.json",
"repo_id": "text-generation-inference",
"token_count": 7698
} | 286 |
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
} | 287 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_fp8_kv_cache_handle(launcher):
with launcher(
"neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV",
num_shard=2,
kv_cache_dtype="fp8_e4m3fn",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama... | text-generation-inference/integration-tests/models/test_flash_llama_fp8_kv_cache.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_fp8_kv_cache.py",
"repo_id": "text-generation-inference",
"token_count": 986
} | 288 |
import pytest
@pytest.fixture(scope="module")
def flash_phi35_moe_handle(launcher):
with launcher(
"microsoft/Phi-3.5-MoE-instruct",
num_shard=4,
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_phi35_moe(flash_phi35_moe_handle):
await flash_phi35_moe_han... | text-generation-inference/integration-tests/models/test_flash_phi35_moe.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_phi35_moe.py",
"repo_id": "text-generation-inference",
"token_count": 921
} | 289 |
import pytest
import requests
@pytest.fixture(scope="module")
def lora_mistral_handle(launcher):
with launcher(
"mistralai/Mistral-7B-v0.1",
lora_adapters=[
"predibase/dbpedia",
"predibase/customer_support",
],
cuda_graphs=[0],
) as handle:
yield... | text-generation-inference/integration-tests/models/test_lora_mistral.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_lora_mistral.py",
"repo_id": "text-generation-inference",
"token_count": 1873
} | 290 |
[pytest]
addopts = --snapshot-warn-unused
asyncio_mode = auto
markers =
private: marks tests as requiring an admin hf token (deselect with '-m "not private"')
| text-generation-inference/integration-tests/pytest.ini/0 | {
"file_path": "text-generation-inference/integration-tests/pytest.ini",
"repo_id": "text-generation-inference",
"token_count": 58
} | 291 |
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
[[package]]
name = "certifi"
version = "2024.8.30"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
{file = "certifi-2024.8.30-py3-none-any.whl", hash = "... | text-generation-inference/load_tests/poetry.lock/0 | {
"file_path": "text-generation-inference/load_tests/poetry.lock",
"repo_id": "text-generation-inference",
"token_count": 27378
} | 292 |
// 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_stream... | 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": 8233
} | 293 |
exllamav2_commit := v0.1.8
build-exllamav2:
git clone https://github.com/turboderp/exllamav2.git exllamav2 && \
cd exllamav2 && git fetch && git checkout $(exllamav2_commit) && \
git submodule update --init --recursive && \
pip install -r requirements.txt && \
CUDA_ARCH_LIST="8.0;9.0a" NVCC_GENCODE="-gencode=arc... | text-generation-inference/server/Makefile-exllamav2/0 | {
"file_path": "text-generation-inference/server/Makefile-exllamav2",
"repo_id": "text-generation-inference",
"token_count": 302
} | 294 |
#include "q4_matmul.cuh"
#include "column_remap.cuh"
#include <ATen/cuda/CUDAContext.h>
#include "../util.cuh"
#include "../matrix.cuh"
#include "../cu_compat.cuh"
#include "../cuda_buffers.cuh"
#if defined(USE_ROCM)
#include "../hip_compat.cuh"
#endif
const int THREADS_X = 32; // Block size and thread count alo... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cu",
"repo_id": "text-generation-inference",
"token_count": 4211
} | 295 |
#include "compat.cuh"
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result, const half qs_h)
{
half2 result = {};
const half2* a2_ptr = (const half2*)a_ptr;
#pragma unroll
for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
return __hfm... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel.cuh",
"repo_id": "text-generation-inference",
"token_count": 11459
} | 296 |
# test_watermark_logits_processor.py
import os
import numpy as np
import torch
from text_generation_server.utils.watermark import WatermarkLogitsProcessor
GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
DELTA = os.getenv("WATERMARK_DELTA", 2.0)
def test_seed_rng():
input_ids = [101, 2036, 3731, 102, 2003, 103]
p... | text-generation-inference/server/tests/utils/test_watermark.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_watermark.py",
"repo_id": "text-generation-inference",
"token_count": 781
} | 297 |
import intel_extension_for_pytorch as ipex
import torch
from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
from text_generation_server.layers.attention import Seqlen
from typing import Optional
from text_generation_server.models.globals import (
ATTENTION,
BLOCK_SIZE,
)
if ATTENTION... | text-generation-inference/server/text_generation_server/layers/attention/ipex.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/ipex.py",
"repo_id": "text-generation-inference",
"token_count": 2563
} | 298 |
from dataclasses import dataclass
from typing import List, Optional, Union
import torch
import torch.nn as nn
from text_generation_server.layers.marlin.util import _check_marlin_kernels
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.kernels import load_kernel
from text_... | text-generation-inference/server/text_generation_server/layers/marlin/marlin.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/marlin.py",
"repo_id": "text-generation-inference",
"token_count": 6121
} | 299 |
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