text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
|---|---|---|---|
#!/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_observation_processor.py/0 | {
"file_path": "lerobot/tests/processor/test_observation_processor.py",
"repo_id": "lerobot",
"token_count": 6090
} | 193 |
# 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/utils/test_random_utils.py/0 | {
"file_path": "lerobot/tests/utils/test_random_utils.py",
"repo_id": "lerobot",
"token_count": 1453
} | 194 |
# 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 applicabl... | open-r1/scripts/run_benchmarks.py/0 | {
"file_path": "open-r1/scripts/run_benchmarks.py",
"repo_id": "open-r1",
"token_count": 815
} | 195 |
#!/bin/bash
#SBATCH --job-name=open_r1
#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 --error=./logs/%x-%j.err
#SBATCH --requeue
#SBATCH --time=3-00:00:00
if [[ "$*" == *"--help"* ]]; then
... | open-r1/slurm/train.slurm/0 | {
"file_path": "open-r1/slurm/train.slurm",
"repo_id": "open-r1",
"token_count": 2351
} | 196 |
import asyncio
import os
import random
import re
import subprocess
from collections import Counter
from functools import lru_cache
import aiohttp
class PistonError(Exception):
pass
@lru_cache(maxsize=1)
def get_piston_client_from_env(session=None):
piston_endpoints = os.getenv("PISTON_ENDPOINTS")
if pi... | open-r1/src/open_r1/utils/competitive_programming/piston_client.py/0 | {
"file_path": "open-r1/src/open_r1/utils/competitive_programming/piston_client.py",
"repo_id": "open-r1",
"token_count": 4290
} | 197 |
<!---
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": 3732
} | 198 |
<!--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... | peft/docs/source/developer_guides/checkpoint.md/0 | {
"file_path": "peft/docs/source/developer_guides/checkpoint.md",
"repo_id": "peft",
"token_count": 4146
} | 199 |
<!--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/package_reference/multitask_prompt_tuning.md/0 | {
"file_path": "peft/docs/source/package_reference/multitask_prompt_tuning.md",
"repo_id": "peft",
"token_count": 533
} | 200 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/quicktour.md/0 | {
"file_path": "peft/docs/source/quicktour.md",
"repo_id": "peft",
"token_count": 2385
} | 201 |
import argparse
import os
from typing import Optional
from huggingface_hub import HfFolder, whoami
from transformers import PretrainedConfig
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organi... | peft/examples/boft_controlnet/utils/args_loader.py/0 | {
"file_path": "peft/examples/boft_controlnet/utils/args_loader.py",
"repo_id": "peft",
"token_count": 7255
} | 202 |
import os
import torch
from accelerate import Accelerator
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import LoraConfig, TaskType, get_pef... | peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py/0 | {
"file_path": "peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py",
"repo_id": "peft",
"token_count": 2543
} | 203 |
# 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/loftq_finetuning/quantize_save_load.py/0 | {
"file_path": "peft/examples/loftq_finetuning/quantize_save_load.py",
"repo_id": "peft",
"token_count": 2835
} | 204 |
<jupyter_start><jupyter_text>Using PEFT with custom models `peft` allows us to fine-tune models efficiently with LoRA. In this short notebook, we will demonstrate how to train a simple multilayer perceptron (MLP) using `peft`. Imports Make sure that you have the latest version of `peft` installed. To ensure that, run ... | peft/examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb/0 | {
"file_path": "peft/examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb",
"repo_id": "peft",
"token_count": 4087
} | 205 |
import os
from enum import Enum
import packaging.version
import torch
import transformers
from datasets import DatasetDict, load_dataset, load_from_disk
from datasets.builder import DatasetGenerationError
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
from peft impor... | peft/examples/sft/utils.py/0 | {
"file_path": "peft/examples/sft/utils.py",
"repo_id": "peft",
"token_count": 3912
} | 206 |
{
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": null,
"beta1": 0.85,
"beta2": 0.85,
"bias": "none",
"corda_config": null,
"deltaT": 1,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"inference_mode": false,
"init_lora_weights": true,
"init_r": 6... | peft/method_comparison/MetaMathQA/experiments/adalora/llama-3.2-3B-rank32/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/adalora/llama-3.2-3B-rank32/adapter_config.json",
"repo_id": "peft",
"token_count": 384
} | 207 |
{
"alpha": 64,
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": null,
"decompose_both": false,
"decompose_factor": -1,
"exclude_modules": null,
"inference_mode": false,
"init_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"module_dropout": 0.0,
"modul... | peft/method_comparison/MetaMathQA/experiments/lokr/llama-3.2-3B-rank32/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/lokr/llama-3.2-3B-rank32/adapter_config.json",
"repo_id": "peft",
"token_count": 254
} | 208 |
import pandas as pd
import pytest
from .sanitizer import parse_and_filter
@pytest.fixture
def df_products():
data = {
'product_id': [101, 102, 103, 104, 105, 106],
'category': ['Electronics', 'Books', 'Electronics', 'Home Goods', 'Books', 'Electronics'],
'price': [799.99, 19.99, 49.50, 12... | peft/method_comparison/test_sanitizer.py/0 | {
"file_path": "peft/method_comparison/test_sanitizer.py",
"repo_id": "peft",
"token_count": 554
} | 209 |
# 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/scripts/launch_notebook_mp.py/0 | {
"file_path": "peft/scripts/launch_notebook_mp.py",
"repo_id": "peft",
"token_count": 493
} | 210 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/peft_model.py/0 | {
"file_path": "peft/src/peft/peft_model.py",
"repo_id": "peft",
"token_count": 72428
} | 211 |
# 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
} | 212 |
# 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
} | 213 |
# 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
} | 214 |
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/lora/corda.py/0 | {
"file_path": "peft/src/peft/tuners/lora/corda.py",
"repo_id": "peft",
"token_count": 6533
} | 215 |
# 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/model.py/0 | {
"file_path": "peft/src/peft/tuners/oft/model.py",
"repo_id": "peft",
"token_count": 7891
} | 216 |
# 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/randlora/bnb.py/0 | {
"file_path": "peft/src/peft/tuners/randlora/bnb.py",
"repo_id": "peft",
"token_count": 9574
} | 217 |
# 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/layer.py/0 | {
"file_path": "peft/src/peft/tuners/trainable_tokens/layer.py",
"repo_id": "peft",
"token_count": 4476
} | 218 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/xlora/model.py/0 | {
"file_path": "peft/src/peft/tuners/xlora/model.py",
"repo_id": "peft",
"token_count": 9094
} | 219 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/regression/test_regression.py/0 | {
"file_path": "peft/tests/regression/test_regression.py",
"repo_id": "peft",
"token_count": 10766
} | 220 |
# 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_incremental_pca.py/0 | {
"file_path": "peft/tests/test_incremental_pca.py",
"repo_id": "peft",
"token_count": 2775
} | 221 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_stablediffusion.py/0 | {
"file_path": "peft/tests/test_stablediffusion.py",
"repo_id": "peft",
"token_count": 7695
} | 222 |
# 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
} | 223 |
# 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
} | 224 |
# (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
} | 225 |
""" 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
} | 226 |
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
} | 227 |
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
} | 228 |
""" 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
} | 229 |
""" 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
} | 230 |
""" 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
} | 231 |
""" 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
} | 232 |
""" 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
} | 233 |
""" 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
} | 234 |
""" 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
} | 235 |
"""
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": 11683
} | 236 |
"""
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, NormMlpClassifierHead, C... | pytorch-image-models/timm/models/rdnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/rdnet.py",
"repo_id": "pytorch-image-models",
"token_count": 9547
} | 237 |
"""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
} | 238 |
""" 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": 9084
} | 239 |
""" 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
} | 240 |
""" 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
} | 241 |
# How do multi-step agents work?
The ReAct framework ([Yao et al., 2022](https://huggingface.co/papers/2210.03629)) is currently the main approach to building agents.
The name is based on the concatenation of two words, "Reason" and "Act." Indeed, agents following this architecture will solve their task in as many st... | smolagents/docs/source/en/conceptual_guides/react.md/0 | {
"file_path": "smolagents/docs/source/en/conceptual_guides/react.md",
"repo_id": "smolagents",
"token_count": 807
} | 242 |
# Inspecting runs with OpenTelemetry
[[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).
## Why log your agent runs?
Agent runs are complicated to debug.
Validating that ... | smolagents/docs/source/en/tutorials/inspect_runs.md/0 | {
"file_path": "smolagents/docs/source/en/tutorials/inspect_runs.md",
"repo_id": "smolagents",
"token_count": 2101
} | 243 |
# OpenTelemetry के साथ runs का निरीक्षण
[[open-in-colab]]
> [!TIP]
> यदि आप एजेंट्स बनाने में नए हैं, तो पहले [एजेंट्स का परिचय](../conceptual_guides/intro_agents) और [smolagents की गाइडेड टूर](../guided_tour) पढ़ना सुनिश्चित करें।
### Agents runs को लॉग क्यों करें?
Agent runs को डीबग करना जटिल होता है।
यह सत्यापि... | smolagents/docs/source/hi/tutorials/inspect_runs.md/0 | {
"file_path": "smolagents/docs/source/hi/tutorials/inspect_runs.md",
"repo_id": "smolagents",
"token_count": 3246
} | 244 |
# Agents - 导览
[[open-in-colab]]
在本导览中，您将学习如何构建一个 agent（智能体），如何运行它，以及如何自定义它以使其更好地适应您的使用场景。
> [!TIP]
> 译者注：Agent 的业内术语是“智能体”。本译文将保留 agent，不作翻译，以带来更高效的阅读体验。(在中文为主的文章中，It's easier to 注意到英文。Attention Is All You Need!)
> [!TIP]
> 中文社区发布了关于 smolagents 的介绍和实践讲解视频(来源：[Issue#80](https://github.com/huggingface/smolagents/issu... | smolagents/docs/source/zh/guided_tour.md/0 | {
"file_path": "smolagents/docs/source/zh/guided_tour.md",
"repo_id": "smolagents",
"token_count": 10501
} | 245 |
from openinference.instrumentation.smolagents import SmolagentsInstrumentor
from phoenix.otel import register
register()
SmolagentsInstrumentor().instrument(skip_dep_check=True)
from smolagents import (
CodeAgent,
InferenceClientModel,
ToolCallingAgent,
VisitWebpageTool,
WebSearchTool,
)
# The... | smolagents/examples/inspect_multiagent_run.py/0 | {
"file_path": "smolagents/examples/inspect_multiagent_run.py",
"repo_id": "smolagents",
"token_count": 335
} | 246 |
import base64
import json
import mimetypes
import os
import uuid
from io import BytesIO
import PIL.Image
import requests
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
from smolagents import Tool, tool
load_dotenv(override=True)
def process_images_and_text(image_path, query, client):
... | smolagents/examples/open_deep_research/scripts/visual_qa.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/visual_qa.py",
"repo_id": "smolagents",
"token_count": 2558
} | 247 |
# 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/src/smolagents/agent_types.py/0 | {
"file_path": "smolagents/src/smolagents/agent_types.py",
"repo_id": "smolagents",
"token_count": 3867
} | 248 |
#!/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/utils.py/0 | {
"file_path": "smolagents/src/smolagents/utils.py",
"repo_id": "smolagents",
"token_count": 6942
} | 249 |
import json
from textwrap import dedent
import pytest
from mcp import StdioServerParameters
from smolagents.mcp_client import MCPClient
@pytest.fixture
def echo_server_script():
return dedent(
'''
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Echo Server")
@mcp.tool()
... | smolagents/tests/test_mcp_client.py/0 | {
"file_path": "smolagents/tests/test_mcp_client.py",
"repo_id": "smolagents",
"token_count": 1956
} | 250 |
include Makefile-flash-att
include Makefile-flash-att-v2
include Makefile-vllm
include Makefile-awq
include Makefile-eetq
include Makefile-selective-scan
PROTO_PATH ?= ../proto/v3
unit-tests:
pytest -s -vv -m "not private" tests
gen-server:
# Compile protos
pip install grpcio-tools==1.62.2 mypy-protobuf==3.6.0 't... | text-generation-inference/backends/gaudi/server/Makefile/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/Makefile",
"repo_id": "text-generation-inference",
"token_count": 468
} | 251 |
# coding=utf-8
# Copyright 2023, 2024 DeepSeek-AI 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/LI... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/fused_moe.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/fused_moe.py",
"repo_id": "text-generation-inference",
"token_count": 2009
} | 252 |
import asyncio
from pathlib import Path
from typing import List
from grpc import aio
from grpc_reflection.v1alpha import reflection
from loguru import logger
from .generator import Generator, NeuronGenerator
from .interceptor import ExceptionInterceptor
from .pb import generate_pb2, generate_pb2_grpc
class TextGene... | text-generation-inference/backends/neuron/server/text_generation_server/server.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/server.py",
"repo_id": "text-generation-inference",
"token_count": 1222
} | 253 |
#!/usr/bin/env python
import logging
import os
import sys
from text_generation_server.tgi_env import (
available_cores,
get_env_dict,
get_neuron_config_for_model,
neuron_config_to_env,
neuronxcc_version,
parse_cmdline_and_set_env,
tgi_env_vars,
)
logger = logging.getLogger(__name__)
d... | text-generation-inference/backends/neuron/tgi_entry_point.py/0 | {
"file_path": "text-generation-inference/backends/neuron/tgi_entry_point.py",
"repo_id": "text-generation-inference",
"token_count": 515
} | 254 |
pub use looper::TensorRtLlmBackendV2;
pub mod errors;
mod looper;
mod utils;
#[cxx::bridge(namespace = "huggingface::tgi::backends::trtllm")]
mod ffi {
#[cxx_name = "finish_reason_t"]
#[derive(Debug, Clone, Copy)]
pub enum FinishReason {
/// The request is not finished.
#[cxx_name = "kNOT_... | text-generation-inference/backends/trtllm/src/lib.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 1463
} | 255 |
use std::sync::Arc;
use criterion::{black_box, criterion_group, criterion_main, Criterion};
use rand::Rng;
use text_generation_router_v3::block_allocator::Allocator;
use text_generation_router_v3::radix::RadixAllocator;
fn prefix_cache_benchmark(c: &mut Criterion) {
// let prefixes: Vec<Vec<u32>> = (0..8192)
... | text-generation-inference/backends/v3/benches/prefix_cache.rs/0 | {
"file_path": "text-generation-inference/backends/v3/benches/prefix_cache.rs",
"repo_id": "text-generation-inference",
"token_count": 806
} | 256 |
mod app;
mod event;
mod generation;
mod table;
mod utils;
use crate::app::App;
use crate::event::Event;
use ratatui::backend::CrosstermBackend;
use ratatui::crossterm::ExecutableCommand;
use ratatui::Terminal;
use std::io;
use text_generation_client::v3::{GrammarType, NextTokenChooserParameters, ShardedClient};
use to... | text-generation-inference/benchmark/src/lib.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 1979
} | 257 |
from typing import Dict
# Text Generation Inference Errors
class ValidationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class GenerationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class OverloadedError(Exception):
def _... | text-generation-inference/clients/python/text_generation/errors.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/errors.py",
"repo_id": "text-generation-inference",
"token_count": 1080
} | 258 |
# Non-core Model Serving
TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced b... | text-generation-inference/docs/source/basic_tutorials/non_core_models.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/non_core_models.md",
"repo_id": "text-generation-inference",
"token_count": 472
} | 259 |
# Streaming
## What is Streaming?
Token streaming is the mode in which the server returns the tokens one by one as the model generates them. This enables showing progressive generations to the user rather than waiting for the whole generation. Streaming is an essential aspect of the end-user experience as it reduces... | text-generation-inference/docs/source/conceptual/streaming.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/streaming.md",
"repo_id": "text-generation-inference",
"token_count": 1861
} | 260 |
# Collection of Usage Statistics
Text Generation Inference collects anonymous usage statistics to help us improve the service. The collected data is used to improve TGI and to understand what causes failures. The data is collected transparently and any sensitive information is omitted.
Usage statistics are collected... | text-generation-inference/docs/source/usage_statistics.md/0 | {
"file_path": "text-generation-inference/docs/source/usage_statistics.md",
"repo_id": "text-generation-inference",
"token_count": 966
} | 261 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 185,
"logprob": -1.546875,
"special": false,
"text": "\n"
},
{
"id": 549,
"logprob": -... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2.json",
"repo_id": "text-generation-inference",
"token_count": 858
} | 262 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "Here's a description of what's shown in the image:\n\nThe image depicts a brown cow standing on a sandy beach. The beach has turquoise water and a distant island visible in the backgrou... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow.json",
"repo_id": "text-generation-inference",
"token_count": 334
} | 263 |
{
"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
} | 264 |
{
"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
} | 265 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 288,
"logprob": -0.2854004,
"special": false,
"text": "ing"
},
{
"id": 264,
"logprob": -0... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 860
} | 266 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2502,
"logprob": null,
"text": " red"
},
{
"id": 13,
"logprob": -2.734375,
"text": ","
},
{
"id": 8862... | text-generation-inference/integration-tests/models/__snapshots__/test_mamba/test_mamba_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mamba/test_mamba_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 1155
} | 267 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 8,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 330,
"logprob": -0.107421875,
"special": false,
"text": " A"
},
{
"id": 11426,
"logp... | text-generation-inference/integration-tests/models/__snapshots__/test_smolvlm/test_flash_smolvlm_next_simple_url.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_smolvlm/test_flash_smolvlm_next_simple_url.json",
"repo_id": "text-generation-inference",
"token_count": 718
} | 268 |
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_wna16_int_24_handle(launcher):
with launcher(
"danieldk/Llama-3.1-8B-w4a16-int-24",
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_24.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_compressed_tensors_wna16_int_24.py",
"repo_id": "text-generation-inference",
"token_count": 1080
} | 269 |
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
} | 270 |
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
} | 271 |
import pytest
@pytest.fixture(scope="module")
def fused_kernel_mamba_handle(launcher):
with launcher("state-spaces/mamba-130m-hf", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def fused_kernel_mamba(fused_kernel_mamba_handle):
await fused_kernel_mamba_handle.health(300)... | text-generation-inference/integration-tests/models/test_mamba.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_mamba.py",
"repo_id": "text-generation-inference",
"token_count": 825
} | 272 |
[tool.poetry]
name = "text-generation-inference-benchmarks"
version = "0.1.0"
description = ""
authors = ["Hugo Larcher <hugo.larcher@huggingface.co>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.11"
docker = "^7.1.0"
loguru = "^0.7.2"
psutil = "^6.0.0"
gputil = "^1.4.0"
pandas = "^2.2.3"
pyarrow = "^... | text-generation-inference/load_tests/pyproject.toml/0 | {
"file_path": "text-generation-inference/load_tests/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 195
} | 273 |
use crate::infer::InferError;
use crate::{
FunctionDefinition, FunctionRef, FunctionsMap, JsonSchemaTool, Properties, Tool, ToolChoice,
};
use serde_json::{json, Map, Value};
use std::collections::HashMap;
pub(crate) struct ToolGrammar {}
impl ToolGrammar {
// find a tool by name
fn find_tool_by_name(tool... | text-generation-inference/router/src/infer/tool_grammar.rs/0 | {
"file_path": "text-generation-inference/router/src/infer/tool_grammar.rs",
"repo_id": "text-generation-inference",
"token_count": 2647
} | 274 |
flash_att_commit := ceee0de88c037ee6eda5e75c813a8648e4bcb1c9
build-flash-attention:
if [ ! -d 'flash-attention' ]; then \
pip install -U packaging ninja --no-cache-dir && \
git clone https://github.com/Narsil/flash-attention.git; \
fi
cd flash-attention && git fetch && git checkout $(flash_att_commit) && \
MA... | text-generation-inference/server/Makefile-flash-att/0 | {
"file_path": "text-generation-inference/server/Makefile-flash-att",
"repo_id": "text-generation-inference",
"token_count": 236
} | 275 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matmul_cuh
#define _q4_matmul_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#include "q4_matrix.cuh"
#include "../tuning.h"
void q4_matmul_cuda
(
ExL... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matmul.cuh",
"repo_id": "text-generation-inference",
"token_count": 322
} | 276 |
#include "compat.cuh"
__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result)
{
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 __hadd2(result, g_resu... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel_gptq.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm_kernel_gptq.cuh",
"repo_id": "text-generation-inference",
"token_count": 4839
} | 277 |
import pytest
import torch
from text_generation_server.utils.weights import (
DefaultWeightsLoader,
Weights,
WeightsLoader,
)
from text_generation_server.layers.gptq import GPTQWeight, GPTQWeightsLoader
from text_generation_server.layers.exl2 import Exl2Weight, Exl2WeightsLoader
from text_generation_server.... | text-generation-inference/server/tests/utils/test_weights.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_weights.py",
"repo_id": "text-generation-inference",
"token_count": 17962
} | 278 |
from typing import Tuple
from dataclasses import dataclass, field
from loguru import logger
import torch
from text_generation_server.layers.fp8 import fp8_quantize
from text_generation_server.models.globals import ATTENTION, BLOCK_SIZE
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_... | text-generation-inference/server/text_generation_server/layers/attention/kv_cache.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/kv_cache.py",
"repo_id": "text-generation-inference",
"token_count": 5908
} | 279 |
from dataclasses import dataclass
import os
from typing import Optional, Tuple, Type, Union, List
import torch
from loguru import logger
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.kernels import load_kernel
from text_generation_server.utils.weights import (
Weig... | text-generation-inference/server/text_generation_server/layers/fp8.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/fp8.py",
"repo_id": "text-generation-inference",
"token_count": 10546
} | 280 |
import functools
from typing import List, Tuple
import numpy
import torch
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.kernels import load_kernel
if SYSTEM == "cuda":
quantization = load_kernel(
module="quantization", repo_id="kernels-community/quantizatio... | text-generation-inference/server/text_generation_server/layers/marlin/util.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/util.py",
"repo_id": "text-generation-inference",
"token_count": 1826
} | 281 |
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and 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/L... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_vision.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_vision.py",
"repo_id": "text-generation-inference",
"token_count": 9625
} | 282 |
from io import BytesIO
from PIL import Image
import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
AutoConfig,
AutoProcessor,
AutoTokenizer,
PreTrainedTokenizerBase,
ProcessorMixin,
)
from typing import Optional, Tuple, List, Type, Dic... | text-generation-inference/server/text_generation_server/models/idefics_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/idefics_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 17112
} | 283 |
nodeLinker: node-modules
npmAuditRegistry: 'https://registry.npmjs.org'
yarnPath: .yarn/releases/yarn-3.5.1.cjs
| tokenizers/bindings/node/.yarnrc.yml/0 | {
"file_path": "tokenizers/bindings/node/.yarnrc.yml",
"repo_id": "tokenizers",
"token_count": 53
} | 284 |
/* eslint-disable @typescript-eslint/no-empty-function */
/* eslint-disable @typescript-eslint/no-explicit-any */
import { BPE, Unigram, WordPiece } from '../../'
const MOCKS_DIR = __dirname + '/__mocks__'
describe('WordPiece', () => {
describe('fromFile', () => {
it('throws if called with only one argument', ... | tokenizers/bindings/node/lib/bindings/models.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/models.test.ts",
"repo_id": "tokenizers",
"token_count": 818
} | 285 |
# `tokenizers-linux-arm-gnueabihf`
This is the **armv7-unknown-linux-gnueabihf** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-arm-gnueabihf/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm-gnueabihf/README.md",
"repo_id": "tokenizers",
"token_count": 42
} | 286 |
{
"name": "tokenizers",
"version": "0.15.3-dev0",
"repository": {
"type": "git",
"url": "git+https://github.com/huggingface/tokenizers.git"
},
"bugs": {
"url": "https://github.com/huggingface/tokenizers/issues"
},
"homepage": "https://github.com/huggingface/tokenizers/tree/master/bindings/node... | tokenizers/bindings/node/package.json/0 | {
"file_path": "tokenizers/bindings/node/package.json",
"repo_id": "tokenizers",
"token_count": 1532
} | 287 |
{
"compilerOptions": {
"target": "ES2018",
"strict": true,
"moduleResolution": "node",
"module": "CommonJS",
"noUnusedLocals": true,
"noUnusedParameters": true,
"esModuleInterop": true,
"allowSyntheticDefaultImports": true
},
"include": ["."],
"exclude": ["node_modules"]
}
| tokenizers/bindings/node/tsconfig.json/0 | {
"file_path": "tokenizers/bindings/node/tsconfig.json",
"repo_id": "tokenizers",
"token_count": 129
} | 288 |
import datasets
from tokenizers import Tokenizer, models, normalizers, pre_tokenizers
# Build a tokenizer
bpe_tokenizer = Tokenizer(models.BPE())
bpe_tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
bpe_tokenizer.normalizer = normalizers.Lowercase()
# Initialize a dataset
dataset = datasets.load_dataset("wikit... | tokenizers/bindings/python/examples/train_with_datasets.py/0 | {
"file_path": "tokenizers/bindings/python/examples/train_with_datasets.py",
"repo_id": "tokenizers",
"token_count": 207
} | 289 |
# Generated content DO NOT EDIT
class Normalizer:
"""
Base class for all normalizers
This class is not supposed to be instantiated directly. Instead, any implementation of a
Normalizer will return an instance of this class when instantiated.
"""
def normalize(self, normalized):
"""
... | tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 8593
} | 290 |
use std::sync::{Arc, RwLock};
use crate::pre_tokenizers::from_string;
use crate::tokenizer::PyTokenizer;
use crate::utils::PyPattern;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::de::Error;
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use tk::decoders::bpe::BPEDecoder;
u... | tokenizers/bindings/python/src/decoders.rs/0 | {
"file_path": "tokenizers/bindings/python/src/decoders.rs",
"repo_id": "tokenizers",
"token_count": 11001
} | 291 |
use serde::de::value::Error;
use serde::{ser, Serialize};
type Result<T> = ::std::result::Result<T, Error>;
pub struct Serializer {
// This string starts empty and JSON is appended as values are serialized.
output: String,
/// Each levels remembers its own number of elements
num_elements: Vec<usize>,
... | tokenizers/bindings/python/src/utils/serde_pyo3.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/serde_pyo3.rs",
"repo_id": "tokenizers",
"token_count": 10084
} | 292 |
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