text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
|---|---|---|---|
# HopeJR
## Prerequisites
- [Hardware Setup](https://github.com/TheRobotStudio/HOPEJr)
## Install LeRobot
Follow the [installation instructions](https://github.com/huggingface/lerobot#installation) to install LeRobot.
Install LeRobot with HopeJR dependencies:
```bash
pip install -e ".[hopejr]"
```
## Device Conf... | lerobot/docs/source/hope_jr.mdx/0 | {
"file_path": "lerobot/docs/source/hope_jr.mdx",
"repo_id": "lerobot",
"token_count": 3224
} | 213 |
# SO-101
In the steps below, we explain how to assemble our flagship robot, the SO-101.
## Source the parts
Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts.
And advise if it's your... | lerobot/docs/source/so101.mdx/0 | {
"file_path": "lerobot/docs/source/so101.mdx",
"repo_id": "lerobot",
"token_count": 4594
} | 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/src/lerobot/cameras/opencv/camera_opencv.py/0 | {
"file_path": "lerobot/src/lerobot/cameras/opencv/camera_opencv.py",
"repo_id": "lerobot",
"token_count": 8038
} | 215 |
#!/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/factory.py/0 | {
"file_path": "lerobot/src/lerobot/datasets/factory.py",
"repo_id": "lerobot",
"token_count": 1940
} | 216 |
# 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/envs/configs.py/0 | {
"file_path": "lerobot/src/lerobot/envs/configs.py",
"repo_id": "lerobot",
"token_count": 3802
} | 217 |
# 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/conversion_scripts/conversion_utils.py/0 | {
"file_path": "lerobot/src/lerobot/policies/pi0/conversion_scripts/conversion_utils.py",
"repo_id": "lerobot",
"token_count": 1158
} | 218 |
## Paper
https://www.nicklashansen.com/td-mpc/
## Citation
```bibtex
@inproceedings{Hansen2022tdmpc,
title={Temporal Difference Learning for Model Predictive Control},
author={Nicklas Hansen and Xiaolong Wang and Hao Su},
booktitle={ICML},
year={2022}
}
```
| lerobot/src/lerobot/policies/tdmpc/README.md/0 | {
"file_path": "lerobot/src/lerobot/policies/tdmpc/README.md",
"repo_id": "lerobot",
"token_count": 97
} | 219 |
from .config import RobotConfig
from .robot import Robot
from .utils import make_robot_from_config
| lerobot/src/lerobot/robots/__init__.py/0 | {
"file_path": "lerobot/src/lerobot/robots/__init__.py",
"repo_id": "lerobot",
"token_count": 27
} | 220 |
# LeKiwi
In the steps below, we explain how to assemble the LeKiwi mobile robot.
## Source the parts
Follow this [README](https://github.com/SIGRobotics-UIUC/LeKiwi). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts.
And advise if it's your first t... | lerobot/src/lerobot/robots/lekiwi/lekiwi.mdx/0 | {
"file_path": "lerobot/src/lerobot/robots/lekiwi/lekiwi.mdx",
"repo_id": "lerobot",
"token_count": 4213
} | 221 |
# 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/robots/stretch3/configuration_stretch3.py/0 | {
"file_path": "lerobot/src/lerobot/robots/stretch3/configuration_stretch3.py",
"repo_id": "lerobot",
"token_count": 812
} | 222 |
# 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/scripts/server/configs.py/0 | {
"file_path": "lerobot/src/lerobot/scripts/server/configs.py",
"repo_id": "lerobot",
"token_count": 2783
} | 223 |
// 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 req... | lerobot/src/lerobot/transport/services.proto/0 | {
"file_path": "lerobot/src/lerobot/transport/services.proto",
"repo_id": "lerobot",
"token_count": 781
} | 224 |
#!/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/train_utils.py/0 | {
"file_path": "lerobot/src/lerobot/utils/train_utils.py",
"repo_id": "lerobot",
"token_count": 2187
} | 225 |
# 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/datasets/test_delta_timestamps.py/0 | {
"file_path": "lerobot/tests/datasets/test_delta_timestamps.py",
"repo_id": "lerobot",
"token_count": 4089
} | 226 |
# 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/tests/mocks/mock_motors_bus.py/0 | {
"file_path": "lerobot/tests/mocks/mock_motors_bus.py",
"repo_id": "lerobot",
"token_count": 1828
} | 227 |
#!/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.py/0 | {
"file_path": "lerobot/tests/processor/test_pipeline.py",
"repo_id": "lerobot",
"token_count": 25837
} | 228 |
#!/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/utils/test_replay_buffer.py/0 | {
"file_path": "lerobot/tests/utils/test_replay_buffer.py",
"repo_id": "lerobot",
"token_count": 10170
} | 229 |
# coding=utf-8
# 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 r... | open-r1/scripts/upload_details.py/0 | {
"file_path": "open-r1/scripts/upload_details.py",
"repo_id": "open-r1",
"token_count": 615
} | 230 |
from itertools import islice
def batched(iterable, n):
"Batch data into lists of length n. The last batch may be shorter."
# batched('ABCDEFG', 3) --> ABC DEF G
if n < 1:
return iterable
it = iter(iterable)
while batch := list(islice(it, n)):
yield batch
| open-r1/src/open_r1/utils/competitive_programming/utils.py/0 | {
"file_path": "open-r1/src/open_r1/utils/competitive_programming/utils.py",
"repo_id": "open-r1",
"token_count": 117
} | 231 |
# PEFT Docker images
Here we store all PEFT Docker images used in our testing infrastructure. We use python 3.11 for now on all our images.
- `peft-cpu`: PEFT compiled on CPU with all other HF libraries installed on main branch
- `peft-gpu`: PEFT complied for NVIDIA GPUs with all other HF libraries installed on main ... | peft/docker/README.md/0 | {
"file_path": "peft/docker/README.md",
"repo_id": "peft",
"token_count": 155
} | 232 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | peft/docs/source/developer_guides/contributing.md/0 | {
"file_path": "peft/docs/source/developer_guides/contributing.md",
"repo_id": "peft",
"token_count": 1743
} | 233 |
<!--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/c3a.md/0 | {
"file_path": "peft/docs/source/package_reference/c3a.md",
"repo_id": "peft",
"token_count": 774
} | 234 |
<!--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/task_guides/ia3.md/0 | {
"file_path": "peft/docs/source/task_guides/ia3.md",
"repo_id": "peft",
"token_count": 3256
} | 235 |
import random
import numpy as np
import torch
import wandb
from datasets import load_dataset
from diffusers import DDIMScheduler
from PIL import Image
from torchvision import transforms
from utils.pipeline_controlnet import LightControlNetPipeline
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols... | peft/examples/boft_controlnet/utils/dataset.py/0 | {
"file_path": "peft/examples/boft_controlnet/utils/dataset.py",
"repo_id": "peft",
"token_count": 3160
} | 236 |
<jupyter_start><jupyter_code>from transformers import AutoModelForSeq2SeqLM
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from transformers import AutoToke... | peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb",
"repo_id": "peft",
"token_count": 2489
} | 237 |
# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/examples/eva_finetuning/eva_finetuning.py/0 | {
"file_path": "peft/examples/eva_finetuning/eva_finetuning.py",
"repo_id": "peft",
"token_count": 1010
} | 238 |
# adapted from [peft's boft_dreambooth](https://github.com/huggingface/peft/tree/main/examples/boft_dreambooth)
from pathlib import Path
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the i... | peft/examples/hra_dreambooth/utils/dataset.py/0 | {
"file_path": "peft/examples/hra_dreambooth/utils/dataset.py",
"repo_id": "peft",
"token_count": 1928
} | 239 |
# 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/train_gsm8k_llama.py/0 | {
"file_path": "peft/examples/loftq_finetuning/train_gsm8k_llama.py",
"repo_id": "peft",
"token_count": 14677
} | 240 |
<jupyter_start><jupyter_text>Dreambooth with OFTThis Notebook assumes that you already ran the train_dreambooth.py script to create your own adapter.<jupyter_code>from diffusers import DiffusionPipeline
from diffusers.utils import check_min_version, get_logger
from peft import PeftModel
# Will error if the minimal ver... | peft/examples/oft_dreambooth/oft_dreambooth_inference.ipynb/0 | {
"file_path": "peft/examples/oft_dreambooth/oft_dreambooth_inference.ipynb",
"repo_id": "peft",
"token_count": 433
} | 241 |
<jupyter_start><jupyter_text>IntroductionIn this notebook, we will learn how to use [LoRA](https://huggingface.co/papers/2106.09685) from 🤗 PEFT to fine-tune a SegFormer model variant for semantic segmentation by ONLY using **14%** of the original trainable parameters of the model. LoRA adds low-rank "update matrices"... | peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb/0 | {
"file_path": "peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb",
"repo_id": "peft",
"token_count": 8138
} | 242 |
compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
deepspeed_multinode_launcher: standard
offload_optimizer_device: none
offload_param_device: none
zero3_init... | peft/examples/sft/configs/deepspeed_config_z3_qlora.yaml/0 | {
"file_path": "peft/examples/sft/configs/deepspeed_config_z3_qlora.yaml",
"repo_id": "peft",
"token_count": 331
} | 243 |
# Sparse High Rank Adapters
## Introduction
Sparse High Rank Adapters or [SHiRA](https://arxiv.org/abs/2406.13175) is an alternate type of adapter and has been found to have significant advantages over the low rank adapters. Specifically, SHiRA achieves better accuracy than LoRA for a variety of vision and language ta... | peft/examples/shira_finetuning/README.md/0 | {
"file_path": "peft/examples/shira_finetuning/README.md",
"repo_id": "peft",
"token_count": 1165
} | 244 |
{
"adapter_layers": 28,
"adapter_len": 100,
"auto_mapping": null,
"base_model_name_or_path": null,
"inference_mode": false,
"peft_type": "ADAPTION_PROMPT",
"revision": null,
"target_modules": null,
"task_type": "CAUSAL_LM"
} | peft/method_comparison/MetaMathQA/experiments/adaptionprompt/llama-3.2-3B-lr_0.0005/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/adaptionprompt/llama-3.2-3B-lr_0.0005/adapter_config.json",
"repo_id": "peft",
"token_count": 107
} | 245 |
{
"auto_mapping": null,
"base_model_name_or_path": null,
"fan_in_fan_out": false,
"inference_mode": false,
"init_weights": true,
"mask_type": "random",
"modules_to_save": null,
"peft_type": "SHIRA",
"r": 32,
"random_seed": 42,
"revision": null,
"target_modules": null,
"task_type": null
} | peft/method_comparison/MetaMathQA/experiments/shira/llama-3.2-3B-lr_0.0003-random_seed_42/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/shira/llama-3.2-3B-lr_0.0003-random_seed_42/adapter_config.json",
"repo_id": "peft",
"token_count": 135
} | 246 |
## 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
} | 247 |
import argparse
import json
import os
from datetime import date
from pathlib import Path
from tabulate import tabulate
MAX_LEN_MESSAGE = 2900 # slack endpoint has a limit of 3001 characters
parser = argparse.ArgumentParser()
parser.add_argument(
"--slack_channel_name",
default="peft-ci-daily",
)
def main... | peft/scripts/log_reports.py/0 | {
"file_path": "peft/scripts/log_reports.py",
"repo_id": "peft",
"token_count": 2520
} | 248 |
# 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/model.py/0 | {
"file_path": "peft/src/peft/tuners/cpt/model.py",
"repo_id": "peft",
"token_count": 3563
} | 249 |
# 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/layer.py/0 | {
"file_path": "peft/src/peft/tuners/ln_tuning/layer.py",
"repo_id": "peft",
"token_count": 1888
} | 250 |
# 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/dora.py/0 | {
"file_path": "peft/src/peft/tuners/lora/dora.py",
"repo_id": "peft",
"token_count": 3719
} | 251 |
# 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/config.py/0 | {
"file_path": "peft/src/peft/tuners/randlora/config.py",
"repo_id": "peft",
"token_count": 3781
} | 252 |
# 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/model.py/0 | {
"file_path": "peft/src/peft/tuners/trainable_tokens/model.py",
"repo_id": "peft",
"token_count": 5013
} | 253 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/utils/__init__.py/0 | {
"file_path": "peft/src/peft/utils/__init__.py",
"repo_id": "peft",
"token_count": 1874
} | 254 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_adaption_prompt.py/0 | {
"file_path": "peft/tests/test_adaption_prompt.py",
"repo_id": "peft",
"token_count": 8196
} | 255 |
# 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_initialization.py/0 | {
"file_path": "peft/tests/test_initialization.py",
"repo_id": "peft",
"token_count": 87745
} | 256 |
# 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/tests/test_target_parameters.py/0 | {
"file_path": "peft/tests/test_target_parameters.py",
"repo_id": "peft",
"token_count": 10629
} | 257 |
- sections:
- local: index
title: Home
- local: quickstart
title: Quickstart
- local: installation
title: Installation
- local: changes
title: Changelog
title: Get started
- sections:
- local: feature_extraction
title: Using Pretrained Models as Feature Extractors
- local: training_sc... | pytorch-image-models/hfdocs/source/_toctree.yml/0 | {
"file_path": "pytorch-image-models/hfdocs/source/_toctree.yml",
"repo_id": "pytorch-image-models",
"token_count": 1701
} | 258 |
# ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https:/... | pytorch-image-models/hfdocs/source/models/ecaresnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ecaresnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3644
} | 259 |
# Inception v4
**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
## How do I use this model on an image?
To loa... | pytorch-image-models/hfdocs/source/models/inception-v4.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/inception-v4.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1628
} | 260 |
# ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the [1×1 convolution](https://paperswithcode.co... | pytorch-image-models/hfdocs/source/models/resnet-d.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/resnet-d.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3935
} | 261 |
# (Tensorflow) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://pap... | pytorch-image-models/hfdocs/source/models/tf-inception-v3.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-inception-v3.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1959
} | 262 |
""" ONNX-runtime validation script
This script was created to verify accuracy and performance of exported ONNX
models running with the onnxruntime. It utilizes the PyTorch dataloader/processing
pipeline for a fair comparison against the originals.
Copyright 2020 Ross Wightman
"""
import argparse
import numpy as np
im... | pytorch-image-models/onnx_validate.py/0 | {
"file_path": "pytorch-image-models/onnx_validate.py",
"repo_id": "pytorch-image-models",
"token_count": 1960
} | 263 |
"""Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a ... | pytorch-image-models/tests/test_models.py/0 | {
"file_path": "pytorch-image-models/tests/test_models.py",
"repo_id": "pytorch-image-models",
"token_count": 13631
} | 264 |
import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class OrderedDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In suc... | pytorch-image-models/timm/data/distributed_sampler.py/0 | {
"file_path": "pytorch-image-models/timm/data/distributed_sampler.py",
"repo_id": "pytorch-image-models",
"token_count": 2276
} | 265 |
""" Dataset reader for HF IterableDataset
"""
import math
import os
from itertools import repeat, chain
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
from datasets.distributed import split_dataset_by_node
from datasets.splits import Sp... | pytorch-image-models/timm/data/readers/reader_hfids.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_hfids.py",
"repo_id": "pytorch-image-models",
"token_count": 3798
} | 266 |
from typing import Final, Optional, Type
import torch
from torch import nn as nn
from torch.nn import functional as F
from ._fx import register_notrace_function
from .config import use_fused_attn
from .pos_embed_sincos import apply_rot_embed_cat
@torch.fx.wrap
@register_notrace_function
def maybe_add_mask(scores: t... | pytorch-image-models/timm/layers/attention.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention.py",
"repo_id": "pytorch-image-models",
"token_count": 4241
} | 267 |
""" NormAct (Normalization + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
instances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN or EvoNorms.
Hacked together by / Copyr... | pytorch-image-models/timm/layers/create_norm_act.py/0 | {
"file_path": "pytorch-image-models/timm/layers/create_norm_act.py",
"repo_id": "pytorch-image-models",
"token_count": 2027
} | 268 |
""" Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
@misc{2102.08602,
Author = {Irwan Bello},
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
Year = {2021},
}
Status:
This impl is a WIP. Code snippets in the... | pytorch-image-models/timm/layers/lambda_layer.py/0 | {
"file_path": "pytorch-image-models/timm/layers/lambda_layer.py",
"repo_id": "pytorch-image-models",
"token_count": 2611
} | 269 |
""" Relative position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
import os
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .grid import ndgrid
from .interpolate import RegularGridInterpolator
from .mlp i... | pytorch-image-models/timm/layers/pos_embed_rel.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pos_embed_rel.py",
"repo_id": "pytorch-image-models",
"token_count": 9303
} | 270 |
""" Cross Entropy w/ smoothing or soft targets
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothingCrossEntropy(nn.Module):
""" NLL loss with label smoothing.
"""
def __init__(self, smoothing=0.1):
super(Lab... | pytorch-image-models/timm/loss/cross_entropy.py/0 | {
"file_path": "pytorch-image-models/timm/loss/cross_entropy.py",
"repo_id": "pytorch-image-models",
"token_count": 470
} | 271 |
"""Pytorch Densenet implementation w/ tweaks
This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
fixed kwargs passthrough and addition of dynamic global avg/max pool.
"""
import re
from collections import OrderedDict
from typing import Any, Dict, Optional, Tuple, Union
import tor... | pytorch-image-models/timm/models/densenet.py/0 | {
"file_path": "pytorch-image-models/timm/models/densenet.py",
"repo_id": "pytorch-image-models",
"token_count": 9539
} | 272 |
""" Global Context ViT
From scratch implementation of GCViT in the style of timm swin_transformer_v2_cr.py
Global Context Vision Transformers -https://arxiv.org/abs/2206.09959
@article{hatamizadeh2022global,
title={Global Context Vision Transformers},
author={Hatamizadeh, Ali and Yin, Hongxu and Kautz, Jan and M... | pytorch-image-models/timm/models/gcvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/gcvit.py",
"repo_id": "pytorch-image-models",
"token_count": 11847
} | 273 |
""" MaxVit and CoAtNet Vision Transformer - CNN Hybrids in PyTorch
This is a from-scratch implementation of both CoAtNet and MaxVit in PyTorch.
99% of the implementation was done from papers, however last minute some adjustments were made
based on the (as yet unfinished?) public code release https://github.com/google... | pytorch-image-models/timm/models/maxxvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/maxxvit.py",
"repo_id": "pytorch-image-models",
"token_count": 48679
} | 274 |
from ._registry import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
| pytorch-image-models/timm/models/registry.py/0 | {
"file_path": "pytorch-image-models/timm/models/registry.py",
"repo_id": "pytorch-image-models",
"token_count": 41
} | 275 |
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | pytorch-image-models/timm/models/swin_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 22341
} | 276 |
"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | pytorch-image-models/timm/models/xception.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception.py",
"repo_id": "pytorch-image-models",
"token_count": 3992
} | 277 |
""" PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb
This optimizer code was adapted from the following (starting with latest)
* https://github.com/HabanaAI/Model-References/blob/2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
* https://github.com/NVIDIA/DeepLearningExample... | pytorch-image-models/timm/optim/lamb.py/0 | {
"file_path": "pytorch-image-models/timm/optim/lamb.py",
"repo_id": "pytorch-image-models",
"token_count": 4651
} | 278 |
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
from .scheduler_factory import create_scheduler, create_scheduler_v2, schedul... | pytorch-image-models/timm/scheduler/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 112
} | 279 |
""" Distributed training/validation utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from typing import Optional
import torch
from torch import distributed as dist
from .model import unwrap_model
_logger = logging.getLogger(__name__)
def reduce_tensor(tensor, n):
rt = tenso... | pytorch-image-models/timm/utils/distributed.py/0 | {
"file_path": "pytorch-image-models/timm/utils/distributed.py",
"repo_id": "pytorch-image-models",
"token_count": 2680
} | 280 |
# Async Applications with Agents
This guide demonstrates how to integrate a synchronous agent from the `smolagents` library into an asynchronous Python web application using Starlette.
The example is designed to help users new to async Python and agent integration understand best practices for combining synchronous ag... | smolagents/docs/source/en/examples/async_agent.md/0 | {
"file_path": "smolagents/docs/source/en/examples/async_agent.md",
"repo_id": "smolagents",
"token_count": 809
} | 281 |
# 📚 Manage your agent's memory
[[open-in-colab]]
In the end, an agent can be defined by simple components: it has tools, prompts.
And most importantly, it has a memory of past steps, drawing a history of planning, execution, and errors.
### Replay your agent's memory
We propose several features to inspect a past a... | smolagents/docs/source/en/tutorials/memory.md/0 | {
"file_path": "smolagents/docs/source/en/tutorials/memory.md",
"repo_id": "smolagents",
"token_count": 1510
} | 282 |
# सुरक्षित कोड एक्जीक्यूशन
[[open-in-colab]]
> [!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।
### कोड Agents
[कई](https://huggingface.co/papers/2402.01030) [शोध](https://huggingface.co/p... | smolagents/docs/source/hi/tutorials/secure_code_execution.md/0 | {
"file_path": "smolagents/docs/source/hi/tutorials/secure_code_execution.md",
"repo_id": "smolagents",
"token_count": 5644
} | 283 |
# `smolagents`
这是构建强大 agent 的最简单框架！顺便问一下，什么是 "agent"？我们在[此页面](conceptual_guides/intro_agents)提供了我们的定义，您还可以找到关于何时使用或不使用它们的建议（剧透：通常不使用 agent 会更好）。
> [!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!)
本库提供：
✨ **简洁性**：Agent 逻辑仅需约千行代码。我们将抽象保持在原始代码之上的最... | smolagents/docs/source/zh/index.md/0 | {
"file_path": "smolagents/docs/source/zh/index.md",
"repo_id": "smolagents",
"token_count": 1623
} | 284 |
import os
from smolagents import CodeAgent, LiteLLMRouterModel, WebSearchTool
# Make sure to setup the necessary environment variables!
llm_loadbalancer_model_list = [
{
"model_name": "model-group-1",
"litellm_params": {
"model": "gpt-4o-mini",
"api_key": os.getenv("OPENA... | smolagents/examples/multi_llm_agent.py/0 | {
"file_path": "smolagents/examples/multi_llm_agent.py",
"repo_id": "smolagents",
"token_count": 702
} | 285 |
<jupyter_start><jupyter_text>Compare a text-based vs a vision-based browserWarning: this notebook is experimental, it probably won't work out of the box!<jupyter_code>!pip install "smolagents[litellm,toolkit]" -q
import datasets
eval_ds = datasets.load_dataset("gaia-benchmark/GAIA", "2023_all")["validation"]
to_keep ... | smolagents/examples/open_deep_research/visual_vs_text_browser.ipynb/0 | {
"file_path": "smolagents/examples/open_deep_research/visual_vs_text_browser.ipynb",
"repo_id": "smolagents",
"token_count": 2407
} | 286 |
#!/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/agents.py/0 | {
"file_path": "smolagents/src/smolagents/agents.py",
"repo_id": "smolagents",
"token_count": 35399
} | 287 |
import argparse
from io import BytesIO
from time import sleep
import helium
import PIL.Image
from dotenv import load_dotenv
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from smolagents import CodeAgent, WebSearchTool, tool
from smolagents.a... | smolagents/src/smolagents/vision_web_browser.py/0 | {
"file_path": "smolagents/src/smolagents/vision_web_browser.py",
"repo_id": "smolagents",
"token_count": 2770
} | 288 |
import json
import pytest
from PIL import Image
from smolagents.agents import ToolCall
from smolagents.memory import (
ActionStep,
AgentMemory,
ChatMessage,
MemoryStep,
MessageRole,
PlanningStep,
SystemPromptStep,
TaskStep,
)
from smolagents.monitoring import Timing, TokenUsage
class... | smolagents/tests/test_memory.py/0 | {
"file_path": "smolagents/tests/test_memory.py",
"repo_id": "smolagents",
"token_count": 3931
} | 289 |
<!---
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 ... | text-generation-inference/CONTRIBUTING.md/0 | {
"file_path": "text-generation-inference/CONTRIBUTING.md",
"repo_id": "text-generation-inference",
"token_count": 1396
} | 290 |
{
"__inputs": [
{
"name": "DS_PROMETHEUS_EKS API INFERENCE PROD",
"label": "Prometheus EKS API Inference Prod",
"description": "",
"type": "datasource",
"pluginId": "prometheus",
"pluginName": "Prometheus"
}
],
"__elements": {},
"__requires": [
{
"type": "pa... | text-generation-inference/assets/tgi_grafana.json/0 | {
"file_path": "text-generation-inference/assets/tgi_grafana.json",
"repo_id": "text-generation-inference",
"token_count": 62818
} | 291 |
# Fork that adds only the correct stream to this kernel in order
# to make cuda graphs work.
awq_commit := bd1dc2d5254345cc76ab71894651fb821275bdd4
awq:
rm -rf llm-awq
git clone https://github.com/huggingface/llm-awq
build-awq: awq
cd llm-awq/ && git fetch && git checkout $(awq_commit)
cd llm-awq/awq/kernels && p... | text-generation-inference/backends/gaudi/server/Makefile-awq/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/Makefile-awq",
"repo_id": "text-generation-inference",
"token_count": 183
} | 292 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/lora.py
# License: Apache License Version 2.0, January 2004
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Type, Union
import torch
from peft impor... | text-generation-inference/backends/gaudi/server/text_generation_server/adapters/lora.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/adapters/lora.py",
"repo_id": "text-generation-inference",
"token_count": 8028
} | 293 |
from typing import List, Optional, Union
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationType
from text_generation_server.layers.fp8 import (
Fp8Weight,
_load_scalar_or_matrix_scale,
requantize_with_max_scale,
)
from text_generation_server.utils.weights import Weight... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/compressed_tensors/w8an_fp.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/compressed_tensors/w8an_fp.py",
"repo_id": "text-generation-inference",
"token_count": 4701
} | 294 |
from typing import Optional
import torch
import torch.nn as nn
from text_generation_server.utils.weights import UnquantizedWeight, Weights
from vllm_hpu_extension.ops import VllmMixtureOfExpertsOp
import habana_frameworks.torch as htorch
import torch.nn.functional as F
import os
class UnquantizedSparseMoELayer(nn.M... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py",
"repo_id": "text-generation-inference",
"token_count": 2816
} | 295 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 6328
} | 296 |
# coding=utf-8
# Copyright 2024 Starcoder2 AI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# t... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 9861
} | 297 |
# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
import asyncio
import os
import torch
import time
import signal
from grpc import aio
from loguru import logger
from grpc_reflection.v1alpha import reflection
from pathlib import Path
from typing import List, Optional
from text_generation_server.cache import C... | text-generation-inference/backends/gaudi/server/text_generation_server/server.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/server.py",
"repo_id": "text-generation-inference",
"token_count": 5307
} | 298 |
from typing import Optional
SUPPORT_CHUNKING: Optional[bool] = None
MAX_PREFILL_TOKENS: Optional[int] = None
def set_support_chunking(support_chunking: bool):
global SUPPORT_CHUNKING
SUPPORT_CHUNKING = support_chunking
def get_support_chunking() -> bool:
global SUPPORT_CHUNKING
return SUPPORT_CHUNK... | text-generation-inference/backends/gaudi/server/text_generation_server/utils/prefill_chunking.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/utils/prefill_chunking.py",
"repo_id": "text-generation-inference",
"token_count": 221
} | 299 |
use crate::llamacpp;
use async_trait::async_trait;
use std::ffi::CString;
use std::mem::replace;
use std::str::FromStr;
use std::sync::{mpsc, Once};
use text_generation_router::infer::{Backend, GeneratedText, InferError, InferStreamResponse};
use text_generation_router::validation::ValidGenerateRequest;
use text_gener... | text-generation-inference/backends/llamacpp/src/backend.rs/0 | {
"file_path": "text-generation-inference/backends/llamacpp/src/backend.rs",
"repo_id": "text-generation-inference",
"token_count": 13858
} | 300 |
#!/usr/bin/env python
import argparse
import logging
import os
import sys
from typing import Any, Dict, List, Optional
from optimum.neuron.modeling_decoder import get_available_cores
from optimum.neuron.cache import get_hub_cached_entries
from optimum.neuron.configuration_utils import NeuronConfig
from optimum.neuron... | text-generation-inference/backends/neuron/server/text_generation_server/tgi_env.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/tgi_env.py",
"repo_id": "text-generation-inference",
"token_count": 4375
} | 301 |
use async_trait::async_trait;
use cxx::UniquePtr;
use hashbrown::HashMap;
use std::hint;
use std::ops::Deref;
use std::path::Path;
use tokenizers::Tokenizer;
use tokio::sync::mpsc::{unbounded_channel, UnboundedReceiver, UnboundedSender};
use tokio::sync::TryAcquireError;
use tokio::task::spawn_blocking;
use tokio::time... | text-generation-inference/backends/trtllm/src/looper.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/looper.rs",
"repo_id": "text-generation-inference",
"token_count": 6376
} | 302 |
/// Text Generation Inference benchmarking tool
///
/// Inspired by the great Oha app: https://github.com/hatoo/oha
/// and: https://github.com/orhun/rust-tui-template
use clap::Parser;
use std::path::Path;
use text_generation_client::v3::ShardedClient;
use tokenizers::{FromPretrainedParameters, Tokenizer};
use tracing... | text-generation-inference/benchmark/src/main.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 3164
} | 303 |
import os
import requests
from typing import Dict, Optional, List
from huggingface_hub.utils import build_hf_headers
from text_generation import Client, AsyncClient, __version__
from text_generation.types import DeployedModel
from text_generation.errors import NotSupportedError, parse_error
INFERENCE_ENDPOINT = os.e... | text-generation-inference/clients/python/text_generation/inference_api.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/inference_api.py",
"repo_id": "text-generation-inference",
"token_count": 2182
} | 304 |
# Tensor Parallelism
Tensor parallelism is a technique used to fit a large model in multiple GPUs. For example, when multiplying the input tensors with the first weight tensor, the matrix multiplication is equivalent to splitting the weight tensor column-wise, multiplying each column with the input separately, and the... | text-generation-inference/docs/source/conceptual/tensor_parallelism.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/tensor_parallelism.md",
"repo_id": "text-generation-inference",
"token_count": 272
} | 305 |
{
"nodes": {
"cachix": {
"inputs": {
"devenv": [
"crate2nix"
],
"flake-compat": [
"crate2nix"
],
"nixpkgs": "nixpkgs",
"pre-commit-hooks": [
"crate2nix"
]
},
"locked": {
"lastModified": 1709700175,
... | text-generation-inference/flake.lock/0 | {
"file_path": "text-generation-inference/flake.lock",
"repo_id": "text-generation-inference",
"token_count": 16562
} | 306 |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "As of your last question, the weather in Brooklyn, New York, is typically hot and humid throughout the year. The suburbs around New York City are jealously sheltered, and at least in ... | text-generation-inference/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json",
"repo_id": "text-generation-inference",
"token_count": 364
} | 307 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5380,
"logprob": -0.23840332,
"special": false,
"text": "?\n"
},
{
"id": 34564,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 853
} | 308 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 4,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 2143,
"logprob": -1.828125,
"special": false,
"text": " sent"
},
{
"id": 10081,
"logpro... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 424
} | 309 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "That's a fantastic question! However, the image doesn't show a dog. It shows a **Brown Swiss cow** standing on a beach. \n\nBrown Swiss cows are known for their beautiful reddish-brown ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow_dog.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma3/test_flash_gemma3_image_cow_dog.json",
"repo_id": "text-generation-inference",
"token_count": 340
} | 310 |
[
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "Jeff Walker's Product Launch Formula is a comprehensive system",
"name": null,
"role": "assistant",
"tool_calls": null
},
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_prefix/test_flash_llama_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_prefix/test_flash_llama_load.json",
"repo_id": "text-generation-inference",
"token_count": 32395
} | 311 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 198,
"logprob": -2.9023438,
"special": false,
"text": "\n"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_load.json",
"repo_id": "text-generation-inference",
"token_count": 4044
} | 312 |
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