text stringlengths 5 424k | id stringlengths 13 178 | metadata dict | __index_level_0__ int64 0 672 |
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#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/src/lerobot/teleoperators/phone/teleop_phone.py/0 | {
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"token_count": 6547
} | 210 |
#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/src/lerobot/utils/rotation.py/0 | {
"file_path": "lerobot/src/lerobot/utils/rotation.py",
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"token_count": 4440
} | 211 |
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#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/tests/datasets/test_compute_stats.py/0 | {
"file_path": "lerobot/tests/datasets/test_compute_stats.py",
"repo_id": "lerobot",
"token_count": 5023
} | 215 |
#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/tests/mocks/mock_dynamixel.py/0 | {
"file_path": "lerobot/tests/mocks/mock_dynamixel.py",
"repo_id": "lerobot",
"token_count": 11311
} | 216 |
#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/tests/processor/test_act_processor.py/0 | {
"file_path": "lerobot/tests/processor/test_act_processor.py",
"repo_id": "lerobot",
"token_count": 5203
} | 217 |
#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | lerobot/tests/processor/test_smolvla_processor.py/0 | {
"file_path": "lerobot/tests/processor/test_smolvla_processor.py",
"repo_id": "lerobot",
"token_count": 5858
} | 218 |
# 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_io_utils.py/0 | {
"file_path": "lerobot/tests/utils/test_io_utils.py",
"repo_id": "lerobot",
"token_count": 1063
} | 219 |
# Model arguments
model_name_or_path: Qwen/Qwen2.5-Coder-7B-Instruct
model_revision: main
torch_dtype: bfloat16
attn_implementation: flash_attention_2
# Data training arguments
dataset_name: open-r1/codeforces
dataset_prompt_column: prompt
dataset_config: verifiable-prompts
dataset_test_split: test
dataset_train_split:... | open-r1/recipes/Qwen2.5-Coder-7B-Instruct/grpo/config_codeforces.yaml/0 | {
"file_path": "open-r1/recipes/Qwen2.5-Coder-7B-Instruct/grpo/config_codeforces.yaml",
"repo_id": "open-r1",
"token_count": 926
} | 220 |
# 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/pass_rate_filtering/compute_pass_rate.py/0 | {
"file_path": "open-r1/scripts/pass_rate_filtering/compute_pass_rate.py",
"repo_id": "open-r1",
"token_count": 3331
} | 221 |
#!/bin/bash
#SBATCH --job-name=r1-server
#SBATCH --partition=hopper-prod
#SBATCH --qos=normal
#SBATCH --nodes=2
#SBATCH --gpus-per-node=8
#SBATCH --exclusive
#SBATCH --output=./logs/%x_%j_%n.out
#SBATCH --error=./logs/%x_%j_%n.err
#SBATCH --time=7-00:00:00
#SBATCH --ntasks-per-node=1
set -exuo pipefail
MODEL_PATH="de... | open-r1/slurm/serve_r1.slurm/0 | {
"file_path": "open-r1/slurm/serve_r1.slurm",
"repo_id": "open-r1",
"token_count": 1544
} | 222 |
from collections import defaultdict
from functools import lru_cache
from datasets import load_dataset
def add_includes(code: str, problem_id: str) -> str:
"""
Fix common compilation errors for IOI problems.
"""
if not code:
return code
# has most of the useful functions
code_header = ... | open-r1/src/open_r1/utils/competitive_programming/ioi_utils.py/0 | {
"file_path": "open-r1/src/open_r1/utils/competitive_programming/ioi_utils.py",
"repo_id": "open-r1",
"token_count": 520
} | 223 |
<!---
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
} | 224 |
<!--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
} | 225 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/examples/boft_controlnet/test_controlnet.py/0 | {
"file_path": "peft/examples/boft_controlnet/test_controlnet.py",
"repo_id": "peft",
"token_count": 1827
} | 226 |
#!/usr/bin/env python
# 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 require... | peft/examples/boft_dreambooth/train_dreambooth.py/0 | {
"file_path": "peft/examples/boft_dreambooth/train_dreambooth.py",
"repo_id": "peft",
"token_count": 12892
} | 227 |
<jupyter_start><jupyter_code>import torch
from datasets import load_dataset
from transformers import set_seed, AutoModelForSeq2SeqLM, AutoTokenizer
from peft import get_peft_model, MultitaskPromptTuningConfig, TaskType, MultitaskPromptTuningInit
set_seed(42)
device = torch.accelerator.current_accelerator().type if has... | peft/examples/conditional_generation/multitask_prompt_tuning.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/multitask_prompt_tuning.ipynb",
"repo_id": "peft",
"token_count": 3398
} | 228 |
<jupyter_start><jupyter_text>PEFT with DNA Language Models This notebook demonstrates how to utilize parameter-efficient fine-tuning techniques (PEFT) from the PEFT library to fine-tune a DNA Language Model (DNA-LM). The fine-tuned DNA-LM will be applied to solve a task from the nucleotide benchmark dataset. Parameter-... | peft/examples/dna_language_models/dna_lm.ipynb/0 | {
"file_path": "peft/examples/dna_language_models/dna_lm.ipynb",
"repo_id": "peft",
"token_count": 3835
} | 229 |
<jupyter_start><jupyter_code>import os
from PIL import Image
import torch
from accelerate.logging import get_logger
from diffusers import StableDiffusionPipeline
from diffusers.utils import check_min_version
from peft import PeftModel
# Will error if the minimal version of diffusers is not installed. Remove at your... | peft/examples/hra_dreambooth/dreambooth_inference.ipynb/0 | {
"file_path": "peft/examples/hra_dreambooth/dreambooth_inference.ipynb",
"repo_id": "peft",
"token_count": 1150
} | 230 |
<jupyter_start><jupyter_text>Finetuning Whisper-large-V2 on Colab using PEFT-Lora + BNB INT8 training In this Colab, we present a step-by-step guide on how to fine-tune Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers and 🤗 PEFT. Using 🤗 PEFT and `bitsandbytes`, you can train the `whisper-l... | peft/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb/0 | {
"file_path": "peft/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb",
"repo_id": "peft",
"token_count": 7766
} | 231 |
# 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/examples/miss_finetuning/miss_finetuning.py/0 | {
"file_path": "peft/examples/miss_finetuning/miss_finetuning.py",
"repo_id": "peft",
"token_count": 1540
} | 232 |
# RandLora: Full-rank parameter-efficient fine-tuning of large models
## Introduction
[RandLora](https://huggingface.co/papers/2502.00987) is a parameter-efficient fine-tuning technique that is similar to LoRA and VeRA but performs full rank updates to improve performance. RandLora can be particulary usefull when ada... | peft/examples/randlora_finetuning/README.md/0 | {
"file_path": "peft/examples/randlora_finetuning/README.md",
"repo_id": "peft",
"token_count": 1887
} | 233 |
<jupyter_start><jupyter_text>Using VeRA for sequence classification In this example, we fine-tune Roberta on a sequence classification task using VeRA. Imports<jupyter_code>import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from peft import (
get_peft_model,
VeraConfig,
Peft... | peft/examples/sequence_classification/VeRA.ipynb/0 | {
"file_path": "peft/examples/sequence_classification/VeRA.ipynb",
"repo_id": "peft",
"token_count": 2571
} | 234 |
# Makefile for running MetaMathQA experiments.
# --- Configuration ---
PYTHON := python
RUN_SCRIPT := run.py
EXPERIMENTS_DIR := experiments
RESULTS_DIR := results
# --- Automatic Experiment and Result Discovery ---
# 1. Find all experiment directories by looking for adapter_config.json files.
# This gives us a li... | peft/method_comparison/MetaMathQA/Makefile/0 | {
"file_path": "peft/method_comparison/MetaMathQA/Makefile",
"repo_id": "peft",
"token_count": 1187
} | 235 |
{
"auto_mapping": null,
"base_model_name_or_path": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"feedforward_modules": null,
"inference_mode": false,
"init_ia3_weights": true,
"modules_to_save": null,
"peft_type": "IA3",
"revision": null,
"target_modules": null,
"task_type": null
} | peft/method_comparison/MetaMathQA/experiments/ia3/llama-3.2-3B-default/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/ia3/llama-3.2-3B-default/adapter_config.json",
"repo_id": "peft",
"token_count": 131
} | 236 |
{
"auto_mapping": null,
"base_model_name_or_path": null,
"encoder_hidden_size": 3072,
"inference_mode": false,
"num_attention_heads": 24,
"num_layers": 28,
"num_transformer_submodules": 1,
"num_virtual_tokens": 200,
"peft_type": "PREFIX_TUNING",
"prefix_projection": false,
"revision": null,
"tas... | peft/method_comparison/MetaMathQA/experiments/prefixtuning/llama-3.2-3B-lr_0.001/adapter_config.json/0 | {
"file_path": "peft/method_comparison/MetaMathQA/experiments/prefixtuning/llama-3.2-3B-lr_0.001/adapter_config.json",
"repo_id": "peft",
"token_count": 157
} | 237 |
{
"model_id": "meta-llama/Llama-3.2-3B",
"dtype": "float16",
"seed": 42,
"num_inference_runs": 10,
"max_new_tokens": 20,
"category_generation_params": {
"short": {"max_new_tokens": 20},
"medium": {"max_new_tokens": 50},
"long": {"max_new_tokens": 100}
}
}
| peft/method_comparison/text_generation_benchmark/default_benchmark_params.json/0 | {
"file_path": "peft/method_comparison/text_generation_benchmark/default_benchmark_params.json",
"repo_id": "peft",
"token_count": 163
} | 238 |
# 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/__init__.py/0 | {
"file_path": "peft/src/peft/__init__.py",
"repo_id": "peft",
"token_count": 2490
} | 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/src/peft/tuners/adalora/__init__.py/0 | {
"file_path": "peft/src/peft/tuners/adalora/__init__.py",
"repo_id": "peft",
"token_count": 498
} | 240 |
# 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/layer.py/0 | {
"file_path": "peft/src/peft/tuners/boft/layer.py",
"repo_id": "peft",
"token_count": 20314
} | 241 |
# 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/fourierft/layer.py/0 | {
"file_path": "peft/src/peft/tuners/fourierft/layer.py",
"repo_id": "peft",
"token_count": 3696
} | 242 |
# 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/loha/config.py/0 | {
"file_path": "peft/src/peft/tuners/loha/config.py",
"repo_id": "peft",
"token_count": 2743
} | 243 |
# 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/eva.py/0 | {
"file_path": "peft/src/peft/tuners/lora/eva.py",
"repo_id": "peft",
"token_count": 13577
} | 244 |
# 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/p_tuning/model.py/0 | {
"file_path": "peft/src/peft/tuners/p_tuning/model.py",
"repo_id": "peft",
"token_count": 2476
} | 245 |
# 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/model.py/0 | {
"file_path": "peft/src/peft/tuners/randlora/model.py",
"repo_id": "peft",
"token_count": 6741
} | 246 |
# 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/utils/hotswap.py/0 | {
"file_path": "peft/src/peft/utils/hotswap.py",
"repo_id": "peft",
"token_count": 10563
} | 247 |
# 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_auto.py/0 | {
"file_path": "peft/tests/test_auto.py",
"repo_id": "peft",
"token_count": 4124
} | 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/tests/test_integrations.py/0 | {
"file_path": "peft/tests/test_integrations.py",
"repo_id": "peft",
"token_count": 1367
} | 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/tests/test_torch_compile.py/0 | {
"file_path": "peft/tests/test_torch_compile.py",
"repo_id": "peft",
"token_count": 11218
} | 250 |
*This guideline is very much a work-in-progress.*
Contributions to `timm` for code, documentation, tests are more than welcome!
There haven't been any formal guidelines to date so please bear with me, and feel free to add to this guide.
# Coding style
Code linting and auto-format (black) are not currently in place ... | pytorch-image-models/CONTRIBUTING.md/0 | {
"file_path": "pytorch-image-models/CONTRIBUTING.md",
"repo_id": "pytorch-image-models",
"token_count": 1502
} | 251 |
# Sharing and Loading Models From the Hugging Face Hub
The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub.
In this short guide, we'll see how to:
1. Share a `timm` model on the Hub
2. How to load that model back from the Hub
## Authent... | pytorch-image-models/hfdocs/source/hf_hub.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/hf_hub.mdx",
"repo_id": "pytorch-image-models",
"token_count": 593
} | 252 |
# TResNet
A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-down... | pytorch-image-models/hfdocs/source/models/tresnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tresnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4203
} | 253 |
import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv',
],
'results-imagenet-r-clean.csv... | pytorch-image-models/results/generate_csv_results.py/0 | {
"file_path": "pytorch-image-models/results/generate_csv_results.py",
"repo_id": "pytorch-image-models",
"token_count": 1453
} | 254 |
from .version import __version__ as __version__
from .layers import (
is_scriptable as is_scriptable,
is_exportable as is_exportable,
set_scriptable as set_scriptable,
set_exportable as set_exportable,
)
from .models import (
create_model as create_model,
list_models as list_models,
list_pre... | pytorch-image-models/timm/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 219
} | 255 |
""" Mixup and Cutmix
Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyTorch
Hacked together by / Co... | pytorch-image-models/timm/data/mixup.py/0 | {
"file_path": "pytorch-image-models/timm/data/mixup.py",
"repo_id": "pytorch-image-models",
"token_count": 8184
} | 256 |
""" A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | pytorch-image-models/timm/data/readers/reader_image_tar.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_tar.py",
"repo_id": "pytorch-image-models",
"token_count": 1071
} | 257 |
""" Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""... | pytorch-image-models/timm/layers/attention_pool2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool2d.py",
"repo_id": "pytorch-image-models",
"token_count": 5737
} | 258 |
""" EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato ... | pytorch-image-models/timm/layers/evo_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/evo_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 6684
} | 259 |
""" Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kern... | pytorch-image-models/timm/layers/median_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/median_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 883
} | 260 |
""" Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d... | pytorch-image-models/timm/layers/separable_conv.py/0 | {
"file_path": "pytorch-image-models/timm/layers/separable_conv.py",
"repo_id": "pytorch-image-models",
"token_count": 1138
} | 261 |
import dataclasses
import logging
import os
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TypeVar, Union
from torch import nn as nn
from torch.hub import load_state_dict_from_url
from timm.models._features import FeatureListNet, FeatureDictNet,... | pytorch-image-models/timm/models/_builder.py/0 | {
"file_path": "pytorch-image-models/timm/models/_builder.py",
"repo_id": "pytorch-image-models",
"token_count": 9114
} | 262 |
""" Model Registry
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
import re
import sys
import warnings
from collections import defaultdict, deque
from copy import deepcopy
from dataclasses import replace
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Sequence, Union, Tuple... | pytorch-image-models/timm/models/_registry.py/0 | {
"file_path": "pytorch-image-models/timm/models/_registry.py",
"repo_id": "pytorch-image-models",
"token_count": 5725
} | 263 |
""" EdgeNeXt
Paper: `EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications`
- https://arxiv.org/abs/2206.10589
Original code and weights from https://github.com/mmaaz60/EdgeNeXt
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
import math
from funct... | pytorch-image-models/timm/models/edgenext.py/0 | {
"file_path": "pytorch-image-models/timm/models/edgenext.py",
"repo_id": "pytorch-image-models",
"token_count": 12219
} | 264 |
from ._builder import *
from ._helpers import *
from ._manipulate import *
from ._prune import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
| pytorch-image-models/timm/models/helpers.py/0 | {
"file_path": "pytorch-image-models/timm/models/helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 62
} | 265 |
""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2019, Ross Wightman
"""
from functools import partial
from typing import Any, Dict, Callable, List, Optional, Tuple, Union... | pytorch-image-models/timm/models/mobilenetv3.py/0 | {
"file_path": "pytorch-image-models/timm/models/mobilenetv3.py",
"repo_id": "pytorch-image-models",
"token_count": 32542
} | 266 |
""" RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={230... | pytorch-image-models/timm/models/repvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/repvit.py",
"repo_id": "pytorch-image-models",
"token_count": 9407
} | 267 |
""" TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import itertools
from functools import partial
from typing import Dict, List, ... | pytorch-image-models/timm/models/tiny_vit.py/0 | {
"file_path": "pytorch-image-models/timm/models/tiny_vit.py",
"repo_id": "pytorch-image-models",
"token_count": 13498
} | 268 |
from .adabelief import AdaBelief
from .adafactor import Adafactor
from .adafactor_bv import AdafactorBigVision
from .adahessian import Adahessian
from .adamp import AdamP
from .adamw import AdamWLegacy
from .adan import Adan
from .adopt import Adopt
from .lamb import Lamb
from .laprop import LaProp
from .lars import La... | pytorch-image-models/timm/optim/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/optim/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 385
} | 269 |
""" Lion Optimizer
Paper: `Symbolic Discovery of Optimization Algorithms` - https://arxiv.org/abs/2302.06675
Original Impl: https://github.com/google/automl/tree/master/lion
References for added functionality:
Cautious Optimizers: https://arxiv.org/abs/2411.16085
Why Gradients Rapidly Increase Near the End of ... | pytorch-image-models/timm/optim/lion.py/0 | {
"file_path": "pytorch-image-models/timm/optim/lion.py",
"repo_id": "pytorch-image-models",
"token_count": 4091
} | 270 |
""" Plateau Scheduler
Adapts PyTorch plateau scheduler and allows application of noise, warmup.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from typing import List
from .scheduler import Scheduler
class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation ... | pytorch-image-models/timm/scheduler/plateau_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/plateau_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1807
} | 271 |
""" Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | pytorch-image-models/timm/utils/metrics.py/0 | {
"file_path": "pytorch-image-models/timm/utils/metrics.py",
"repo_id": "pytorch-image-models",
"token_count": 374
} | 272 |
# Using different models
[[open-in-colab]]
`smolagents` provides a flexible framework that allows you to use various language models from different providers.
This guide will show you how to use different model types with your agents.
## Available model types
`smolagents` supports several model types out of the box... | smolagents/docs/source/en/examples/using_different_models.md/0 | {
"file_path": "smolagents/docs/source/en/examples/using_different_models.md",
"repo_id": "smolagents",
"token_count": 1337
} | 273 |
# Agents का परिचय
## 🤔 Agents क्या हैं?
AI का उपयोग करने वाली किसी भी कुशल प्रणाली को LLM को वास्तविक दुनिया तक किसी प्रकार की पहुंच प्रदान करने की आवश्यकता होगी: उदाहरण के लिए बाहरी जानकारी प्राप्त करने के लिए एक खोज टूल को कॉल करने की संभावना, या किसी कार्य को हल करने के लिए कुछ प्रोग्राम पर कार्य करने की। दूसरे श... | smolagents/docs/source/hi/conceptual_guides/intro_agents.md/0 | {
"file_path": "smolagents/docs/source/hi/conceptual_guides/intro_agents.md",
"repo_id": "smolagents",
"token_count": 11425
} | 274 |
# 멀티 에이전트 시스템 오케스트레이션 🤖🤝🤖
[[Colab에서 열기]]
이 노트북에서는 **멀티 에이전트 웹 브라우저**를 만들어보겠습니다. 이는 웹을 사용하여 문제를 해결하기 위해 여러 에이전트가 협력하는 에이전트 시스템입니다!
멀티 에이전트는 간단한 계층 구조로 구성됩니다.
```
+----------------+
| Manager agent |
+----------------+
|
_______________|____... | smolagents/docs/source/ko/examples/multiagents.md/0 | {
"file_path": "smolagents/docs/source/ko/examples/multiagents.md",
"repo_id": "smolagents",
"token_count": 5355
} | 275 |
# Agents（智能体）
<Tip warning={true}>
Smolagents 是一个实验性的 API，可能会随时发生变化。由于 API 或底层模型可能发生变化，代理返回的结果也可能有所不同。
</Tip>
要了解有关智能体和工具的更多信息，请务必阅读[入门指南](../index)。本页面包含基础类的 API 文档。
## 智能体（Agents）
我们的智能体继承自 [`MultiStepAgent`]，这意味着它们可以执行多步操作，每一步包含一个思考（thought），然后是一个工具调用和执行。请阅读[概念指南](../conceptual_guides/react)以了解更多信息。
我们提供两种类型的... | smolagents/docs/source/zh/reference/agents.md/0 | {
"file_path": "smolagents/docs/source/zh/reference/agents.md",
"repo_id": "smolagents",
"token_count": 830
} | 276 |
import requests
# from smolagents.agents import ToolCallingAgent
from smolagents import CodeAgent, InferenceClientModel, tool
# Choose which LLM engine to use!
model = InferenceClientModel()
# model = TransformersModel(model_id="meta-llama/Llama-3.2-2B-Instruct")
# For anthropic: change model_id below to 'anthropic... | smolagents/examples/multiple_tools.py/0 | {
"file_path": "smolagents/examples/multiple_tools.py",
"repo_id": "smolagents",
"token_count": 3110
} | 277 |
# Human-in-the-Loop: Customize Agent Plan Interactively
This example demonstrates advanced usage of the smolagents library, specifically showing how to implement Human-in-the-Loop strategies to:
1. **Interrupt agent execution after plan creation** using step callbacks
2. **Allow user interaction** to review and modif... | smolagents/examples/plan_customization/README.md/0 | {
"file_path": "smolagents/examples/plan_customization/README.md",
"repo_id": "smolagents",
"token_count": 1114
} | 278 |
#!/usr/bin/env python
# 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/L... | smolagents/src/smolagents/cli.py/0 | {
"file_path": "smolagents/src/smolagents/cli.py",
"repo_id": "smolagents",
"token_count": 2110
} | 279 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | smolagents/tests/test_models.py/0 | {
"file_path": "smolagents/tests/test_models.py",
"repo_id": "smolagents",
"token_count": 16498
} | 280 |
[workspace]
members = [
"benchmark",
"backends/v2",
"backends/v3",
"backends/grpc-metadata",
"backends/trtllm",
"backends/llamacpp",
"launcher",
"router"
]
default-members = [
"benchmark",
"backends/v2",
"backends/v3",
"backends/grpc-metadata",
# "backends/trtllm",
... | text-generation-inference/Cargo.toml/0 | {
"file_path": "text-generation-inference/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 512
} | 281 |
[package]
name = "text-generation-client"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
async-trait = "^0.1"
base64 = { workspace = true }
futures = "^0.3"
grpc-metadata = { path = "../grpc-metadata" }
prost = "^0.12"
thiserror = "^1.0"
tokio = { ve... | text-generation-inference/backends/client/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/client/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 202
} | 282 |
fbgemm_commit := v0.8.0
build-fbgemm:
@if [ ! -d "fbgemm" ]; then \
git clone https://github.com/pytorch/FBGEMM.git fbgemm; \
fi
cd fbgemm && git fetch && git checkout $(fbgemm_commit) && \
git submodule update --init --recursive && \
cd fbgemm_gpu && \
pip install -r requirements.txt && \
CUDA_ARCH_LIST="8.... | text-generation-inference/backends/gaudi/server/Makefile-fbgemm/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/Makefile-fbgemm",
"repo_id": "text-generation-inference",
"token_count": 337
} | 283 |
import torch
from typing import Dict, Optional, TypeVar
from text_generation_server.models.types import Batch
B = TypeVar("B", bound=Batch)
class Cache:
def __init__(self):
self.cache: Dict[int, B] = {}
def pop(self, batch_id: int) -> Optional[B]:
return self.cache.pop(batch_id, None)
... | text-generation-inference/backends/gaudi/server/text_generation_server/cache.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/cache.py",
"repo_id": "text-generation-inference",
"token_count": 359
} | 284 |
from dataclasses import dataclass
from typing import List, Union
import torch
from text_generation_server.utils.weights import Weight, Weights, WeightsLoader
@dataclass
class Exl2Weight(Weight):
"""
Exllama2 exl2 quantized weights.
"""
q_weight: torch.Tensor
q_scale: torch.Tensor
q_invperm: ... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/exl2.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/exl2.py",
"repo_id": "text-generation-inference",
"token_count": 1050
} | 285 |
import torch
import json
from typing import Tuple, Optional
from text_generation_server.layers.tensor_parallel import TensorParallelHead
from text_generation_server.layers.medusa import MedusaHeadV1, MedusaHeadV2
from text_generation_server.layers.mlp import MLPSpeculatorHead
class SpeculativeHead(torch.nn.Module):
... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/speculative.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/speculative.py",
"repo_id": "text-generation-inference",
"token_count": 851
} | 286 |
# 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_llama_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 11549
} | 287 |
# 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/LICENSE-2.0
#
# Unless r... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/idefics3.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/idefics3.py",
"repo_id": "text-generation-inference",
"token_count": 10886
} | 288 |
# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
from text_generation_server.utils.convert import convert_file, convert_files
from text_generation_server.utils.dist import initialize_torch_distributed
from text_generation_server.utils.weights import Weights
from text_generation_server.utils.peft import downloa... | text-generation-inference/backends/gaudi/server/text_generation_server/utils/__init__.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/utils/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 516
} | 289 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/utils/segments.py
# License: Apache License Version 2.0, January 2004
from typing import List, Tuple, Union
import torch
def find_segments(
adapter_indices: Union[torch.Tensor, List[int]],
) -> Tuple[List[int], List[int]]:
... | text-generation-inference/backends/gaudi/server/text_generation_server/utils/segments.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/utils/segments.py",
"repo_id": "text-generation-inference",
"token_count": 1082
} | 290 |
mod backend;
mod llamacpp;
mod quantize;
use quantize::QuantizeType;
use backend::{
BackendError, LlamacppBackend, LlamacppConfig, LlamacppGGMLType, LlamacppNuma,
LlamacppSplitMode,
};
use clap::Parser;
use hf_hub::api::tokio::ApiBuilder;
use hf_hub::{Repo, RepoType};
use std::path::Path;
use text_generation_... | text-generation-inference/backends/llamacpp/src/main.rs/0 | {
"file_path": "text-generation-inference/backends/llamacpp/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 4967
} | 291 |
import copy
import logging
import subprocess
import sys
from tempfile import TemporaryDirectory
import os
import pytest
from transformers import AutoTokenizer
from optimum.neuron.cache import synchronize_hub_cache
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s] %(levelname)s [%(filename)s.%(... | text-generation-inference/backends/neuron/tests/fixtures/model.py/0 | {
"file_path": "text-generation-inference/backends/neuron/tests/fixtures/model.py",
"repo_id": "text-generation-inference",
"token_count": 1570
} | 292 |
# Text Generation Inference - TensorRT-LLM Backend Implementation
## Description
This folder provides the sources of the TensorRT-LLM backend implementation powered by TensorRT-LLM Executor new API
## Simplified Request Sequence
```mermaid
sequenceDiagram
actor User
participant TextGenerationInference.HttpS... | text-generation-inference/backends/trtllm/README.md/0 | {
"file_path": "text-generation-inference/backends/trtllm/README.md",
"repo_id": "text-generation-inference",
"token_count": 1019
} | 293 |
///
/// Extract the first line of the provided string reference.
/// If there is no lines in the buffer, it returns a string
/// which content is defined by the content of `fail`
/// # Arguments
///
/// * `s`: The string buffer to extract the first-line from
/// * `fail`: A string content which is returned if no lines ... | text-generation-inference/backends/trtllm/src/utils.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/utils.rs",
"repo_id": "text-generation-inference",
"token_count": 201
} | 294 |
use std::sync::Arc;
use tokio::sync::{mpsc, oneshot};
use crate::radix::RadixAllocator;
use text_generation_router::usage_stats::Env;
#[derive(Debug, Clone)]
pub struct BlockAllocation {
pub allocation_id: u64,
pub blocks: Vec<u32>,
pub slots: Vec<u32>,
/// Prefix that was cached and for which the KV ... | text-generation-inference/backends/v3/src/block_allocator.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/block_allocator.rs",
"repo_id": "text-generation-inference",
"token_count": 3274
} | 295 |
/// MIT License
//
// Copyright (c) 2020 hatoo
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merg... | text-generation-inference/benchmark/src/utils.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/utils.rs",
"repo_id": "text-generation-inference",
"token_count": 598
} | 296 |
{
"git+https://github.com/dottxt-ai/outlines-core.git?rev=ba10c619fc9bf3c487e43f49bdecb95a24bb465c#outlines-core@0.1.0": "1j9dcd831b0bmmjk2n4aag3x47qnqmkpg4gqpvwwyic7744llbfm"
} | text-generation-inference/crate-hashes.json/0 | {
"file_path": "text-generation-inference/crate-hashes.json",
"repo_id": "text-generation-inference",
"token_count": 106
} | 297 |
# Train Medusa
This tutorial will show you how to train a Medusa model on a dataset of your choice. Please check out the [speculation documentation](../conceptual/speculation) for more information on how Medusa works and speculation in general.
## What are the benefits of training a Medusa model?
Training Medusa hea... | text-generation-inference/docs/source/basic_tutorials/train_medusa.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/train_medusa.md",
"repo_id": "text-generation-inference",
"token_count": 3478
} | 298 |
# Installation from source
<Tip warning={true}>
Installing TGI from source is not the recommended usage. We strongly recommend to use TGI through Docker, check the [Quick Tour](./quicktour), [Installation for Nvidia GPUs](./installation_nvidia) and [Installation for AMD GPUs](./installation_amd) to learn how to use T... | text-generation-inference/docs/source/installation.md/0 | {
"file_path": "text-generation-inference/docs/source/installation.md",
"repo_id": "text-generation-inference",
"token_count": 727
} | 299 |
pytest_plugins = [
"fixtures.neuron.service",
"fixtures.neuron.export_models",
"fixtures.gaudi.service",
]
# ruff: noqa: E402
from _pytest.fixtures import SubRequest
from huggingface_hub.inference._generated.types.chat_completion import (
ChatCompletionStreamOutput,
ChatCompletionOutput,
)
from open... | text-generation-inference/integration-tests/conftest.py/0 | {
"file_path": "text-generation-inference/integration-tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 13377
} | 300 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 23090,
"logprob": -1.8251953,
"special": false,
"text": " Hello"
},
{
"id": 23,
"logpr... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json",
"repo_id": "text-generation-inference",
"token_count": 850
} | 301 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 604,
"logprob": -0.28271484,
"special": false,
"text": " for"
},
{
"id": 573,
"logprob": ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 867
} | 302 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 25,
"logprob": -0.88183594,
"special": false,
"text": ":"
},
{
"id": 2209,
"logprob": -2.... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 868
} | 303 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 13,
"logprob": -1.1582031,
"special": false,
"text": "\n"
},
{
"id": 2772,
"logprob": -0.... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_medusa/test_flash_medusa_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 847
} | 304 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 60,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 222,
"logprob": 0.0,
"special": false,
"text": "\n"
},
{
"id": 222,
"logprob": 0.0,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder2_lora/test_flash_starcoder2_default_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder2_lora/test_flash_starcoder2_default_params.json",
"repo_id": "text-generation-inference",
"token_count": 4513
} | 305 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 19,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 415,
"logprob": -0.03665161,
"special": false,
"text": " The"
},
{
"id": 12072,
"lo... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_two_images.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_two_images.json",
"repo_id": "text-generation-inference",
"token_count": 1559
} | 306 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": null,
"role": "assistant",
"tool_calls": [
{
"function": {
"arguments": "{\"location\":\"Brooklyn, NY\",\"format\":\"fahrenheit\"}",
... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto_nostream.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto_nostream.json",
"repo_id": "text-generation-inference",
"token_count": 421
} | 307 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "The image shows a brown cow standing on the beach with a white face and black and white marking on its ears. The cow has a white patch around its nose and mouth. The ocean and blue sky ... | text-generation-inference/integration-tests/models/__snapshots__/test_transformers_llama4/test_flash_llama4_image_cow.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_transformers_llama4/test_flash_llama4_image_cow.json",
"repo_id": "text-generation-inference",
"token_count": 314
} | 308 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle_sharded(launcher):
with launcher(
"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
num_shard=2,
quantize="awq",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_ll... | text-generation-inference/integration-tests/models/test_flash_awq_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_awq_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 624
} | 309 |
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