text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
|---|---|---|
<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": 2545
} |
# Copyright 2023 The HuggingFace Team, the AllenNLP library authors. 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
#
... | peft/scripts/stale.py/0 | {
"file_path": "peft/scripts/stale.py",
"repo_id": "peft",
"token_count": 890
} |
# 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
} |
# 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": 19780
} |
# 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/lokr/layer.py/0 | {
"file_path": "peft/src/peft/tuners/lokr/layer.py",
"repo_id": "peft",
"token_count": 7994
} |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/src/peft/tuners/lora/tp_layer.py/0 | {
"file_path": "peft/src/peft/tuners/lora/tp_layer.py",
"repo_id": "peft",
"token_count": 8506
} |
# 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/poly/layer.py/0 | {
"file_path": "peft/src/peft/tuners/poly/layer.py",
"repo_id": "peft",
"token_count": 3184
} |
# 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/vera/config.py/0 | {
"file_path": "peft/src/peft/tuners/vera/config.py",
"repo_id": "peft",
"token_count": 3107
} |
# 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/peft_types.py/0 | {
"file_path": "peft/src/peft/utils/peft_types.py",
"repo_id": "peft",
"token_count": 2234
} |
# Note: These tests were copied from test_common_gpu.py and test_gpu_examples.py as they can run on CPU too.
#
# 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 cop... | peft/tests/test_gptqmodel.py/0 | {
"file_path": "peft/tests/test_gptqmodel.py",
"repo_id": "peft",
"token_count": 6031
} |
# 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": 11038
} |
*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": 1223
} |
# 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
} |
# # Ensemble Adversarial Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception arch... | pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2211
} |
# RexNet
**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.paperswithcode.com/method/relu6).
## How do... | pytorch-image-models/hfdocs/source/models/rexnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/rexnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3086
} |
# 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": 4202
} |
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
} |
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
} |
""" 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": 7225
} |
""" Tensorflow Preprocessing Adapter
Allows use of Tensorflow preprocessing pipeline in PyTorch Transform
Copyright of original Tensorflow code below.
Hacked together by / Copyright 2020 Ross Wightman
"""
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.... | pytorch-image-models/timm/data/tf_preprocessing.py/0 | {
"file_path": "pytorch-image-models/timm/data/tf_preprocessing.py",
"repo_id": "pytorch-image-models",
"token_count": 3775
} |
""" Conv2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
from .config import is_exportable, is_scriptable
from .padding import pad_same, pad_same_arg, get_padding_value
_USE_EXPORT_CONV = Fa... | pytorch-image-models/timm/layers/conv2d_same.py/0 | {
"file_path": "pytorch-image-models/timm/layers/conv2d_same.py",
"repo_id": "pytorch-image-models",
"token_count": 1560
} |
""" Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching existing torch norm layers
* s... | pytorch-image-models/timm/layers/grn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/grn.py",
"repo_id": "pytorch-image-models",
"token_count": 565
} |
""" Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple, Union
import torch
import torch.nn.functional as F
from .helpers import to_2tuple
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = ... | pytorch-image-models/timm/layers/padding.py/0 | {
"file_path": "pytorch-image-models/timm/layers/padding.py",
"repo_id": "pytorch-image-models",
"token_count": 1439
} |
import collections.abc
import math
import re
from collections import defaultdict
from itertools import chain
from typing import Any, Callable, Dict, Iterator, Optional, Tuple, Type, Union
import torch
import torch.utils.checkpoint
from torch import nn as nn
from timm.layers import use_reentrant_ckpt
__all__ = ['mod... | pytorch-image-models/timm/models/_manipulate.py/0 | {
"file_path": "pytorch-image-models/timm/models/_manipulate.py",
"repo_id": "pytorch-image-models",
"token_count": 4675
} |
""" ConvNeXt
Papers:
* `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
@Article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Confer... | pytorch-image-models/timm/models/convnext.py/0 | {
"file_path": "pytorch-image-models/timm/models/convnext.py",
"repo_id": "pytorch-image-models",
"token_count": 27273
} |
# FastViT for PyTorch
#
# Original implementation and weights from https://github.com/apple/ml-fastvit
#
# For licensing see accompanying LICENSE file at https://github.com/apple/ml-fastvit/tree/main
# Original work is copyright (C) 2023 Apple Inc. All Rights Reserved.
#
import os
from functools import partial
from typ... | pytorch-image-models/timm/models/fastvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/fastvit.py",
"repo_id": "pytorch-image-models",
"token_count": 29338
} |
""" Pytorch Inception-V4 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET... | pytorch-image-models/timm/models/inception_v4.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_v4.py",
"repo_id": "pytorch-image-models",
"token_count": 5547
} |
""" Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | pytorch-image-models/timm/models/pvt_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/pvt_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 9062
} |
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... | pytorch-image-models/timm/models/swin_transformer_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 21258
} |
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partia... | pytorch-image-models/timm/models/xception_aligned.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception_aligned.py",
"repo_id": "pytorch-image-models",
"token_count": 7780
} |
""" PyTorch impl of LaProp optimizer
Code simplified from https://github.com/Z-T-WANG/LaProp-Optimizer, MIT License
Paper: LaProp: Separating Momentum and Adaptivity in Adam, https://arxiv.org/abs/2002.04839
@article{ziyin2020laprop,
title={LaProp: a Better Way to Combine Momentum with Adaptive Gradient},
author... | pytorch-image-models/timm/optim/laprop.py/0 | {
"file_path": "pytorch-image-models/timm/optim/laprop.py",
"repo_id": "pytorch-image-models",
"token_count": 2264
} |
""" Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise, k-decay.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class CosineLRSchedu... | pytorch-image-models/timm/scheduler/cosine_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/cosine_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 2070
} |
""" JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT executor. These ... | pytorch-image-models/timm/utils/jit.py/0 | {
"file_path": "pytorch-image-models/timm/utils/jit.py",
"repo_id": "pytorch-image-models",
"token_count": 1035
} |
# Web Browser Automation with Agents 🤖🌐
[[open-in-colab]]
In this notebook, we'll create an **agent-powered web browser automation system**! This system can navigate websites, interact with elements, and extract information automatically.
The agent will be able to:
- [x] Navigate to web pages
- [x] Click on eleme... | smolagents/docs/source/en/examples/web_browser.md/0 | {
"file_path": "smolagents/docs/source/en/examples/web_browser.md",
"repo_id": "smolagents",
"token_count": 2146
} |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | smolagents/docs/source/hi/examples/text_to_sql.md/0 | {
"file_path": "smolagents/docs/source/hi/examples/text_to_sql.md",
"repo_id": "smolagents",
"token_count": 5208
} |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | smolagents/docs/source/zh/guided_tour.md/0 | {
"file_path": "smolagents/docs/source/zh/guided_tour.md",
"repo_id": "smolagents",
"token_count": 9318
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | smolagents/src/smolagents/__init__.py/0 | {
"file_path": "smolagents/src/smolagents/__init__.py",
"repo_id": "smolagents",
"token_count": 303
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | smolagents/src/smolagents/utils.py/0 | {
"file_path": "smolagents/src/smolagents/utils.py",
"repo_id": "smolagents",
"token_count": 5527
} |
[package]
name = "grpc-metadata"
version = "0.1.0"
edition = "2021"
[dependencies]
opentelemetry = "^0.20"
tonic = "^0.10"
tracing = "^0.1"
tracing-opentelemetry = "^0.21"
| text-generation-inference/backends/grpc-metadata/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/grpc-metadata/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 83
} |
use std::path::PathBuf;
use thiserror::Error;
use text_generation_router::server;
#[derive(Debug, Error)]
pub enum TensorRtLlmBackendError {
#[error("Provided engine folder {0} doesn't exist")]
EngineFolderDoesntExists(PathBuf),
#[error("Provided executorWorker binary path {0} doesn't exist")]
Executo... | text-generation-inference/backends/trtllm/src/errors.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/errors.rs",
"repo_id": "text-generation-inference",
"token_count": 285
} |
[package]
name = "text-generation-router-v3"
description = "Text Generation Webserver"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[lib]
path = "src/lib.rs"
[[bin]]
name = "text-generation-router"
path = "src/main.rs"
[dependencies]
async-trait = "0.1.74"
asyn... | text-generation-inference/backends/v3/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/v3/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 911
} |
use std::time::{Duration, Instant};
use text_generation_client::v3::{
Batch, CachedBatch, NextTokenChooserParameters, Request, ShardedClient,
StoppingCriteriaParameters,
};
use text_generation_client::{Chunk, ClientError, Input};
use tokenizers::{Tokenizer, TruncationDirection};
use tokio::sync::{broadcast, mps... | text-generation-inference/benchmark/src/generation.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/generation.rs",
"repo_id": "text-generation-inference",
"token_count": 3420
} |
import json
import requests
import warnings
from aiohttp import ClientSession, ClientTimeout
from pydantic import ValidationError
from typing import Dict, Optional, List, AsyncIterator, Iterator, Union
from text_generation import DEPRECATION_WARNING
from text_generation.types import (
StreamResponse,
Response... | text-generation-inference/clients/python/text_generation/client.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/client.py",
"repo_id": "text-generation-inference",
"token_count": 19241
} |
# Model safety.
[Pytorch uses pickle](https://pytorch.org/docs/master/generated/torch.load.html) by default meaning that for quite a long while
*Every* model using that format is potentially executing unintended code while purely loading the model.
There is a big red warning on Python's page for pickle [link](https:/... | text-generation-inference/docs/source/basic_tutorials/safety.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/safety.md",
"repo_id": "text-generation-inference",
"token_count": 465
} |
# Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
![Text Generation Inference](https://hugging... | text-generation-inference/docs/source/index.md/0 | {
"file_path": "text-generation-inference/docs/source/index.md",
"repo_id": "text-generation-inference",
"token_count": 562
} |
{
inputs = {
crate2nix = {
url = "github:nix-community/crate2nix";
inputs.nixpkgs.follows = "tgi-nix/nixpkgs";
};
nix-filter.url = "github:numtide/nix-filter";
tgi-nix.url = "github:huggingface/text-generation-inference-nix";
nixpkgs.follows = "tgi-nix/nixpkgs";
flake-utils.url = "... | text-generation-inference/flake.nix/0 | {
"file_path": "text-generation-inference/flake.nix",
"repo_id": "text-generation-inference",
"token_count": 2853
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5380,
"logprob": 0.0,
"special": false,
"text": "?\n"
},
{
"id": 34564,
"logprob": 0.0,
... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 853
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -1.9306641,
"special": false,
"text": "\n"
},
{
"id": 5618,
"logprob": ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq/test_flash_llama_awq.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_awq/test_flash_llama_awq.json",
"repo_id": "text-generation-inference",
"token_count": 868
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 369,
"logprob": -2.1816406,
"special": false,
"text": " for"
},
{
"id": 279,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8.json",
"repo_id": "text-generation-inference",
"token_count": 862
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 42,
"logprob": -0.88378906,
"special": false,
"text": "I"
},
{
"id": 1353,
"logprob": ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox.json",
"repo_id": "text-generation-inference",
"token_count": 854
} |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "The image showcases a stunning cityscape, featuring the iconic Statue of Liberty in the foreground. The image displays Lady Liberty's imposing presence, with her towering base standing ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2_vl/test_flash_qwen2_vl_bay.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2_vl/test_flash_qwen2_vl_bay.json",
"repo_id": "text-generation-inference",
"token_count": 376
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 2,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 284,
"logprob": -1.1679688,
"special": false,
"text": "\n "
},
{
"id": 0,
"logprob... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_starcoder_gptq/test_flash_starcoder_gptq.json",
"repo_id": "text-generation-inference",
"token_count": 259
} |
{
"details": {
"finish_reason": "length",
"generated_tokens": 40,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -0.27416992,
"special": false,
"text": "\n"
},
{
"id": 13,
"logprob": -0.17016602,
"special": ... | text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_customer_support_adapter.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_customer_support_adapter.json",
"repo_id": "text-generation-inference",
"token_count": 3128
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 510,
"logprob": -0.5878906,
"special": false,
"text": "The"
},
{
"id": 3159,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_neox_sharded/test_neox.json",
"repo_id": "text-generation-inference",
"token_count": 857
} |
{
"choices": [
{
"delta": {
"content": null,
"role": "assistant",
"tool_calls": {
"function": {
"arguments": "<|eot_id|>",
"name": null
},
"id": "",
"index": 0,
"type": "function"
}
},
"fini... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_sea_creatures_stream_required.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_sea_creatures_stream_required.json",
"repo_id": "text-generation-inference",
"token_count": 325
} |
import pytest
@pytest.fixture(scope="module")
def flash_gemma_handle(launcher):
with launcher("google/gemma-2b", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_gemma(flash_gemma_handle):
await flash_gemma_handle.health(300)
return flash_gemma_handle.client
... | text-generation-inference/integration-tests/models/test_flash_gemma.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_gemma.py",
"repo_id": "text-generation-inference",
"token_count": 678
} |
import pytest
@pytest.fixture(scope="module")
def flash_mixtral_handle(launcher):
with launcher("mistralai/Mixtral-8x7B-v0.1", num_shard=8) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_mixtral(flash_mixtral_handle):
await flash_mixtral_handle.health(300)
return flash_m... | text-generation-inference/integration-tests/models/test_flash_mixtral.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_mixtral.py",
"repo_id": "text-generation-inference",
"token_count": 926
} |
import pytest
import json
from text_generation.types import GrammarType
@pytest.fixture(scope="module")
def non_flash_llama_grammar_handle(launcher):
with launcher(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
num_shard=1,
disable_grammar_support=False,
use_flash_attention=False,
) a... | text-generation-inference/integration-tests/models/test_grammar_llama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_grammar_llama.py",
"repo_id": "text-generation-inference",
"token_count": 1346
} |
import pytest
import requests
import json
@pytest.fixture(scope="module")
def flash_llama_grammar_tools_handle(launcher):
with launcher(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
num_shard=2,
disable_grammar_support=False,
) as handle:
yield handle
@pytest.fixture(scope="modul... | text-generation-inference/integration-tests/models/test_tools_llama.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_tools_llama.py",
"repo_id": "text-generation-inference",
"token_count": 7205
} |
# https://www.gutenberg.org/cache/epub/103/pg103.txt
from openai import OpenAI
import os
import requests
if not os.path.exists("pg103.txt"):
response = requests.get("https://www.gutenberg.org/cache/epub/103/pg103.txt")
with open("pg103.txt", "w") as f:
f.write(response.text)
length = 130000
with open... | text-generation-inference/load_tests/long_prompt2.py/0 | {
"file_path": "text-generation-inference/load_tests/long_prompt2.py",
"repo_id": "text-generation-inference",
"token_count": 250
} |
eetq_commit := 81e0b14d64088d58ef6acd2c8f3e788d59324407
eetq:
# Clone eetq
pip install packaging
git clone https://github.com/NetEase-FuXi/EETQ.git eetq
build-eetq: eetq
cd eetq && git fetch && git checkout $(eetq_commit) && git submodule update --init --recursive
cd eetq && python setup.py build
install-eet... | text-generation-inference/server/Makefile-eetq/0 | {
"file_path": "text-generation-inference/server/Makefile-eetq",
"repo_id": "text-generation-inference",
"token_count": 156
} |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "column_remap.cuh"
#include "../util.cuh"
const int SHUF_BLOCKSIZE_X = 256;
const int SHUF_BLOCKSIZE_Y = 16;
__global__ void column_remap_kernel
(
const half* __restrict__ x,
half* __restrict__ x_new,
const int x_width,
... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu",
"repo_id": "text-generation-inference",
"token_count": 696
} |
#include "q_gemm.cuh"
#include "util.cuh"
#include "matrix_view.cuh"
#include "../config.h"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define GPTQ_BLOCK_KN_SIZE 128
#define GPTQ_BLOCK_M_SIZE_MAX 8
#... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu",
"repo_id": "text-generation-inference",
"token_count": 3563
} |
[project]
name = "text-generation-server"
version = "2.0.5-dev0"
description = "Text Generation Inference Python gRPC Server"
readme = "README.md"
requires-python = ">=3.9"
authors = [
{name = "Olivier Dehaene", email = "olivier@huggingface.co"},
{name = "Nicolas Patry", email = "nicolas@huggingface.co"},
]
depende... | text-generation-inference/server/pyproject.toml/0 | {
"file_path": "text-generation-inference/server/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 1427
} |
import torch
from text_generation_server.utils.tokens import (
StopSequenceCriteria,
StoppingCriteria,
FinishReason,
batch_top_tokens,
)
def test_stop_sequence_criteria():
criteria = StopSequenceCriteria("/test;")
assert not criteria("/")
assert not criteria("/test")
assert criteria("... | text-generation-inference/server/tests/utils/test_tokens.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_tokens.py",
"repo_id": "text-generation-inference",
"token_count": 1427
} |
from typing import Optional
from contextvars import ContextVar
from contextlib import contextmanager
import flashinfer
import torch
prefill_state: ContextVar[flashinfer.BatchPrefillWithRaggedKVCacheWrapper] = ContextVar(
"prefill_state"
)
prefill_with_paged_kv_state: ContextVar[
flashinfer.BatchPrefillWithPa... | text-generation-inference/server/text_generation_server/layers/attention/flashinfer.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/flashinfer.py",
"repo_id": "text-generation-inference",
"token_count": 3030
} |
from dataclasses import dataclass
import torch
from EETQ import quant_weights, w8_a16_gemm
from text_generation_server.utils.weights import UnquantizedWeight
@dataclass
class EETQWeight(UnquantizedWeight):
weight: torch.Tensor
def get_linear(self, bias: torch.Tensor):
try:
from text_gene... | text-generation-inference/server/text_generation_server/layers/eetq.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/eetq.py",
"repo_id": "text-generation-inference",
"token_count": 574
} |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy
import torch
import torch.nn as nn
from loguru import logger
from text_generation_server.layers.marlin.util import (
_check_marlin_kernels,
marlin_zero_points,
permute_scales,
unpack_cols,
)
from text_generation_ser... | text-generation-inference/server/text_generation_server/layers/marlin/gptq.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/gptq.py",
"repo_id": "text-generation-inference",
"token_count": 7162
} |
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.layers.attention import (
paged_attention,
attention,
Seqlen,
)
from tex... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 6794
} |
# 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/server/text_generation_server/models/custom_modeling/mllama.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/mllama.py",
"repo_id": "text-generation-inference",
"token_count": 18144
} |
import inspect
import torch
from abc import ABC, abstractmethod
from typing import List, Tuple, Optional, TypeVar, Type, Dict
from collections import defaultdict
from transformers import PreTrainedTokenizerBase
from loguru import logger
from text_generation_server.models.globals import (
ATTENTION,
PREFIX_CAC... | text-generation-inference/server/text_generation_server/models/model.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/model.py",
"repo_id": "text-generation-inference",
"token_count": 2809
} |
from functools import lru_cache
from text_generation_server.utils.dist import RANK
@lru_cache(10)
def log_once(log, msg: str, master=True):
if master:
log_master(log, msg)
else:
log(msg)
def log_master(log, msg: str):
if RANK == 0:
log(msg)
| text-generation-inference/server/text_generation_server/utils/log.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/log.py",
"repo_id": "text-generation-inference",
"token_count": 126
} |
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: HuggingFace's Tokenizers
message: >-
Fast State-of-the-Art Tokenizers optimized for Research
and Production.
type: software
authors:
- given-names: Anthony
family-names: Moi
... | tokenizers/CITATION.cff/0 | {
"file_path": "tokenizers/CITATION.cff",
"repo_id": "tokenizers",
"token_count": 293
} |
<p align="center">
<br>
<img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/>
<br>
<p>
<p align="center">
<a href="https://badge.fury.io/js/tokenizers">
<img alt="Build" src="https://badge.fury.io/js/tokenizers.svg">
</a>
<a href="https://github.com/huggingface/to... | tokenizers/bindings/node/README.md/0 | {
"file_path": "tokenizers/bindings/node/README.md",
"repo_id": "tokenizers",
"token_count": 651
} |
/* eslint-disable @typescript-eslint/no-explicit-any */
/* eslint-disable @typescript-eslint/no-empty-function */
import { TruncationStrategy, BPE, Encoding, AddedToken, Tokenizer } from '../../'
// jest.mock('../../bindings/tokenizer');
// jest.mock('../../bindings/models', () => ({
// __esModule: true,
// Model... | tokenizers/bindings/node/lib/bindings/tokenizer.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/tokenizer.test.ts",
"repo_id": "tokenizers",
"token_count": 5268
} |
use crate::tokenizer::PaddingOptions;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use tokenizers::utils::truncation::TruncationDirection;
use tokenizers::Encoding;
#[napi(js_name = "Encoding")]
#[derive(Clone, Default)]
pub struct JsEncoding {
pub(crate) encoding: Option<Encoding>,
}
impl From<Encoding> fo... | tokenizers/bindings/node/src/encoding.rs/0 | {
"file_path": "tokenizers/bindings/node/src/encoding.rs",
"repo_id": "tokenizers",
"token_count": 3778
} |
from .. import decoders
Decoder = decoders.Decoder
ByteLevel = decoders.ByteLevel
Replace = decoders.Replace
WordPiece = decoders.WordPiece
ByteFallback = decoders.ByteFallback
Fuse = decoders.Fuse
Strip = decoders.Strip
Metaspace = decoders.Metaspace
BPEDecoder = decoders.BPEDecoder
CTC = decoders.CTC
Sequence = dec... | tokenizers/bindings/python/py_src/tokenizers/decoders/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/decoders/__init__.py",
"repo_id": "tokenizers",
"token_count": 140
} |
# Generated content DO NOT EDIT
class PostProcessor:
"""
Base class for all post-processors
This class is not supposed to be instantiated directly. Instead, any implementation of
a PostProcessor will return an instance of this class when instantiated.
"""
def num_special_tokens_to_add(self, is_... | tokenizers/bindings/python/py_src/tokenizers/processors/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/processors/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 4779
} |
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::sync::{Arc, RwLock};
use crate::token::PyToken;
use crate::trainers::PyTrainer;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::{Deserialize, Serialize};
use tk::models::bpe::{BpeBuilder, Merges, Vocab, BPE};
use tk::mod... | tokenizers/bindings/python/src/models.rs/0 | {
"file_path": "tokenizers/bindings/python/src/models.rs",
"repo_id": "tokenizers",
"token_count": 15947
} |
from tokenizers import ByteLevelBPETokenizer
from ..utils import data_dir, multiprocessing_with_parallelism, roberta_files
class TestByteLevelBPE:
def test_basic_encode(self, roberta_files):
tokenizer = ByteLevelBPETokenizer.from_file(roberta_files["vocab"], roberta_files["merges"])
output = toke... | tokenizers/bindings/python/tests/implementations/test_byte_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_byte_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 1653
} |
# Pre-tokenizers
<tokenizerslangcontent>
<python>
## BertPreTokenizer
[[autodoc]] tokenizers.pre_tokenizers.BertPreTokenizer
## ByteLevel
[[autodoc]] tokenizers.pre_tokenizers.ByteLevel
## CharDelimiterSplit
[[autodoc]] tokenizers.pre_tokenizers.CharDelimiterSplit
## Digits
[[autodoc]] tokenizers.pre_tokenizers... | tokenizers/docs/source-doc-builder/api/pre-tokenizers.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/pre-tokenizers.mdx",
"repo_id": "tokenizers",
"token_count": 371
} |
The tokenization pipeline
====================================================================================================
When calling :entity:`Tokenizer.encode` or :entity:`Tokenizer.encode_batch`, the input text(s) go
through the following pipeline:
- :ref:`normalization`
- :ref:`pre-tokenization`
- :ref:`mode... | tokenizers/docs/source/pipeline.rst/0 | {
"file_path": "tokenizers/docs/source/pipeline.rst",
"repo_id": "tokenizers",
"token_count": 6322
} |
use tokenizers::models::wordpiece::WordPiece;
use tokenizers::{AddedToken, Tokenizer};
fn main() {
let start = std::time::Instant::now();
let mut tokenizer = Tokenizer::new(WordPiece::default());
// Mix special and not special
// You can make sure ids are in order, and special status is correct.
l... | tokenizers/tokenizers/examples/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/serialization.rs",
"repo_id": "tokenizers",
"token_count": 299
} |
#![allow(clippy::map_entry)]
use super::{Pair, WithFirstLastIterator, Word, BPE};
use crate::parallelism::*;
use crate::tokenizer::{AddedToken, Result, Trainer};
use crate::utils::progress::{ProgressBar, ProgressStyle};
use serde::{Deserialize, Serialize};
use std::cmp::Ordering;
use std::collections::{BinaryHeap, Has... | tokenizers/tokenizers/src/models/bpe/trainer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/trainer.rs",
"repo_id": "tokenizers",
"token_count": 15462
} |
use crate::processors::byte_level::bytes_char;
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::macro_rules_attribute;
use std::collections::{HashMap, HashSet};
#[derive(Clone, Debug)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct ByteLevel;
lazy_static! {
static ref BYTES_C... | tokenizers/tokenizers/src/normalizers/byte_level.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/byte_level.rs",
"repo_id": "tokenizers",
"token_count": 3445
} |
use crate::utils::SysRegex;
use serde::{Deserialize, Deserializer, Serialize};
use crate::tokenizer::{
pattern::Invert, PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior,
};
/// Represents the different patterns that `Split` can use
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq)]
pub... | tokenizers/tokenizers/src/pre_tokenizers/split.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/split.rs",
"repo_id": "tokenizers",
"token_count": 4042
} |
use std::marker::PhantomData;
use serde::{
self,
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use super::{added_vocabulary::AddedTokenWithId, TokenizerImpl};
use crate::{Decoder, Model, Normalizer, PostProcessor, PreTokenizer, TokenizerBui... | tokenizers/tokenizers/src/tokenizer/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/serialization.rs",
"repo_id": "tokenizers",
"token_count": 3738
} |
mod common;
use common::*;
use tokenizers::decoders::byte_level::ByteLevel;
use tokenizers::decoders::DecoderWrapper;
use tokenizers::models::bpe::BPE;
use tokenizers::models::wordlevel::WordLevel;
use tokenizers::models::wordpiece::WordPiece;
use tokenizers::models::ModelWrapper;
use tokenizers::normalizers::bert::Be... | tokenizers/tokenizers/tests/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/serialization.rs",
"repo_id": "tokenizers",
"token_count": 3890
} |
# Using quantized models (dtypes)
Before Transformers.js v3, we used the `quantized` option to specify whether to use a quantized (q8) or full-precision (fp32) variant of the model by setting `quantized` to `true` or `false`, respectively. Now, we've added the ability to select from a much larger list with the `dtype`... | transformers.js/docs/source/guides/dtypes.md/0 | {
"file_path": "transformers.js/docs/source/guides/dtypes.md",
"repo_id": "transformers.js",
"token_count": 1698
} |
.sidebar {
background-color: #181818;
color: #CCCCCC;
}
body{
background-color: #1F1F1F;
color: white;
}
.progress-container {
position: relative;
font-size: 16px;
color: white;
/* background-color: #e9ecef; */
border-radius: 8px;
text-align: left;
overflow: hidden;
}
.progress-bar {
padding:... | transformers.js/examples/code-completion/src/App.css/0 | {
"file_path": "transformers.js/examples/code-completion/src/App.css",
"repo_id": "transformers.js",
"token_count": 208
} |
import { useState, useRef, useEffect, useCallback } from 'react'
import './App.css'
const PLACEHOLDER_TEXTS = [
"'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
"The novel 'Moby-... | transformers.js/examples/cross-encoder/src/App.jsx/0 | {
"file_path": "transformers.js/examples/cross-encoder/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 2232
} |
# Transformers.js - Sample browser extension
An example project to show how to run 🤗 Transformers in a browser extension. Although we only provide instructions for running in Chrome, it should be similar for other browsers.
## Getting Started
1. Clone the repo and enter the project directory:
```bash
git cl... | transformers.js/examples/extension/README.md/0 | {
"file_path": "transformers.js/examples/extension/README.md",
"repo_id": "transformers.js",
"token_count": 624
} |
import { useEffect, useState, useRef } from 'react';
import { AutoTokenizer, MusicgenForConditionalGeneration, BaseStreamer } from '@xenova/transformers';
import { encodeWAV, share } from './utils.js';
import './App.css';
const MODEL_ID = 'Xenova/musicgen-small';
// Adapted from https://huggingface.co/spaces/faceboo... | transformers.js/examples/musicgen-web/src/App.jsx/0 | {
"file_path": "transformers.js/examples/musicgen-web/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 3165
} |
'use client'
import { useState, useEffect, useCallback, useRef } from 'react'
import { Modal } from './components/Modal';
import { SearchBar } from './components/SearchBar';
import { ImageGrid } from './components/ImageGrid';
export default function Home() {
// Application state
const [ready, setReady] = useStat... | transformers.js/examples/semantic-image-search-client/src/app/page.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/src/app/page.js",
"repo_id": "transformers.js",
"token_count": 772
} |
// Helper script to update the database with image embeddings
import { AutoProcessor, RawImage, CLIPVisionModelWithProjection } from '@xenova/transformers';
import { createClient } from '@supabase/supabase-js'
if (!process.env.SUPABASE_SECRET_KEY) {
throw new Error('Missing `SUPABASE_SECRET_KEY` environment varia... | transformers.js/examples/semantic-image-search/scripts/update-database.mjs/0 | {
"file_path": "transformers.js/examples/semantic-image-search/scripts/update-database.mjs",
"repo_id": "transformers.js",
"token_count": 778
} |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Transformers.js | Real-time object detection</title>
</head>
<body>
<h1>
Real-time object detection w/
<a href="https://github.com/huggingface/transformers.j... | transformers.js/examples/video-object-detection/index.html/0 | {
"file_path": "transformers.js/examples/video-object-detection/index.html",
"repo_id": "transformers.js",
"token_count": 608
} |
* {
box-sizing: border-box;
padding: 0;
margin: 0;
font-family: sans-serif;
}
html,
body {
height: 100%;
}
body {
padding: 16px 32px;
}
body,
#container {
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
}
#controls {
display: flex;
padding: 1rem;
gap: 1... | transformers.js/examples/webgpu-video-depth-estimation/style.css/0 | {
"file_path": "transformers.js/examples/webgpu-video-depth-estimation/style.css",
"repo_id": "transformers.js",
"token_count": 372
} |
export default function CrossIcon(props) {
return (
<svg
{...props}
xmlns="http://www.w3.org/2000/svg"
width="24"
height="24"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
strokeWidth="2"
stro... | transformers.js/examples/webgpu-vlm/src/components/icons/CrossIcon.jsx/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/components/icons/CrossIcon.jsx",
"repo_id": "transformers.js",
"token_count": 304
} |
import { useEffect, useState, useRef, useCallback } from 'react';
import Progress from './components/Progress';
import MediaInput from './components/MediaInput';
import Transcript from './components/Transcript';
import LanguageSelector from './components/LanguageSelector';
async function hasWebGPU() {
if (!navigat... | transformers.js/examples/whisper-word-timestamps/src/App.jsx/0 | {
"file_path": "transformers.js/examples/whisper-word-timestamps/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 3492
} |
# Support exporting vision and text models separately:
# Adapted from https://github.com/huggingface/optimum/issues/1186#issuecomment-1637641760
from optimum.exporters.onnx.model_configs import SiglipTextOnnxConfig, ViTOnnxConfig
from typing import Dict
class SiglipVisionOnnxConfig(ViTOnnxConfig):
pass
class S... | transformers.js/scripts/extra/siglip.py/0 | {
"file_path": "transformers.js/scripts/extra/siglip.py",
"repo_id": "transformers.js",
"token_count": 496
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.