text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
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# 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/optimizers/loraplus.py/0 | {
"file_path": "peft/src/peft/optimizers/loraplus.py",
"repo_id": "peft",
"token_count": 1910
} |
# 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": 3638
} |
# 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": 2612
} |
# 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/gptq.py/0 | {
"file_path": "peft/src/peft/tuners/lora/gptq.py",
"repo_id": "peft",
"token_count": 1841
} |
# 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/vblora/config.py/0 | {
"file_path": "peft/src/peft/tuners/vblora/config.py",
"repo_id": "peft",
"token_count": 3931
} |
# 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/incremental_pca.py/0 | {
"file_path": "peft/src/peft/utils/incremental_pca.py",
"repo_id": "peft",
"token_count": 6139
} |
# 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_cpt.py/0 | {
"file_path": "peft/tests/test_cpt.py",
"repo_id": "peft",
"token_count": 4903
} |
# 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_mixed.py/0 | {
"file_path": "peft/tests/test_mixed.py",
"repo_id": "peft",
"token_count": 16988
} |
# Deep Layer Aggregation
Extending “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through ... | pytorch-image-models/hfdocs/source/models/dla.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/dla.mdx",
"repo_id": "pytorch-image-models",
"token_count": 6760
} |
# 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 architecture).
## How do I... | pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1684
} |
# Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-li... | pytorch-image-models/hfdocs/source/models/res2next.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/res2next.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1713
} |
# (Tensorflow) EfficientNet Lite
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly ... | pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3372
} |
#!/usr/bin/env python3
"""PyTorch Inference Script
An example inference script that outputs top-k class ids for images in a folder into a csv.
Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
"""
import argparse
import json
import logging
import os
import time
from contextlib import su... | pytorch-image-models/inference.py/0 | {
"file_path": "pytorch-image-models/inference.py",
"repo_id": "pytorch-image-models",
"token_count": 7092
} |
""" Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from typing import Optional
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, KMNIST, FashionMNIST, ImageFolder
try:
from torchvision.datasets import Places365
has_places365 = True
except ImportError:
has_places3... | pytorch-image-models/timm/data/dataset_factory.py/0 | {
"file_path": "pytorch-image-models/timm/data/dataset_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 4027
} |
""" 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
} |
""" Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers f... | pytorch-image-models/timm/layers/bottleneck_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/bottleneck_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 2907
} |
""" Filter Response Norm in PyTorch
Based on `Filter Response Normalization Layer` - https://arxiv.org/abs/1911.09737
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
from .create_act import create_act_layer
from .trace_utils import _assert
def inv_instance_rms(x, eps: float... | pytorch-image-models/timm/layers/filter_response_norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/filter_response_norm.py",
"repo_id": "pytorch-image-models",
"token_count": 1182
} |
from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identi... | pytorch-image-models/timm/layers/ml_decoder.py/0 | {
"file_path": "pytorch-image-models/timm/layers/ml_decoder.py",
"repo_id": "pytorch-image-models",
"token_count": 3048
} |
""" Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
Thi... | pytorch-image-models/timm/layers/split_batchnorm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/split_batchnorm.py",
"repo_id": "pytorch-image-models",
"token_count": 1394
} |
import os
from pathlib import Path
from typing import Any, Dict, Optional, Union
from urllib.parse import urlsplit
from timm.layers import set_layer_config
from ._helpers import load_checkpoint
from ._hub import load_model_config_from_hf
from ._pretrained import PretrainedCfg
from ._registry import is_model, model_ent... | pytorch-image-models/timm/models/_factory.py/0 | {
"file_path": "pytorch-image-models/timm/models/_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 2169
} |
""" Bring-Your-Own-Blocks Network
A flexible network w/ dataclass based config for stacking those NN blocks.
This model is currently used to implement the following networks:
GPU Efficient (ResNets) - gernet_l/m/s (original versions called genet, but this was already used (by SENet author)).
Paper: `Neural Architect... | pytorch-image-models/timm/models/byobnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/byobnet.py",
"repo_id": "pytorch-image-models",
"token_count": 52981
} |
""" The EfficientNet Family in PyTorch
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* EfficientNet-V2
- `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/A... | pytorch-image-models/timm/models/efficientnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientnet.py",
"repo_id": "pytorch-image-models",
"token_count": 57808
} |
""" HRNet
Copied from https://github.com/HRNet/HRNet-Image-Classification
Original header:
Copyright (c) Microsoft
Licensed under the MIT License.
Written by Bin Xiao (Bin.Xiao@microsoft.com)
Modified by Ke Sun (sunk@mail.ustc.edu.cn)
"""
import logging
from typing import List
import torch
import torch.nn as... | pytorch-image-models/timm/models/hrnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/hrnet.py",
"repo_id": "pytorch-image-models",
"token_count": 17688
} |
""" Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... | pytorch-image-models/timm/models/nest.py/0 | {
"file_path": "pytorch-image-models/timm/models/nest.py",
"repo_id": "pytorch-image-models",
"token_count": 10094
} |
"""PyTorch SelecSLS Net example for ImageNet Classification
License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode)
Author: Dushyant Mehta (@mehtadushy)
SelecSLS (core) Network Architecture as proposed in "XNect: Real-time Multi-person 3D
Human Pose Estimation with a Single RGB Camera, Mehta et al."... | pytorch-image-models/timm/models/selecsls.py/0 | {
"file_path": "pytorch-image-models/timm/models/selecsls.py",
"repo_id": "pytorch-image-models",
"token_count": 6461
} |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... | pytorch-image-models/timm/models/vision_transformer_sam.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer_sam.py",
"repo_id": "pytorch-image-models",
"token_count": 13920
} |
""" AdamW Optimizer
Impl copied from PyTorch master
NOTE: This impl has been deprecated in favour of torch.optim.AdamW and remains as a reference
"""
import math
from typing import Tuple
import torch
from torch.optim.optimizer import Optimizer
from ._types import ParamsT
class AdamWLegacy(Optimizer):
r"""Imple... | pytorch-image-models/timm/optim/adamw.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adamw.py",
"repo_id": "pytorch-image-models",
"token_count": 2672
} |
""" RMSProp modified to behave like Tensorflow impl
Originally cut & paste from PyTorch RMSProp
https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py
Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE
Modifications Copyright 2021... | pytorch-image-models/timm/optim/rmsprop_tf.py/0 | {
"file_path": "pytorch-image-models/timm/optim/rmsprop_tf.py",
"repo_id": "pytorch-image-models",
"token_count": 3431
} |
""" Checkpoint Saver
Track top-n training checkpoints and maintain recovery checkpoints on specified intervals.
Hacked together by / Copyright 2020 Ross Wightman
"""
import glob
import logging
import operator
import os
import shutil
import torch
from .model import unwrap_model, get_state_dict
_logger = logging.g... | pytorch-image-models/timm/utils/checkpoint_saver.py/0 | {
"file_path": "pytorch-image-models/timm/utils/checkpoint_saver.py",
"repo_id": "pytorch-image-models",
"token_count": 3258
} |
#!/usr/bin/env python3
""" ImageNet Validation Script
This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
canonical PyTorch, standard Python style, and good perform... | pytorch-image-models/validate.py/0 | {
"file_path": "pytorch-image-models/validate.py",
"repo_id": "pytorch-image-models",
"token_count": 9687
} |
- title: Get started
sections:
- local: index
title: 🤗 Agents
- local: guided_tour
title: गाइडेड टूर
- title: Tutorials
sections:
- local: tutorials/building_good_agents
title: ✨ अच्छे Agents का निर्माण
- local: tutorials/inspect_runs
title: 📊 OpenTelemetry के साथ runs का निरीक्षण
- loca... | smolagents/docs/source/hi/_toctree.yml/0 | {
"file_path": "smolagents/docs/source/hi/_toctree.yml",
"repo_id": "smolagents",
"token_count": 783
} |
<!--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/conceptual_guides/intro_agents.md/0 | {
"file_path": "smolagents/docs/source/zh/conceptual_guides/intro_agents.md",
"repo_id": "smolagents",
"token_count": 5282
} |
from dotenv import load_dotenv
from smolagents import CodeAgent, HfApiModel, Tool
from smolagents.default_tools import VisitWebpageTool
load_dotenv()
class GetCatImageTool(Tool):
name = "get_cat_image"
description = "Get a cat image"
inputs = {}
output_type = "image"
def __init__(self):
... | smolagents/examples/e2b_example.py/0 | {
"file_path": "smolagents/examples/e2b_example.py",
"repo_id": "smolagents",
"token_count": 496
} |
<jupyter_start><jupyter_code>!pip install "smolagents[litellm]" -q
import datasets
eval_ds = datasets.load_dataset("gaia-benchmark/GAIA", "2023_all")["validation"]
to_keep = [
"What's the last line of the rhyme under the flavor",
'Of the authors (First M. Last) that worked on the paper "Pie Menus or Linear Me... | smolagents/examples/open_deep_research/visual_vs_text_browser.ipynb/0 | {
"file_path": "smolagents/examples/open_deep_research/visual_vs_text_browser.ipynb",
"repo_id": "smolagents",
"token_count": 2367
} |
#!/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/monitoring.py/0 | {
"file_path": "smolagents/src/smolagents/monitoring.py",
"repo_id": "smolagents",
"token_count": 3705
} |
import subprocess
def test_import_smolagents_without_extras():
# Run the import statement in an isolated virtual environment
result = subprocess.run(
["uv", "run", "--isolated", "--no-editable", "-"], input="import smolagents", text=True, capture_output=True
)
# Check if the import was success... | smolagents/tests/test_import.py/0 | {
"file_path": "smolagents/tests/test_import.py",
"repo_id": "smolagents",
"token_count": 200
} |
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.84.0 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef AS planner
COPY Cargo.lock Cargo.lock
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
... | text-generation-inference/Dockerfile/0 | {
"file_path": "text-generation-inference/Dockerfile",
"repo_id": "text-generation-inference",
"token_count": 3963
} |
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod client;
mod sharded_client;
pub use client::Client;
pub use pb::generate::v2::HealthResponse;
pub use pb::generate::v2::{
Batch, CachedBatch, FinishReason, GeneratedText, Generation, GrammarType, InfoResponse,
NextTokenChooserParameters, Request, Stop... | text-generation-inference/backends/client/src/v2/mod.rs/0 | {
"file_path": "text-generation-inference/backends/client/src/v2/mod.rs",
"repo_id": "text-generation-inference",
"token_count": 134
} |
#ifndef TGI_BACKEND_TRTLLM
#define TGI_BACKEND_TRTLLM
#include <cmath>
#include <cstdint>
#include <expected>
#include <fstream>
#include <list>
#include <span>
#include <nlohmann/json.hpp>
#include <spdlog/spdlog.h>
#include <spdlog/fmt/fmt.h>
#include <tensorrt_llm/executor/executor.h>
namespace huggingface::tgi:... | text-generation-inference/backends/trtllm/csrc/backend.hpp/0 | {
"file_path": "text-generation-inference/backends/trtllm/csrc/backend.hpp",
"repo_id": "text-generation-inference",
"token_count": 3772
} |
//! Text Generation gRPC client library
use async_trait::async_trait;
use thiserror::Error;
use tonic::transport;
use tonic::Status;
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod grpc_client;
mod sharded_client;
pub use grpc_client::Client;
pub use pb::generate::v2::{
Batch, CachedBatch, FinishReas... | text-generation-inference/backends/v2/src/client/mod.rs/0 | {
"file_path": "text-generation-inference/backends/v2/src/client/mod.rs",
"repo_id": "text-generation-inference",
"token_count": 1194
} |
use crate::block_allocator::{Allocator, BlockAllocation};
use slotmap::{DefaultKey, SlotMap};
use std::hash::{Hash, Hasher};
use std::{
collections::{BTreeSet, HashMap},
sync::Arc,
};
fn hash(slice: &[u32]) -> u64 {
assert!(!slice.is_empty());
if slice.len() == 1 {
slice[0] as u64
} else {
... | text-generation-inference/backends/v3/src/radix.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/radix.rs",
"repo_id": "text-generation-inference",
"token_count": 14966
} |
import pytest
from text_generation import Client, AsyncClient
from text_generation.errors import NotFoundError, ValidationError
from text_generation.types import FinishReason, InputToken
def test_generate(llama_7b_url, hf_headers):
client = Client(llama_7b_url, hf_headers)
response = client.generate("test", ... | text-generation-inference/clients/python/tests/test_client.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_client.py",
"repo_id": "text-generation-inference",
"token_count": 2110
} |
# Consuming Text Generation Inference
There are many ways to consume Text Generation Inference (TGI) server in your applications. After launching the server, you can use the [Messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api) `/v1/chat/completions` route and make a `POST` request to g... | text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md",
"repo_id": "text-generation-inference",
"token_count": 2375
} |
# Text-generation-launcher arguments
<!-- WRAP CODE BLOCKS -->
```shell
Text Generation Launcher
Usage: text-generation-launcher [OPTIONS]
Options:
```
## MODEL_ID
```shell
--model-id <MODEL_ID>
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `Open... | text-generation-inference/docs/source/reference/launcher.md/0 | {
"file_path": "text-generation-inference/docs/source/reference/launcher.md",
"repo_id": "text-generation-inference",
"token_count": 7683
} |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"text": " A Beginner’s Guide\nDeep learning is a subset"
},
{
"finish_reason": "length",
"index": 1,
"logprobs": null,
"text": " This is a question that has puzzled many people for"
... | text-generation-inference/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json",
"repo_id": "text-generation-inference",
"token_count": 434
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 34564,
"logprob": -1.765625,
"special": false,
"text": "Deep"
},
{
"id": 6975,
"logpro... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int_24/test_compressed_tensors_wna16_int_24.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int_24/test_compressed_tensors_wna16_int_24.json",
"repo_id": "text-generation-inference",
"token_count": 868
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 7539,
"logprob": -0.609375,
"special": false,
"text": " forms"
},
{
"id": 708,
"logprob":... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 849
} |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5351562,
"special": false,
"text": " for"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json",
"repo_id": "text-generation-inference",
"token_count": 4045
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 25584,
"logprob": 0.0,
"special": false,
"text": "Grad"
},
{
"id": 993,
"logprob": 0.0,
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 849
} |
{
"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
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 5,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": 0,
"tokens": [
{
"id": 926,
"logprob": -4.3554688,
"special... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base.json",
"repo_id": "text-generation-inference",
"token_count": 532
} |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "I am an AI assistant",
"name": null,
"role": "assistant",
"tool_calls": null
},
"usage": null
}
],
"created": 1728497062,
"id": "",
"mode... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json",
"repo_id": "text-generation-inference",
"token_count": 275
} |
import pytest
import requests
@pytest.fixture(scope="module")
def llama_continue_final_message_handle(launcher):
with launcher("TinyLlama/TinyLlama-1.1B-Chat-v1.0") as handle:
yield handle
@pytest.fixture(scope="module")
async def llama_continue_final_message(llama_continue_final_message_handle):
aw... | text-generation-inference/integration-tests/models/test_continue_final_message.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_continue_final_message.py",
"repo_id": "text-generation-inference",
"token_count": 1102
} |
import pytest
@pytest.fixture(scope="module")
def flash_llama_marlin24_handle(launcher):
with launcher(
"nm-testing/Llama-2-7b-pruned2.4-Marlin_24", quantize="marlin"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_marlin(flash_llama_marlin24_handle):
awai... | text-generation-inference/integration-tests/models/test_flash_llama_marlin_24.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_marlin_24.py",
"repo_id": "text-generation-inference",
"token_count": 754
} |
import pytest
@pytest.fixture(scope="module")
def flash_santacoder_handle(launcher):
with launcher("bigcode/santacoder") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_santacoder(flash_santacoder_handle):
await flash_santacoder_handle.health(300)
return flash_santacoder_... | text-generation-inference/integration-tests/models/test_flash_santacoder.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_santacoder.py",
"repo_id": "text-generation-inference",
"token_count": 403
} |
import pytest
@pytest.fixture(scope="module")
def neox_handle(launcher):
with launcher(
"stabilityai/stablelm-tuned-alpha-3b", num_shard=1, use_flash_attention=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def neox(neox_handle):
await neox_handle.health(300)
r... | text-generation-inference/integration-tests/models/test_neox.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_neox.py",
"repo_id": "text-generation-inference",
"token_count": 515
} |
import argparse
import datetime
import json
import os
import traceback
from typing import Dict, Tuple, List
import GPUtil
import docker
from docker.models.containers import Container
from loguru import logger
import pandas as pd
class InferenceEngineRunner:
def __init__(self, model: str):
self.model = mo... | text-generation-inference/load_tests/benchmarks.py/0 | {
"file_path": "text-generation-inference/load_tests/benchmarks.py",
"repo_id": "text-generation-inference",
"token_count": 4444
} |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import torch
extra_cuda_cflags = []
extra_cflags = []
if torch.version.hip:
extra_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
extra_cuda_cflags = ["-DLEGACY_HIPBLAS_DIRECT=ON"]
extra_compile_args = {
"cxx": extra_... | text-generation-inference/server/exllama_kernels/setup.py/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/setup.py",
"repo_id": "text-generation-inference",
"token_count": 470
} |
#ifndef _qdq_8_cuh
#define _qdq_8_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_8BIT == 1
// Not implemented
#else
__forceinline__ __device__ void shuffle_8bit_4
(
uint32_t* q,
int stride
)
{
}
__forceinline__ __device__ void dequant_8bit_8
(
const uint32_t q_0,
const uint32_t ... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_8.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_8.cuh",
"repo_id": "text-generation-inference",
"token_count": 337
} |
import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.seq2seq_lm import Seq2SeqLM, Seq2SeqLMBatch
@pytest.fixture(scope="session")
def mt0_small_tokenizer():
tokenizer = AutoTokenizer.from_pr... | text-generation-inference/server/tests/models/test_seq2seq_lm.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_seq2seq_lm.py",
"repo_id": "text-generation-inference",
"token_count": 5528
} |
from text_generation_server.layers.tensor_parallel import (
TensorParallelColumnLinear,
TensorParallelRowLinear,
TensorParallelEmbedding,
)
from text_generation_server.layers.linear import (
get_linear,
FastLinear,
)
from text_generation_server.layers.speculative import SpeculativeHead
# Just to ad... | text-generation-inference/server/text_generation_server/layers/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 346
} |
from typing import List, Optional, Union, TypeVar
from dataclasses import dataclass
from loguru import logger
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationType
from text_generation_server.layers.fp8 import _load_scalar_or_matrix_scale
from text_generation_server.utils.log imp... | text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8a8_int.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8a8_int.py",
"repo_id": "text-generation-inference",
"token_count": 3942
} |
import torch
from torch import nn
from accelerate import init_empty_weights
from text_generation_server.utils.import_utils import (
SYSTEM,
)
# Monkey patching
@classmethod
def load_layer_norm(cls, prefix, weights, eps):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.... | text-generation-inference/server/text_generation_server/layers/layernorm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/layernorm.py",
"repo_id": "text-generation-inference",
"token_count": 3189
} |
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/server/text_generation_server/layers/speculative.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/speculative.py",
"repo_id": "text-generation-inference",
"token_count": 851
} |
# 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/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 11907
} |
# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of ... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_perceiver.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_perceiver.py",
"repo_id": "text-generation-inference",
"token_count": 5152
} |
import torch
import os
from loguru import logger
from typing import Dict, Optional
from text_generation_server.utils.log import log_master
REQUEST_LOGPROBS = os.getenv("REQUEST_LOGPROBS", "0").lower() in {"1", "true"}
ATTENTION = os.environ["ATTENTION"]
# default_prefix_caching = "1" if ATTENTION in {"flashinfer", "f... | text-generation-inference/server/text_generation_server/models/globals.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/globals.py",
"repo_id": "text-generation-inference",
"token_count": 900
} |
from typing import Iterable
from loguru import logger
from text_generation_server.pb import generate_pb2
def concat_text_chunks(chunks: Iterable[generate_pb2.InputChunk]) -> str:
"""
Concatenate text in text chunks. Non-text chunks are dropped.
"""
text = None
for chunk in chunks:
chunk_... | text-generation-inference/server/text_generation_server/utils/chunks.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/chunks.py",
"repo_id": "text-generation-inference",
"token_count": 332
} |
# coding=utf-8
# Copyright 2023 Authors of "A Watermark for Large Language Models"
# available at https://arxiv.org/abs/2301.10226
#
# 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... | text-generation-inference/server/text_generation_server/utils/watermark.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/watermark.py",
"repo_id": "text-generation-inference",
"token_count": 1489
} |
import {
PaddingDirection,
WordPiece,
punctuationPreTokenizer,
sequencePreTokenizer,
whitespacePreTokenizer,
Encoding,
EncodeOptions,
Tokenizer,
} from '../../'
import { InputSequence } from '../../types'
const MOCKS_DIR = __dirname + '/__mocks__'
describe('Can modify pretokenizers on the fly', () => ... | tokenizers/bindings/node/lib/bindings/encoding.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/encoding.test.ts",
"repo_id": "tokenizers",
"token_count": 3021
} |
{
"name": "tokenizers-freebsd-x64",
"version": "0.13.4-rc1",
"os": [
"freebsd"
],
"cpu": [
"x64"
],
"main": "tokenizers.freebsd-x64.node",
"files": [
"tokenizers.freebsd-x64.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",
"N... | tokenizers/bindings/node/npm/freebsd-x64/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/freebsd-x64/package.json",
"repo_id": "tokenizers",
"token_count": 272
} |
{
"name": "tokenizers-win32-x64-msvc",
"version": "0.13.4-rc1",
"os": [
"win32"
],
"cpu": [
"x64"
],
"main": "tokenizers.win32-x64-msvc.node",
"files": [
"tokenizers.win32-x64-msvc.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",... | tokenizers/bindings/node/npm/win32-x64-msvc/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-x64-msvc/package.json",
"repo_id": "tokenizers",
"token_count": 277
} |
use napi::bindgen_prelude::*;
use napi_derive::napi;
use tokenizers as tk;
use tokenizers::Encoding;
use crate::encoding::JsEncoding;
#[napi]
pub fn slice(s: String, begin_index: Option<i32>, end_index: Option<i32>) -> Result<String> {
let len = s.chars().count();
let get_index = |x: i32| -> usize {
if x >= ... | tokenizers/bindings/node/src/utils.rs/0 | {
"file_path": "tokenizers/bindings/node/src/utils.rs",
"repo_id": "tokenizers",
"token_count": 503
} |
import argparse
import glob
from os.path import join
from tokenizers import ByteLevelBPETokenizer
parser = argparse.ArgumentParser()
parser.add_argument(
"--files",
default=None,
metavar="path",
type=str,
required=True,
help="The files to use as training; accept '**/*.txt' type of patterns \
... | tokenizers/bindings/python/examples/train_bytelevel_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/examples/train_bytelevel_bpe.py",
"repo_id": "tokenizers",
"token_count": 521
} |
from .. import normalizers
Normalizer = normalizers.Normalizer
BertNormalizer = normalizers.BertNormalizer
NFD = normalizers.NFD
NFKD = normalizers.NFKD
NFC = normalizers.NFC
NFKC = normalizers.NFKC
Sequence = normalizers.Sequence
Lowercase = normalizers.Lowercase
Prepend = normalizers.Prepend
Strip = normalizers.Str... | tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.py",
"repo_id": "tokenizers",
"token_count": 304
} |
[isort]
default_section = FIRSTPARTY
ensure_newline_before_comments = True
force_grid_wrap = 0
include_trailing_comma = True
known_first_party = transformers
known_third_party =
absl
conllu
datasets
elasticsearch
fairseq
faiss-cpu
fastprogress
fire
fugashi
git
h5py
matplo... | tokenizers/bindings/python/setup.cfg/0 | {
"file_path": "tokenizers/bindings/python/setup.cfg",
"repo_id": "tokenizers",
"token_count": 386
} |
use pyo3::exceptions;
use pyo3::prelude::*;
use tk::utils::SysRegex;
/// Instantiate a new Regex with the given pattern
#[pyclass(module = "tokenizers", name = "Regex")]
pub struct PyRegex {
pub inner: SysRegex,
pub pattern: String,
}
#[pymethods]
impl PyRegex {
#[new]
#[pyo3(text_signature = "(self, ... | tokenizers/bindings/python/src/utils/regex.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/regex.rs",
"repo_id": "tokenizers",
"token_count": 273
} |
from tokenizers import Tokenizer
from ..utils import data_dir, doc_wiki_tokenizer
disable_printing = True
original_print = print
def print(*args, **kwargs):
if not disable_printing:
original_print(*args, **kwargs)
class TestQuicktour:
# This method contains everything we don't want to run
@sta... | tokenizers/bindings/python/tests/documentation/test_quicktour.py/0 | {
"file_path": "tokenizers/bindings/python/tests/documentation/test_quicktour.py",
"repo_id": "tokenizers",
"token_count": 3290
} |
# Encoding
<tokenizerslangcontent>
<python>
## Encoding
[[autodoc]] tokenizers.Encoding
- all
- attention_mask
- ids
- n_sequences
- offsets
- overflowing
- sequence_ids
- special_tokens_mask
- tokens
- type_ids
- word_ids
- words
</python>
<rust>
The Rust API Reference... | tokenizers/docs/source-doc-builder/api/encoding.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/encoding.mdx",
"repo_id": "tokenizers",
"token_count": 190
} |
from docutils import nodes
import sphinx
from sphinx.locale import _
from conf import rust_version
logger = sphinx.util.logging.getLogger(__name__)
class RustRef:
def __call__(self, name, rawtext, text, lineno, inliner, options={}, content=[]):
doctype = name.split("_")[1]
parts = text.split(":... | tokenizers/docs/source/_ext/rust_doc.py/0 | {
"file_path": "tokenizers/docs/source/_ext/rust_doc.py",
"repo_id": "tokenizers",
"token_count": 1221
} |
Tokenizers
====================================================================================================
Fast State-of-the-art tokenizers, optimized for both research and production
`🤗 Tokenizers`_ provides an implementation of today's most used tokenizers, with
a focus on performance and versatility. These t... | tokenizers/docs/source/index.rst/0 | {
"file_path": "tokenizers/docs/source/index.rst",
"repo_id": "tokenizers",
"token_count": 404
} |
use std::time::{Duration, Instant};
use criterion::black_box;
use tokenizers::{
Decoder, EncodeInput, Model, Normalizer, PostProcessor, PreTokenizer, TokenizerImpl, Trainer,
};
pub fn iter_bench_encode<M, N, PT, PP, D>(
iters: u64,
tokenizer: &TokenizerImpl<M, N, PT, PP, D>,
lines: &[EncodeInput],
) ... | tokenizers/tokenizers/benches/common/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/common/mod.rs",
"repo_id": "tokenizers",
"token_count": 964
} |
use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize)]
/// The WordPiece decoder takes care of decoding a list of wordpiece tokens
/// back into a readable string.
#[serde(tag = "type")]
#[non_exhaustive]
pub struct WordPiece {
/// The prefix ... | tokenizers/tokenizers/src/decoders/wordpiece.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/wordpiece.rs",
"repo_id": "tokenizers",
"token_count": 1275
} |
use super::WordLevel;
use crate::utils::parallelism::*;
use crate::{AddedToken, Result, Trainer};
use serde::{Deserialize, Serialize};
use std::cmp::Ordering;
use std::collections::HashMap;
#[non_exhaustive]
#[derive(Debug, Clone, Builder, Serialize, Deserialize)]
pub struct WordLevelTrainer {
/// The minimum freq... | tokenizers/tokenizers/src/models/wordlevel/trainer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordlevel/trainer.rs",
"repo_id": "tokenizers",
"token_count": 2735
} |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
/// Pre tokenizes the numbers into single tokens. If individual_digits is set
/// to true, then all digits are ... | tokenizers/tokenizers/src/pre_tokenizers/digits.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/digits.rs",
"repo_id": "tokenizers",
"token_count": 1667
} |
use crate::parallelism::*;
use crate::tokenizer::{Offsets, Token};
use crate::utils::padding::PaddingDirection;
use crate::utils::truncation::TruncationDirection;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::ops::Range;
/// Represents the output of a `Tokenizer`.
#[derive(Default, Parti... | tokenizers/tokenizers/src/tokenizer/encoding.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/encoding.rs",
"repo_id": "tokenizers",
"token_count": 17192
} |
mod common;
use common::*;
use tokenizers::tokenizer::AddedToken;
#[test]
fn add_tokens() {
let mut tokenizer = get_empty();
assert_eq!(
tokenizer.add_special_tokens(&[
AddedToken::from("<cls>", true),
AddedToken::from("<sep>", true)
]),
2
);
assert_eq!... | tokenizers/tokenizers/tests/added_tokens.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/added_tokens.rs",
"repo_id": "tokenizers",
"token_count": 1770
} |
# Building a React application
In this tutorial, we'll be building a simple React application that performs multilingual translation using Transformers.js! The final product will look something like this:
Use... | transformers.js/docs/source/tutorials/react.md/0 | {
"file_path": "transformers.js/docs/source/tutorials/react.md",
"repo_id": "transformers.js",
"token_count": 5891
} |
@font-face {
font-family: "bootstrap-icons";
src: url("./bootstrap-icons.woff2") format("woff2"),
url("./bootstrap-icons.woff") format("woff");
}
.bi::before,
[class^="bi-"]::before,
[class*=" bi-"]::before {
display: inline-block;
font-family: bootstrap-icons !important;
font-style: normal;
font-we... | transformers.js/examples/demo-site/public/css/bootstrap-icons.css/0 | {
"file_path": "transformers.js/examples/demo-site/public/css/bootstrap-icons.css",
"repo_id": "transformers.js",
"token_count": 29385
} |
/////////////////////////////////////////////////////////////////
// Worker.js file for doing all transformer-based computations //
// Needed to ensure the UI thread is not blocked when running //
/////////////////////////////////////////////////////////////////
import { pipeline, env } from "@xenova/transformers";
... | transformers.js/examples/demo-site/src/worker.js/0 | {
"file_path": "transformers.js/examples/demo-site/src/worker.js",
"repo_id": "transformers.js",
"token_count": 6373
} |
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<title>Transformers.js | Sample Electron application</title>
<!-- Load styles -->
<link rel="stylesheet" href="index.css" />
<!-- Load the client script -->
<script src="./client.js" defer></script>
</head>
<body>
<div class="contain... | transformers.js/examples/electron/src/index.html/0 | {
"file_path": "transformers.js/examples/electron/src/index.html",
"repo_id": "transformers.js",
"token_count": 220
} |
/** @type {import('next').NextConfig} */
const nextConfig = {
// (Optional) Export as a standalone site
// See https://nextjs.org/docs/pages/api-reference/next-config-js/output#automatically-copying-traced-files
output: 'standalone', // Feel free to modify/remove this option
// Indicate that these ... | transformers.js/examples/next-server/next.config.js/0 | {
"file_path": "transformers.js/examples/next-server/next.config.js",
"repo_id": "transformers.js",
"token_count": 164
} |
{
"name": "commonjs",
"version": "1.0.0",
"description": "Server-side inference with Transformers.js (CommonJS)",
"main": "app.js",
"keywords": [],
"author": "Xenova",
"license": "ISC",
"dependencies": {
"@xenova/transformers": "^2.0.0"
}
}
| transformers.js/examples/node/commonjs/package.json/0 | {
"file_path": "transformers.js/examples/node/commonjs/package.json",
"repo_id": "transformers.js",
"token_count": 109
} |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Transformers.js - Background Removal</title>
</head>
<body>
<h1>Background Removal w/ <a href="https://github.com/huggingface/transformers.js" target="_blank">🤗 Tra... | transformers.js/examples/remove-background-client/index.html/0 | {
"file_path": "transformers.js/examples/remove-background-client/index.html",
"repo_id": "transformers.js",
"token_count": 719
} |
'use client'
import Image from 'next/image'
import { downloadImage } from '../utils.js'
export function Modal({ currentImage, setCurrentImage }) {
const photo_url = currentImage ? `https://unsplash.com/photos/${currentImage.id}` : null;
const photo_image_url = currentImage ? `https://images.unsplash.com/${cur... | transformers.js/examples/semantic-image-search-client/src/app/components/Modal.jsx/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/src/app/components/Modal.jsx",
"repo_id": "transformers.js",
"token_count": 2014
} |
/** @type {import('tailwindcss').Config} */
module.exports = {
content: [
'./src/pages/**/*.{js,ts,jsx,tsx,mdx}',
'./src/components/**/*.{js,ts,jsx,tsx,mdx}',
'./src/app/**/*.{js,ts,jsx,tsx,mdx}',
],
theme: {
extend: {
boxShadow: {
highlight: 'inset 0 0 0 1px rgba(255, 255, 255, 0.1)... | transformers.js/examples/semantic-image-search/tailwind.config.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search/tailwind.config.js",
"repo_id": "transformers.js",
"token_count": 188
} |
<!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 CLIP</title>
</head>
<body>
<h1>
Real-time zero-shot image classification (WebGPU)
</h1>
<h3>
Runs locally in your browser w/... | transformers.js/examples/webgpu-clip/index.html/0 | {
"file_path": "transformers.js/examples/webgpu-clip/index.html",
"repo_id": "transformers.js",
"token_count": 417
} |
import { marked } from 'marked';
import DOMPurify from 'dompurify';
import BotIcon from './icons/BotIcon';
import UserIcon from './icons/UserIcon';
import './Chat.css';
export default function Chat({ messages }) {
const empty = messages.length === 0;
return (<div className={`flex-1 p-6 max-w-[960px] w-full ... | transformers.js/examples/webgpu-vlm/src/components/Chat.jsx/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/components/Chat.jsx",
"repo_id": "transformers.js",
"token_count": 1430
} |
# TODO: Enable once https://github.com/huggingface/optimum/pull/1552 is merged
# # 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 CLAPTextWithProjectionOnnxConfig, Audio... | transformers.js/scripts/extra/clap.py/0 | {
"file_path": "transformers.js/scripts/extra/clap.py",
"repo_id": "transformers.js",
"token_count": 684
} |
/**
* @file Helper module for using model configs. For more information, see the corresponding
* [Python documentation](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoConfig).
*
* **Example:** Load an `AutoConfig`.
*
* ```javascript
* import { AutoConfig } from '@huggingface/... | transformers.js/src/configs.js/0 | {
"file_path": "transformers.js/src/configs.js",
"repo_id": "transformers.js",
"token_count": 7215
} |
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