text stringlengths 5 409k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 572 |
|---|---|---|---|
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import use_fused_attn
from .mlp import Mlp
from .weight_init import trunc_normal_tf_
class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[boo... | pytorch-image-models/timm/layers/attention_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 1923
} | 187 |
"""
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by Chris Ha https://github.com/V... | pytorch-image-models/timm/layers/eca.py/0 | {
"file_path": "pytorch-image-models/timm/layers/eca.py",
"repo_id": "pytorch-image-models",
"token_count": 2411
} | 188 |
""" Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting
weight &... | pytorch-image-models/timm/layers/linear.py/0 | {
"file_path": "pytorch-image-models/timm/layers/linear.py",
"repo_id": "pytorch-image-models",
"token_count": 282
} | 189 |
""" Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d... | pytorch-image-models/timm/layers/separable_conv.py/0 | {
"file_path": "pytorch-image-models/timm/layers/separable_conv.py",
"repo_id": "pytorch-image-models",
"token_count": 1138
} | 190 |
import dataclasses
import logging
import os
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from torch import nn as nn
from torch.hub import load_state_dict_from_url
from timm.models._features import FeatureListNet, FeatureDictNet, FeatureHookNet... | pytorch-image-models/timm/models/_builder.py/0 | {
"file_path": "pytorch-image-models/timm/models/_builder.py",
"repo_id": "pytorch-image-models",
"token_count": 8732
} | 191 |
""" Model Registry
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
import re
import sys
import warnings
from collections import defaultdict, deque
from copy import deepcopy
from dataclasses import replace
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Sequence, Union, Tuple... | pytorch-image-models/timm/models/_registry.py/0 | {
"file_path": "pytorch-image-models/timm/models/_registry.py",
"repo_id": "pytorch-image-models",
"token_count": 5725
} | 192 |
""" EdgeNeXt
Paper: `EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications`
- https://arxiv.org/abs/2206.10589
Original code and weights from https://github.com/mmaaz60/EdgeNeXt
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
import math
from funct... | pytorch-image-models/timm/models/edgenext.py/0 | {
"file_path": "pytorch-image-models/timm/models/edgenext.py",
"repo_id": "pytorch-image-models",
"token_count": 11055
} | 193 |
""" PP-HGNet (V1 & V2)
Reference:
https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/ImageNet1k/PP-HGNetV2.md
The Paddle Implement of PP-HGNet (https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5.1/docs/en/models/PP-HGNet_en.md)
PP-HGNet: https://github.com/PaddlePaddle/PaddleClas/blob/r... | pytorch-image-models/timm/models/hgnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/hgnet.py",
"repo_id": "pytorch-image-models",
"token_count": 13177
} | 194 |
""" MobileViT
Paper:
V1: `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
V2: `Separable Self-attention for Mobile Vision Transformers` - https://arxiv.org/abs/2206.02680
MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets... | pytorch-image-models/timm/models/mobilevit.py/0 | {
"file_path": "pytorch-image-models/timm/models/mobilevit.py",
"repo_id": "pytorch-image-models",
"token_count": 12812
} | 195 |
"""PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
fro... | pytorch-image-models/timm/models/resnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/resnet.py",
"repo_id": "pytorch-image-models",
"token_count": 45896
} | 196 |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https:... | pytorch-image-models/timm/models/vision_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 85072
} | 197 |
""" Adafactor (Big Vision variant) for PyTorch
Adapted from the implementation in big vision: https://github.com/google-research/big_vision
Described in 'Scaling Vision Transformers': https://arxiv.org/abs/2106.04560
Adaptation and PyTorch modifications by Ross Wightman
"""
from typing import List, Optional, Tuple, ... | pytorch-image-models/timm/optim/adafactor_bv.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adafactor_bv.py",
"repo_id": "pytorch-image-models",
"token_count": 6213
} | 198 |
""" Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... | pytorch-image-models/timm/optim/nvnovograd.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nvnovograd.py",
"repo_id": "pytorch-image-models",
"token_count": 2509
} | 199 |
from .agc import adaptive_clip_grad
from .attention_extract import AttentionExtract
from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .decay_batch import decay_batch_step, check_batch_size_retry
from .distributed import distribute_bn,... | pytorch-image-models/timm/utils/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/utils/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 264
} | 200 |
""" Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | pytorch-image-models/timm/utils/summary.py/0 | {
"file_path": "pytorch-image-models/timm/utils/summary.py",
"repo_id": "pytorch-image-models",
"token_count": 633
} | 201 |
<!--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/en/tutorials/secure_code_execution.mdx/0 | {
"file_path": "smolagents/docs/source/en/tutorials/secure_code_execution.mdx",
"repo_id": "smolagents",
"token_count": 1304
} | 202 |
<!--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/tutorials/tools.mdx/0 | {
"file_path": "smolagents/docs/source/hi/tutorials/tools.mdx",
"repo_id": "smolagents",
"token_count": 10845
} | 203 |
# You can use most Debian-based base images
FROM e2bdev/code-interpreter:latest
# Install dependencies and customize sandbox
RUN pip install git+https://github.com/huggingface/smolagents.git | smolagents/e2b.Dockerfile/0 | {
"file_path": "smolagents/e2b.Dockerfile",
"repo_id": "smolagents",
"token_count": 57
} | 204 |
# Shamelessly stolen from Microsoft Autogen team: thanks to them for this great resource!
# https://github.com/microsoft/autogen/blob/gaia_multiagent_v01_march_1st/autogen/browser_utils.py
import copy
from smolagents.models import MessageRole, Model
def prepare_response(original_task: str, inner_messages, reformulat... | smolagents/examples/open_deep_research/scripts/reformulator.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/reformulator.py",
"repo_id": "smolagents",
"token_count": 1514
} | 205 |
#!/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/e2b_executor.py/0 | {
"file_path": "smolagents/src/smolagents/e2b_executor.py",
"repo_id": "smolagents",
"token_count": 2958
} | 206 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | smolagents/tests/test_all_docs.py/0 | {
"file_path": "smolagents/tests/test_all_docs.py",
"repo_id": "smolagents",
"token_count": 2615
} | 207 |
<div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a>
# Text Generation Inference
<a href="https://github.com/huggingface/text-generation-inference">
<... | text-generation-inference/README.md/0 | {
"file_path": "text-generation-inference/README.md",
"repo_id": "text-generation-inference",
"token_count": 4591
} | 208 |
[package]
name = "text-generation-router-llamacpp"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[build-dependencies]
bindgen = "0.71.1"
pkg-config = "0.3.31"
[dependencies]
async-trait = "0.1.85"
clap = "4.5.27"
num_cpus = "1.16.0"
text-generation-router = { pat... | text-generation-inference/backends/llamacpp/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/llamacpp/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 197
} | 209 |
import os
import shutil
import time
from typing import Optional
from huggingface_hub import snapshot_download
from huggingface_hub.constants import HF_HUB_CACHE
from loguru import logger
from transformers import AutoConfig
from optimum.neuron import NeuronModelForCausalLM
from optimum.neuron.utils import get_hub_cach... | text-generation-inference/backends/neuron/server/text_generation_server/model.py/0 | {
"file_path": "text-generation-inference/backends/neuron/server/text_generation_server/model.py",
"repo_id": "text-generation-inference",
"token_count": 1869
} | 210 |
[package]
name = "text-generation-backends-trtllm"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
async-trait = "0.1"
clap = { version = "4.5", features = ["derive"] }
cxx = "1.0"
hashbrown = "0.15"
hf-hub = { workspace = true }
text-generation-route... | text-generation-inference/backends/trtllm/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/trtllm/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 275
} | 211 |
use std::path::{Path, PathBuf};
use clap::Parser;
use hf_hub::api::tokio::{Api, ApiBuilder};
use hf_hub::{Cache, Repo, RepoType};
use tracing::info;
use text_generation_backends_trtllm::errors::TensorRtLlmBackendError;
use text_generation_backends_trtllm::TensorRtLlmBackendV2;
use text_generation_router::server::{
... | text-generation-inference/backends/trtllm/src/main.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 5530
} | 212 |
/// Batching and inference logic
use crate::client::{
Batch, CachedBatch, ClientError, Generation, Health, InfoResponse, ShardedClient,
};
use crate::queue::{Entry, Queue};
use async_trait::async_trait;
use nohash_hasher::IntMap;
use std::sync::Arc;
use text_generation_router::infer::{Backend, GeneratedText, InferE... | text-generation-inference/backends/v3/src/backend.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/backend.rs",
"repo_id": "text-generation-inference",
"token_count": 11182
} | 213 |
use crate::app::Data;
use tabled::settings::Merge;
use tabled::{builder::Builder, settings::Style, Table};
#[allow(clippy::too_many_arguments)]
pub(crate) fn parameters_table(
tokenizer_name: String,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: us... | text-generation-inference/benchmark/src/table.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/table.rs",
"repo_id": "text-generation-inference",
"token_count": 2288
} | 214 |
from enum import Enum
from pydantic import BaseModel, field_validator, ConfigDict
from typing import Optional, List, Union, Any
from text_generation.errors import ValidationError
# enum for grammar type
class GrammarType(str, Enum):
Json = "json"
Regex = "regex"
# Grammar type and value
class Grammar(BaseM... | text-generation-inference/clients/python/text_generation/types.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/types.py",
"repo_id": "text-generation-inference",
"token_count": 5255
} | 215 |
# Train Medusa
This tutorial will show you how to train a Medusa model on a dataset of your choice. Please check out the [speculation documentation](../conceptual/speculation) for more information on how Medusa works and speculation in general.
## What are the benefits of training a Medusa model?
Training Medusa hea... | text-generation-inference/docs/source/basic_tutorials/train_medusa.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/train_medusa.md",
"repo_id": "text-generation-inference",
"token_count": 3478
} | 216 |
# Installation from source
<Tip warning={true}>
Installing TGI from source is not the recommended usage. We strongly recommend to use TGI through Docker, check the [Quick Tour](./quicktour), [Installation for Nvidia GPUs](./installation_nvidia) and [Installation for AMD GPUs](./installation_amd) to learn how to use T... | text-generation-inference/docs/source/installation.md/0 | {
"file_path": "text-generation-inference/docs/source/installation.md",
"repo_id": "text-generation-inference",
"token_count": 727
} | 217 |
pytest_plugins = ["fixtures.neuron.service", "fixtures.neuron.model"]
# ruff: noqa: E402
from _pytest.fixtures import SubRequest
import requests
class SessionTimeoutFix(requests.Session):
def request(self, *args, **kwargs):
timeout = kwargs.pop("timeout", 120)
return super().request(*args, **kwarg... | text-generation-inference/integration-tests/conftest.py/0 | {
"file_path": "text-generation-inference/integration-tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 12036
} | 218 |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": " the royal mouse? It is a little more slender and only weighs around 1.5 pounds for males and 1.3 pounds",
"role": "assistant"
}
}
],
"created": 1732541190,
"i... | text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt_continue.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt_continue.json",
"repo_id": "text-generation-inference",
"token_count": 260
} | 219 |
{
"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
} | 220 |
{
"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
} | 221 |
{
"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
} | 222 |
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_wna16_int_24_handle(launcher):
with launcher(
"danieldk/Llama-3.1-8B-w4a16-int-24",
num_shard=2,
quantize="compressed-tensors",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def compressed_... | text-generation-inference/integration-tests/models/test_compressed_tensors_wna16_int_24.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_compressed_tensors_wna16_int_24.py",
"repo_id": "text-generation-inference",
"token_count": 1080
} | 223 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_marlin_handle(launcher):
with launcher(
"neuralmagic/llama-2-7b-chat-marlin", num_shard=2, quantize="marlin"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_marlin(flash_llama_marlin_handle):
aw... | text-generation-inference/integration-tests/models/test_flash_llama_marlin.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_marlin.py",
"repo_id": "text-generation-inference",
"token_count": 748
} | 224 |
import pytest
@pytest.fixture(scope="module")
def flash_qwen2_5_vl_handle(launcher):
with launcher("Qwen/Qwen2.5-VL-3B-Instruct") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_qwen2_5(flash_qwen2_5_vl_handle):
await flash_qwen2_5_vl_handle.health(300)
return flash_qwen2... | text-generation-inference/integration-tests/models/test_flash_qwen2_5_vl.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_qwen2_5_vl.py",
"repo_id": "text-generation-inference",
"token_count": 2258
} | 225 |
import pytest
@pytest.fixture(scope="module")
def mpt_sharded_handle(launcher):
with launcher("mosaicml/mpt-7b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def mpt_sharded(mpt_sharded_handle):
await mpt_sharded_handle.health(300)
return mpt_sharded_handle.client
... | text-generation-inference/integration-tests/models/test_mpt.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_mpt.py",
"repo_id": "text-generation-inference",
"token_count": 541
} | 226 |
use std::fmt;
use std::process::Command;
pub(crate) struct Env {
cargo_target: &'static str,
cargo_version: &'static str,
git_sha: &'static str,
docker_label: &'static str,
nvidia_env: String,
xpu_env: String,
}
impl Env {
pub fn new() -> Self {
let nvidia_env = nvidia_smi();
... | text-generation-inference/launcher/src/env_runtime.rs/0 | {
"file_path": "text-generation-inference/launcher/src/env_runtime.rs",
"repo_id": "text-generation-inference",
"token_count": 861
} | 227 |
{
lib,
mkShell,
black,
cmake,
isort,
ninja,
which,
cudaPackages,
openssl,
pkg-config,
poetry,
protobuf,
python3,
pyright,
redocly,
ruff,
rust-bin,
server,
# Enable dependencies for building CUDA packages. Useful for e.g.
# developing marlin/moe-kernels in-place.
withCuda ? fal... | text-generation-inference/nix/impure-shell.nix/0 | {
"file_path": "text-generation-inference/nix/impure-shell.nix",
"repo_id": "text-generation-inference",
"token_count": 1111
} | 228 |
/// HTTP Server logic
use crate::config::Config;
use crate::infer::{Backend, Infer, InferError, InferResponse, InferStreamResponse};
#[cfg(feature = "kserve")]
use crate::kserve::{
kerve_server_metadata, kserve_health_live, kserve_health_ready, kserve_model_infer,
kserve_model_metadata, kserve_model_metadata_re... | text-generation-inference/router/src/server.rs/0 | {
"file_path": "text-generation-inference/router/src/server.rs",
"repo_id": "text-generation-inference",
"token_count": 48354
} | 229 |
commit_rocm := de990cd12537f78f74e40b5c8ee1a62d63d734dd
build-vllm-rocm:
if [ ! -d 'vllm' ]; then \
pip install -U ninja packaging --no-cache-dir && \
git clone https://github.com/mht-sharma/vllm.git vllm; \
fi
cd vllm && git fetch && git checkout $(commit_rocm) && \
PYTORCH_ROCM_ARCH="gfx90a;gfx942" python s... | text-generation-inference/server/Makefile-vllm/0 | {
"file_path": "text-generation-inference/server/Makefile-vllm",
"repo_id": "text-generation-inference",
"token_count": 221
} | 230 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _hip_compat_cuh
#define _hip_compat_cuh
// Workaround for a bug in hipamd, backported from upstream, this is fixed in ROCm 5.6.
__device__ __forceinline__ __half __compat_hrcp(__half x) {
return __half_raw{
static_cast<_Float1... | text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh",
"repo_id": "text-generation-inference",
"token_count": 1710
} | 231 |
#ifndef _qdq_3_cuh
#define _qdq_3_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_3BIT == 1
// Permutation:
//
// v9997775 55333111 u8886664 44222000 (u, v lsb)
// vjjjhhhf ffdddbbb uiiiggge eecccaaa
// vtttrrrp ppnnnlll usssqqqo oommmkkk
__forceinline__ __device__ void shuffle_3bit_32
(
uin... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh",
"repo_id": "text-generation-inference",
"token_count": 3335
} | 232 |
import pytest
import os
from text_generation_server.pb import generate_pb2
os.environ["PREFIX_CACHING"] = "1"
os.environ["ATTENTION"] = "flashinfer"
@pytest.fixture
def default_pb_parameters():
return generate_pb2.NextTokenChooserParameters(
temperature=1.0,
repetition_penalty=1.0,
top_k=... | text-generation-inference/server/tests/conftest.py/0 | {
"file_path": "text-generation-inference/server/tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 235
} | 233 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/lora.py
# License: Apache License Version 2.0, January 2004
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Type, Union
from loguru import logger
im... | text-generation-inference/server/text_generation_server/adapters/lora.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/adapters/lora.py",
"repo_id": "text-generation-inference",
"token_count": 8265
} | 234 |
# Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
from typing import Optional
import torch
import torch.nn as nn
import awq_inference_engine # with CUDA kernels
# class ScaledActivation(nn.Module):
# def __init__(self, module, scales... | text-generation-inference/server/text_generation_server/layers/awq/quantize/cuda.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/awq/quantize/cuda.py",
"repo_id": "text-generation-inference",
"token_count": 750
} | 235 |
# Adapted from turboderp exllama: https://github.com/turboderp/exllamav2
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
from loguru import logger
from text_generation_server.layers.exl2 import Exl2Weight
from text_generation_server.layers.gptq import GPTQWeight
from ... | text-generation-inference/server/text_generation_server/layers/gptq/exllamav2.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/gptq/exllamav2.py",
"repo_id": "text-generation-inference",
"token_count": 3935
} | 236 |
from typing import Optional
import torch
import torch.nn as nn
from text_generation_server.utils.weights import Weights
from text_generation_server.layers.fp8 import (
Fp8Weight,
fp8_quantize,
quant_dtype,
normalize_e4m3fn_to_native_float8,
)
try:
from .unquantized import fused_moe
except Excepti... | text-generation-inference/server/text_generation_server/layers/moe/fp8.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/moe/fp8.py",
"repo_id": "text-generation-inference",
"token_count": 2685
} | 237 |
# 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/idefics2.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics2.py",
"repo_id": "text-generation-inference",
"token_count": 15298
} | 238 |
# 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/qwen2_vl.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/qwen2_vl.py",
"repo_id": "text-generation-inference",
"token_count": 10239
} | 239 |
import torch
from PIL import Image
from io import BytesIO
from opentelemetry import trace
from typing import Iterable, Optional, Tuple, List, Type, Dict
from transformers import PreTrainedTokenizerBase
from transformers.image_processing_utils import select_best_resolution
from text_generation_server.pb import generat... | text-generation-inference/server/text_generation_server/models/vlm_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/vlm_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 10527
} | 240 |
import os
from typing import Union
from loguru import logger
import torch
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
def download_and_unload_peft(model_id, revision, trust_remote_code):
torch_dtype = torch.float16
logger.info("Trying to load a... | text-generation-inference/server/text_generation_server/utils/peft.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/peft.py",
"repo_id": "text-generation-inference",
"token_count": 981
} | 241 |
# EditorConfig helps developers define and maintain consistent
# coding styles between different editors or IDEs
# http://editorconfig.org
root = true
[*]
indent_style = space
indent_size = 2
end_of_line = lf
charset = utf-8
trim_trailing_whitespace = true
insert_final_newline = true
[*.md]
trim_trailing_whitespace =... | tokenizers/bindings/node/.editorconfig/0 | {
"file_path": "tokenizers/bindings/node/.editorconfig",
"repo_id": "tokenizers",
"token_count": 108
} | 242 |
/* tslint:disable */
/* eslint-disable */
/* prettier-ignore */
/* auto-generated by NAPI-RS */
const { existsSync, readFileSync } = require('fs')
const { join } = require('path')
const { platform, arch } = process
let nativeBinding = null
let localFileExisted = false
let loadError = null
function isMusl() {
// ... | tokenizers/bindings/node/index.js/0 | {
"file_path": "tokenizers/bindings/node/index.js",
"repo_id": "tokenizers",
"token_count": 5374
} | 243 |
{
"name": "tokenizers-android-arm64",
"version": "0.13.4-rc1",
"os": [
"android"
],
"cpu": [
"arm64"
],
"main": "tokenizers.android-arm64.node",
"files": [
"tokenizers.android-arm64.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI"... | tokenizers/bindings/node/npm/android-arm64/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/android-arm64/package.json",
"repo_id": "tokenizers",
"token_count": 264
} | 244 |
{
"name": "tokenizers-linux-x64-musl",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"x64"
],
"main": "tokenizers.linux-x64-musl.node",
"files": [
"tokenizers.linux-x64-musl.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"NAPI",... | tokenizers/bindings/node/npm/linux-x64-musl/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-x64-musl/package.json",
"repo_id": "tokenizers",
"token_count": 291
} | 245 |
use crate::arc_rwlock_serde;
use serde::{Deserialize, Serialize};
extern crate tokenizers as tk;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use std::sync::{Arc, RwLock};
use tk::processors::PostProcessorWrapper;
use tk::Encoding;
#[derive(Clone, Serialize, Deserialize)]
#[napi]
pub struct Processor {
#[se... | tokenizers/bindings/node/src/processors.rs/0 | {
"file_path": "tokenizers/bindings/node/src/processors.rs",
"repo_id": "tokenizers",
"token_count": 1336
} | 246 |
<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/py/tokenizers">
<img alt="Build" src="https://badge.fury.io/py/tokenizers.svg">
</a>
<a href="https://github.c... | tokenizers/bindings/python/README.md/0 | {
"file_path": "tokenizers/bindings/python/README.md",
"repo_id": "tokenizers",
"token_count": 1621
} | 247 |
from typing import Dict, Iterator, List, Optional, Tuple, Union
from tokenizers import AddedToken, Tokenizer, decoders, pre_tokenizers, processors, trainers
from tokenizers.models import BPE
from tokenizers.normalizers import Lowercase, Sequence, unicode_normalizer_from_str
from .base_tokenizer import BaseTokenizer
... | tokenizers/bindings/python/py_src/tokenizers/implementations/byte_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/byte_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 1978
} | 248 |
# Generated content DO NOT EDIT
class Trainer:
"""
Base class for all trainers
This class is not supposed to be instantiated directly. Instead, any implementation of a
Trainer will return an instance of this class when instantiated.
"""
class BpeTrainer(Trainer):
"""
Trainer capable of tra... | tokenizers/bindings/python/py_src/tokenizers/trainers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/trainers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 2178
} | 249 |
use serde::Serialize;
use std::collections::{hash_map::DefaultHasher, HashMap};
use std::hash::{Hash, Hasher};
use numpy::{npyffi, PyArray1, PyArrayMethods};
use pyo3::class::basic::CompareOp;
use pyo3::exceptions;
use pyo3::intern;
use pyo3::prelude::*;
use pyo3::types::*;
use tk::models::bpe::BPE;
use tk::tokenizer:... | tokenizers/bindings/python/src/tokenizer.rs/0 | {
"file_path": "tokenizers/bindings/python/src/tokenizer.rs",
"repo_id": "tokenizers",
"token_count": 28074
} | 250 |
import json
import pickle
import pytest
from tokenizers.pre_tokenizers import (
BertPreTokenizer,
ByteLevel,
CharDelimiterSplit,
Digits,
Metaspace,
PreTokenizer,
Punctuation,
Sequence,
Split,
UnicodeScripts,
Whitespace,
WhitespaceSplit,
)
class TestByteLevel:
def ... | tokenizers/bindings/python/tests/bindings/test_pre_tokenizers.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_pre_tokenizers.py",
"repo_id": "tokenizers",
"token_count": 5390
} | 251 |
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for those with `?=`
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
BUILDDIR ?= build
SOURCEDIR = source
# Put it first so that "make" without argument is like "make html_all".
h... | tokenizers/docs/Makefile/0 | {
"file_path": "tokenizers/docs/Makefile",
"repo_id": "tokenizers",
"token_count": 393
} | 252 |
<!-- DISABLE-FRONTMATTER-SECTIONS -->
# Tokenizers
Fast State-of-the-art tokenizers, optimized for both research and
production
[🤗 Tokenizers](https://github.com/huggingface/tokenizers) provides an
implementation of today's most used tokenizers, with a focus on
performance and versatility. These tokenizers are also... | tokenizers/docs/source-doc-builder/index.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/index.mdx",
"repo_id": "tokenizers",
"token_count": 250
} | 253 |
Input sequences
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
These types represent all the different kinds of sequence that can be used as input of a Tokenizer.
Globally, any sequence can be either a string or a list of strings, according to the operating
mode of... | tokenizers/docs/source/api/python.inc/0 | {
"file_path": "tokenizers/docs/source/api/python.inc",
"repo_id": "tokenizers",
"token_count": 562
} | 254 |
pub fn set_panic_hook() {
// When the `console_error_panic_hook` feature is enabled, we can call the
// `set_panic_hook` function at least once during initialization, and then
// we will get better error messages if our code ever panics.
//
// For more details see
// https://github.com/rustwasm/... | tokenizers/tokenizers/examples/unstable_wasm/src/utils.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/src/utils.rs",
"repo_id": "tokenizers",
"token_count": 150
} | 255 |
use crate::tokenizer::{Decoder, Result};
use monostate::MustBe;
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize, Default)]
/// ByteFallback is a simple trick which converts tokens looking like `<0x61>`
/// to pure bytes, and attempts to make them into a string. If the tokens
/// can... | tokenizers/tokenizers/src/decoders/byte_fallback.rs/0 | {
"file_path": "tokenizers/tokenizers/src/decoders/byte_fallback.rs",
"repo_id": "tokenizers",
"token_count": 1938
} | 256 |
use super::{
lattice::Lattice,
trainer::UnigramTrainer,
trie::{Trie, TrieBuilder},
};
use crate::tokenizer::{Model, Result, Token};
use crate::utils::cache::{Cache, MAX_LENGTH};
use std::collections::HashMap;
use std::convert::TryInto;
use std::fs::read_to_string;
use std::path::{Path, PathBuf};
type Toke... | tokenizers/tokenizers/src/models/unigram/model.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/model.rs",
"repo_id": "tokenizers",
"token_count": 12037
} | 257 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
use unicode_normalization_alignments::char::is_combining_mark;
#[derive(Copy, Clone, Debug, Deserialize, Serialize)]
#[serde(tag = "type")]
#[non_exhaustive]
pub struct Strip {
... | tokenizers/tokenizers/src/normalizers/strip.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/strip.rs",
"repo_id": "tokenizers",
"token_count": 2512
} | 258 |
use crate::tokenizer::{Encoding, PostProcessor, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::iter::FromIterator;
#[derive(Serialize, Deserialize, Clone, Debug, PartialEq, Eq)]
#[serde(tag = "type")]
pub struct BertProcessing {
pub sep: (String, u32),
pub cls: (String, u... | tokenizers/tokenizers/src/processors/bert.rs/0 | {
"file_path": "tokenizers/tokenizers/src/processors/bert.rs",
"repo_id": "tokenizers",
"token_count": 7483
} | 259 |
pub(crate) mod cache;
#[cfg(feature = "http")]
pub(crate) mod from_pretrained;
#[cfg(feature = "unstable_wasm")]
mod fancy;
#[cfg(feature = "unstable_wasm")]
pub use fancy::SysRegex;
#[cfg(not(feature = "unstable_wasm"))]
mod onig;
#[cfg(not(feature = "unstable_wasm"))]
pub use crate::utils::onig::SysRegex;
pub mod i... | tokenizers/tokenizers/src/utils/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/mod.rs",
"repo_id": "tokenizers",
"token_count": 3092
} | 260 |
It's super simple to translate from existing code! Just like the python library, we support the `pipeline` API. Pipelines group together a pretrained model with preprocessing of inputs and postprocessing of outputs, making it the easiest way to run models with the library.
<table>
<tr>
<th width="440px" align="center... | transformers.js/docs/snippets/1_quick-tour.snippet/0 | {
"file_path": "transformers.js/docs/snippets/1_quick-tour.snippet",
"repo_id": "transformers.js",
"token_count": 837
} | 261 |
# Building a browser extension
*Full tutorial coming soon...* In the meantime, check out the example application: https://github.com/huggingface/transformers.js/tree/main/examples/extension
| transformers.js/docs/source/tutorials/browser-extension.md/0 | {
"file_path": "transformers.js/docs/source/tutorials/browser-extension.md",
"repo_id": "transformers.js",
"token_count": 50
} | 262 |
import { env, pipeline } from '@xenova/transformers';
// Skip local model check since we are downloading the model from the Hugging Face Hub.
env.allowLocalModels = false;
class MyFeatureExtractionPipeline {
static task = 'feature-extraction';
static model = 'nomic-ai/nomic-embed-text-v1.5';
static instan... | transformers.js/examples/adaptive-retrieval/src/worker.js/0 | {
"file_path": "transformers.js/examples/adaptive-retrieval/src/worker.js",
"repo_id": "transformers.js",
"token_count": 595
} | 263 |
{
"name": "demo-site",
"private": true,
"version": "0.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "vite build",
"preview": "vite preview"
},
"devDependencies": {
"vite": "^4.3.2"
},
"dependencies": {
"@xenova/transformers": "^2.0.0-alpha.3",
"chart.js": "^4.3.0... | transformers.js/examples/demo-site/package.json/0 | {
"file_path": "transformers.js/examples/demo-site/package.json",
"repo_id": "transformers.js",
"token_count": 183
} | 264 |
{
"name": "electron",
"version": "1.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "electron",
"version": "1.0.0",
"license": "MIT",
"dependencies": {
"@xenova/transformers": "^2.6.2",
"electron-squirrel-startup": "^1.0.0"
},
"... | transformers.js/examples/electron/package-lock.json/0 | {
"file_path": "transformers.js/examples/electron/package-lock.json",
"repo_id": "transformers.js",
"token_count": 127856
} | 265 |
import { SamModel, AutoProcessor, RawImage, Tensor } from '@huggingface/transformers';
// We adopt the singleton pattern to enable lazy-loading of the model and processor.
export class SegmentAnythingSingleton {
static model_id = 'Xenova/slimsam-77-uniform';
static model;
static processor;
static getI... | transformers.js/examples/segment-anything-client/worker.js/0 | {
"file_path": "transformers.js/examples/segment-anything-client/worker.js",
"repo_id": "transformers.js",
"token_count": 1374
} | 266 |
module.exports = {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
}
| transformers.js/examples/semantic-image-search-client/postcss.config.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/postcss.config.js",
"repo_id": "transformers.js",
"token_count": 38
} | 267 |
import './globals.css'
import { Inter } from 'next/font/google'
const inter = Inter({ subsets: ['latin'] })
export const metadata = {
title: 'Semantic Image Search',
description: 'Search for images using text (built w/ Transformers.js and Supabase)',
}
export default function RootLayout({ children }) {
return ... | transformers.js/examples/semantic-image-search/src/app/layout.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search/src/app/layout.js",
"repo_id": "transformers.js",
"token_count": 139
} | 268 |
import {
AutoTokenizer,
AutoModelForCausalLM,
TextStreamer,
StoppingCriteria,
} from '@xenova/transformers';
class CallbackTextStreamer extends TextStreamer {
constructor(tokenizer, cb) {
super(tokenizer, {
skip_prompt: true,
skip_special_tokens: true,
});
... | transformers.js/examples/webgpu-chat/src/worker.js/0 | {
"file_path": "transformers.js/examples/webgpu-chat/src/worker.js",
"repo_id": "transformers.js",
"token_count": 1857
} | 269 |
<!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 background removal</title>
</head>
<body>
<h1>
Real-time background removal w/
<a href="https://github.com/huggingface/transforme... | transformers.js/examples/webgpu-video-background-removal/index.html/0 | {
"file_path": "transformers.js/examples/webgpu-video-background-removal/index.html",
"repo_id": "transformers.js",
"token_count": 553
} | 270 |
import {
AutoTokenizer,
AutoProcessor,
WhisperForConditionalGeneration,
TextStreamer,
full,
} from '@huggingface/transformers';
const MAX_NEW_TOKENS = 64;
/**
* This class uses the Singleton pattern to ensure that only one instance of the model is loaded.
*/
class AutomaticSpeechRecognitionPip... | transformers.js/examples/webgpu-whisper/src/worker.js/0 | {
"file_path": "transformers.js/examples/webgpu-whisper/src/worker.js",
"repo_id": "transformers.js",
"token_count": 1466
} | 271 |
import { pipeline } from '@xenova/transformers';
const PER_DEVICE_CONFIG = {
webgpu: {
dtype: {
encoder_model: 'fp32',
decoder_model_merged: 'q4',
},
device: 'webgpu',
},
wasm: {
dtype: 'q8',
device: 'wasm',
},
};
/**
* This class uses ... | transformers.js/examples/whisper-word-timestamps/src/worker.js/0 | {
"file_path": "transformers.js/examples/whisper-word-timestamps/src/worker.js",
"repo_id": "transformers.js",
"token_count": 970
} | 272 |
/*
* For a detailed explanation regarding each configuration property, visit:
* https://jestjs.io/docs/configuration
*/
export default {
// All imported modules in your tests should be mocked automatically
// automock: false,
// Stop running tests after `n` failures
// bail: 0,
// Automatically clear mo... | transformers.js/jest.config.mjs/0 | {
"file_path": "transformers.js/jest.config.mjs",
"repo_id": "transformers.js",
"token_count": 1658
} | 273 |
/**
* @file Handler file for choosing the correct version of ONNX Runtime, based on the environment.
* Ideally, we could import the `onnxruntime-web` and `onnxruntime-node` packages only when needed,
* but dynamic imports don't seem to work with the current webpack version and/or configuration.
* This is possibly d... | transformers.js/src/backends/onnx.js/0 | {
"file_path": "transformers.js/src/backends/onnx.js",
"repo_id": "transformers.js",
"token_count": 3082
} | 274 |
import { IMAGE_PROCESSOR_NAME } from '../../utils/constants.js';
import { getModelJSON } from '../../utils/hub.js';
import { Processor } from '../../base/processing_utils.js';
import * as AllProcessors from '../processors.js';
import * as AllImageProcessors from '../image_processors.js';
import * as AllFeatureExtrac... | transformers.js/src/models/auto/processing_auto.js/0 | {
"file_path": "transformers.js/src/models/auto/processing_auto.js",
"repo_id": "transformers.js",
"token_count": 1334
} | 275 |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
import { center_to_corners_format } from "../../base/image_processors_utils.js";
/**
* Get token ids of phrases from posmaps and input... | transformers.js/src/models/grounding_dino/processing_grounding_dino.js/0 | {
"file_path": "transformers.js/src/models/grounding_dino/processing_grounding_dino.js",
"repo_id": "transformers.js",
"token_count": 1714
} | 276 |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
export class MobileViTImageProcessor extends ImageProcessor { }
export class MobileViTFeatureExtractor extends MobileViTImageProcessor { }
| transformers.js/src/models/mobilevit/image_processing_mobilevit.js/0 | {
"file_path": "transformers.js/src/models/mobilevit/image_processing_mobilevit.js",
"repo_id": "transformers.js",
"token_count": 64
} | 277 |
import {
ImageProcessor,
post_process_object_detection,
} from "../../base/image_processors_utils.js";
export class RTDetrImageProcessor extends ImageProcessor {
/** @type {typeof post_process_object_detection} */
post_process_object_detection(...args) {
return post_process_object_detection(.... | transformers.js/src/models/rt_detr/image_processing_rt_detr.js/0 | {
"file_path": "transformers.js/src/models/rt_detr/image_processing_rt_detr.js",
"repo_id": "transformers.js",
"token_count": 122
} | 278 |
import { FeatureExtractor, validate_audio_inputs } from '../../base/feature_extraction_utils.js';
import { Tensor } from '../../utils/tensor.js';
import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio.js';
export class WeSpeakerFeatureExtractor extends FeatureExtractor {
constructor(con... | transformers.js/src/models/wespeaker/feature_extraction_wespeaker.js/0 | {
"file_path": "transformers.js/src/models/wespeaker/feature_extraction_wespeaker.js",
"repo_id": "transformers.js",
"token_count": 1713
} | 279 |
/**
* A base class for creating callable objects.
* See [here](https://stackoverflow.com/q/76073890) for more information.
*
* @type {new () => {(...args: any[]): any, _call(...args: any[]): any}}
*/
export const Callable = /** @type {any} */ (class {
/**
* Creates a new instance of the Callable class.
... | transformers.js/src/utils/generic.js/0 | {
"file_path": "transformers.js/src/utils/generic.js",
"repo_id": "transformers.js",
"token_count": 439
} | 280 |
import { AutoImageProcessor, DonutFeatureExtractor } from "../../../src/transformers.js";
import { load_cached_image } from "../../asset_cache.js";
import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
// DonutFeatureExtractor
// - tests thumbnail resizing (do_t... | transformers.js/tests/models/donut/test_image_processing_donut.js/0 | {
"file_path": "transformers.js/tests/models/donut/test_image_processing_donut.js",
"repo_id": "transformers.js",
"token_count": 493
} | 281 |
import { PaliGemmaProcessor, PaliGemmaForConditionalGeneration, RawImage } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
const text = "<image>What is on the flower?";
// Empt... | transformers.js/tests/models/paligemma/test_modeling_paligemma.js/0 | {
"file_path": "transformers.js/tests/models/paligemma/test_modeling_paligemma.js",
"repo_id": "transformers.js",
"token_count": 790
} | 282 |
import { T5Tokenizer, T5Model, T5ForConditionalGeneration } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
describe("T5Model", () => {
const model_id = "hf-internal-testing/t... | transformers.js/tests/models/t5/test_modeling_t5.js/0 | {
"file_path": "transformers.js/tests/models/t5/test_modeling_t5.js",
"repo_id": "transformers.js",
"token_count": 1496
} | 283 |
import { pipeline, AudioClassificationPipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
const PIPELINE_ID = "audio-classification";
export default () => {
describe("Audio Classification", () => {
... | transformers.js/tests/pipelines/test_pipelines_audio_classification.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_audio_classification.js",
"repo_id": "transformers.js",
"token_count": 1277
} | 284 |
import { pipeline, TextGenerationPipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
const PIPELINE_ID = "text-generation";
export default () => {
describe("Text Generation", () => {
const model_i... | transformers.js/tests/pipelines/test_pipelines_text_generation.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_text_generation.js",
"repo_id": "transformers.js",
"token_count": 1602
} | 285 |
# 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 applicabl... | transformers/benchmark/benchmark.py/0 | {
"file_path": "transformers/benchmark/benchmark.py",
"repo_id": "transformers",
"token_count": 5440
} | 286 |
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