text stringlengths 5 409k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 572 |
|---|---|---|---|
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794 and https://arxiv.org/pdf/2208.07220
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
... | pytorch-image-models/timm/layers/patch_dropout.py/0 | {
"file_path": "pytorch-image-models/timm/layers/patch_dropout.py",
"repo_id": "pytorch-image-models",
"token_count": 858
} | 187 |
import torch
import math
import warnings
from torch import nn
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presen... | pytorch-image-models/timm/layers/weight_init.py/0 | {
"file_path": "pytorch-image-models/timm/layers/weight_init.py",
"repo_id": "pytorch-image-models",
"token_count": 2577
} | 188 |
import copy
from collections import deque, defaultdict
from dataclasses import dataclass, field, replace, asdict
from typing import Any, Deque, Dict, Tuple, Optional, Union
__all__ = ['PretrainedCfg', 'filter_pretrained_cfg', 'DefaultCfg']
@dataclass
class PretrainedCfg:
"""
"""
# weight source location... | pytorch-image-models/timm/models/_pretrained.py/0 | {
"file_path": "pytorch-image-models/timm/models/_pretrained.py",
"repo_id": "pytorch-image-models",
"token_count": 1341
} | 189 |
""" CrossViT Model
@inproceedings{
chen2021crossvit,
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
booktitle={International Conference on Computer Vision (ICCV)},
year={2021}
}
Paper l... | pytorch-image-models/timm/models/crossvit.py/0 | {
"file_path": "pytorch-image-models/timm/models/crossvit.py",
"repo_id": "pytorch-image-models",
"token_count": 12489
} | 190 |
# NOTE timm.models.layers is DEPRECATED, please use timm.layers, this is here to reduce breakages in transition
from timm.layers.activations import *
from timm.layers.adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from timm.layers.attention_p... | pytorch-image-models/timm/models/layers/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/models/layers/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 1220
} | 191 |
"""
RDNet
Copyright (c) 2024-present NAVER Cloud Corp.
Apache-2.0
"""
from functools import partial
from typing import List, Optional, Tuple, Union, Callable
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, NormMlpClassifierHead, C... | pytorch-image-models/timm/models/rdnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/rdnet.py",
"repo_id": "pytorch-image-models",
"token_count": 9340
} | 192 |
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... | pytorch-image-models/timm/models/swin_transformer_v2_cr.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2_cr.py",
"repo_id": "pytorch-image-models",
"token_count": 21262
} | 193 |
""" Cross-Covariance Image Transformer (XCiT) in PyTorch
Paper:
- https://arxiv.org/abs/2106.09681
Same as the official implementation, with some minor adaptations, original copyright below
- https://github.com/facebookresearch/xcit/blob/master/xcit.py
Modifications and additions for timm hacked together by ... | pytorch-image-models/timm/models/xcit.py/0 | {
"file_path": "pytorch-image-models/timm/models/xcit.py",
"repo_id": "pytorch-image-models",
"token_count": 20442
} | 194 |
""" PyTorch LARS / LARC Optimizer
An implementation of LARS (SGD) + LARC in PyTorch
Based on:
* PyTorch SGD: https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
* NVIDIA APEX LARC: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
Additional cleanup and modifications to properly su... | pytorch-image-models/timm/optim/lars.py/0 | {
"file_path": "pytorch-image-models/timm/optim/lars.py",
"repo_id": "pytorch-image-models",
"token_count": 2549
} | 195 |
""" MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List
class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
... | pytorch-image-models/timm/scheduler/multistep_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/multistep_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 1036
} | 196 |
""" Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
if record.levelno =... | pytorch-image-models/timm/utils/log.py/0 | {
"file_path": "pytorch-image-models/timm/utils/log.py",
"repo_id": "pytorch-image-models",
"token_count": 383
} | 197 |
<!--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/index.mdx/0 | {
"file_path": "smolagents/docs/source/en/index.mdx",
"repo_id": "smolagents",
"token_count": 1250
} | 198 |
<!--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/guided_tour.mdx/0 | {
"file_path": "smolagents/docs/source/hi/guided_tour.mdx",
"repo_id": "smolagents",
"token_count": 16513
} | 199 |
<!--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/index.mdx/0 | {
"file_path": "smolagents/docs/source/zh/index.mdx",
"repo_id": "smolagents",
"token_count": 1807
} | 200 |
<jupyter_start><jupyter_code># !pip install plotly kaleido datasets nbformat -U -q
import os
import datasets
import pandas as pd
from dotenv import load_dotenv
from huggingface_hub import login
load_dotenv(override=True)
login(os.getenv("HF_TOKEN"))
pd.set_option("max_colwidth", None)
OUTPUT_DIR = "output"
eval_ds... | smolagents/examples/open_deep_research/analysis.ipynb/0 | {
"file_path": "smolagents/examples/open_deep_research/analysis.ipynb",
"repo_id": "smolagents",
"token_count": 3691
} | 201 |
[build-system]
requires = ["setuptools"]
build-backend = "setuptools.build_meta"
[project]
name = "smolagents"
version = "1.10.0.dev0"
description = "🤗 smolagents: a barebones library for agents. Agents write python code to call tools or orchestrate other agents."
authors = [
{ name="Aymeric Roucher", email="aymeri... | smolagents/pyproject.toml/0 | {
"file_path": "smolagents/pyproject.toml",
"repo_id": "smolagents",
"token_count": 1021
} | 202 |
#!/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/tools.py/0 | {
"file_path": "smolagents/src/smolagents/tools.py",
"repo_id": "smolagents",
"token_count": 17933
} | 203 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | smolagents/tests/test_models.py/0 | {
"file_path": "smolagents/tests/test_models.py",
"repo_id": "smolagents",
"token_count": 4862
} | 204 |
# Build the image and get out the docker file:
#
# docker build -t tgi-nix-builder -f Dockerfile.nix
# docker run --log-driver=none tgi-nix-builder | docker load
FROM nixos/nix:2.18.8 AS builder
RUN echo "experimental-features = nix-command flakes" >> /etc/nix/nix.conf
RUN nix profile install nixpkgs#cachix
RUN cachix... | text-generation-inference/Dockerfile.nix/0 | {
"file_path": "text-generation-inference/Dockerfile.nix",
"repo_id": "text-generation-inference",
"token_count": 268
} | 205 |
#[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
} | 206 |
from helpers import create_request
from text_generation_server.generator import NeuronGenerator
from text_generation_server.pb.generate_pb2 import Batch
def test_decode(neuron_model_config):
"""Verify that a decoding for a single request generates the expected output."""
config_name = neuron_model_config["nam... | text-generation-inference/backends/neuron/tests/server/test_decode.py/0 | {
"file_path": "text-generation-inference/backends/neuron/tests/server/test_decode.py",
"repo_id": "text-generation-inference",
"token_count": 1018
} | 207 |
#ifndef TGI_BACKEND_TRTLLM_FFI
#define TGI_BACKEND_TRTLLM_FFI
#include <memory>
#include <thread>
#include <nvml.h>
#include <tensorrt_llm/common/tllmException.h>
#include <tensorrt_llm/plugins/api/tllmPlugin.h>
#include <spdlog/spdlog.h>
#include <backend.hpp>
#include <hardware.hpp>
namespace rust::behavior {
... | text-generation-inference/backends/trtllm/csrc/ffi.hpp/0 | {
"file_path": "text-generation-inference/backends/trtllm/csrc/ffi.hpp",
"repo_id": "text-generation-inference",
"token_count": 3501
} | 208 |
/// Multi shard Client
use crate::client::{ClientError, Result};
use crate::client::{Health, ShardInfo};
use crate::client::grpc_client::{DecodeTimings, PrefillTimings};
use crate::client::InfoResponse;
use crate::client::{
Batch, CachedBatch, Client, Generation, GrammarType, HealthResponse,
NextTokenChooserPa... | text-generation-inference/backends/v2/src/client/sharded_client.rs/0 | {
"file_path": "text-generation-inference/backends/v2/src/client/sharded_client.rs",
"repo_id": "text-generation-inference",
"token_count": 3986
} | 209 |
[package]
name = "text-generation-benchmark"
description = "Text Generation Benchmarking tool"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[lib]
path = "src/lib.rs"
[[bin]]
name = "text-generation-benchmark"
path = "src/main.rs"
[dependencies]
average = "0.14"... | text-generation-inference/benchmark/Cargo.toml/0 | {
"file_path": "text-generation-inference/benchmark/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 335
} | 210 |
from text_generation.errors import (
parse_error,
GenerationError,
IncompleteGenerationError,
OverloadedError,
ValidationError,
BadRequestError,
ShardNotReadyError,
ShardTimeoutError,
NotFoundError,
RateLimitExceededError,
UnknownError,
)
def test_generation_error():
pa... | text-generation-inference/clients/python/tests/test_errors.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_errors.py",
"repo_id": "text-generation-inference",
"token_count": 598
} | 211 |
# TensorRT-LLM backend
The NVIDIA TensorRT-LLM (TRTLLM) backend is a high-performance backend for LLMs
that uses NVIDIA's TensorRT library for inference acceleration.
It makes use of specific optimizations for NVIDIA GPUs, such as custom kernels.
To use the TRTLLM backend **you need to compile** `engines` for the mod... | text-generation-inference/docs/source/backends/trtllm.md/0 | {
"file_path": "text-generation-inference/docs/source/backends/trtllm.md",
"repo_id": "text-generation-inference",
"token_count": 2127
} | 212 |
# HTTP API Reference
#### Table of Contents
- [Text Generation Inference custom API](#text-generation-inference-custom-api)
- [OpenAI Messages API](#openai-messages-api)
- [Making a Request](#making-a-request)
- [Streaming](#streaming)
- [Synchronous](#synchronous)
- [Hugging Face Inference Endpoints](#huggin... | text-generation-inference/docs/source/reference/api_reference.md/0 | {
"file_path": "text-generation-inference/docs/source/reference/api_reference.md",
"repo_id": "text-generation-inference",
"token_count": 1840
} | 213 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 23090,
"logprob": -1.8251953,
"special": false,
"text": " Hello"
},
{
"id": 23,
"logpr... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_falcon/test_flash_falcon.json",
"repo_id": "text-generation-inference",
"token_count": 850
} | 214 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 29946,
"logprob": -1.4765625,
"special": false,
"text": "4"
},
{
"id": 29906,
"logprob... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_regex.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar_regex.json",
"repo_id": "text-generation-inference",
"token_count": 860
} | 215 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 1313,
"logprob": -2.3613281,
"special": false,
"text": "It"
},
{
"id": 3969,
"logprob": -... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_mixtral/test_flash_mixtral_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_mixtral/test_flash_mixtral_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 858
} | 216 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "stop_sequence",
"generated_tokens": 6,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 284,
"logprob": -0.28955078,
"special": false,
"text": " to"
},
{
"id": 3758,
"logp... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 568
} | 217 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "{ \"unit\": \"fahrenheit\", \"temperature\": [ 72, 79, 88 ] }",
"role": "assistant"
}
}
],
"created": 1732525803,
"id": "",
"model": "TinyLlama/TinyLlama-1.1B-... | text-generation-inference/integration-tests/models/__snapshots__/test_grammar_response_format_llama/test_grammar_response_format_llama_json.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_grammar_response_format_llama/test_grammar_response_format_llama_json.json",
"repo_id": "text-generation-inference",
"token_count": 255
} | 218 |
import pytest
@pytest.fixture(scope="module")
def bloom_560m_sharded_handle(launcher):
with launcher("bigscience/bloom-560m", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def bloom_560m_sharded(bloom_560m_sharded_handle):
await bloom_560m_sharded_handle.health(240)
... | text-generation-inference/integration-tests/models/test_bloom_560m_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_bloom_560m_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 527
} | 219 |
import pytest
@pytest.fixture(scope="module")
def flash_gpt2_handle(launcher):
with launcher("openai-community/gpt2", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_gpt2(flash_gpt2_handle):
await flash_gpt2_handle.health(300)
return flash_gpt2_handle.client
... | text-generation-inference/integration-tests/models/test_flash_gpt2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_gpt2.py",
"repo_id": "text-generation-inference",
"token_count": 476
} | 220 |
import pytest
@pytest.fixture(scope="module")
def flash_neox_handle(launcher):
with launcher("stabilityai/stablelm-tuned-alpha-3b", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_neox(flash_neox_handle):
await flash_neox_handle.health(300)
return flash_neox_... | text-generation-inference/integration-tests/models/test_flash_neox.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_neox.py",
"repo_id": "text-generation-inference",
"token_count": 514
} | 221 |
import pytest
@pytest.fixture(scope="module")
def idefics_handle(launcher):
with launcher(
"HuggingFaceM4/idefics-9b-instruct", num_shard=2, dtype="float16"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def idefics(idefics_handle):
await idefics_handle.health(300)
r... | text-generation-inference/integration-tests/models/test_idefics.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_idefics.py",
"repo_id": "text-generation-inference",
"token_count": 782
} | 222 |
import os
import pytest
from huggingface_hub.errors import ValidationError
@pytest.fixture(scope="module", params=["hub-neuron", "hub", "local-neuron"])
async def tgi_service(request, neuron_launcher, neuron_model_config):
"""Expose a TGI service corresponding to a model configuration
For each model configu... | text-generation-inference/integration-tests/neuron/integration/test_implicit_env.py/0 | {
"file_path": "text-generation-inference/integration-tests/neuron/integration/test_implicit_env.py",
"repo_id": "text-generation-inference",
"token_count": 839
} | 223 |
# https://www.gutenberg.org/cache/epub/103/pg103.txt
from openai import OpenAI
import os
import requests
if not os.path.exists("pg103.txt"):
response = requests.get("https://www.gutenberg.org/cache/epub/103/pg103.txt")
with open("pg103.txt", "w") as f:
f.write(response.text)
length = 130000
with open... | text-generation-inference/load_tests/long_prompt2.py/0 | {
"file_path": "text-generation-inference/load_tests/long_prompt2.py",
"repo_id": "text-generation-inference",
"token_count": 250
} | 224 |
eetq_commit := 465e9726bf7ae30803a2d0dd9e5d4315aef17491
eetq:
# Clone eetq
pip install packaging
git clone https://github.com/NetEase-FuXi/EETQ.git eetq
build-eetq: eetq
cd eetq && git fetch && git checkout $(eetq_commit) && git submodule update --init --recursive
cd eetq && python setup.py build
install-eet... | text-generation-inference/server/Makefile-eetq/0 | {
"file_path": "text-generation-inference/server/Makefile-eetq",
"repo_id": "text-generation-inference",
"token_count": 154
} | 225 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include "column_remap.cuh"
#include "../util.cuh"
const int SHUF_BLOCKSIZE_X = 256;
const int SHUF_BLOCKSIZE_Y = 16;
__global__ void column_remap_kernel
(
const half* __restrict__ x,
half* __restrict__ x_new,
const int x_width,
... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/column_remap.cu",
"repo_id": "text-generation-inference",
"token_count": 696
} | 226 |
#include "q_gemm.cuh"
#include "util.cuh"
#include "matrix_view.cuh"
#include "../config.h"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define GPTQ_BLOCK_KN_SIZE 128
#define GPTQ_BLOCK_M_SIZE_MAX 8
#... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_gemm.cu",
"repo_id": "text-generation-inference",
"token_count": 3563
} | 227 |
[
{
"repo_id": "kernels-community/paged-attention",
"sha": "331b7e63a6b592799c8bc992f681bb1ee2c865a2",
"files": [
{
"filename": "build/torch25-cxx11-cu118-x86_64-linux/paged_attention/__init__.py",
"blob_id": "9de56043369487facc1f163df6bd319c9806e5ca"
},
{
"filena... | text-generation-inference/server/hf-kernels.lock/0 | {
"file_path": "text-generation-inference/server/hf-kernels.lock",
"repo_id": "text-generation-inference",
"token_count": 213661
} | 228 |
import torch
from text_generation_server.layers import (
TensorParallelEmbedding,
)
class ProcessGroup:
def __init__(self, rank: int, world_size: int):
self._rank = rank
self.world_size = world_size
def size(self) -> int:
return self.world_size
def rank(self) -> int:
... | text-generation-inference/server/tests/utils/test_layers.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_layers.py",
"repo_id": "text-generation-inference",
"token_count": 1146
} | 229 |
#!/usr/bin/env python
"""
Fused Attention
===============
This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao
(https://tridao.me/publications/flash2/flash2.pdf)
Credits: OpenAI kernel team, AMD ML Frameworks Triton team
Features supported:
1) Fwd with causal masking
2) Any sequence lengt... | text-generation-inference/server/text_generation_server/layers/attention/flash_attn_triton.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/flash_attn_triton.py",
"repo_id": "text-generation-inference",
"token_count": 14692
} | 230 |
from accelerate import init_empty_weights
import torch
@classmethod
def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.bias")
with init_empty_weights():
conv2d = cls(
... | text-generation-inference/server/text_generation_server/layers/conv.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/conv.py",
"repo_id": "text-generation-inference",
"token_count": 518
} | 231 |
from typing import Optional
import torch
import torch.nn as nn
from text_generation_server.layers.fp8 import fp8_quantize
from text_generation_server.layers.marlin.gptq import _check_valid_shape
from text_generation_server.layers.marlin.util import (
_check_marlin_kernels,
permute_scales,
)
from text_generatio... | text-generation-inference/server/text_generation_server/layers/marlin/fp8.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/fp8.py",
"repo_id": "text-generation-inference",
"token_count": 1856
} | 232 |
import torch
import time
import torch.distributed
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
AutoConfig,
AutoTokenizer,
AutoModelForCausalLM,
PreTrainedTokenizerBase,
)
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.ut... | text-generation-inference/server/text_generation_server/models/causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 16985
} | 233 |
# coding=utf-8
# Copyright 2024 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 requi... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 1969
} | 234 |
import torch
import torch.distributed
from mamba_ssm.ops.triton.selective_state_update import selective_state_update
from mamba_ssm.ops.selective_scan_interface import selective_scan_fn
from torch import nn
from typing import Optional, Tuple, Any
from transformers.configuration_utils import PretrainedConfig
import tor... | text-generation-inference/server/text_generation_server/models/custom_modeling/mamba_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/mamba_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 4238
} | 235 |
import torch
import triton
import triton.language as tl
from loguru import logger
from typing import List, Optional
from torch.utils._triton import has_triton as has_triton_torch
from text_generation_server.utils.import_utils import (
SYSTEM,
)
from text_generation_server.utils.log import log_master
_HAS_TRITON... | text-generation-inference/server/text_generation_server/models/metadata_kernels.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/metadata_kernels.py",
"repo_id": "text-generation-inference",
"token_count": 4276
} | 236 |
import time
import os
from datetime import timedelta
from loguru import logger
from pathlib import Path
from typing import Optional, List
from huggingface_hub import file_download, hf_api, HfApi, hf_hub_download
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub.utils import (
LocalE... | text-generation-inference/server/text_generation_server/utils/hub.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/hub.py",
"repo_id": "text-generation-inference",
"token_count": 3419
} | 237 |
/* eslint-disable @typescript-eslint/no-explicit-any */
import { bertProcessing, byteLevelProcessing, robertaProcessing, sequenceProcessing, templateProcessing } from '../../'
describe('bertProcessing', () => {
it('instantiates correctly with only two parameters', () => {
const processor = bertProcessing(['sep'... | tokenizers/bindings/node/lib/bindings/post-processors.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/post-processors.test.ts",
"repo_id": "tokenizers",
"token_count": 1022
} | 238 |
# `tokenizers-linux-arm64-gnu`
This is the **aarch64-unknown-linux-gnu** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-arm64-gnu/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-gnu/README.md",
"repo_id": "tokenizers",
"token_count": 35
} | 239 |
use serde::de::Deserializer;
use serde::ser::Serializer;
use serde::{Deserialize, Serialize};
use std::sync::{Arc, RwLock};
pub fn serialize<S, T>(val: &Option<Arc<RwLock<T>>>, s: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
T: Serialize,
{
T::serialize(&*(val.clone().unwrap()).read().unwrap(), s)
}
pub f... | tokenizers/bindings/node/src/arc_rwlock_serde.rs/0 | {
"file_path": "tokenizers/bindings/node/src/arc_rwlock_serde.rs",
"repo_id": "tokenizers",
"token_count": 220
} | 240 |
# This file is generated by running "yarn install" inside your project.
# Manual changes might be lost - proceed with caution!
__metadata:
version: 6
cacheKey: 8
"@aashutoshrathi/word-wrap@npm:^1.2.3":
version: 1.2.6
resolution: "@aashutoshrathi/word-wrap@npm:1.2.6"
checksum: ada901b9e7c680d190f1d012c84217c... | tokenizers/bindings/node/yarn.lock/0 | {
"file_path": "tokenizers/bindings/node/yarn.lock",
"repo_id": "tokenizers",
"token_count": 125917
} | 241 |
from enum import Enum
from typing import List, Tuple, Union
Offsets = Tuple[int, int]
TextInputSequence = str
"""A :obj:`str` that represents an input sequence """
PreTokenizedInputSequence = Union[List[str], Tuple[str]]
"""A pre-tokenized input sequence. Can be one of:
- A :obj:`List` of :obj:`str`
- A :o... | tokenizers/bindings/python/py_src/tokenizers/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/__init__.py",
"repo_id": "tokenizers",
"token_count": 984
} | 242 |
# Generated content DO NOT EDIT
class PreTokenizer:
"""
Base class for all pre-tokenizers
This class is not supposed to be instantiated directly. Instead, any implementation of a
PreTokenizer will return an instance of this class when instantiated.
"""
def pre_tokenize(self, pretok):
""... | tokenizers/bindings/python/py_src/tokenizers/pre_tokenizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/pre_tokenizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 9661
} | 243 |
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::type_object::PyTypeInfo;
use std::ffi::CString;
use std::fmt::{Display, Formatter, Result as FmtResult};
use tokenizers::tokenizer::Result;
#[derive(Debug)]
pub struct PyError(pub String);
impl PyError {
#[allow(dead_code)]
pub fn from(s: &str) -> Self {
... | tokenizers/bindings/python/src/error.rs/0 | {
"file_path": "tokenizers/bindings/python/src/error.rs",
"repo_id": "tokenizers",
"token_count": 548
} | 244 |
from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers, processors
from tokenizers.implementations import BaseTokenizer
class TestBaseTokenizer:
def test_get_set_components(self):
toki = Tokenizer(models.BPE())
toki.normalizer = normalizers.NFC()
toki.pre_tokenizer... | tokenizers/bindings/python/tests/implementations/test_base_tokenizer.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_base_tokenizer.py",
"repo_id": "tokenizers",
"token_count": 550
} | 245 |
# Normalizers
<tokenizerslangcontent>
<python>
## BertNormalizer
[[autodoc]] tokenizers.normalizers.BertNormalizer
## Lowercase
[[autodoc]] tokenizers.normalizers.Lowercase
## NFC
[[autodoc]] tokenizers.normalizers.NFC
## NFD
[[autodoc]] tokenizers.normalizers.NFD
## NFKC
[[autodoc]] tokenizers.normalizers.NF... | tokenizers/docs/source-doc-builder/api/normalizers.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/normalizers.mdx",
"repo_id": "tokenizers",
"token_count": 350
} | 246 |
🤗 Tokenizers is tested on Python 3.5+.
You should install 🤗 Tokenizers in a
`virtual environment <https://docs.python.org/3/library/venv.html>`_. If you're unfamiliar with
Python virtual environments, check out the
`user guide <https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/>`__.
C... | tokenizers/docs/source/installation/python.inc/0 | {
"file_path": "tokenizers/docs/source/installation/python.inc",
"repo_id": "tokenizers",
"token_count": 383
} | 247 |
#[macro_use]
extern crate criterion;
use criterion::Criterion;
use std::collections::HashMap;
use std::fs::read_to_string;
use std::time::{Duration, Instant};
use tokenizers::models::unigram::Unigram;
use tokenizers::models::unigram::UnigramTrainer;
pub fn bench_train(c: &mut Criterion) {
let trainer = UnigramTra... | tokenizers/tokenizers/benches/unigram_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/unigram_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 1172
} | 248 |
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Hello wasm-pack!</title>
</head>
<body>
<noscript>This page contains webassembly and javascript content, please enable javascript in your browser.</noscript>
<script src="./bootstrap.js"></script>
</body>
</html>
| tokenizers/tokenizers/examples/unstable_wasm/www/index.html/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/index.html",
"repo_id": "tokenizers",
"token_count": 110
} | 249 |
use super::{super::OrderedVocabIter, trainer::BpeTrainer, Error, Pair, Word};
use crate::tokenizer::{Model, Result, Token};
use crate::utils::cache::{Cache, DEFAULT_CACHE_CAPACITY, MAX_LENGTH};
use crate::utils::iter::ResultShunt;
use serde_json::Value;
use std::borrow::Cow;
use std::{
collections::HashMap,
fs:... | tokenizers/tokenizers/src/models/bpe/model.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/model.rs",
"repo_id": "tokenizers",
"token_count": 17728
} | 250 |
use super::WordPiece;
use crate::models::bpe::{BpeTrainer, BpeTrainerBuilder, BPE};
use crate::tokenizer::{AddedToken, Result, Trainer};
use serde::{Deserialize, Serialize};
use std::collections::HashSet;
/// A `WordPieceTrainerBuilder` can be used to create a `WordPieceTrainer` with a custom
/// configuration.
pub st... | tokenizers/tokenizers/src/models/wordpiece/trainer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordpiece/trainer.rs",
"repo_id": "tokenizers",
"token_count": 2499
} | 251 |
use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
use unicode_categories::UnicodeCategories;
fn is_punc(x: char) -> bool {
char::is_ascii_punctuation(&x) || x.is_punctuation()
}
#[derive(Copy, Cl... | tokenizers/tokenizers/src/pre_tokenizers/punctuation.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/punctuation.rs",
"repo_id": "tokenizers",
"token_count": 1103
} | 252 |
use crate::utils::SysRegex;
use crate::{Offsets, Result};
use regex::Regex;
/// Pattern used to split a NormalizedString
pub trait Pattern {
/// Slice the given string in a list of pattern match positions, with
/// a boolean indicating whether this is a match or not.
///
/// This method *must* cover th... | tokenizers/tokenizers/src/tokenizer/pattern.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/pattern.rs",
"repo_id": "tokenizers",
"token_count": 3903
} | 253 |
#![cfg(feature = "http")]
use tokenizers::{FromPretrainedParameters, Result, Tokenizer};
#[test]
fn test_from_pretrained() -> Result<()> {
let tokenizer = Tokenizer::from_pretrained("bert-base-cased", None)?;
let encoding = tokenizer.encode("Hey there dear friend!", false)?;
assert_eq!(
encoding.ge... | tokenizers/tokenizers/tests/from_pretrained.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/from_pretrained.rs",
"repo_id": "tokenizers",
"token_count": 683
} | 254 |
# Using quantized models (dtypes)
Before Transformers.js v3, we used the `quantized` option to specify whether to use a quantized (q8) or full-precision (fp32) variant of the model by setting `quantized` to `true` or `false`, respectively. Now, we've added the ability to select from a much larger list with the `dtype`... | transformers.js/docs/source/guides/dtypes.md/0 | {
"file_path": "transformers.js/docs/source/guides/dtypes.md",
"repo_id": "transformers.js",
"token_count": 1698
} | 255 |
.sidebar {
background-color: #181818;
color: #CCCCCC;
}
body{
background-color: #1F1F1F;
color: white;
}
.progress-container {
position: relative;
font-size: 16px;
color: white;
/* background-color: #e9ecef; */
border-radius: 8px;
text-align: left;
overflow: hidden;
}
.progress-bar {
padding:... | transformers.js/examples/code-completion/src/App.css/0 | {
"file_path": "transformers.js/examples/code-completion/src/App.css",
"repo_id": "transformers.js",
"token_count": 208
} | 256 |
import { useState, useRef, useEffect, useCallback } from 'react'
import './App.css'
const PLACEHOLDER_TEXTS = [
"'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
"The novel 'Moby-... | transformers.js/examples/cross-encoder/src/App.jsx/0 | {
"file_path": "transformers.js/examples/cross-encoder/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 2232
} | 257 |
# Transformers.js - Sample browser extension
An example project to show how to run 🤗 Transformers in a browser extension. Although we only provide instructions for running in Chrome, it should be similar for other browsers.
## Getting Started
1. Clone the repo and enter the project directory:
```bash
git cl... | transformers.js/examples/extension/README.md/0 | {
"file_path": "transformers.js/examples/extension/README.md",
"repo_id": "transformers.js",
"token_count": 624
} | 258 |
import { useEffect, useState, useRef } from 'react';
import { AutoTokenizer, MusicgenForConditionalGeneration, BaseStreamer } from '@xenova/transformers';
import { encodeWAV, share } from './utils.js';
import './App.css';
const MODEL_ID = 'Xenova/musicgen-small';
// Adapted from https://huggingface.co/spaces/faceboo... | transformers.js/examples/musicgen-web/src/App.jsx/0 | {
"file_path": "transformers.js/examples/musicgen-web/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 3165
} | 259 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Transformers.js - Sample react application</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.jsx"></script>
</body>... | transformers.js/examples/react-translator/index.html/0 | {
"file_path": "transformers.js/examples/react-translator/index.html",
"repo_id": "transformers.js",
"token_count": 133
} | 260 |
{
"extends": "next/core-web-vitals"
}
| transformers.js/examples/semantic-image-search-client/.eslintrc.json/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/.eslintrc.json",
"repo_id": "transformers.js",
"token_count": 20
} | 261 |
'use client'
import { useState, useEffect, useCallback, useRef } from 'react'
import { Modal } from './components/Modal';
import { SearchBar } from './components/SearchBar';
import { ImageGrid } from './components/ImageGrid';
export default function Home() {
// Application state
const [ready, setReady] = useStat... | transformers.js/examples/semantic-image-search-client/src/app/page.js/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/src/app/page.js",
"repo_id": "transformers.js",
"token_count": 772
} | 262 |
// Helper script to update the database with image embeddings
import { AutoProcessor, RawImage, CLIPVisionModelWithProjection } from '@xenova/transformers';
import { createClient } from '@supabase/supabase-js'
if (!process.env.SUPABASE_SECRET_KEY) {
throw new Error('Missing `SUPABASE_SECRET_KEY` environment varia... | transformers.js/examples/semantic-image-search/scripts/update-database.mjs/0 | {
"file_path": "transformers.js/examples/semantic-image-search/scripts/update-database.mjs",
"repo_id": "transformers.js",
"token_count": 778
} | 263 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Transformers.js | Real-time object detection</title>
</head>
<body>
<h1>
Real-time object detection w/
<a href="https://github.com/huggingface/transformers.j... | transformers.js/examples/video-object-detection/index.html/0 | {
"file_path": "transformers.js/examples/video-object-detection/index.html",
"repo_id": "transformers.js",
"token_count": 608
} | 264 |
* {
box-sizing: border-box;
padding: 0;
margin: 0;
font-family: sans-serif;
}
html,
body {
height: 100%;
}
body {
padding: 16px 32px;
}
body,
#container {
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
}
#controls {
display: flex;
padding: 1rem;
gap: 1... | transformers.js/examples/webgpu-video-depth-estimation/style.css/0 | {
"file_path": "transformers.js/examples/webgpu-video-depth-estimation/style.css",
"repo_id": "transformers.js",
"token_count": 372
} | 265 |
export default function CrossIcon(props) {
return (
<svg
{...props}
xmlns="http://www.w3.org/2000/svg"
width="24"
height="24"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
strokeWidth="2"
stro... | transformers.js/examples/webgpu-vlm/src/components/icons/CrossIcon.jsx/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/components/icons/CrossIcon.jsx",
"repo_id": "transformers.js",
"token_count": 304
} | 266 |
import { useEffect, useState, useRef, useCallback } from 'react';
import Progress from './components/Progress';
import MediaInput from './components/MediaInput';
import Transcript from './components/Transcript';
import LanguageSelector from './components/LanguageSelector';
async function hasWebGPU() {
if (!navigat... | transformers.js/examples/whisper-word-timestamps/src/App.jsx/0 | {
"file_path": "transformers.js/examples/whisper-word-timestamps/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 3492
} | 267 |
# Support exporting vision and text models separately:
# Adapted from https://github.com/huggingface/optimum/issues/1186#issuecomment-1637641760
from optimum.exporters.onnx.model_configs import SiglipTextOnnxConfig, ViTOnnxConfig
from typing import Dict
class SiglipVisionOnnxConfig(ViTOnnxConfig):
pass
class S... | transformers.js/scripts/extra/siglip.py/0 | {
"file_path": "transformers.js/scripts/extra/siglip.py",
"repo_id": "transformers.js",
"token_count": 496
} | 268 |
/**
* @module generation/parameters
*/
/**
* @typedef {Object} GenerationFunctionParameters
* @property {import('../utils/tensor.js').Tensor} [inputs=null] (`Tensor` of varying shape depending on the modality, *optional*):
* The sequence used as a prompt for the generation or as model inputs to the encoder. If `... | transformers.js/src/generation/parameters.js/0 | {
"file_path": "transformers.js/src/generation/parameters.js",
"repo_id": "transformers.js",
"token_count": 701
} | 269 |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
export class DonutImageProcessor extends ImageProcessor {
pad_image(pixelData, imgDims, padSize, options = {}) {
const [imageHeight, imageWidth, imageChannels] = imgDims;
let image_mean = this.image_mean;
if (!Arr... | transformers.js/src/models/donut/image_processing_donut.js/0 | {
"file_path": "transformers.js/src/models/donut/image_processing_donut.js",
"repo_id": "transformers.js",
"token_count": 482
} | 270 |
import { MaskFormerImageProcessor } from "../maskformer/image_processing_maskformer.js";
// NOTE: extends MaskFormerImageProcessor
export class Mask2FormerImageProcessor extends MaskFormerImageProcessor { }
| transformers.js/src/models/mask2former/image_processing_mask2former.js/0 | {
"file_path": "transformers.js/src/models/mask2former/image_processing_mask2former.js",
"repo_id": "transformers.js",
"token_count": 53
} | 271 |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
import { RawImage } from "../../utils/image.js";
const IMAGE_TOKEN = "<|image|>";
const IMAGE_TOKEN_PATTERN = /<\|image_\d+\|>/g;
expo... | transformers.js/src/models/phi3_v/processing_phi3_v.js/0 | {
"file_path": "transformers.js/src/models/phi3_v/processing_phi3_v.js",
"repo_id": "transformers.js",
"token_count": 827
} | 272 |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
export class Swin2SRImageProcessor extends ImageProcessor {
pad_image(pixelData, imgDims, padSize, options = {}) {
// NOTE: In this case, `padSize` represents the size of the sliding window for the local attention.
// In o... | transformers.js/src/models/swin2sr/image_processing_swin2sr.js/0 | {
"file_path": "transformers.js/src/models/swin2sr/image_processing_swin2sr.js",
"repo_id": "transformers.js",
"token_count": 462
} | 273 |
/**
* @file Entry point for the Transformers.js library. Only the exports from this file
* are available to the end user, and are grouped as follows:
*
* 1. [Pipelines](./pipelines)
* 2. [Environment variables](./env)
* 3. [Models](./models)
* 4. [Tokenizers](./tokenizers)
* 5. [Processors](./processors)
*
... | transformers.js/src/transformers.js/0 | {
"file_path": "transformers.js/src/transformers.js",
"repo_id": "transformers.js",
"token_count": 422
} | 274 |
import { init } from "./init.js";
import { collect_and_execute_tests } from "./test_utils.js";
init();
await collect_and_execute_tests("Image processors", "image_processing");
| transformers.js/tests/image_processors.test.js/0 | {
"file_path": "transformers.js/tests/image_processors.test.js",
"repo_id": "transformers.js",
"token_count": 55
} | 275 |
import { CodeGenTokenizer, CodeGenForCausalLM } 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("CodeGenForCausalLM", () => {
const model_id = "hf-internal-testing/ti... | transformers.js/tests/models/codegen/test_modeling_codegen.js/0 | {
"file_path": "transformers.js/tests/models/codegen/test_modeling_codegen.js",
"repo_id": "transformers.js",
"token_count": 768
} | 276 |
import { GemmaTokenizer, Gemma2ForCausalLM } 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("Gemma2ForCausalLM", () => {
const model_id = "hf-internal-testing/tiny-r... | transformers.js/tests/models/gemma2/test_modeling_gemma2.js/0 | {
"file_path": "transformers.js/tests/models/gemma2/test_modeling_gemma2.js",
"repo_id": "transformers.js",
"token_count": 796
} | 277 |
import { AutoProcessor, VLChatProcessor } from "../../../src/transformers.js";
import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
describe("VLChatProcessor", () => {
const model_id = "onnx-community/Janus-1.3B-ONNX";
/** @type {VLChatProcessor} */
l... | transformers.js/tests/models/janus/test_processor_janus.js/0 | {
"file_path": "transformers.js/tests/models/janus/test_processor_janus.js",
"repo_id": "transformers.js",
"token_count": 849
} | 278 |
import { T5Tokenizer, MusicgenForConditionalGeneration, full } 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("MusicgenForConditionalGeneration", () => {
const model... | transformers.js/tests/models/musicgen/test_modeling_musicgen.js/0 | {
"file_path": "transformers.js/tests/models/musicgen/test_modeling_musicgen.js",
"repo_id": "transformers.js",
"token_count": 1011
} | 279 |
import { Qwen2VLProcessor, Qwen2VLForConditionalGeneration, 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 CONVERSATION = [
{
role: "user",
conte... | transformers.js/tests/models/qwen2_vl/test_modeling_qwen2_vl.js/0 | {
"file_path": "transformers.js/tests/models/qwen2_vl/test_modeling_qwen2_vl.js",
"repo_id": "transformers.js",
"token_count": 1367
} | 280 |
import { AutoFeatureExtractor, WeSpeakerFeatureExtractor } from "../../../src/transformers.js";
import { MAX_FEATURE_EXTRACTOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
// WeSpeakerFeatureExtractor
describe("WeSpeakerFeatureExtractor", () => {
const model_id = "onnx-com... | transformers.js/tests/models/wespeaker_resnet/test_feature_extraction_wespeaker_resnet.js/0 | {
"file_path": "transformers.js/tests/models/wespeaker_resnet/test_feature_extraction_wespeaker_resnet.js",
"repo_id": "transformers.js",
"token_count": 1014
} | 281 |
import { pipeline, ImageToImagePipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
import { load_cached_image } from "../asset_cache.js";
const PIPELINE_ID = "image-to-image";
export default () => {
d... | transformers.js/tests/pipelines/test_pipelines_image_to_image.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_image_to_image.js",
"repo_id": "transformers.js",
"token_count": 898
} | 282 |
import fs from "fs";
import path from "path";
import { fileURLToPath } from "url";
export async function loadAudio(url) {
// NOTE: Since the Web Audio API is not available in Node.js, we will need to use the `wavefile` library to obtain the raw audio data.
// For more information, see: https://huggingface.co/docs/... | transformers.js/tests/test_utils.js/0 | {
"file_path": "transformers.js/tests/test_utils.js",
"repo_id": "transformers.js",
"token_count": 1612
} | 283 |
import re
import argparse
def parse_pytest_output(file_path):
skipped_tests = {}
skipped_count = 0
with open(file_path, 'r') as file:
for line in file:
match = re.match(r'^SKIPPED \[(\d+)\] (tests/.*): (.*)$', line)
if match:
skipped_count += 1
... | transformers/.circleci/parse_test_outputs.py/0 | {
"file_path": "transformers/.circleci/parse_test_outputs.py",
"repo_id": "transformers",
"token_count": 1145
} | 284 |
FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands)
SHELL ["sh", "-lc"]
# The following `ARG` are mainly used to specify the versions explicit... | transformers/docker/transformers-all-latest-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-all-latest-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 1260
} | 285 |
# Translating the Transformers documentation into your language
As part of our mission to democratize machine learning, we aim to make the Transformers library available in many more languages! Follow the steps below to help translate the documentation into your language.
## Open an Issue
1. Navigate to the Issues p... | transformers/docs/TRANSLATING.md/0 | {
"file_path": "transformers/docs/TRANSLATING.md",
"repo_id": "transformers",
"token_count": 745
} | 286 |
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