text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
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# 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_local_python_executor.py/0 | {
"file_path": "smolagents/tests/test_local_python_executor.py",
"repo_id": "smolagents",
"token_count": 17042
} |
# 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
} |
/// Multi shard Client
use crate::{v2, Health, ShardInfo};
use crate::{ClientError, Result};
use crate::v2::InfoResponse;
use async_trait::async_trait;
use futures::future::join_all;
use tonic::transport::Uri;
use tracing::instrument;
use v2::client::{DecodeTimings, PrefillTimings};
use v2::{
Batch, CachedBatch, C... | text-generation-inference/backends/client/src/v2/sharded_client.rs/0 | {
"file_path": "text-generation-inference/backends/client/src/v2/sharded_client.rs",
"repo_id": "text-generation-inference",
"token_count": 3969
} |
#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
} |
[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
} |
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
} |
# Serving Private & Gated Models
If the model you wish to serve is behind gated access or the model repository on Hugging Face Hub is private, and you have access to the model, you can provide your Hugging Face Hub access token. You can generate and copy a read token from [Hugging Face Hub tokens page](https://hugging... | text-generation-inference/docs/source/basic_tutorials/gated_model_access.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/gated_model_access.md",
"repo_id": "text-generation-inference",
"token_count": 290
} |
[
{
"choices": [
{
"finish_reason": "",
"index": 0,
"logprobs": null,
"text": " A"
}
],
"created": 1725883643,
"id": "",
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"object": "text_completion",
"system_fingerprint": "2.2.1-dev0-native"
},... | text-generation-inference/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json",
"repo_id": "text-generation-inference",
"token_count": 7100
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": 0,
"tokens": [
{
"id": 5380,
"logprob": 0.0,
"special": false,
"text": "?\n"
},
{
"id": 34564,
"logprob": 0.0,
... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int_24/test_compressed_tensors_wna16_int_24_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int_24/test_compressed_tensors_wna16_int_24_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 849
} |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 1736,
"logprob": -2.09375,
"special": false,
"text": " form"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma/test_flash_gemma_load.json",
"repo_id": "text-generation-inference",
"token_count": 4072
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 5229,
"logprob": -2.7988281,
"special": false,
"text": " failed"
},
{
"id": 29901,
"lo... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin_24/test_flash_llama_marlin.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_marlin_24/test_flash_llama_marlin.json",
"repo_id": "text-generation-inference",
"token_count": 869
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 9,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 2684,
"logprob": -0.24902344,
"special": false,
"text": " There"
},
{
"id": 374,
"lo... | text-generation-inference/integration-tests/models/__snapshots__/test_idefics3/test_flash_idefics3_next_simple_url.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_idefics3/test_flash_idefics3_next_simple_url.json",
"repo_id": "text-generation-inference",
"token_count": 796
} |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": 0,
"tokens": [
{
"id": 16017,
"logprob": 0.0,
"special": fals... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 910
} |
{
"choices": [
{
"delta": {
"content": " assistant",
"role": "assistant",
"tool_calls": null
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1728497531,
"id": "",
"model": "meta-llama/Llama-3.1-8B-Instruct",
"object": "chat... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information_stream.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information_stream.json",
"repo_id": "text-generation-inference",
"token_count": 205
} |
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle(launcher):
with launcher(
"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
num_shard=1,
quantize="awq",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_awq(... | text-generation-inference/integration-tests/models/test_flash_awq.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_awq.py",
"repo_id": "text-generation-inference",
"token_count": 866
} |
import pytest
@pytest.fixture(scope="module")
def flash_starcoder_handle(launcher):
with launcher("bigcode/starcoder", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder(flash_starcoder_handle):
await flash_starcoder_handle.health(300)
return flash_sta... | text-generation-inference/integration-tests/models/test_flash_starcoder.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_starcoder.py",
"repo_id": "text-generation-inference",
"token_count": 602
} |
import pytest
@pytest.fixture(scope="module")
def neox_sharded_handle(launcher):
with launcher(
"OpenAssistant/oasst-sft-1-pythia-12b", num_shard=2, use_flash_attention=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def neox_sharded(neox_sharded_handle):
await neox... | text-generation-inference/integration-tests/models/test_neox_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_neox_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 523
} |
import { check } from 'k6';
import { scenario } from 'k6/execution';
import http from 'k6/http';
import { Trend, Counter } from 'k6/metrics';
const host = __ENV.HOST;
const model_id = __ENV.MODEL_ID;
const timePerToken = new Trend('time_per_token', true);
const tokens = new Counter('tokens');
const new_tokens = new Co... | text-generation-inference/load_tests/common.js/0 | {
"file_path": "text-generation-inference/load_tests/common.js",
"repo_id": "text-generation-inference",
"token_count": 1530
} |
[package]
name = "text-generation-router"
description = "Text Generation Webserver"
build = "build.rs"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
anyhow = "1"
async-trait = "0.1.74"
async-stream = "0.3.5"
axum = { version = "0.7", features = ["js... | text-generation-inference/router/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 935
} |
#!/bin/bash
if [[ -z "${HF_MODEL_ID}" ]]; then
echo "HF_MODEL_ID must be set"
exit 1
fi
export MODEL_ID="${HF_MODEL_ID}"
if [[ -n "${HF_MODEL_REVISION}" ]]; then
export REVISION="${HF_MODEL_REVISION}"
fi
if [[ -n "${SM_NUM_GPUS}" ]]; then
export NUM_SHARD="${SM_NUM_GPUS}"
fi
if [[ -n "${HF_MODEL_QUANTIZE}" ... | text-generation-inference/sagemaker-entrypoint.sh/0 | {
"file_path": "text-generation-inference/sagemaker-entrypoint.sh",
"repo_id": "text-generation-inference",
"token_count": 239
} |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
extra_compile_args = ["-std=c++17"]
setup(
name="custom_kernels",
ext_modules=[
CUDAExtension(
name="custom_kernels.fused_bloom_attention_cuda",
sources=["custom_kernels/fused_bloom... | text-generation-inference/server/custom_kernels/setup.py/0 | {
"file_path": "text-generation-inference/server/custom_kernels/setup.py",
"repo_id": "text-generation-inference",
"token_count": 309
} |
#ifndef _config_h
#define _config_h
#define MAX_Q_GEMM_ROWS 50
#define MAX_Q_GEMM_WEIGHTS 4 // must be <= MAX_Q_GEMM_ROWS
#define QMODE_2BIT 1
#define QMODE_3BIT 1
#define QMODE_4BIT 1
#define QMODE_5BIT 1
#define QMODE_6BIT 0
#define QMODE_8BIT 0
#endif
| text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/config.h",
"repo_id": "text-generation-inference",
"token_count": 119
} |
#ifndef _qdq_util_cuh
#define _qdq_util_cuh
union half2_uint32
{
uint32_t as_uint32;
half2 as_half2;
__device__ half2_uint32(uint32_t val) : as_uint32(val) {}
__device__ half2_uint32(half2 val) : as_half2(val) {}
__device__ half2_uint32() : as_uint32(0) {}
};
union half_uint16
{
uint16_t as_ui... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_util.cuh",
"repo_id": "text-generation-inference",
"token_count": 602
} |
import pytest
from unittest.mock import Mock
from text_generation_server.utils.adapter import (
get_attn_weights,
get_mlp_weights,
parse_lora_adapters,
AdapterInfo,
)
def test_parse_lora_adapters_empty():
assert parse_lora_adapters(None) == []
assert parse_lora_adapters("") == []
def test_pa... | text-generation-inference/server/tests/utils/test_adapter.py/0 | {
"file_path": "text-generation-inference/server/tests/utils/test_adapter.py",
"repo_id": "text-generation-inference",
"token_count": 4022
} |
import os
from text_generation_server.utils.import_utils import SYSTEM
from .common import Seqlen
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
raise ImportError("`USE_FLASH_ATTENTION` is false.")
if SYSTEM == "cuda":
from .cuda import (
SUPPORTS_WINDOWING,
attention,
pa... | text-generation-inference/server/text_generation_server/layers/attention/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/attention/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 404
} |
from typing import List, Optional, Union
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationType
from text_generation_server.layers.fp8 import (
Fp8Weight,
_load_scalar_or_matrix_scale,
requantize_with_max_scale,
normalize_e4m3fn_to_native_float8,
)
from text_genera... | text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8an_fp.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/compressed_tensors/w8an_fp.py",
"repo_id": "text-generation-inference",
"token_count": 3370
} |
import torch
from text_generation_server.utils.import_utils import SYSTEM
from torch.nn import functional as F
import os
if SYSTEM == "rocm":
ROCM_USE_SKINNY_GEMM = os.getenv("ROCM_USE_SKINNY_GEMM", "True").lower() in (
"true",
"1",
)
if ROCM_USE_SKINNY_GEMM:
try:
impor... | text-generation-inference/server/text_generation_server/layers/linear.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/linear.py",
"repo_id": "text-generation-inference",
"token_count": 1954
} |
import torch
from torch.nn import functional as F
from typing import Iterable, List
from text_generation_server.layers.linear import get_linear, FastLinear
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM == "ipex":
import intel_extension_for_pytorch as ipex
class LayerConcat(torch.nn.Modul... | text-generation-inference/server/text_generation_server/layers/tensor_parallel.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/tensor_parallel.py",
"repo_id": "text-generation-inference",
"token_count": 4175
} |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 8969
} |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_processing.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_processing.py",
"repo_id": "text-generation-inference",
"token_count": 8120
} |
from io import BytesIO
from PIL import Image
import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
AutoConfig,
AutoProcessor,
AutoTokenizer,
PreTrainedTokenizerBase,
ProcessorMixin,
)
from typing import Optional, Tuple, List, Type, Dic... | text-generation-inference/server/text_generation_server/models/idefics_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/idefics_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 17112
} |
import datetime
import torch
import os
from loguru import logger
from pathlib import Path
from safetensors.torch import save_file, load_file, _find_shared_tensors, _is_complete
from typing import List, Dict
from collections import defaultdict
def _remove_duplicate_names(
state_dict: Dict[str, torch.Tensor],
... | text-generation-inference/server/text_generation_server/utils/convert.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/convert.py",
"repo_id": "text-generation-inference",
"token_count": 1775
} |
import torch
from abc import ABC, abstractmethod
from contextlib import contextmanager
from pathlib import Path
from typing import Dict, List, Optional, Union, Type
from safetensors import safe_open
from dataclasses import dataclass
from text_generation_server.utils.import_utils import SYSTEM
class WeightsLoader(AB... | text-generation-inference/server/text_generation_server/utils/weights.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/weights.py",
"repo_id": "text-generation-inference",
"token_count": 6743
} |
/* eslint-disable @typescript-eslint/no-empty-function */
/* eslint-disable @typescript-eslint/no-explicit-any */
import { BPE, Unigram, WordPiece } from '../../'
const MOCKS_DIR = __dirname + '/__mocks__'
describe('WordPiece', () => {
describe('fromFile', () => {
it('throws if called with only one argument', ... | tokenizers/bindings/node/lib/bindings/models.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/models.test.ts",
"repo_id": "tokenizers",
"token_count": 818
} |
{
"name": "tokenizers",
"version": "0.15.3-dev0",
"repository": {
"type": "git",
"url": "git+https://github.com/huggingface/tokenizers.git"
},
"bugs": {
"url": "https://github.com/huggingface/tokenizers/issues"
},
"homepage": "https://github.com/huggingface/tokenizers/tree/master/bindings/node... | tokenizers/bindings/node/package.json/0 | {
"file_path": "tokenizers/bindings/node/package.json",
"repo_id": "tokenizers",
"token_count": 1532
} |
{
"compilerOptions": {
"target": "ES2018",
"strict": true,
"moduleResolution": "node",
"module": "CommonJS",
"noUnusedLocals": true,
"noUnusedParameters": true,
"esModuleInterop": true,
"allowSyntheticDefaultImports": true
},
"include": ["."],
"exclude": ["node_modules"]
}
| tokenizers/bindings/node/tsconfig.json/0 | {
"file_path": "tokenizers/bindings/node/tsconfig.json",
"repo_id": "tokenizers",
"token_count": 129
} |
import datasets
from tokenizers import Tokenizer, models, normalizers, pre_tokenizers
# Build a tokenizer
bpe_tokenizer = Tokenizer(models.BPE())
bpe_tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
bpe_tokenizer.normalizer = normalizers.Lowercase()
# Initialize a dataset
dataset = datasets.load_dataset("wikit... | tokenizers/bindings/python/examples/train_with_datasets.py/0 | {
"file_path": "tokenizers/bindings/python/examples/train_with_datasets.py",
"repo_id": "tokenizers",
"token_count": 207
} |
# Generated content DO NOT EDIT
class Normalizer:
"""
Base class for all normalizers
This class is not supposed to be instantiated directly. Instead, any implementation of a
Normalizer will return an instance of this class when instantiated.
"""
def normalize(self, normalized):
"""
... | tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/normalizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 8593
} |
use std::sync::{Arc, RwLock};
use crate::pre_tokenizers::from_string;
use crate::tokenizer::PyTokenizer;
use crate::utils::PyPattern;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::de::Error;
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use tk::decoders::bpe::BPEDecoder;
u... | tokenizers/bindings/python/src/decoders.rs/0 | {
"file_path": "tokenizers/bindings/python/src/decoders.rs",
"repo_id": "tokenizers",
"token_count": 11043
} |
use serde::de::value::Error;
use serde::{ser, Serialize};
type Result<T> = ::std::result::Result<T, Error>;
pub struct Serializer {
// This string starts empty and JSON is appended as values are serialized.
output: String,
/// Each levels remembers its own number of elements
num_elements: Vec<usize>,
... | tokenizers/bindings/python/src/utils/serde_pyo3.rs/0 | {
"file_path": "tokenizers/bindings/python/src/utils/serde_pyo3.rs",
"repo_id": "tokenizers",
"token_count": 10084
} |
# flake8: noqa
import gzip
import os
import datasets
import pytest
from ..utils import data_dir, train_files
class TestTrainFromIterators:
@staticmethod
def get_tokenizer_trainer():
# START init_tokenizer_trainer
from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers... | tokenizers/bindings/python/tests/documentation/test_tutorial_train_from_iterators.py/0 | {
"file_path": "tokenizers/bindings/python/tests/documentation/test_tutorial_train_from_iterators.py",
"repo_id": "tokenizers",
"token_count": 1595
} |
# Input Sequences
<tokenizerslangcontent>
<python>
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 the tokenizer: `raw text` vs `pre-tokenized`.
## TextInputSequen... | tokenizers/docs/source-doc-builder/api/input-sequences.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/input-sequences.mdx",
"repo_id": "tokenizers",
"token_count": 402
} |
import re
from sphinx.directives.other import TocTree
class TocTreeTags(TocTree):
hasPat = re.compile("^\s*:(.+):(.+)$")
def filter_entries(self, entries):
filtered = []
for e in entries:
m = self.hasPat.match(e)
if m != None:
if self.env.app.tags.has(m... | tokenizers/docs/source/_ext/toctree_tags.py/0 | {
"file_path": "tokenizers/docs/source/_ext/toctree_tags.py",
"repo_id": "tokenizers",
"token_count": 345
} |
Installation
====================================================================================================
.. only:: python
.. include:: python.inc
.. only:: rust
.. include:: rust.inc
.. only:: node
.. include:: node.inc
| tokenizers/docs/source/installation/main.rst/0 | {
"file_path": "tokenizers/docs/source/installation/main.rst",
"repo_id": "tokenizers",
"token_count": 54
} |
#[macro_use]
extern crate criterion;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
use std::time::{Duration, Instant};
use criterion::black_box;
use criterion::Criterion;
use tokenizers::processors::template::TemplateProcessing;
use tokenizers::{EncodeInput, Encoding, PostProcessor, Token... | tokenizers/tokenizers/benches/layout_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/layout_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 1158
} |
<div align="center">
<h1><code>create-wasm-app</code></h1>
<strong>An <code>npm init</code> template for kick starting a project that uses NPM packages containing Rust-generated WebAssembly and bundles them with Webpack.</strong>
<p>
<a href="https://travis-ci.org/rustwasm/create-wasm-app"><img src="https:... | tokenizers/tokenizers/examples/unstable_wasm/www/README.md/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/README.md",
"repo_id": "tokenizers",
"token_count": 893
} |
#![warn(clippy::all)]
#![allow(clippy::upper_case_acronyms)]
#![doc(html_favicon_url = "https://huggingface.co/favicon.ico")]
#![doc(html_logo_url = "https://huggingface.co/landing/assets/huggingface_logo.svg")]
//! The core of `tokenizers`, written in Rust.
//! Provides an implementation of today's most used tokenize... | tokenizers/tokenizers/src/lib.rs/0 | {
"file_path": "tokenizers/tokenizers/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 2226
} |
//! [WordPiece](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37842.pdf)
//! model.
use crate::models::bpe::BPE;
use crate::tokenizer::{Model, Result, Token};
use std::{
borrow::Cow,
collections::HashMap,
fs::File,
io::prelude::*,
io::{BufRead, BufReader},
path... | tokenizers/tokenizers/src/models/wordpiece/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordpiece/mod.rs",
"repo_id": "tokenizers",
"token_count": 4422
} |
use crate::tokenizer::{Decoder, PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use serde::{de, Deserialize, Deserializer, Serialize};
/// Enum representing options for the metaspace prepending scheme.
#[derive(Debug, Clone, PartialEq, Serialize, Eq, Deserialize, Copy)]
#[serde(rename_all = "snake_c... | tokenizers/tokenizers/src/pre_tokenizers/metaspace.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/metaspace.rs",
"repo_id": "tokenizers",
"token_count": 6687
} |
//! Represents a tokenization pipeline.
//!
//! A [`Tokenizer`](struct.Tokenizer.html) is composed of some of the following parts.
//! - [`Normalizer`](trait.Normalizer.html): Takes care of the text normalization (like unicode normalization).
//! - [`PreTokenizer`](trait.PreTokenizer.html): Takes care of the pre to... | tokenizers/tokenizers/src/tokenizer/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/mod.rs",
"repo_id": "tokenizers",
"token_count": 22643
} |
use tokenizers::decoders::wordpiece::WordPiece as WordPieceDecoder;
use tokenizers::models::bpe::BPE;
use tokenizers::models::wordpiece::WordPiece;
use tokenizers::normalizers::bert::BertNormalizer;
use tokenizers::pre_tokenizers::bert::BertPreTokenizer;
use tokenizers::pre_tokenizers::byte_level::ByteLevel;
use tokeni... | tokenizers/tokenizers/tests/common/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/common/mod.rs",
"repo_id": "tokenizers",
"token_count": 811
} |
# Building a Vanilla JavaScript Application
In this tutorial, you’ll build a simple web application that detects objects in images using Transformers.js! To follow along, all you need is a code editor, a browser, and a simple server (e.g., VS Code Live Server).
Here's how it works: the user clicks “Upload image” and ... | transformers.js/docs/source/tutorials/vanilla-js.md/0 | {
"file_path": "transformers.js/docs/source/tutorials/vanilla-js.md",
"repo_id": "transformers.js",
"token_count": 3621
} |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Transformers.js - Code completion playground</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.jsx"></script>
</bod... | transformers.js/examples/code-completion/index.html/0 | {
"file_path": "transformers.js/examples/code-completion/index.html",
"repo_id": "transformers.js",
"token_count": 133
} |
import path from 'path';
// Needed for deploying to GitHub pages
const BASE_PATH = process.env.BASE_PATH ?? '';
export default {
// config options
base: BASE_PATH,
root: path.join(__dirname, 'src'),
build: {
outDir: path.join(__dirname, 'dist')
},
publicDir: path.join(__dirname, 'publi... | transformers.js/examples/demo-site/vite.config.js/0 | {
"file_path": "transformers.js/examples/demo-site/vite.config.js",
"repo_id": "transformers.js",
"token_count": 126
} |
const { app, BrowserWindow, ipcMain } = require('electron');
const path = require('path');
const { session } = require('electron');
const { run } = require('./model.js');
// Handle creating/removing shortcuts on Windows when installing/uninstalling.
if (require('electron-squirrel-startup')) {
app.quit();
}
const... | transformers.js/examples/electron/src/index.js/0 | {
"file_path": "transformers.js/examples/electron/src/index.js",
"repo_id": "transformers.js",
"token_count": 796
} |
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>MusicGen Web | In-browser text-to-music w/ 🤗 Transformers.js!</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.jsx"... | transformers.js/examples/musicgen-web/index.html/0 | {
"file_path": "transformers.js/examples/musicgen-web/index.html",
"repo_id": "transformers.js",
"token_count": 145
} |
{
"name": "next",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint"
},
"dependencies": {
"@huggingface/transformers": "^3.0.0-alpha.5",
"autoprefixer": "10.4.14",
"eslint": "8.45.0",
"eslint-... | transformers.js/examples/next-client/package.json/0 | {
"file_path": "transformers.js/examples/next-client/package.json",
"repo_id": "transformers.js",
"token_count": 280
} |
import './style.css';
import { AutoModel, AutoProcessor, env, RawImage } from '@xenova/transformers';
// Since we will download the model from the Hugging Face Hub, we can skip the local model check
env.allowLocalModels = false;
// Proxy the WASM backend to prevent the UI from freezing
env.backends.onnx.wasm.proxy =... | transformers.js/examples/remove-background-client/main.js/0 | {
"file_path": "transformers.js/examples/remove-background-client/main.js",
"repo_id": "transformers.js",
"token_count": 1399
} |
'use client'
export function SearchBar({ search }) {
return (<form
onSubmit={e => {
e.preventDefault();
const formData = new FormData(e.target);
const text = formData.get('text');
search(text);
}}
className='relative mb-2'
>
<div c... | transformers.js/examples/semantic-image-search-client/src/app/components/SearchBar.jsx/0 | {
"file_path": "transformers.js/examples/semantic-image-search-client/src/app/components/SearchBar.jsx",
"repo_id": "transformers.js",
"token_count": 734
} |
{
"name": "semantic-image-search",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint"
},
"dependencies": {
"@supabase/supabase-js": "^2.31.0",
"@xenova/transformers": "^2.5.0",
"autoprefixer": "10... | transformers.js/examples/semantic-image-search/package.json/0 | {
"file_path": "transformers.js/examples/semantic-image-search/package.json",
"repo_id": "transformers.js",
"token_count": 319
} |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<link rel="stylesheet" href="style.css" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Transformers.js - Object Detection demo</title>
</head>
<body>
<main class="container">
<label class="... | transformers.js/examples/vanilla-js/index.html/0 | {
"file_path": "transformers.js/examples/vanilla-js/index.html",
"repo_id": "transformers.js",
"token_count": 316
} |
import {
AutoTokenizer,
CLIPTextModelWithProjection,
AutoProcessor,
CLIPVisionModelWithProjection,
RawImage,
dot,
softmax,
} from '@xenova/transformers';
import './style.css';
// Reference the elements that we will need
const status = document.getElementById('status');
const container = d... | transformers.js/examples/webgpu-clip/main.js/0 | {
"file_path": "transformers.js/examples/webgpu-clip/main.js",
"repo_id": "transformers.js",
"token_count": 2330
} |
import { useState } from "react";
import CrossIcon from "./icons/CrossIcon"
export default function ImagePreview({ src, onRemove, ...props }) {
const [hover, setHover] = useState(false);
return (
<div
{...props}
onMouseEnter={() => setHover(true)}
onMouseLeave={() =... | transformers.js/examples/webgpu-vlm/src/components/ImagePreview.jsx/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/components/ImagePreview.jsx",
"repo_id": "transformers.js",
"token_count": 272
} |
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<link rel="icon" type="image/png" href="/logo.png" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Whisper WebGPU</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src... | transformers.js/examples/webgpu-whisper/index.html/0 | {
"file_path": "transformers.js/examples/webgpu-whisper/index.html",
"repo_id": "transformers.js",
"token_count": 153
} |
# 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 CLIPTextOnnxConfig, ViTOnnxConfig
from typing import Dict
class CLIPVisionOnnxConfig(ViTOnnxConfig):
pass
class CLIPT... | transformers.js/scripts/extra/clip.py/0 | {
"file_path": "transformers.js/scripts/extra/clip.py",
"repo_id": "transformers.js",
"token_count": 439
} |
/**
* @file Module used to configure Transformers.js.
*
* **Example:** Disable remote models.
* ```javascript
* import { env } from '@huggingface/transformers';
* env.allowRemoteModels = false;
* ```
*
* **Example:** Set local model path.
* ```javascript
* import { env } from '@huggingface/transformers';
... | transformers.js/src/env.js/0 | {
"file_path": "transformers.js/src/env.js",
"repo_id": "transformers.js",
"token_count": 2090
} |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
import { mergeArrays } from "../../utils/core.js";
import { Tensor } from "../../utils/tensor.js";
import { RawImage } from "../../util... | transformers.js/src/models/janus/processing_janus.js/0 | {
"file_path": "transformers.js/src/models/janus/processing_janus.js",
"repo_id": "transformers.js",
"token_count": 2202
} |
import {
ImageProcessor,
post_process_object_detection,
} from "../../base/image_processors_utils.js";
export class OwlViTImageProcessor extends ImageProcessor {
/** @type {typeof post_process_object_detection} */
post_process_object_detection(...args) {
return post_process_object_detection(..... | transformers.js/src/models/owlvit/image_processing_owlvit.js/0 | {
"file_path": "transformers.js/src/models/owlvit/image_processing_owlvit.js",
"repo_id": "transformers.js",
"token_count": 141
} |
import {
ImageProcessor,
post_process_semantic_segmentation,
} from "../../base/image_processors_utils.js";
export class SegformerImageProcessor extends ImageProcessor {
/** @type {typeof post_process_semantic_segmentation} */
post_process_semantic_segmentation(...args) {
return post_process_... | transformers.js/src/models/segformer/image_processing_segformer.js/0 | {
"file_path": "transformers.js/src/models/segformer/image_processing_segformer.js",
"repo_id": "transformers.js",
"token_count": 145
} |
import {
ImageProcessor,
post_process_object_detection,
} from "../../base/image_processors_utils.js";
export class YolosImageProcessor extends ImageProcessor {
/** @type {typeof post_process_object_detection} */
post_process_object_detection(...args) {
return post_process_object_detection(...... | transformers.js/src/models/yolos/image_processing_yolos.js/0 | {
"file_path": "transformers.js/src/models/yolos/image_processing_yolos.js",
"repo_id": "transformers.js",
"token_count": 140
} |
import { RawImage } from "../src/transformers.js";
const BASE_URL = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/";
const TEST_IMAGES = Object.freeze({
white_image: BASE_URL + "white-image.png",
blue_image: BASE_URL + "blue-image.png",
pattern_3x3: BASE_URL + "pattern_3x3.png",
pat... | transformers.js/tests/asset_cache.js/0 | {
"file_path": "transformers.js/tests/asset_cache.js",
"repo_id": "transformers.js",
"token_count": 880
} |
import { AutoFeatureExtractor, ClapFeatureExtractor } from "../../../src/transformers.js";
import { load_cached_audio } from "../../asset_cache.js";
import { MAX_FEATURE_EXTRACTOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
// ClapFeatureExtractor
describe("ClapFeatureExtract... | transformers.js/tests/models/clap/test_feature_extraction_clap.js/0 | {
"file_path": "transformers.js/tests/models/clap/test_feature_extraction_clap.js",
"repo_id": "transformers.js",
"token_count": 1203
} |
import { Florence2Processor, Florence2ForConditionalGeneration, RawImage, 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 () => {
const texts = ["Describe with a paragraph what is ... | transformers.js/tests/models/florence2/test_modeling_florence2.js/0 | {
"file_path": "transformers.js/tests/models/florence2/test_modeling_florence2.js",
"repo_id": "transformers.js",
"token_count": 1366
} |
import { PreTrainedTokenizer, HeliumForCausalLM } 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("HeliumForCausalLM", () => {
const model_id = "hf-internal-testing/t... | transformers.js/tests/models/helium/test_modeling_helium.js/0 | {
"file_path": "transformers.js/tests/models/helium/test_modeling_helium.js",
"repo_id": "transformers.js",
"token_count": 783
} |
import { AutoFeatureExtractor, MoonshineFeatureExtractor } from "../../../src/transformers.js";
import { load_cached_audio } from "../../asset_cache.js";
import { MAX_FEATURE_EXTRACTOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
// MoonshineFeatureExtractor
describe("Moonshin... | transformers.js/tests/models/moonshine/test_feature_extraction_moonshine.js/0 | {
"file_path": "transformers.js/tests/models/moonshine/test_feature_extraction_moonshine.js",
"repo_id": "transformers.js",
"token_count": 465
} |
import { AutoProcessor, Phi3VProcessor } 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 () => {
const model_id = "onnx-community/Phi-3.5-vision-instruct";
describe("Phi... | transformers.js/tests/models/phi3_v/test_processor_phi3_v.js/0 | {
"file_path": "transformers.js/tests/models/phi3_v/test_processor_phi3_v.js",
"repo_id": "transformers.js",
"token_count": 1404
} |
import { AutoImageProcessor, VitMatteImageProcessor } 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 () => {
// VitMatteImageProcessor
// - tests custom overrides
// ... | transformers.js/tests/models/vitmatte/test_image_processing_vitmatte.js/0 | {
"file_path": "transformers.js/tests/models/vitmatte/test_image_processing_vitmatte.js",
"repo_id": "transformers.js",
"token_count": 1213
} |
import { pipeline, FillMaskPipeline } 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 = "fill-mask";
export default () => {
describe("Fill Mask", () => {
describe("Standard", () => {
... | transformers.js/tests/pipelines/test_pipelines_fill_mask.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_fill_mask.js",
"repo_id": "transformers.js",
"token_count": 3763
} |
import { pipeline, ZeroShotAudioClassificationPipeline } 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_audio } from "../asset_cache.js";
const PIPELINE_ID = "zero-shot-audio-classificatio... | transformers.js/tests/pipelines/test_pipelines_zero_shot_audio_classification.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_zero_shot_audio_classification.js",
"repo_id": "transformers.js",
"token_count": 827
} |
import TerserPlugin from "terser-webpack-plugin";
import { fileURLToPath } from "url";
import path from "path";
import fs from "fs";
const __dirname = path.dirname(fileURLToPath(import.meta.url));
/**
* Plugin to post-process build files. Required to solve certain issues with ESM module output.
* See https://github... | transformers.js/webpack.config.js/0 | {
"file_path": "transformers.js/webpack.config.js",
"repo_id": "transformers.js",
"token_count": 2312
} |
{
"annotations": {
"list": [
{
"builtIn": 1,
"datasource": {
"type": "grafana",
"uid": "-- Grafana --"
},
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"type": "dashboard"... | transformers/benchmark/grafana_dashboard.json/0 | {
"file_path": "transformers/benchmark/grafana_dashboard.json",
"repo_id": "transformers",
"token_count": 42595
} |
apiVersion: v1
kind: PersistentVolume
metadata:
name: huggingface-cluster-disk
spec:
storageClassName: ""
capacity:
storage: 500Gi
accessModes:
- ReadOnlyMany
claimRef:
namespace: default
name: huggingface-cluster-disk-claim
gcePersistentDisk:
pdName: huggingface-cluster-disk
fsType:... | transformers/docker/transformers-pytorch-tpu/dataset.yaml/0 | {
"file_path": "transformers/docker/transformers-pytorch-tpu/dataset.yaml",
"repo_id": "transformers",
"token_count": 274
} |
<!--Copyright 2022 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... | transformers/docs/source/ar/tasks/question_answering.md/0 | {
"file_path": "transformers/docs/source/ar/tasks/question_answering.md",
"repo_id": "transformers",
"token_count": 8612
} |
<!--Copyright 2020 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... | transformers/docs/source/de/add_new_model.md/0 | {
"file_path": "transformers/docs/source/de/add_new_model.md",
"repo_id": "transformers",
"token_count": 23975
} |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/de/transformers_agents.md/0 | {
"file_path": "transformers/docs/source/de/transformers_agents.md",
"repo_id": "transformers",
"token_count": 6629
} |
<!--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... | transformers/docs/source/en/conversations.md/0 | {
"file_path": "transformers/docs/source/en/conversations.md",
"repo_id": "transformers",
"token_count": 4633
} |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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