text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
|---|---|---|---|
""" EVA
EVA ViT from https://github.com/baaivision/EVA , paper: https://arxiv.org/abs/2211.07636
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
Tiejun ... | pytorch-image-models/timm/models/eva.py/0 | {
"file_path": "pytorch-image-models/timm/models/eva.py",
"repo_id": "pytorch-image-models",
"token_count": 47165
} | 260 |
"""
InceptionNeXt paper: https://arxiv.org/abs/2303.16900
Original implementation & weights from: https://github.com/sail-sg/inceptionnext
"""
from functools import partial
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT... | pytorch-image-models/timm/models/inception_next.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_next.py",
"repo_id": "pytorch-image-models",
"token_count": 8611
} | 261 |
""" Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... | pytorch-image-models/timm/models/nest.py/0 | {
"file_path": "pytorch-image-models/timm/models/nest.py",
"repo_id": "pytorch-image-models",
"token_count": 11329
} | 262 |
"""PyTorch SelecSLS Net example for ImageNet Classification
License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode)
Author: Dushyant Mehta (@mehtadushy)
SelecSLS (core) Network Architecture as proposed in "XNect: Real-time Multi-person 3D
Human Pose Estimation with a Single RGB Camera, Mehta et al."... | pytorch-image-models/timm/models/selecsls.py/0 | {
"file_path": "pytorch-image-models/timm/models/selecsls.py",
"repo_id": "pytorch-image-models",
"token_count": 6452
} | 263 |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https:... | pytorch-image-models/timm/models/vision_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 91969
} | 264 |
""" Adafactor (Big Vision variant) for PyTorch
Adapted from the implementation in big vision: https://github.com/google-research/big_vision
Described in 'Scaling Vision Transformers': https://arxiv.org/abs/2106.04560
References for added functionality:
Cautious Optimizers: https://arxiv.org/abs/2411.16085
Wh... | pytorch-image-models/timm/optim/adafactor_bv.py/0 | {
"file_path": "pytorch-image-models/timm/optim/adafactor_bv.py",
"repo_id": "pytorch-image-models",
"token_count": 6669
} | 265 |
""" Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... | pytorch-image-models/timm/optim/nvnovograd.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nvnovograd.py",
"repo_id": "pytorch-image-models",
"token_count": 2509
} | 266 |
from .agc import adaptive_clip_grad
from .attention_extract import AttentionExtract
from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .decay_batch import decay_batch_step, check_batch_size_retry
from .distributed import distribute_bn,... | pytorch-image-models/timm/utils/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/utils/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 264
} | 267 |
""" Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | pytorch-image-models/timm/utils/summary.py/0 | {
"file_path": "pytorch-image-models/timm/utils/summary.py",
"repo_id": "pytorch-image-models",
"token_count": 633
} | 268 |
<!---
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 ... | smolagents/README.md/0 | {
"file_path": "smolagents/README.md",
"repo_id": "smolagents",
"token_count": 4304
} | 269 |
# Installation Options
The `smolagents` library can be installed using pip. Here are the different installation methods and options available.
## Prerequisites
- Python 3.10 or newer
- Python package manager: [`pip`](https://pip.pypa.io/en/stable/) or [`uv`](https://docs.astral.sh/uv/)
## Virtual Environment
It's s... | smolagents/docs/source/en/installation.md/0 | {
"file_path": "smolagents/docs/source/en/installation.md",
"repo_id": "smolagents",
"token_count": 2204
} | 270 |
# Text-to-SQL
[[open-in-colab]]
इस ट्यूटोरियल में, हम देखेंगे कि कैसे `smolagents` का उपयोग करके एक एजेंट को SQL का उपयोग करने के लिए लागू किया जा सकता है।
> आइए सबसे महत्वपूर्ण प्रश्न से शुरू करें: इसे साधारण क्यों नहीं रखें और एक सामान्य text-to-SQL पाइपलाइन का उपयोग करें?
एक सामान्य text-to-SQL पाइपलाइन कमजोर हो... | smolagents/docs/source/hi/examples/text_to_sql.md/0 | {
"file_path": "smolagents/docs/source/hi/examples/text_to_sql.md",
"repo_id": "smolagents",
"token_count": 5027
} | 271 |
# Agent 简介
> [!TIP]
> 译者注：Agent 的业内术语是“智能体”。本译文将保留 agent，不作翻译，以带来更高效的阅读体验。(在中文为主的文章中，It's easier to 注意到英文。Attention Is All You Need!)
## 🤔 什么是 agent？
任何使用 AI 的高效系统都需要为 LLM 提供某种访问现实世界的方式：例如调用搜索工具获取外部信息，或者操作某些程序以完成任务。换句话说，LLM 应该具有 **_Agent 能力_**。Agent 程序是 LLM 通往外部世界的门户。
> [!TIP]
> AI agent 是 **LLM 输出控制工作流的程序**。
任何利... | smolagents/docs/source/zh/conceptual_guides/intro_agents.md/0 | {
"file_path": "smolagents/docs/source/zh/conceptual_guides/intro_agents.md",
"repo_id": "smolagents",
"token_count": 5097
} | 272 |
# This is a config for E2B sandbox template.
# You can use template ID (qywp2ctmu2q7jzprcf4j) to create a sandbox:
# Python SDK
# from e2b import Sandbox, AsyncSandbox
# sandbox = Sandbox("qywp2ctmu2q7jzprcf4j") # Sync sandbox
# sandbox = await AsyncSandbox.create("qywp2ctmu2q7jzprcf4j") # Async sandbox
# JS SDK
# im... | smolagents/e2b.toml/0 | {
"file_path": "smolagents/e2b.toml",
"repo_id": "smolagents",
"token_count": 254
} | 273 |
import re
import string
import warnings
def normalize_number_str(number_str: str) -> float:
# we replace these common units and commas to allow
# conversion to float
for char in ["$", "%", ","]:
number_str = number_str.replace(char, "")
try:
return float(number_str)
except ValueErr... | smolagents/examples/open_deep_research/scripts/gaia_scorer.py/0 | {
"file_path": "smolagents/examples/open_deep_research/scripts/gaia_scorer.py",
"repo_id": "smolagents",
"token_count": 1643
} | 274 |
<jupyter_start><jupyter_code>!pip install -e .. datasets sympy numpy matplotlib seaborn -q # Install dev version of smolagents + some packages
# Benchmark date
# - set a concrete date:
DATE = "2024-12-26"
# - or use default: today
# DATE = None
# Evaluation dataset
# - the dataset is gated, so you must first visit it... | smolagents/examples/smolagents_benchmark/score.ipynb/0 | {
"file_path": "smolagents/examples/smolagents_benchmark/score.ipynb",
"repo_id": "smolagents",
"token_count": 4251
} | 275 |
system_prompt: |-
You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
To solve the task, you must plan forward to pr... | smolagents/src/smolagents/prompts/code_agent.yaml/0 | {
"file_path": "smolagents/src/smolagents/prompts/code_agent.yaml",
"repo_id": "smolagents",
"token_count": 4865
} | 276 |
# 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_default_tools.py/0 | {
"file_path": "smolagents/tests/test_default_tools.py",
"repo_id": "smolagents",
"token_count": 1943
} | 277 |
# 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_utils.py/0 | {
"file_path": "smolagents/tests/test_utils.py",
"repo_id": "smolagents",
"token_count": 7047
} | 278 |
FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu24.04 AS deps
ARG llamacpp_version=b4827
ARG llamacpp_cuda=OFF
ARG llamacpp_native=ON
ARG llamacpp_cpu_arm_arch=native
ARG cuda_arch=75-real;80-real;86-real;89-real;90-real
WORKDIR /opt/src
ENV DEBIAN_FRONTEND=noninteractive
RUN apt update && apt upgrade -y && apt install -y... | text-generation-inference/Dockerfile_llamacpp/0 | {
"file_path": "text-generation-inference/Dockerfile_llamacpp",
"repo_id": "text-generation-inference",
"token_count": 1128
} | 279 |
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod client;
mod sharded_client;
pub use client::Client;
pub use pb::generate::v3::{
input_chunk::Chunk, Batch, CachedBatch, FinishReason, GeneratedText, Generation, GrammarType,
HealthResponse, Image, InfoResponse, Input, InputChunk, NextTokenChooserParam... | text-generation-inference/backends/client/src/v3/mod.rs/0 | {
"file_path": "text-generation-inference/backends/client/src/v3/mod.rs",
"repo_id": "text-generation-inference",
"token_count": 142
} | 280 |
from typing import Tuple
from dataclasses import dataclass, field
import torch
from text_generation_server.models.globals import BLOCK_SIZE
from text_generation_server.utils.weights import Weights
@dataclass
class KVScales:
"""
Key-value scales for FP8 KV cache.
This data class stores key and value sca... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/attention/kv_cache.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/attention/kv_cache.py",
"repo_id": "text-generation-inference",
"token_count": 2679
} | 281 |
import torch
from torch.nn import functional as F
class FastLinear(torch.nn.Module):
def __init__(
self,
weight,
bias,
) -> None:
super().__init__()
self.weight = torch.nn.Parameter(weight, requires_grad=False)
if bias is not None:
self.bias = torch.... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/linear.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/linear.py",
"repo_id": "text-generation-inference",
"token_count": 469
} | 282 |
# coding=utf-8
# Copyright 2022 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/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 12281
} | 283 |
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.layers.attention import (
paged_attention,
attention,
set_block_mapping,... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 7035
} | 284 |
import torch
from PIL import Image
from io import BytesIO
from dataclasses import dataclass
from opentelemetry import trace
from typing import Iterable, Optional, Tuple, List, Type, Dict
from transformers import PreTrainedTokenizerBase
from transformers.image_processing_utils import select_best_resolution
from text_ge... | text-generation-inference/backends/gaudi/server/text_generation_server/models/flash_vlm_causal_lm.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/flash_vlm_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 20373
} | 285 |
import torch
def get_hpu_free_memory(device, memory_fraction):
free_hpu_memory, _ = torch.hpu.mem_get_info()
return free_hpu_memory
def synchronize_hpu(device):
torch.hpu.synchronize()
def noop(*args, **kwargs):
pass
empty_cache = noop
synchronize = synchronize_hpu
get_free_memory = get_hpu_free... | text-generation-inference/backends/gaudi/server/text_generation_server/utils/import_utils.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/utils/import_utils.py",
"repo_id": "text-generation-inference",
"token_count": 131
} | 286 |
#!/bin/bash
ldconfig 2>/dev/null || echo 'unable to refresh ld cache, not a big deal in most cases'
# Check if --sharded argument is present in the command line arguments
if [[ "$*" == *"--sharded true"* ]]; then
echo 'setting PT_HPU_ENABLE_LAZY_COLLECTIVES=1 for sharding'
export PT_HPU_ENABLE_LAZY_COLLECTIVES=1
... | text-generation-inference/backends/gaudi/tgi-entrypoint.sh/0 | {
"file_path": "text-generation-inference/backends/gaudi/tgi-entrypoint.sh",
"repo_id": "text-generation-inference",
"token_count": 127
} | 287 |
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": 932
} | 288 |
#ifndef TGI_BACKEND_TRTLLM
#define TGI_BACKEND_TRTLLM
#include <cmath>
#include <cstdint>
#include <expected>
#include <fstream>
#include <list>
#include <span>
#include <nlohmann/json.hpp>
#include <spdlog/spdlog.h>
#include <spdlog/fmt/fmt.h>
#include <tensorrt_llm/executor/executor.h>
namespace huggingface::tgi:... | text-generation-inference/backends/trtllm/csrc/backend.hpp/0 | {
"file_path": "text-generation-inference/backends/trtllm/csrc/backend.hpp",
"repo_id": "text-generation-inference",
"token_count": 3772
} | 289 |
use crate::block_allocator::{Allocator, BlockAllocation};
use slotmap::{DefaultKey, SlotMap};
use std::hash::{Hash, Hasher};
use std::{
collections::{BTreeSet, HashMap},
sync::Arc,
};
fn hash(slice: &[u32]) -> u64 {
assert!(!slice.is_empty());
if slice.len() == 1 {
slice[0] as u64
} else {
... | text-generation-inference/backends/v3/src/radix.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/radix.rs",
"repo_id": "text-generation-inference",
"token_count": 18123
} | 290 |
import pytest
from text_generation import Client, AsyncClient
from text_generation.errors import NotFoundError, ValidationError
from text_generation.types import FinishReason
def test_generate(llama_7b_url, hf_headers):
client = Client(llama_7b_url, hf_headers)
response = client.generate("test", max_new_toke... | text-generation-inference/clients/python/tests/test_client.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_client.py",
"repo_id": "text-generation-inference",
"token_count": 2112
} | 291 |
# Llamacpp Backend
The llamacpp backend facilitates the deployment of large language models
(LLMs) by integrating [llama.cpp][llama.cpp], an advanced inference engine
optimized for both CPU and GPU computation. This backend is a component
of Hugging Face’s **Text Generation Inference (TGI)** suite,
specifically design... | text-generation-inference/docs/source/backends/llamacpp.md/0 | {
"file_path": "text-generation-inference/docs/source/backends/llamacpp.md",
"repo_id": "text-generation-inference",
"token_count": 2663
} | 292 |
# Guidance
## What is Guidance?
Guidance is a feature that allows users to constrain the generation of a large language model with a specified grammar. This feature is particularly useful when you want to generate text that follows a specific structure or uses a specific set of words or produce output in a specific f... | text-generation-inference/docs/source/conceptual/guidance.md/0 | {
"file_path": "text-generation-inference/docs/source/conceptual/guidance.md",
"repo_id": "text-generation-inference",
"token_count": 1237
} | 293 |
# Multi-backend support
TGI (Text Generation Inference) offers flexibility by supporting multiple backends for serving large language models (LLMs).
With multi-backend support, you can choose the backend that best suits your needs,
whether you prioritize performance, ease of use, or compatibility with specific hardwar... | text-generation-inference/docs/source/multi_backend_support.md/0 | {
"file_path": "text-generation-inference/docs/source/multi_backend_support.md",
"repo_id": "text-generation-inference",
"token_count": 381
} | 294 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 323,
"logprob": -1.1171875,
"special": false,
"text": " and"
},
{
"id": 1268,
"logprob... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8a8_int/test_compressed_tensors_w8a8_int.json",
"repo_id": "text-generation-inference",
"token_count": 873
} | 295 |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": " the royal mouse? It is a little more slender and only weighs around 1.5 pounds for males and 1.3 pounds",
"role": "assistant"
}
}
],
"created": 1732541190,
"i... | text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt_continue.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_continue_final_message/test_llama_completion_single_prompt_continue.json",
"repo_id": "text-generation-inference",
"token_count": 260
} | 296 |
{
"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
} | 297 |
{
"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
} | 298 |
{
"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
} | 299 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 1241,
"logprob": -0.9863281,
"special": false,
"text": "():"
},
{
"id": 258,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder.json",
"repo_id": "text-generation-inference",
"token_count": 866
} | 300 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "{ \"unit\": \"fahrenheit\", \"temperature\": [ 72, 79, 88 ] }",
"role": "assistant"
}
}
],
"created": 1740095072,
"id": "",
"model": "TinyLlama/TinyLlama-1.1B-... | text-generation-inference/integration-tests/models/__snapshots__/test_grammar_response_format_llama/test_grammar_response_format_llama_json.1.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_grammar_response_format_llama/test_grammar_response_format_llama_json.1.json",
"repo_id": "text-generation-inference",
"token_count": 255
} | 301 |
[
{
"choices": [
{
"delta": {
"content": "Once",
"role": "assistant",
"tool_calls": null
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1741695408,
"id": "",
"model": "meta-llama/Llama-3.1-8B-... | text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_sea_creatures_stream_auto.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_sea_creatures_stream_auto.json",
"repo_id": "text-generation-inference",
"token_count": 4845
} | 302 |
import pytest
@pytest.fixture(scope="module")
def chat_handle(launcher):
with launcher(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def chat_client(chat_handle):
await chat_handle.health(300)
return chat_handle.client
| text-generation-inference/integration-tests/models/test_chat_stream_options.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_chat_stream_options.py",
"repo_id": "text-generation-inference",
"token_count": 128
} | 303 |
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
} | 304 |
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
} | 305 |
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": 783
} | 306 |
# import base64
# from io import BytesIO
# from PIL import Image
#
# import pytest
#
#
# @pytest.fixture(scope="module")
# def flash_llama4_handle(launcher):
# with launcher("ll-re/Llama-4-Scout-17B-16E-Instruct", num_shard=8) as handle:
# yield handle
#
#
# @pytest.fixture(scope="module")
# async def flash... | text-generation-inference/integration-tests/models/test_transformers_llama4.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_transformers_llama4.py",
"repo_id": "text-generation-inference",
"token_count": 2864
} | 307 |
import json
def main():
with open("./ShareGPT_V3_unfiltered_cleaned_split.json", "r") as f:
data = json.load(f)
# Select only the first 2k conversations that start with a human.
max = 2000
conversations = []
for conversation in data:
conv = conversation.get("conversations")
... | text-generation-inference/load_tests/filter.py/0 | {
"file_path": "text-generation-inference/load_tests/filter.py",
"repo_id": "text-generation-inference",
"token_count": 307
} | 308 |
# Router
Also named `webserver` throughout the docs.
This router is handling most of the logic to handle the "batches" tell
when to pass new `prefill` requests and pausing `decode` requests, which ones etc...
It uses gRPC to communicate with the shards which can therefore be kept
much simpler and focus on having the... | text-generation-inference/router/README.md/0 | {
"file_path": "text-generation-inference/router/README.md",
"repo_id": "text-generation-inference",
"token_count": 1175
} | 309 |
#!/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
} | 310 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _cuda_compat_cuh
#define _cuda_compat_cuh
// atomicAdd for half types, to support CC < 7.x
__device__ __forceinline__ void atomicAdd_half(half* address, half val)
{
unsigned int * address_as_ui = (unsigned int *) ((char *)address - (... | text-generation-inference/server/exllama_kernels/exllama_kernels/cu_compat.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cu_compat.cuh",
"repo_id": "text-generation-inference",
"token_count": 692
} | 311 |
#ifndef _util_h
#define _util_h
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, __y) printf("%s, %s: %i, %i\n", #__x, #__y, __x, __y)
#define DBGI3(__x, __y, __z) printf("%s, %s, %s: %i, %i, %i\n", #__x, #__y, #__z, __x, __y, __z)
#define DBGF(__x) printf("%s: %... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cpp/util.h/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cpp/util.h",
"repo_id": "text-generation-inference",
"token_count": 296
} | 312 |
#ifndef _util_cuh
#define _util_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#include <ATen/cuda/CUDAContext.h>
#define DIVIDE(x, size) (((x) + (size) - 1) / (size))
#define DBGS(__x) printf("%s\n", __x)
#define DBGI(__x) printf("%s: %i\n", #__x, __x)
#define DBGI2(__x, _... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/util.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/util.cuh",
"repo_id": "text-generation-inference",
"token_count": 1115
} | 313 |
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
} | 314 |
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
} | 315 |
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
} | 316 |
parser: '@typescript-eslint/parser'
parserOptions:
ecmaFeatures:
jsx: true
ecmaVersion: latest
sourceType: module
project: ./tsconfig.json
env:
browser: true
es6: true
node: true
jest: true
ignorePatterns: ['index.js', 'target/']
plugins:
- import
- '@typescript-eslint'
extends:
- eslint:... | tokenizers/bindings/node/.eslintrc.yml/0 | {
"file_path": "tokenizers/bindings/node/.eslintrc.yml",
"repo_id": "tokenizers",
"token_count": 2733
} | 317 |
/* eslint-disable prettier/prettier */
// For a detailed explanation regarding each configuration property, visit:
// https://jestjs.io/docs/en/configuration.html
module.exports = {
// All imported modules in your tests should be mocked automatically
// automock: false,
// Stop running tests after `n` failures
... | tokenizers/bindings/node/jest.config.js/0 | {
"file_path": "tokenizers/bindings/node/jest.config.js",
"repo_id": "tokenizers",
"token_count": 1715
} | 318 |
# `tokenizers-darwin-arm64`
This is the **aarch64-apple-darwin** binary for `tokenizers`
| tokenizers/bindings/node/npm/darwin-arm64/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/darwin-arm64/README.md",
"repo_id": "tokenizers",
"token_count": 33
} | 319 |
# `tokenizers-win32-arm64-msvc`
This is the **aarch64-pc-windows-msvc** binary for `tokenizers`
| tokenizers/bindings/node/npm/win32-arm64-msvc/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/win32-arm64-msvc/README.md",
"repo_id": "tokenizers",
"token_count": 38
} | 320 |
pub mod models;
pub mod tokenizer;
| tokenizers/bindings/node/src/tasks/mod.rs/0 | {
"file_path": "tokenizers/bindings/node/src/tasks/mod.rs",
"repo_id": "tokenizers",
"token_count": 11
} | 321 |
import os
import time
import argparse
from datasets import load_dataset
from tiktoken.load import load_tiktoken_bpe
import tiktoken
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download
from typing import Tuple, List
from multiprocessing import Process
MODEL_ID = "meta-llama/Meta-Llama-3.1-8B"
D... | tokenizers/bindings/python/benches/test_tiktoken.py/0 | {
"file_path": "tokenizers/bindings/python/benches/test_tiktoken.py",
"repo_id": "tokenizers",
"token_count": 2288
} | 322 |
from typing import Dict, Iterator, List, Optional, Tuple, Union
from .. import AddedToken, Tokenizer, decoders, pre_tokenizers, trainers
from ..models import BPE
from ..normalizers import BertNormalizer, Lowercase, Sequence, unicode_normalizer_from_str
from .base_tokenizer import BaseTokenizer
class CharBPETokenizer... | tokenizers/bindings/python/py_src/tokenizers/implementations/char_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/char_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 2501
} | 323 |
[project]
name = "tokenizers"
requires-python = ">=3.9"
authors = [
{ name = "Nicolas Patry", email = "patry.nicolas@protonmail.com" },
{ name = "Anthony Moi", email = "anthony@huggingface.co" },
]
classifiers = [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Intended Audi... | tokenizers/bindings/python/pyproject.toml/0 | {
"file_path": "tokenizers/bindings/python/pyproject.toml",
"repo_id": "tokenizers",
"token_count": 684
} | 324 |
use std::sync::{Arc, RwLock};
use crate::models::PyModel;
use crate::tokenizer::PyAddedToken;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::{Deserialize, Serialize};
use tk::models::TrainerWrapper;
use tk::Trainer;
use tokenizers as tk;
/// Base class for all trainers
///
/// This class i... | tokenizers/bindings/python/src/trainers.rs/0 | {
"file_path": "tokenizers/bindings/python/src/trainers.rs",
"repo_id": "tokenizers",
"token_count": 17893
} | 325 |
import json
import pickle
import pytest
from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.pre_tokenizers import ByteLevel as ByteLevelPreTokenizer
from tokenizers.processors import (
BertProcessing,
ByteLevel,
PostProcessor,
RobertaProcessing,
Sequence,
Templat... | tokenizers/bindings/python/tests/bindings/test_processors.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_processors.py",
"repo_id": "tokenizers",
"token_count": 4406
} | 326 |
## Requirements
In order to generate the documentation, it is necessary to have a Python environment with the
following:
```python
pip install sphinx sphinx_rtd_theme setuptools_rust
```
It is also necessary to have the `tokenizers` library in this same environment, for Sphinx to
generate all the API Reference and li... | tokenizers/docs/README.md/0 | {
"file_path": "tokenizers/docs/README.md",
"repo_id": "tokenizers",
"token_count": 266
} | 327 |
.. only:: python
.. include:: python.inc
.. only:: rust
.. include:: rust.inc
.. only:: node
.. include:: node.inc
| tokenizers/docs/source/api/reference.rst/0 | {
"file_path": "tokenizers/docs/source/api/reference.rst",
"repo_id": "tokenizers",
"token_count": 47
} | 328 |
DATA_DIR = data
BENCHMARK_DIR = benches
TESTS_DIR = tests
dir_guard=@mkdir -p $(@D)
SHARED_RESOURCES = $(DATA_DIR)/gpt2-vocab.json $(DATA_DIR)/gpt2-merges.txt $(DATA_DIR)/bert-base-uncased-vocab.txt $(DATA_DIR)/big.txt $(DATA_DIR)/small.txt $(DATA_DIR)/albert-base-v1-tokenizer.json $(DATA_DIR)/llama-3-tokenizer.json... | tokenizers/tokenizers/Makefile/0 | {
"file_path": "tokenizers/tokenizers/Makefile",
"repo_id": "tokenizers",
"token_count": 1080
} | 329 |
//! Test suite for the Web and headless browsers.
#![cfg(target_arch = "wasm32")]
extern crate wasm_bindgen_test;
use wasm_bindgen_test::*;
wasm_bindgen_test_configure!(run_in_browser);
#[wasm_bindgen_test]
fn pass() {
assert_eq!(1 + 1, 2);
}
| tokenizers/tokenizers/examples/unstable_wasm/tests/web.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/tests/web.rs",
"repo_id": "tokenizers",
"token_count": 109
} | 330 |
use super::model::Unigram;
use serde::{
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
impl Serialize for Unigram {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
{
let mut model ... | tokenizers/tokenizers/src/models/unigram/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/serialization.rs",
"repo_id": "tokenizers",
"token_count": 1824
} | 331 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::macro_rules_attribute;
#[derive(Default, Copy, Clone, Debug)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct NFD;
impl Normalizer for NFD {
fn normalize(&self, normalized: &mut NormalizedString) -> Result<()> {
normalize... | tokenizers/tokenizers/src/normalizers/unicode.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/unicode.rs",
"repo_id": "tokenizers",
"token_count": 1317
} | 332 |
use crate::tokenizer::{Encoding, PostProcessor, Result};
use ahash::AHashMap;
use serde::{Deserialize, Serialize};
use std::iter::FromIterator;
#[derive(Serialize, Deserialize, Clone, Debug, PartialEq, Eq)]
#[serde(tag = "type")]
pub struct BertProcessing {
pub sep: (String, u32),
pub cls: (String, u32),
}
im... | tokenizers/tokenizers/src/processors/bert.rs/0 | {
"file_path": "tokenizers/tokenizers/src/processors/bert.rs",
"repo_id": "tokenizers",
"token_count": 7594
} | 333 |
pub(crate) mod cache;
#[cfg(feature = "http")]
pub(crate) mod from_pretrained;
#[cfg(all(feature = "fancy-regex", not(feature = "onig")))]
mod fancy;
#[cfg(all(feature = "fancy-regex", not(feature = "onig")))]
pub use fancy::SysRegex;
#[cfg(feature = "onig")]
mod onig;
#[cfg(feature = "onig")]
pub use crate::utils::on... | tokenizers/tokenizers/src/utils/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/mod.rs",
"repo_id": "tokenizers",
"token_count": 3161
} | 334 |
// Based on [this tutorial](https://github.com/jsdoc2md/jsdoc-to-markdown/wiki/How-to-create-one-output-file-per-class).
import fs from 'fs';
import path from 'path';
import url from 'url';
import jsdoc2md from 'jsdoc-to-markdown';
const docs = path.dirname(path.dirname(url.fileURLToPath(import.meta.url)));
const ro... | transformers.js/docs/scripts/generate.js/0 | {
"file_path": "transformers.js/docs/scripts/generate.js",
"repo_id": "transformers.js",
"token_count": 790
} | 335 |
# Installation
<include>
{
"path": "../snippets/2_installation.snippet"
}
</include>
| transformers.js/docs/source/installation.md/0 | {
"file_path": "transformers.js/docs/source/installation.md",
"repo_id": "transformers.js",
"token_count": 38
} | 336 |
import { defineConfig } from 'vite'
import react from '@vitejs/plugin-react'
// https://vitejs.dev/config/
export default defineConfig({
plugins: [react()],
})
| transformers.js/examples/cross-encoder/vite.config.js/0 | {
"file_path": "transformers.js/examples/cross-encoder/vite.config.js",
"repo_id": "transformers.js",
"token_count": 54
} | 337 |
# Transformers.js - Sample Electron application
An example project to show how to run 🤗 Transformers in an [Electron](https://www.electronjs.org/) application.
## Getting Started
1. Clone the repo and enter the project directory:
```bash
git clone https://github.com/huggingface/transformers.js.git
cd tr... | transformers.js/examples/electron/README.md/0 | {
"file_path": "transformers.js/examples/electron/README.md",
"repo_id": "transformers.js",
"token_count": 528
} | 338 |
// background.js - Handles requests from the UI, runs the model, then sends back a response
import { pipeline } from '@huggingface/transformers';
class PipelineSingleton {
static task = 'text-classification';
static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
static instance = null;
... | transformers.js/examples/extension/src/background.js/0 | {
"file_path": "transformers.js/examples/extension/src/background.js",
"repo_id": "transformers.js",
"token_count": 1038
} | 339 |
/** @type {import('tailwindcss').Config} */
module.exports = {
content: [
'./src/pages/**/*.{js,ts,jsx,tsx,mdx}',
'./src/components/**/*.{js,ts,jsx,tsx,mdx}',
'./src/app/**/*.{js,ts,jsx,tsx,mdx}',
],
theme: {
extend: {
backgroundImage: {
'gradient-radial': 'radial-gradient(var(--tw-g... | transformers.js/examples/next-client/tailwind.config.js/0 | {
"file_path": "transformers.js/examples/next-client/tailwind.config.js",
"repo_id": "transformers.js",
"token_count": 236
} | 340 |
export default function Progress({ text, percentage }) {
percentage = percentage ?? 0;
return (
<div className="progress-container">
<div className='progress-bar' style={{ 'width': `${percentage}%` }}>{text} ({`${percentage.toFixed(2)}%`})</div>
</div>
);
}
| transformers.js/examples/react-translator/src/components/Progress.jsx/0 | {
"file_path": "transformers.js/examples/react-translator/src/components/Progress.jsx",
"repo_id": "transformers.js",
"token_count": 99
} | 341 |
{
"name": "segment-anything-client",
"private": true,
"version": "0.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "vite build",
"preview": "vite preview"
},
"dependencies": {
"@huggingface/transformers": "^3.0.0-alpha.0"
},
"devDependencies": {
"vite": "^5.2.9"
}
}... | transformers.js/examples/segment-anything-client/package.json/0 | {
"file_path": "transformers.js/examples/segment-anything-client/package.json",
"repo_id": "transformers.js",
"token_count": 152
} | 342 |
export const SPEAKERS = {
"US female 1": "cmu_us_slt_arctic-wav-arctic_a0001",
"US female 2": "cmu_us_clb_arctic-wav-arctic_a0001",
"US male 1": "cmu_us_bdl_arctic-wav-arctic_a0003",
"US male 2": "cmu_us_rms_arctic-wav-arctic_a0003",
"Canadian male": "cmu_us_jmk_arctic-wav-arctic_a0002",
"Scotti... | transformers.js/examples/text-to-speech-client/src/constants.js/0 | {
"file_path": "transformers.js/examples/text-to-speech-client/src/constants.js",
"repo_id": "transformers.js",
"token_count": 247
} | 343 |
import { Fragment } from 'react';
const COLOURS = [
'bg-purple-300',
'bg-green-300',
'bg-yellow-300',
'bg-red-300',
'bg-blue-300',
]
export function Token({ text, position, margin }) {
const textWithLineBreaks = text.split('\n').map((line, index, array) => (
<Fragment key={index}>
... | transformers.js/examples/tokenizer-playground/src/components/Token.jsx/0 | {
"file_path": "transformers.js/examples/tokenizer-playground/src/components/Token.jsx",
"repo_id": "transformers.js",
"token_count": 287
} | 344 |
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<link rel="icon" type="image/svg+xml" href="/vite.svg" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Moondream WebGPU</title>
</head>
<body>
<div id="root"></div>
<script type="module" src... | transformers.js/examples/webgpu-vlm/index.html/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/index.html",
"repo_id": "transformers.js",
"token_count": 157
} | 345 |
# MIT License
#
# Copyright (c) Microsoft Corporation, Hugging Face. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation t... | transformers.js/scripts/float16.py/0 | {
"file_path": "transformers.js/scripts/float16.py",
"repo_id": "transformers.js",
"token_count": 16340
} | 346 |
/**
* @file Definitions of all models available in Transformers.js.
*
* **Example:** Load and run an `AutoModel`.
*
* ```javascript
* import { AutoModel, AutoTokenizer } from '@huggingface/transformers';
*
* let tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased');
* let model = awai... | transformers.js/src/models.js/0 | {
"file_path": "transformers.js/src/models.js",
"repo_id": "transformers.js",
"token_count": 127141
} | 347 |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
export class DPTImageProcessor extends ImageProcessor { }
export class DPTFeatureExtractor extends DPTImageProcessor { } // NOTE: extends DPTImageProcessor
| transformers.js/src/models/dpt/image_processing_dpt.js/0 | {
"file_path": "transformers.js/src/models/dpt/image_processing_dpt.js",
"repo_id": "transformers.js",
"token_count": 70
} | 348 |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
export class JinaCLIPProcessor extends Processor {
static tokenizer_class = AutoTokenizer
static image_processor_class = AutoI... | transformers.js/src/models/jina_clip/processing_jina_clip.js/0 | {
"file_path": "transformers.js/src/models/jina_clip/processing_jina_clip.js",
"repo_id": "transformers.js",
"token_count": 291
} | 349 |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
export class OwlViTProcessor extends Processor {
static tokenizer_class = AutoTokenizer
static image_processor_class = AutoImage... | transformers.js/src/models/owlvit/processing_owlvit.js/0 | {
"file_path": "transformers.js/src/models/owlvit/processing_owlvit.js",
"repo_id": "transformers.js",
"token_count": 95
} | 350 |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
export class SiglipImageProcessor extends ImageProcessor { }
| transformers.js/src/models/siglip/image_processing_siglip.js/0 | {
"file_path": "transformers.js/src/models/siglip/image_processing_siglip.js",
"repo_id": "transformers.js",
"token_count": 44
} | 351 |
const WHISPER_LANGUAGES = [
["en", "english"],
["zh", "chinese"],
["de", "german"],
["es", "spanish"],
["ru", "russian"],
["ko", "korean"],
["fr", "french"],
["ja", "japanese"],
["pt", "portuguese"],
["tr", "turkish"],
["pl", "polish"],
["ca", "catalan"],
["nl", "du... | transformers.js/src/models/whisper/common_whisper.js/0 | {
"file_path": "transformers.js/src/models/whisper/common_whisper.js",
"repo_id": "transformers.js",
"token_count": 1956
} | 352 |
/**
* @file Utility functions to interact with the Hugging Face Hub (https://huggingface.co/models)
*
* @module utils/hub
*/
import fs from 'node:fs';
import path from 'node:path';
import { apis, env } from '../env.js';
import { dispatchCallback } from './core.js';
/**
* @typedef {boolean|number} ExternalData ... | transformers.js/src/utils/hub.js/0 | {
"file_path": "transformers.js/src/utils/hub.js",
"repo_id": "transformers.js",
"token_count": 12319
} | 353 |
import { BertTokenizer, BertModel, BertForMaskedLM, BertForSequenceClassification, BertForTokenClassification, BertForQuestionAnswering } 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 () ... | transformers.js/tests/models/bert/test_modeling_bert.js/0 | {
"file_path": "transformers.js/tests/models/bert/test_modeling_bert.js",
"repo_id": "transformers.js",
"token_count": 3074
} | 354 |
import { AutoImageProcessor, SamImageProcessor } 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 () => {
// SamImageProcessor
// - tests normal padding (do_pad=true, pad... | transformers.js/tests/models/sam/test_image_processing_sam.js/0 | {
"file_path": "transformers.js/tests/models/sam/test_image_processing_sam.js",
"repo_id": "transformers.js",
"token_count": 1475
} | 355 |
import { WhisperTokenizer } from "../../../src/tokenizers.js";
import { BASE_TEST_STRINGS, WHISPER_TEST_STRINGS } from "../test_strings.js";
import { compare } from "../../test_utils.js";
export const TOKENIZER_CLASS = WhisperTokenizer;
export const TEST_CONFIG = {
"onnx-community/whisper-tiny.en": {
SIMPLE: {
... | transformers.js/tests/models/whisper/test_tokenization_whisper.js/0 | {
"file_path": "transformers.js/tests/models/whisper/test_tokenization_whisper.js",
"repo_id": "transformers.js",
"token_count": 25252
} | 356 |
import { pipeline, ObjectDetectionPipeline } 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 = "object-detection";
export default () => ... | transformers.js/tests/pipelines/test_pipelines_object_detection.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_object_detection.js",
"repo_id": "transformers.js",
"token_count": 2625
} | 357 |
import { PriorityQueue, DictionarySplitter, LRUCache } from "../../src/utils/data-structures.js";
describe("Priority queue", () => {
const EXAMPLE_ARRAY = [2, 5, 3, 1, 4];
it("default (max heap)", () => {
const queue = new PriorityQueue();
queue.extend(EXAMPLE_ARRAY);
expect(queue.pop()).toBe(5);
});... | transformers.js/tests/utils/data_structures.test.js/0 | {
"file_path": "transformers.js/tests/utils/data_structures.test.js",
"repo_id": "transformers.js",
"token_count": 1609
} | 358 |
# Security Policy
## Hugging Face Hub, remote artefacts, and remote code
Transformers is open-source software that is tightly coupled to the Hugging Face Hub. While you have the ability to use it
offline with pre-downloaded model weights, it provides a very simple way to download, use, and manage models locally.
Whe... | transformers/SECURITY.md/0 | {
"file_path": "transformers/SECURITY.md",
"repo_id": "transformers",
"token_count": 461
} | 359 |
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