text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
|---|---|---|---|
""" DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl ins... | pytorch-image-models/timm/layers/drop.py/0 | {
"file_path": "pytorch-image-models/timm/layers/drop.py",
"repo_id": "pytorch-image-models",
"token_count": 3016
} | 265 |
import torch
from torch import nn
class LayerScale(nn.Module):
""" LayerScale on tensors with channels in last-dim.
"""
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
) -> None:
super().__init__()
self.inp... | pytorch-image-models/timm/layers/layer_scale.py/0 | {
"file_path": "pytorch-image-models/timm/layers/layer_scale.py",
"repo_id": "pytorch-image-models",
"token_count": 482
} | 266 |
""" Sin-cos, fourier, rotary position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import List, Tuple, Optional, Union
import torch
from torch import nn as nn
from ._fx import register_notrace_function
from .grid import ndgrid
from .trace_utils import ... | pytorch-image-models/timm/layers/pos_embed_sincos.py/0 | {
"file_path": "pytorch-image-models/timm/layers/pos_embed_sincos.py",
"repo_id": "pytorch-image-models",
"token_count": 14271
} | 267 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .cross_entropy import LabelSmoothingCrossEntropy
class JsdCrossEntropy(nn.Module):
""" Jensen-Shannon Divergence + Cross-Entropy Loss
Based on impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
From paper: ... | pytorch-image-models/timm/loss/jsd.py/0 | {
"file_path": "pytorch-image-models/timm/loss/jsd.py",
"repo_id": "pytorch-image-models",
"token_count": 639
} | 268 |
""" Deep Layer Aggregation and DLA w/ Res2Net
DLA original adapted from Official Pytorch impl at: https://github.com/ucbdrive/dla
DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
Res2Net additions from: https://github.com/gasvn/Res2Net/
Res2Net Paper: `Res2Net: A New Multi-scale Backbone Architec... | pytorch-image-models/timm/models/dla.py/0 | {
"file_path": "pytorch-image-models/timm/models/dla.py",
"repo_id": "pytorch-image-models",
"token_count": 9154
} | 269 |
"""
An implementation of GhostNet & GhostNetV2 Models as defined in:
GhostNet: More Features from Cheap Operations. https://arxiv.org/abs/1911.11907
GhostNetV2: Enhance Cheap Operation with Long-Range Attention. https://proceedings.neurips.cc/paper_files/paper/2022/file/40b60852a4abdaa696b5a1a78da34635-Paper-Conference... | pytorch-image-models/timm/models/ghostnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/ghostnet.py",
"repo_id": "pytorch-image-models",
"token_count": 17881
} | 270 |
"""
Poolformer from MetaFormer is Actually What You Need for Vision https://arxiv.org/abs/2111.11418
IdentityFormer, RandFormer, PoolFormerV2, ConvFormer, and CAFormer
from MetaFormer Baselines for Vision https://arxiv.org/abs/2210.13452
All implemented models support feature extraction and variable input resolution.... | pytorch-image-models/timm/models/metaformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/metaformer.py",
"repo_id": "pytorch-image-models",
"token_count": 18680
} | 271 |
"""RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... | pytorch-image-models/timm/models/regnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/regnet.py",
"repo_id": "pytorch-image-models",
"token_count": 26386
} | 272 |
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... | pytorch-image-models/timm/models/swin_transformer_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 23224
} | 273 |
"""Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partia... | pytorch-image-models/timm/models/xception_aligned.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception_aligned.py",
"repo_id": "pytorch-image-models",
"token_count": 7780
} | 274 |
""" PyTorch impl of LaProp optimizer
Code simplified from https://github.com/Z-T-WANG/LaProp-Optimizer, MIT License
Paper: LaProp: Separating Momentum and Adaptivity in Adam, https://arxiv.org/abs/2002.04839
@article{ziyin2020laprop,
title={LaProp: a Better Way to Combine Momentum with Adaptive Gradient},
author... | pytorch-image-models/timm/optim/laprop.py/0 | {
"file_path": "pytorch-image-models/timm/optim/laprop.py",
"repo_id": "pytorch-image-models",
"token_count": 2603
} | 275 |
""" Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise, k-decay.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class CosineLRSchedu... | pytorch-image-models/timm/scheduler/cosine_lr.py/0 | {
"file_path": "pytorch-image-models/timm/scheduler/cosine_lr.py",
"repo_id": "pytorch-image-models",
"token_count": 2070
} | 276 |
""" JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT executor. These ... | pytorch-image-models/timm/utils/jit.py/0 | {
"file_path": "pytorch-image-models/timm/utils/jit.py",
"repo_id": "pytorch-image-models",
"token_count": 1035
} | 277 |
# Orchestrate a multi-agent system 🤖🤝🤖
[[open-in-colab]]
In this notebook we will make a **multi-agent web browser: an agentic system with several agents collaborating to solve problems using the web!**
It will be a simple hierarchy:
```
+----------------+
| Manager agent |
... | smolagents/docs/source/en/examples/multiagents.md/0 | {
"file_path": "smolagents/docs/source/en/examples/multiagents.md",
"repo_id": "smolagents",
"token_count": 2106
} | 278 |
# Secure code execution
[[open-in-colab]]
> [!TIP]
> If you're new to building agents, make sure to first read the [intro to agents](../conceptual_guides/intro_agents) and the [guided tour of smolagents](../guided_tour).
### Code agents
[Multiple](https://huggingface.co/papers/2402.01030) [research](https://hugging... | smolagents/docs/source/en/tutorials/secure_code_execution.md/0 | {
"file_path": "smolagents/docs/source/en/tutorials/secure_code_execution.md",
"repo_id": "smolagents",
"token_count": 5598
} | 279 |
# Tools
[[open-in-colab]]
यहाँ, हम एडवांस्ड tools उपयोग देखेंगे।
> [!TIP]
> यदि आप एजेंट्स बनाने में नए हैं, तो सबसे पहले [एजेंट्स का परिचय](../conceptual_guides/intro_agents) और [smolagents की गाइडेड टूर](../guided_tour) पढ़ना सुनिश्चित करें।
- [Tools](#tools)
- [टूल क्या है, और इसे कैसे बनाएं?](#टूल-क्या-है-औ... | smolagents/docs/source/hi/tutorials/tools.md/0 | {
"file_path": "smolagents/docs/source/hi/tutorials/tools.md",
"repo_id": "smolagents",
"token_count": 10673
} | 280 |
# Agents（智能体）
<Tip warning={true}>
Smolagents 是一个实验性的 API，可能会随时发生变化。由于 API 或底层模型可能发生变化，代理返回的结果也可能有所不同。
</Tip>
要了解有关智能体和工具的更多信息，请务必阅读[入门指南](../index)。本页面包含基础类的 API 文档。
## 智能体（Agents）
我们的智能体继承自 [`MultiStepAgent`]，这意味着它们可以执行多步操作，每一步包含一个思考（thought），然后是一个工具调用和执行。请阅读[概念指南](../conceptual_guides/react)以了解更多信息。
我们提供两种类型的... | smolagents/docs/source/zh/reference/agents.md/0 | {
"file_path": "smolagents/docs/source/zh/reference/agents.md",
"repo_id": "smolagents",
"token_count": 829
} | 281 |
import requests
# from smolagents.agents import ToolCallingAgent
from smolagents import CodeAgent, InferenceClientModel, tool
# Choose which LLM engine to use!
model = InferenceClientModel()
# model = TransformersModel(model_id="meta-llama/Llama-3.2-2B-Instruct")
# For anthropic: change model_id below to 'anthropic... | smolagents/examples/multiple_tools.py/0 | {
"file_path": "smolagents/examples/multiple_tools.py",
"repo_id": "smolagents",
"token_count": 3110
} | 282 |
# Human-in-the-Loop: Customize Agent Plan Interactively
This example demonstrates advanced usage of the smolagents library, specifically showing how to implement Human-in-the-Loop strategies to:
1. **Interrupt agent execution after plan creation** using step callbacks
2. **Allow user interaction** to review and modif... | smolagents/examples/plan_customization/README.md/0 | {
"file_path": "smolagents/examples/plan_customization/README.md",
"repo_id": "smolagents",
"token_count": 1114
} | 283 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | smolagents/src/smolagents/cli.py/0 | {
"file_path": "smolagents/src/smolagents/cli.py",
"repo_id": "smolagents",
"token_count": 2110
} | 284 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | smolagents/tests/test_models.py/0 | {
"file_path": "smolagents/tests/test_models.py",
"repo_id": "smolagents",
"token_count": 15661
} | 285 |
# This file is automatically @generated by Cargo.
# It is not intended for manual editing.
version = 4
[[package]]
name = "addr2line"
version = "0.24.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "dfbe277e56a376000877090da837660b4427aad530e3028d44e0bffe4f89a1c1"
dependencies = [
"giml... | text-generation-inference/Cargo.lock/0 | {
"file_path": "text-generation-inference/Cargo.lock",
"repo_id": "text-generation-inference",
"token_count": 74237
} | 286 |
eetq_commit := 1657b1504faa359e2ce0ac02999439d7ac8c74c0
eetq:
# Clone eetq
pip install packaging
git clone https://github.com/NetEase-FuXi/EETQ.git eetq
build-eetq: eetq
cd eetq && git fetch && git checkout $(eetq_commit) && git submodule update --init --recursive
cd eetq && python setup.py build
install-eet... | text-generation-inference/backends/gaudi/server/Makefile-eetq/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/Makefile-eetq",
"repo_id": "text-generation-inference",
"token_count": 154
} | 287 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/weights.py
# License: Apache License Version 2.0, January 2004
from abc import ABC, abstractclassmethod
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Type... | text-generation-inference/backends/gaudi/server/text_generation_server/adapters/weights.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/adapters/weights.py",
"repo_id": "text-generation-inference",
"token_count": 1824
} | 288 |
from accelerate import init_empty_weights
import torch
@classmethod
def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
weight = weights.get_tensor(f"{prefix}.weight")
bias = weights.get_tensor(f"{prefix}.bias")
with init_empty_weights():
conv2d = cls(
... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/conv.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/conv.py",
"repo_id": "text-generation-inference",
"token_count": 518
} | 289 |
import os
import math
import torch
from torch import nn
from habana_frameworks.torch.hpex.kernels import (
RotaryPosEmbeddingMode,
apply_rotary_pos_emb,
)
def _create_inv_freq(dim, base, device):
inv_freq = 1.0 / (
base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
)
... | text-generation-inference/backends/gaudi/server/text_generation_server/layers/rotary.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/layers/rotary.py",
"repo_id": "text-generation-inference",
"token_count": 12395
} | 290 |
# coding=utf-8
# Copyright 2025 The LLAMA4 and 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... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_llama4_modeling.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/flash_llama4_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 25899
} | 291 |
# coding=utf-8
# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/idefics2.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/models/custom_modeling/idefics2.py",
"repo_id": "text-generation-inference",
"token_count": 15476
} | 292 |
import grpc
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.grpc._aio_server import (
OpenTelemetryAioServerInterceptor,
)
from opentelemetry.semconv.trace import SpanAttributes
from opentelemetry.sdk.resources im... | text-generation-inference/backends/gaudi/server/text_generation_server/tracing.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/tracing.py",
"repo_id": "text-generation-inference",
"token_count": 969
} | 293 |
import json
import os
from dataclasses import dataclass
from typing import Optional, List
from huggingface_hub import hf_hub_download
from text_generation_server.utils.weights import (
WeightsLoader,
)
# TODO: Split this config to have a single config type per quant method
@dataclass
class _QuantizerConfig:
... | text-generation-inference/backends/gaudi/server/text_generation_server/utils/quantization.py/0 | {
"file_path": "text-generation-inference/backends/gaudi/server/text_generation_server/utils/quantization.py",
"repo_id": "text-generation-inference",
"token_count": 2587
} | 294 |
#![allow(non_upper_case_globals)]
#![allow(non_camel_case_types)]
#![allow(non_snake_case)]
#![allow(dead_code)]
include!(concat!(env!("OUT_DIR"), "/llamacpp.rs"));
| text-generation-inference/backends/llamacpp/src/llamacpp.rs/0 | {
"file_path": "text-generation-inference/backends/llamacpp/src/llamacpp.rs",
"repo_id": "text-generation-inference",
"token_count": 77
} | 295 |
pytest_plugins = ["fixtures.model"]
| text-generation-inference/backends/neuron/tests/conftest.py/0 | {
"file_path": "text-generation-inference/backends/neuron/tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 12
} | 296 |
[package]
name = "text-generation-backends-trtllm"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
async-trait = "0.1"
clap = { version = "4.5", features = ["derive"] }
cxx = "1.0"
hashbrown = "0.15"
hf-hub = { workspace = true }
text-generation-route... | text-generation-inference/backends/trtllm/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/trtllm/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 275
} | 297 |
use std::path::{Path, PathBuf};
use clap::Parser;
use hf_hub::api::tokio::{Api, ApiBuilder};
use hf_hub::{Cache, Repo, RepoType};
use tracing::info;
use text_generation_backends_trtllm::errors::TensorRtLlmBackendError;
use text_generation_backends_trtllm::TensorRtLlmBackendV2;
use text_generation_router::server::{
... | text-generation-inference/backends/trtllm/src/main.rs/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/main.rs",
"repo_id": "text-generation-inference",
"token_count": 5674
} | 298 |
/// Batching and inference logic
use crate::client::{
Batch, CachedBatch, ClientError, Generation, Health, InfoResponse, ShardedClient,
};
use crate::queue::{Entry, Queue};
use async_trait::async_trait;
use nohash_hasher::IntMap;
use std::sync::Arc;
use text_generation_router::infer::{Backend, GeneratedText, InferE... | text-generation-inference/backends/v3/src/backend.rs/0 | {
"file_path": "text-generation-inference/backends/v3/src/backend.rs",
"repo_id": "text-generation-inference",
"token_count": 11182
} | 299 |
use crate::app::Data;
use tabled::settings::Merge;
use tabled::{builder::Builder, settings::Style, Table};
#[allow(clippy::too_many_arguments)]
pub(crate) fn parameters_table(
tokenizer_name: String,
sequence_length: u32,
decode_length: u32,
top_n_tokens: Option<u32>,
n_runs: usize,
warmups: us... | text-generation-inference/benchmark/src/table.rs/0 | {
"file_path": "text-generation-inference/benchmark/src/table.rs",
"repo_id": "text-generation-inference",
"token_count": 2288
} | 300 |
from enum import Enum
from pydantic import BaseModel, field_validator, ConfigDict
from typing import Optional, List, Union, Any
from text_generation.errors import ValidationError
# enum for grammar type
class GrammarType(str, Enum):
Json = "json"
Regex = "regex"
# Grammar type and value
class Grammar(BaseM... | text-generation-inference/clients/python/text_generation/types.py/0 | {
"file_path": "text-generation-inference/clients/python/text_generation/types.py",
"repo_id": "text-generation-inference",
"token_count": 5257
} | 301 |
# Model safety.
[Pytorch uses pickle](https://pytorch.org/docs/master/generated/torch.load.html) by default meaning that for quite a long while
*Every* model using that format is potentially executing unintended code while purely loading the model.
There is a big red warning on Python's page for pickle [link](https:/... | text-generation-inference/docs/source/basic_tutorials/safety.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/safety.md",
"repo_id": "text-generation-inference",
"token_count": 465
} | 302 |
# Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
![Text Generation Inference](https://hugging... | text-generation-inference/docs/source/index.md/0 | {
"file_path": "text-generation-inference/docs/source/index.md",
"repo_id": "text-generation-inference",
"token_count": 562
} | 303 |
{
inputs = {
crate2nix = {
url = "github:nix-community/crate2nix";
inputs.nixpkgs.follows = "hf-nix/nixpkgs";
};
nix-filter.url = "github:numtide/nix-filter";
hf-nix.url = "github:huggingface/hf-nix";
nixpkgs.follows = "hf-nix/nixpkgs";
flake-utils.url = "github:numtide/flake-utils... | text-generation-inference/flake.nix/0 | {
"file_path": "text-generation-inference/flake.nix",
"repo_id": "text-generation-inference",
"token_count": 3203
} | 304 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 18682,
"logprob": -0.8769531,
"special": false,
"text": " Deep"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_load.json",
"repo_id": "text-generation-inference",
"token_count": 4051
} | 305 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 185,
"logprob": -1.546875,
"special": false,
"text": "\n"
},
{
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2_load.json",
"repo_id": "text-generation-inference",
"token_count": 4036
} | 306 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 604,
"logprob": -2.4296875,
"special": false,
"text": " for"
},
{
"id": 573,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq.json",
"repo_id": "text-generation-inference",
"token_count": 872
} | 307 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 369,
"logprob": -2.1816406,
"special": false,
"text": " for"
},
{
"id": 279,
"logprob"... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_fp8/test_flash_llama_fp8.json",
"repo_id": "text-generation-inference",
"token_count": 862
} | 308 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 42,
"logprob": -0.88378906,
"special": false,
"text": "I"
},
{
"id": 1353,
"logprob": ... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_neox/test_flash_neox.json",
"repo_id": "text-generation-inference",
"token_count": 854
} | 309 |
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "The image showcases the Statue of Liberty, a colossal bronze statue located in New York Harbor, a heritage building in the United States. The statue has a majestic presence, with one ar... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2_5_vl/test_flash_qwen2_5_vl_bay.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_qwen2_5_vl/test_flash_qwen2_5_vl_bay.json",
"repo_id": "text-generation-inference",
"token_count": 395
} | 310 |
[
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "A chicken sits on a pile of money, looking",
"name": null,
"role": "assistant",
"tool_calls": null
},
"usage": null
... | text-generation-inference/integration-tests/models/__snapshots__/test_mllama/test_mllama_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mllama/test_mllama_load.json",
"repo_id": "text-generation-inference",
"token_count": 662
} | 311 |
{
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "The image is a black background with no discernible objects or features. The image appears to be a blank or empty space, devoid of any visual elements.\n\n**Key Features:**\n\n* **Col... | text-generation-inference/integration-tests/models/__snapshots__/test_transformers_llama4/test_flash_llama4_image_base64_rgba.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_transformers_llama4/test_flash_llama4_image_base64_rgba.json",
"repo_id": "text-generation-inference",
"token_count": 393
} | 312 |
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
} | 313 |
import pytest
@pytest.fixture(scope="module")
def flash_llama_marlin24_handle(launcher):
with launcher(
"nm-testing/Llama-2-7b-pruned2.4-Marlin_24", quantize="marlin"
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_marlin(flash_llama_marlin24_handle):
awai... | text-generation-inference/integration-tests/models/test_flash_llama_marlin_24.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_llama_marlin_24.py",
"repo_id": "text-generation-inference",
"token_count": 754
} | 314 |
import pytest
@pytest.fixture(scope="module")
def flash_qwen2_vl_handle(launcher):
with launcher("Qwen/Qwen2-VL-7B-Instruct") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_qwen2(flash_qwen2_vl_handle):
await flash_qwen2_vl_handle.health(300)
return flash_qwen2_vl_handle... | text-generation-inference/integration-tests/models/test_flash_qwen2_vl.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_qwen2_vl.py",
"repo_id": "text-generation-inference",
"token_count": 2158
} | 315 |
import pytest
@pytest.fixture(scope="module")
def mpt_sharded_handle(launcher):
with launcher("mosaicml/mpt-7b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def mpt_sharded(mpt_sharded_handle):
await mpt_sharded_handle.health(300)
return mpt_sharded_handle.client
... | text-generation-inference/integration-tests/models/test_mpt.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_mpt.py",
"repo_id": "text-generation-inference",
"token_count": 541
} | 316 |
[package]
name = "text-generation-launcher"
description = "Text Generation Launcher"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
clap = { version = "4.4.5", features = ["derive", "env"] }
ctrlc = { version = "3.4.1", features = ["termination"] }
h... | text-generation-inference/launcher/Cargo.toml/0 | {
"file_path": "text-generation-inference/launcher/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 343
} | 317 |
{ pkgs, nix-filter }:
let
filter = nix-filter.lib;
in
with pkgs;
defaultCrateOverrides
// {
aws-lc-rs = attrs: {
# aws-lc-rs does its own custom parsing of Cargo environment
# variables like DEP_.*_INCLUDE. However buildRustCrate does
# not use the version number, so the parsing fails.
postPatch = ... | text-generation-inference/nix/crate-overrides.nix/0 | {
"file_path": "text-generation-inference/nix/crate-overrides.nix",
"repo_id": "text-generation-inference",
"token_count": 937
} | 318 |
/// Text Generation Inference Webserver
pub mod config;
pub mod infer;
pub mod server;
pub mod validation;
#[cfg(feature = "kserve")]
mod kserve;
pub mod logging;
mod chat;
mod sagemaker;
pub mod usage_stats;
mod vertex;
use crate::infer::tool_grammar::ToolGrammar;
use crate::infer::{Infer, InferError};
use pyo3::pr... | text-generation-inference/router/src/lib.rs/0 | {
"file_path": "text-generation-inference/router/src/lib.rs",
"repo_id": "text-generation-inference",
"token_count": 26684
} | 319 |
install-flashinfer:
# We need fsspec as an additional dependency, but
# `pip install flashinfer` cannot resolve it.
uv pip install fsspec sympy==1.13.1 numpy
uv pip install -U setuptools
TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9;9.0+PTX" FLASHINFER_ENABLE_AOT=1 pip install git+https://github.com/flashinfer-ai/flashinf... | text-generation-inference/server/Makefile-flashinfer/0 | {
"file_path": "text-generation-inference/server/Makefile-flashinfer",
"repo_id": "text-generation-inference",
"token_count": 158
} | 320 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matrix_cuh
#define _q4_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
class Q4Matrix
{
public:
int device;
int height;
int width;
int groups;
int groupsize;
uint32_t* cuda_qw... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cuh",
"repo_id": "text-generation-inference",
"token_count": 420
} | 321 |
#ifndef _q_matrix_cuh
#define _q_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#define MAX_SUPERGROUPS 16
class QMatrix
{
public:
int device;
bool is_gptq;
int height;
int width;
int groups;
int gptq_groupsize;
int rows_8;
int rows... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cuh",
"repo_id": "text-generation-inference",
"token_count": 702
} | 322 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/__init__.py
# License: Apache License Version 2.0, January 2004
from text_generation_server.adapters.weights import (
AdapterBatchData,
AdapterBatchMetadata,
)
__all__ = [
"AdapterBatchData",
"AdapterBatchMe... | text-generation-inference/server/text_generation_server/adapters/__init__.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/adapters/__init__.py",
"repo_id": "text-generation-inference",
"token_count": 125
} | 323 |
import torch
from typing import List
AWQ_PACK_ORDER = [0, 2, 4, 6, 1, 3, 5, 7]
REVERSE_AWQ_PACK_ORDER = [0, 4, 1, 5, 2, 6, 3, 7]
def pack(imatrix: torch.Tensor, direction: str = "column"):
"""
Packs a 4-bit integer matrix into a packed 32-bit integer matrix.
Args:
imatrix (torch.Tensor): matrix ... | text-generation-inference/server/text_generation_server/layers/awq/conversion_utils.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/awq/conversion_utils.py",
"repo_id": "text-generation-inference",
"token_count": 1384
} | 324 |
# https://github.com/fpgaminer/GPTQ-triton
"""
Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
"""
import builtins
import math
import time
from typing import Dict
import triton
class Autotuner(triton.KernelInterface):
def __init__(
self,
fn,
... | text-generation-inference/server/text_generation_server/layers/gptq/custom_autotune.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/gptq/custom_autotune.py",
"repo_id": "text-generation-inference",
"token_count": 5117
} | 325 |
import torch
import math
from torch import nn
from torch.nn import functional as F
from typing import Optional, Tuple
from text_generation_server.layers import TensorParallelEmbedding, FastLinear
from text_generation_server.layers.tensor_parallel import TensorParallelHead
from text_generation_server.utils.speculate imp... | text-generation-inference/server/text_generation_server/layers/mlp.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/mlp.py",
"repo_id": "text-generation-inference",
"token_count": 5007
} | 326 |
# 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/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 12518
} | 327 |
from typing import List, Optional, Tuple
import torch
import torch.distributed
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
from text_generation_server.layers import (
SpeculativeHead,
TensorParallelColumnLinear,
... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 11278
} | 328 |
import torch
import torch.distributed
from mamba_ssm.ops.triton.selective_state_update import selective_state_update
from mamba_ssm.ops.selective_scan_interface import selective_scan_fn
from torch import nn
from typing import Optional, Tuple, Any
from transformers.configuration_utils import PretrainedConfig
import tor... | text-generation-inference/server/text_generation_server/models/custom_modeling/mamba_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/mamba_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 4238
} | 329 |
import torch
import triton
import triton.language as tl
from loguru import logger
from typing import List, Optional
from torch.utils._triton import has_triton as has_triton_torch
from text_generation_server.utils.import_utils import (
SYSTEM,
)
from text_generation_server.utils.log import log_master
_HAS_TRITON... | text-generation-inference/server/text_generation_server/models/metadata_kernels.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/metadata_kernels.py",
"repo_id": "text-generation-inference",
"token_count": 4276
} | 330 |
import time
import os
from datetime import timedelta
from loguru import logger
from pathlib import Path
from typing import Optional, List
from huggingface_hub import file_download, hf_api, HfApi, hf_hub_download
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub.utils import (
LocalE... | text-generation-inference/server/text_generation_server/utils/hub.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/hub.py",
"repo_id": "text-generation-inference",
"token_count": 3419
} | 331 |
#!/bin/bash
ldconfig 2>/dev/null || echo 'unable to refresh ld cache, not a big deal in most cases'
source /usr/src/.venv/bin/activate
exec text-generation-launcher $@
| text-generation-inference/tgi-entrypoint.sh/0 | {
"file_path": "text-generation-inference/tgi-entrypoint.sh",
"repo_id": "text-generation-inference",
"token_count": 59
} | 332 |
MIT License
Copyright (c) 2020 N-API for Rust
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 the rights
to use, copy, modify, merge, publish, di... | tokenizers/bindings/node/LICENSE/0 | {
"file_path": "tokenizers/bindings/node/LICENSE",
"repo_id": "tokenizers",
"token_count": 276
} | 333 |
/* eslint-disable @typescript-eslint/no-explicit-any */
import { bertProcessing, byteLevelProcessing, robertaProcessing, sequenceProcessing, templateProcessing } from '../../'
describe('bertProcessing', () => {
it('instantiates correctly with only two parameters', () => {
const processor = bertProcessing(['sep'... | tokenizers/bindings/node/lib/bindings/post-processors.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/post-processors.test.ts",
"repo_id": "tokenizers",
"token_count": 1022
} | 334 |
# `tokenizers-linux-arm64-gnu`
This is the **aarch64-unknown-linux-gnu** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-arm64-gnu/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-gnu/README.md",
"repo_id": "tokenizers",
"token_count": 35
} | 335 |
use serde::de::Deserializer;
use serde::ser::Serializer;
use serde::{Deserialize, Serialize};
use std::sync::{Arc, RwLock};
pub fn serialize<S, T>(val: &Option<Arc<RwLock<T>>>, s: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
T: Serialize,
{
T::serialize(&*(val.clone().unwrap()).read().unwrap(), s)
}
pub f... | tokenizers/bindings/node/src/arc_rwlock_serde.rs/0 | {
"file_path": "tokenizers/bindings/node/src/arc_rwlock_serde.rs",
"repo_id": "tokenizers",
"token_count": 220
} | 336 |
# This file is generated by running "yarn install" inside your project.
# Manual changes might be lost - proceed with caution!
__metadata:
version: 6
cacheKey: 8
"@aashutoshrathi/word-wrap@npm:^1.2.3":
version: 1.2.6
resolution: "@aashutoshrathi/word-wrap@npm:1.2.6"
checksum: ada901b9e7c680d190f1d012c84217c... | tokenizers/bindings/node/yarn.lock/0 | {
"file_path": "tokenizers/bindings/node/yarn.lock",
"repo_id": "tokenizers",
"token_count": 125916
} | 337 |
from enum import Enum
from typing import List, Tuple, Union
Offsets = Tuple[int, int]
TextInputSequence = str
"""A :obj:`str` that represents an input sequence """
PreTokenizedInputSequence = Union[List[str], Tuple[str]]
"""A pre-tokenized input sequence. Can be one of:
- A :obj:`List` of :obj:`str`
- A :o... | tokenizers/bindings/python/py_src/tokenizers/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/__init__.py",
"repo_id": "tokenizers",
"token_count": 984
} | 338 |
# Generated content DO NOT EDIT
class PreTokenizer:
"""
Base class for all pre-tokenizers
This class is not supposed to be instantiated directly. Instead, any implementation of a
PreTokenizer will return an instance of this class when instantiated.
"""
def pre_tokenize(self, pretok):
""... | tokenizers/bindings/python/py_src/tokenizers/pre_tokenizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/pre_tokenizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 10983
} | 339 |
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::type_object::PyTypeInfo;
use std::ffi::CString;
use std::fmt::{Display, Formatter, Result as FmtResult};
use tokenizers::tokenizer::Result;
#[derive(Debug)]
pub struct PyError(pub String);
impl PyError {
#[allow(dead_code)]
pub fn from(s: &str) -> Self {
... | tokenizers/bindings/python/src/error.rs/0 | {
"file_path": "tokenizers/bindings/python/src/error.rs",
"repo_id": "tokenizers",
"token_count": 545
} | 340 |
from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers, processors
from tokenizers.implementations import BaseTokenizer
class TestBaseTokenizer:
def test_get_set_components(self):
toki = Tokenizer(models.BPE())
toki.normalizer = normalizers.NFC()
toki.pre_tokenizer... | tokenizers/bindings/python/tests/implementations/test_base_tokenizer.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_base_tokenizer.py",
"repo_id": "tokenizers",
"token_count": 550
} | 341 |
# Normalizers
<tokenizerslangcontent>
<python>
## BertNormalizer
[[autodoc]] tokenizers.normalizers.BertNormalizer
## Lowercase
[[autodoc]] tokenizers.normalizers.Lowercase
## NFC
[[autodoc]] tokenizers.normalizers.NFC
## NFD
[[autodoc]] tokenizers.normalizers.NFD
## NFKC
[[autodoc]] tokenizers.normalizers.NF... | tokenizers/docs/source-doc-builder/api/normalizers.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/normalizers.mdx",
"repo_id": "tokenizers",
"token_count": 350
} | 342 |
🤗 Tokenizers is tested on Python 3.5+.
You should install 🤗 Tokenizers in a
`virtual environment <https://docs.python.org/3/library/venv.html>`_. If you're unfamiliar with
Python virtual environments, check out the
`user guide <https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/>`__.
C... | tokenizers/docs/source/installation/python.inc/0 | {
"file_path": "tokenizers/docs/source/installation/python.inc",
"repo_id": "tokenizers",
"token_count": 383
} | 343 |
#[macro_use]
extern crate criterion;
mod common;
use common::iter_bench_train;
use criterion::{Criterion, Throughput};
use tokenizers::models::unigram::{Unigram, UnigramTrainerBuilder};
use tokenizers::models::TrainerWrapper;
use tokenizers::pre_tokenizers::whitespace::Whitespace;
use tokenizers::Tokenizer;
// pub ... | tokenizers/tokenizers/benches/unigram_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/unigram_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 951
} | 344 |
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Hello wasm-pack!</title>
</head>
<body>
<noscript>This page contains webassembly and javascript content, please enable javascript in your browser.</noscript>
<script src="./bootstrap.js"></script>
</body>
</html>
| tokenizers/tokenizers/examples/unstable_wasm/www/index.html/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/index.html",
"repo_id": "tokenizers",
"token_count": 110
} | 345 |
use super::{super::OrderedVocabIter, trainer::BpeTrainer, Error, Pair, Word};
use crate::tokenizer::{Model, Result, Token};
use crate::utils::cache::{Cache, DEFAULT_CACHE_CAPACITY, MAX_LENGTH};
use crate::utils::iter::ResultShunt;
use ahash::AHashMap;
use serde_json::Value;
use std::borrow::Cow;
use std::collections::... | tokenizers/tokenizers/src/models/bpe/model.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/model.rs",
"repo_id": "tokenizers",
"token_count": 17796
} | 346 |
use std::collections::HashSet;
use super::WordPiece;
use crate::models::bpe::{BpeTrainer, BpeTrainerBuilder, BPE};
use crate::tokenizer::{AddedToken, Result, Trainer};
use ahash::AHashSet;
use serde::{Deserialize, Serialize};
/// A `WordPieceTrainerBuilder` can be used to create a `WordPieceTrainer` with a custom
///... | tokenizers/tokenizers/src/models/wordpiece/trainer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/wordpiece/trainer.rs",
"repo_id": "tokenizers",
"token_count": 2559
} | 347 |
pub mod bert;
pub mod byte_level;
pub mod delimiter;
pub mod digits;
pub mod fixed_length;
pub mod metaspace;
pub mod punctuation;
pub mod sequence;
pub mod split;
pub mod unicode_scripts;
pub mod whitespace;
use serde::{Deserialize, Deserializer, Serialize};
use crate::pre_tokenizers::bert::BertPreTokenizer;
use cra... | tokenizers/tokenizers/src/pre_tokenizers/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/mod.rs",
"repo_id": "tokenizers",
"token_count": 6852
} | 348 |
use crate::pattern::Pattern;
use crate::{Offsets, Result};
use std::ops::{Bound, RangeBounds};
use unicode_normalization_alignments::UnicodeNormalization;
use serde::{Deserialize, Serialize};
/// The possible offsets referential
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OffsetReferential {
Original,
... | tokenizers/tokenizers/src/tokenizer/normalizer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/normalizer.rs",
"repo_id": "tokenizers",
"token_count": 43150
} | 349 |
use std::iter::FromIterator;
use ahash::AHashMap;
use tokenizers::decoders::byte_fallback::ByteFallback;
use tokenizers::models::bpe::{BpeTrainerBuilder, BPE};
use tokenizers::normalizers::{Sequence, Strip, NFC};
use tokenizers::pre_tokenizers::byte_level::ByteLevel;
use tokenizers::{AddedToken, TokenizerBuilder};
use... | tokenizers/tokenizers/tests/documentation.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/documentation.rs",
"repo_id": "tokenizers",
"token_count": 8476
} | 350 |
- local: index
title: 🤗 Transformers.js
- sections:
- local: installation
title: Installation
- local: pipelines
title: The pipeline API
- local: custom_usage
title: Custom usage
title: Get started
- sections:
- local: tutorials/vanilla-js
title: Building a Vanilla JS Application
- local:... | transformers.js/docs/source/_toctree.yml/0 | {
"file_path": "transformers.js/docs/source/_toctree.yml",
"repo_id": "transformers.js",
"token_count": 825
} | 351 |
{
"name": "code-completion",
"version": "0.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "code-completion",
"version": "0.0.0",
"dependencies": {
"@monaco-editor/react": "^4.5.1",
"@xenova/transformers": "^2.4.4",
"react": "^18.2.0",
... | transformers.js/examples/code-completion/package-lock.json/0 | {
"file_path": "transformers.js/examples/code-completion/package-lock.json",
"repo_id": "transformers.js",
"token_count": 69198
} | 352 |
// This file (model.js) contains all the logic for loading the model and running predictions.
class MyClassificationPipeline {
// NOTE: Replace this with your own task and model
static task = 'text-classification';
static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
static instance... | transformers.js/examples/electron/src/model.js/0 | {
"file_path": "transformers.js/examples/electron/src/model.js",
"repo_id": "transformers.js",
"token_count": 366
} | 353 |
{
"name": "musicgen-web",
"private": true,
"version": "0.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "vite build",
"lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
"preview": "vite preview"
},
"dependencies": {
"@xenova/transformers"... | transformers.js/examples/musicgen-web/package.json/0 | {
"file_path": "transformers.js/examples/musicgen-web/package.json",
"repo_id": "transformers.js",
"token_count": 415
} | 354 |
module.exports = {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
}
| transformers.js/examples/next-client/postcss.config.js/0 | {
"file_path": "transformers.js/examples/next-client/postcss.config.js",
"repo_id": "transformers.js",
"token_count": 38
} | 355 |
{
"name": "esm",
"version": "1.0.0",
"description": "Server-side inference with Transformers.js (ESM)",
"type": "module",
"main": "app.js",
"keywords": [],
"author": "Xenova",
"license": "ISC",
"dependencies": {
"@xenova/transformers": "^2.0.0"
}
}
| transformers.js/examples/node/esm/package.json/0 | {
"file_path": "transformers.js/examples/node/esm/package.json",
"repo_id": "transformers.js",
"token_count": 116
} | 356 |
{
"name": "remove-background-client",
"version": "0.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "remove-background-client",
"version": "0.0.0",
"dependencies": {
"@xenova/transformers": "^2.15.0"
},
"devDependencies": {
"vite": "^... | transformers.js/examples/remove-background-client/package-lock.json/0 | {
"file_path": "transformers.js/examples/remove-background-client/package-lock.json",
"repo_id": "transformers.js",
"token_count": 31164
} | 357 |
@import url('https://fonts.googleapis.com/css2?family=Montserrat&display=swap');
* {
box-sizing: border-box;
padding: 0;
margin: 0;
font-family: 'Montserrat', sans-serif;
}
html {
background: radial-gradient(ellipse at center, #1b2735 0%, #090a0f 100%);
height: 100%;
width: 100%;
}
body {
overflow: h... | transformers.js/examples/semantic-audio-search/style.css/0 | {
"file_path": "transformers.js/examples/semantic-audio-search/style.css",
"repo_id": "transformers.js",
"token_count": 707
} | 358 |
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.6.0';
// Since we will download the model from the Hugging Face Hub, we can skip the local model check
env.allowLocalModels = false;
// Reference the elements that we will need
const status = document.getElementById('status');
const fi... | transformers.js/examples/vanilla-js/index.js/0 | {
"file_path": "transformers.js/examples/vanilla-js/index.js",
"repo_id": "transformers.js",
"token_count": 817
} | 359 |
import { useEffect, useState, useRef } from 'react';
import Chat from './components/Chat';
import ArrowRightIcon from './components/icons/ArrowRightIcon';
import StopIcon from './components/icons/StopIcon';
import Progress from './components/Progress';
const IS_WEBGPU_AVAILABLE = !!navigator.gpu;
const STICKY_SCROLL_... | transformers.js/examples/webgpu-chat/src/App.jsx/0 | {
"file_path": "transformers.js/examples/webgpu-chat/src/App.jsx",
"repo_id": "transformers.js",
"token_count": 4906
} | 360 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Transformers.js | Real-time depth estimation</title>
</head>
<body>
<h1>
Real-time depth estimation w/
<a href="https://huggingface.co/onnx-community/depth-a... | transformers.js/examples/webgpu-video-depth-estimation/index.html/0 | {
"file_path": "transformers.js/examples/webgpu-video-depth-estimation/index.html",
"repo_id": "transformers.js",
"token_count": 534
} | 361 |
function formatBytes(size) {
const i = size == 0 ? 0 : Math.floor(Math.log(size) / Math.log(1024));
return +((size / Math.pow(1024, i)).toFixed(2)) * 1 + ['B', 'kB', 'MB', 'GB', 'TB'][i];
}
export default function Progress({ text, percentage, total }) {
percentage ??= 0;
return (
<div className... | transformers.js/examples/webgpu-vlm/src/components/Progress.jsx/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/components/Progress.jsx",
"repo_id": "transformers.js",
"token_count": 290
} | 362 |
from transformers.convert_slow_tokenizer import Converter
from tokenizers import Tokenizer, pre_tokenizers, processors
from tokenizers.models import WordPiece
class EsmConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(voca... | transformers.js/scripts/extra/esm.py/0 | {
"file_path": "transformers.js/scripts/extra/esm.py",
"repo_id": "transformers.js",
"token_count": 1055
} | 363 |
/**
* @file Helper module for using model configs. For more information, see the corresponding
* [Python documentation](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoConfig).
*
* **Example:** Load an `AutoConfig`.
*
* ```javascript
* import { AutoConfig } from '@huggingface/... | transformers.js/src/configs.js/0 | {
"file_path": "transformers.js/src/configs.js",
"repo_id": "transformers.js",
"token_count": 8170
} | 364 |
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