text stringlengths 5 409k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 572 |
|---|---|---|---|
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/trl/trainer/dpo_trainer.py/0 | {
"file_path": "trl/trl/trainer/dpo_trainer.py",
"repo_id": "trl",
"token_count": 36191
} | 569 |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | trl/trl/trainer/ppo_config.py/0 | {
"file_path": "trl/trl/trainer/ppo_config.py",
"repo_id": "trl",
"token_count": 1824
} | 570 |
<!---
Copyright 2021 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 ... | accelerate/README.md/0 | {
"file_path": "accelerate/README.md",
"repo_id": "accelerate",
"token_count": 4483
} | 0 |
# 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/LICENSE-2.0
#
# Unless required by appl... | accelerate/benchmarks/fp8/torchao/non_distributed.py/0 | {
"file_path": "accelerate/benchmarks/fp8/torchao/non_distributed.py",
"repo_id": "accelerate",
"token_count": 2129
} | 1 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | accelerate/docs/source/basic_tutorials/install.md/0 | {
"file_path": "accelerate/docs/source/basic_tutorials/install.md",
"repo_id": "accelerate",
"token_count": 1129
} | 2 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | accelerate/docs/source/usage_guides/model_size_estimator.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/model_size_estimator.md",
"repo_id": "accelerate",
"token_count": 2017
} | 3 |
# Copyright 2021 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 required by appl... | accelerate/examples/by_feature/gradient_accumulation.py/0 | {
"file_path": "accelerate/examples/by_feature/gradient_accumulation.py",
"repo_id": "accelerate",
"token_count": 3362
} | 4 |
# This config template is for a multi-node setup. This assumes DDP, but can be interop'd with the other configs in this folder
# Generally it's recommended to look at the SLURM config template for a more robust multi-node setup
distributed_type: MULTI_GPU
# We need to specify the current machine's rank
machine_rank: 0
... | accelerate/examples/config_yaml_templates/multi_node.yaml/0 | {
"file_path": "accelerate/examples/config_yaml_templates/multi_node.yaml",
"repo_id": "accelerate",
"token_count": 216
} | 5 |
# 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 required by appl... | accelerate/examples/inference/pippy/bert.py/0 | {
"file_path": "accelerate/examples/inference/pippy/bert.py",
"repo_id": "accelerate",
"token_count": 871
} | 6 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/manim_animations/big_model_inference/stage_4.py/0 | {
"file_path": "accelerate/manim_animations/big_model_inference/stage_4.py",
"repo_id": "accelerate",
"token_count": 2919
} | 7 |
# Copyright 2022 The HuggingFace Team and Brian Chao. 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 requir... | accelerate/src/accelerate/commands/menu/input.py/0 | {
"file_path": "accelerate/src/accelerate/commands/menu/input.py",
"repo_id": "accelerate",
"token_count": 947
} | 8 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/src/accelerate/state.py/0 | {
"file_path": "accelerate/src/accelerate/state.py",
"repo_id": "accelerate",
"token_count": 23317
} | 9 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/src/accelerate/test_utils/scripts/test_notebook.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/scripts/test_notebook.py",
"repo_id": "accelerate",
"token_count": 1371
} | 10 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/src/accelerate/utils/launch.py/0 | {
"file_path": "accelerate/src/accelerate/utils/launch.py",
"repo_id": "accelerate",
"token_count": 13133
} | 11 |
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"optimizer": {
"type": "AdamW",
"params": {
... | accelerate/tests/deepspeed/ds_config_zero3.json/0 | {
"file_path": "accelerate/tests/deepspeed/ds_config_zero3.json",
"repo_id": "accelerate",
"token_count": 825
} | 12 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | accelerate/tests/test_sagemaker.py/0 | {
"file_path": "accelerate/tests/test_sagemaker.py",
"repo_id": "accelerate",
"token_count": 1007
} | 13 |
<jupyter_start><jupyter_text>Dummy Agent LibraryIn this simple example, **we're going to code an Agent from scratch**.This notebook is part of the Hugging Face Agents Course, a free Course from beginner to expert, where you learn to build Agents.<jupyter_code>!pip install -q huggingface_hub<jupyter_output><empty_output... | agents-course/notebooks/unit1/dummy_agent_library.ipynb/0 | {
"file_path": "agents-course/notebooks/unit1/dummy_agent_library.ipynb",
"repo_id": "agents-course",
"token_count": 3120
} | 14 |
# Onboarding: Your First Steps ⛵
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit0/time-to-onboard.jpg" alt="Time to Onboard" width="100%"/>
Now that you have all the details, let's get started! We're going to do four things:
1. **Create your Hugging Face Account** if it's n... | agents-course/units/en/unit0/onboarding.mdx/0 | {
"file_path": "agents-course/units/en/unit0/onboarding.mdx",
"repo_id": "agents-course",
"token_count": 852
} | 15 |
# What are LLMs?
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-check-1.jpg" alt="Unit 1 planning"/>
In the previous section we learned that each Agent needs **an AI Model at its core**, and that LLMs are the most common type of AI models for this purpose.
Now ... | agents-course/units/en/unit1/what-are-llms.mdx/0 | {
"file_path": "agents-course/units/en/unit1/what-are-llms.mdx",
"repo_id": "agents-course",
"token_count": 3784
} | 16 |
# candle
[](https://discord.gg/hugging-face-879548962464493619)
[](https://crates.io/crates/candle-core)
[](htt... | candle/README.md/0 | {
"file_path": "candle/README.md",
"repo_id": "candle",
"token_count": 8420
} | 17 |
# Pytorch cheatsheet
{{#include ../../../README.md:cheatsheet}}
| candle/candle-book/src/guide/cheatsheet.md/0 | {
"file_path": "candle/candle-book/src/guide/cheatsheet.md",
"repo_id": "candle",
"token_count": 26
} | 18 |
#![allow(dead_code)]
use libc::{c_char, c_double, c_float, c_int, c_long, c_ulong};
mod ffi {
use super::*;
extern "C" {
// It would be nice to be able to switch to the NEWLAPACK version of the function but this
// seems to trigger some link error. Available function names can be seen here:
... | candle/candle-core/src/accelerate.rs/0 | {
"file_path": "candle/candle-core/src/accelerate.rs",
"repo_id": "candle",
"token_count": 7639
} | 19 |
//! Implementation of Backend traits for CUDA device
//!
use crate::backend::{BackendDevice, BackendStorage};
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Layout, Result, Shape, WithDType};
pub use candle_kernels as kernels;
pub use cudarc;
use cudarc::cublas::{Gemm, GemmConfig,... | candle/candle-core/src/cuda_backend/mod.rs/0 | {
"file_path": "candle/candle-core/src/cuda_backend/mod.rs",
"repo_id": "candle",
"token_count": 41866
} | 20 |
//! Tensor Opertion Enums and Traits
//!
#![allow(clippy::redundant_closure_call)]
use crate::Tensor;
use half::{bf16, f16};
use num_traits::float::Float;
#[derive(Clone, Copy, PartialEq, Eq)]
pub enum CmpOp {
Eq,
Ne,
Le,
Ge,
Lt,
Gt,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum Redu... | candle/candle-core/src/op.rs/0 | {
"file_path": "candle/candle-core/src/op.rs",
"repo_id": "candle",
"token_count": 13513
} | 21 |
//! The shape of a tensor is a tuple with the size of each of its dimensions.
#![allow(clippy::redundant_closure_call)]
use crate::{Error, Result};
#[derive(Clone, PartialEq, Eq)]
pub struct Shape(Vec<usize>);
pub const SCALAR: Shape = Shape(vec![]);
impl std::fmt::Debug for Shape {
fn fmt(&self, f: &mut std::fm... | candle/candle-core/src/shape.rs/0 | {
"file_path": "candle/candle-core/src/shape.rs",
"repo_id": "candle",
"token_count": 10096
} | 22 |
use candle::{test_device, Device, IndexOp, Result, Tensor};
use candle_core as candle;
fn contiguous(device: &Device) -> Result<()> {
let tensor = Tensor::arange(0u32, 24u32, device)?.reshape((2, 3, 4))?;
assert_eq!(
tensor.to_vec3::<u32>()?,
&[
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 1... | candle/candle-core/tests/layout_tests.rs/0 | {
"file_path": "candle/candle-core/tests/layout_tests.rs",
"repo_id": "candle",
"token_count": 2819
} | 23 |
use hf_hub::{
api::sync::{Api, ApiRepo},
Repo, RepoType,
};
use parquet::file::reader::SerializedFileReader;
use std::fs::File;
#[derive(thiserror::Error, Debug)]
pub enum Error {
#[error("ApiError : {0}")]
ApiError(#[from] hf_hub::api::sync::ApiError),
#[error("IoError : {0}")]
IoError(#[from... | candle/candle-datasets/src/hub.rs/0 | {
"file_path": "candle/candle-datasets/src/hub.rs",
"repo_id": "candle",
"token_count": 900
} | 24 |
# candle-starcoder: code generation model
[StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
model specialized to code generation. The initial model was trained on 80
programming languages.
## Running some example
```bash
cargo run --example bigcode --release -- --prompt "fn fact(n: u64) -... | candle/candle-examples/examples/bigcode/README.md/0 | {
"file_path": "candle/candle-examples/examples/bigcode/README.md",
"repo_id": "candle",
"token_count": 180
} | 25 |
#include <stdint.h>
#include "reduction_utils.cuh"
template <typename scalar_t>
__device__ void
rms_norm_kernel(scalar_t *__restrict__ out, // [num_tokens, hidden_size]
const scalar_t *__restrict__ input, // [num_tokens, hidden_size]
const float epsilon, const uint32_t num_token... | candle/candle-examples/examples/custom-ops/kernels/layernorm_kernels.cu/0 | {
"file_path": "candle/candle-examples/examples/custom-ops/kernels/layernorm_kernels.cu",
"repo_id": "candle",
"token_count": 561
} | 26 |
//! EfficientNet implementation.
//!
//! https://arxiv.org/abs/1905.11946
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::efficientnet::{EfficientNet,... | candle/candle-examples/examples/efficientnet/main.rs/0 | {
"file_path": "candle/candle-examples/examples/efficientnet/main.rs",
"repo_id": "candle",
"token_count": 1421
} | 27 |
from transformers import AutoModelForCausalLM, AutoTokenizer
BASE_MODEL = "google/t5-v1_1-xxl"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
# The tokenizer will be saved in /tmp/tokenizer/tokenizer.json
tokenizer.save_pretrained("/tmp/tokenizer/")
| candle/candle-examples/examples/flux/t5_tokenizer.py/0 | {
"file_path": "candle/candle-examples/examples/flux/t5_tokenizer.py",
"repo_id": "candle",
"token_count": 91
} | 28 |
// https://github.com/karpathy/llama2.c
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::llama2_c as model;
use candle_transformers::models::llama2_c_weights as weights;
use candle_transformers::models::quantized_llama2_c... | candle/candle-examples/examples/llama2-c/main.rs/0 | {
"file_path": "candle/candle-examples/examples/llama2-c/main.rs",
"repo_id": "candle",
"token_count": 6004
} | 29 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::{Parser, ValueEnum};
use candle::{DType, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::models::... | candle/candle-examples/examples/marian-mt/main.rs/0 | {
"file_path": "candle/candle-examples/examples/marian-mt/main.rs",
"repo_id": "candle",
"token_count": 2385
} | 30 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::mobileone;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
S... | candle/candle-examples/examples/mobileone/main.rs/0 | {
"file_path": "candle/candle-examples/examples/mobileone/main.rs",
"repo_id": "candle",
"token_count": 1213
} | 31 |
# candle-parler-tts
[Parler-TTS](https://huggingface.co/parler-tts/parler-tts-large-v1) is a large
text-to-speech model with 2.2B parameters trained on ~45K hours of audio data.
The voice can be controlled by a text prompt.
## Run an example
```bash
cargo run --example parler-tts -r -- \
--prompt "Hey, how are you... | candle/candle-examples/examples/parler-tts/README.md/0 | {
"file_path": "candle/candle-examples/examples/parler-tts/README.md",
"repo_id": "candle",
"token_count": 260
} | 32 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::qwen2::{Config as ConfigBase, ModelForCausalLM as ModelBase};
use candle_transformers::models::qwen2_moe::{Config as Con... | candle/candle-examples/examples/qwen/main.rs/0 | {
"file_path": "candle/candle-examples/examples/qwen/main.rs",
"repo_id": "candle",
"token_count": 4905
} | 33 |
# This script exports pre-trained model weights in the safetensors format.
import numpy as np
import torch
import torchvision
from safetensors import torch as stt
m = torchvision.models.resnet50(pretrained=True)
stt.save_file(m.state_dict(), 'resnet50.safetensors')
m = torchvision.models.resnet101(pretrained=True)
stt... | candle/candle-examples/examples/resnet/export_models.py/0 | {
"file_path": "candle/candle-examples/examples/resnet/export_models.py",
"repo_id": "candle",
"token_count": 166
} | 34 |
# candle-splade
SPLADE is a neural retrieval model which learns query/document sparse expansion via the BERT MLM head and sparse regularization. Sparse representations benefit from several advantages compared to dense approaches: efficient use of inverted index, explicit lexical match, interpretability... They also s... | candle/candle-examples/examples/splade/README.md/0 | {
"file_path": "candle/candle-examples/examples/splade/README.md",
"repo_id": "candle",
"token_count": 474
} | 35 |
# candle-t5
## Encoder-decoder example:
```bash
$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "translate to German: A beautiful candle." --decode
...
Eine schöne Kerze.
9 tokens generated (2.42 token/s)
```
Variants such as [flan-t5](https://huggingface.co/google/flan-t5-small), [flan-ul2](ht... | candle/candle-examples/examples/t5/README.md/0 | {
"file_path": "candle/candle-examples/examples/t5/README.md",
"repo_id": "candle",
"token_count": 608
} | 36 |
#include <cmath>
#include <cute/tensor.hpp>
#include <cutlass/cutlass.h>
#include <cutlass/array.h>
#include "utils.h"
namespace flash {
using namespace cute;
////////////////////////////////////////////////////////////////////////////////////////////////////
template <bool Is_causal>
struct Alibi {
const f... | candle/candle-flash-attn/kernels/alibi.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/alibi.h",
"repo_id": "candle",
"token_count": 1556
} | 37 |
// Copyright (c) 2024, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_fwd_launch_template.h"
template<>
void run_mha_fwd_<cutlass::half_t, 192, true>(Flash_fwd_params ¶ms, cudaStream_t stream... | candle/candle-flash-attn/kernels/flash_fwd_hdim192_fp16_causal_sm80.cu/0 | {
"file_path": "candle/candle-flash-attn/kernels/flash_fwd_hdim192_fp16_causal_sm80.cu",
"repo_id": "candle",
"token_count": 138
} | 38 |
/******************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include <cmath>
#include <cute/tensor.hpp>
#include <cutlass/numeric_types.h>
#include "philox.cuh"
#include... | candle/candle-flash-attn/kernels/softmax.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/softmax.h",
"repo_id": "candle",
"token_count": 4008
} | 39 |
#include<stdint.h>
#include "cuda_fp16.h"
template<typename T>
__device__ void fill_with(T *buf, T value, const size_t numel) {
for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; i += blockDim.x * gridDim.x) {
buf[i] = value;
}
}
extern "C" __global__ void fill_u8(uint8_t *buf, uin... | candle/candle-kernels/src/fill.cu/0 | {
"file_path": "candle/candle-kernels/src/fill.cu",
"repo_id": "candle",
"token_count": 919
} | 40 |
#include <metal_stdlib>
using namespace metal;
METAL_FUNC uint get_strided_index(
uint idx,
constant size_t &num_dims,
constant size_t *dims,
constant size_t *strides
) {
uint strided_i = 0;
for (uint d = 0; d < num_dims; d++) {
uint dim_idx = num_dims - 1 - d;
strided_i += (idx... | candle/candle-metal-kernels/src/indexing.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/indexing.metal",
"repo_id": "candle",
"token_count": 4200
} | 41 |
use candle_metal_kernels::{call_affine, Kernels};
use metal::objc::rc::autoreleasepool;
use metal::{Device, MTLResourceOptions};
use rand;
use std::any::type_name;
use std::time::Instant;
fn main() {
let device = Device::system_default().unwrap();
let kernels = Kernels::new();
let f32_1k = (0..1000).map(|... | candle/candle-metal-kernels/tmp/affine.rs/0 | {
"file_path": "candle/candle-metal-kernels/tmp/affine.rs",
"repo_id": "candle",
"token_count": 1154
} | 42 |
//! Embedding Layer.
use candle::{Result, Tensor};
#[derive(Clone, Debug)]
pub struct Embedding {
embeddings: Tensor,
hidden_size: usize,
}
impl Embedding {
pub fn new(embeddings: Tensor, hidden_size: usize) -> Self {
Self {
embeddings,
hidden_size,
}
}
pub... | candle/candle-nn/src/embedding.rs/0 | {
"file_path": "candle/candle-nn/src/embedding.rs",
"repo_id": "candle",
"token_count": 571
} | 43 |
//! A `VarMap` is a store that holds named variables.
//!
use candle::{DType, Device, Result, Shape, Tensor, Var};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
/// A `VarMap` is a store that holds named variables. Variables can be retrieved from the stores
/// and new variables can be added by providing... | candle/candle-nn/src/var_map.rs/0 | {
"file_path": "candle/candle-nn/src/var_map.rs",
"repo_id": "candle",
"token_count": 1992
} | 44 |
//
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
// SPDX-License-Identifier: Apache-2.0
syntax = "proto3";
package onnx;
// Overview
//
// ONNX is an open specification that is comprised of the following components:
//
// 1) A definition of an extensible computation graph model... | candle/candle-onnx/src/onnx.proto3/0 | {
"file_path": "candle/candle-onnx/src/onnx.proto3",
"repo_id": "candle",
"token_count": 10183
} | 45 |
# Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
from candle import Tensor, DType, QTensor
@staticmethod
def silu(tensor: Tensor) -> Tensor:
"""
Applies the S... | candle/candle-pyo3/py_src/candle/nn/__init__.pyi/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/__init__.pyi",
"repo_id": "candle",
"token_count": 181
} | 46 |
use ::candle::Tensor;
use pyo3::prelude::*;
#[derive(Clone, Debug)]
/// Represents an absolute shape e.g. (1, 2, 3)
pub struct PyShape(Vec<usize>);
impl<'source> pyo3::FromPyObject<'source> for PyShape {
fn extract_bound(ob: &Bound<'source, PyAny>) -> PyResult<Self> {
if ob.is_none() {
return ... | candle/candle-pyo3/src/shape.rs/0 | {
"file_path": "candle/candle-pyo3/src/shape.rs",
"repo_id": "candle",
"token_count": 1664
} | 47 |
//! Based from the Stanford Hazy Research group.
//!
//! See "Simple linear attention language models balance the recall-throughput tradeoff", Arora et al. 2024
//! - Simple linear attention language models balance the recall-throughput tradeoff. [Arxiv](https://arxiv.org/abs/2402.18668)
//! - [Github Rep](https://gith... | candle/candle-transformers/src/models/based.rs/0 | {
"file_path": "candle/candle-transformers/src/models/based.rs",
"repo_id": "candle",
"token_count": 9967
} | 48 |
//! ConvNeXt implementation.
//!
//! This candle implementation uses a pre-trained ConvNeXt network for inference. The
//! classification head has been trained on the ImageNet dataset and returns the
//! probabilities for the top-5 classes.
//!
//! Original code:
//! - 💻 [ConvNeXt](https://github.com/facebookresearch/... | candle/candle-transformers/src/models/convnext.rs/0 | {
"file_path": "candle/candle-transformers/src/models/convnext.rs",
"repo_id": "candle",
"token_count": 4949
} | 49 |
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::{LayerNorm, Linear, RmsNorm, VarBuilder};
// https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/model.py#L12
#[derive(Debug, Clone)]
pub struct Config {
pub in_channels: usize,
pub vec_in_dim: usize,
... | candle/candle-transformers/src/models/flux/model.rs/0 | {
"file_path": "candle/candle-transformers/src/models/flux/model.rs",
"repo_id": "candle",
"token_count": 10740
} | 50 |
//! Mamba inference implementation.
//!
//! See ["Mamba: Linear-Time Sequence Modeling with Selective State Spaces"](https://arxiv.org/abs/2312.00752)
//!
//! Based on reference implementation from the AlbertMamba project
//! A fast implementation of mamba for inference only.
//! Based on Laurent Mazare's rust implemen... | candle/candle-transformers/src/models/mamba.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mamba.rs",
"repo_id": "candle",
"token_count": 3923
} | 51 |
use candle::{Module, Result, Tensor};
use candle_nn as nn;
pub struct Qkv {
pub q: Tensor,
pub k: Tensor,
pub v: Tensor,
}
pub struct Mlp {
fc1: nn::Linear,
act: nn::Activation,
fc2: nn::Linear,
}
impl Mlp {
pub fn new(
in_features: usize,
hidden_features: usize,
v... | candle/candle-transformers/src/models/mmdit/projections.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mmdit/projections.rs",
"repo_id": "candle",
"token_count": 1917
} | 52 |
//! Persimmon Model
//!
//! A transformer language model for efficient inference and general-purpose tasks. The model uses a standard transformer architecture with:
//! - Layer normalization for Q/K attention
//! - RoPE embeddings with partial rotary factor
//! - ReLU activation
//! - Separate number of attention heads... | candle/candle-transformers/src/models/persimmon.rs/0 | {
"file_path": "candle/candle-transformers/src/models/persimmon.rs",
"repo_id": "candle",
"token_count": 1045
} | 53 |
//! Phi3 model implementation with quantization support.
//!
//! Phi3 is a language model intended for research purposes.
//! This implementation provides quantization for reduced memory usage.
//!
//! Key characteristics:
//! - Multi-head attention
//! - RMSNorm for layer normalization
//! - Rotary positional embeddin... | candle/candle-transformers/src/models/quantized_phi3.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_phi3.rs",
"repo_id": "candle",
"token_count": 6108
} | 54 |
use candle::{IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use super::transformer::TwoWayTransformer;
#[derive(Debug)]
struct MlpMaskDecoder {
layers: Vec<super::Linear>,
sigmoid_output: bool,
span: tracing::Span,
}
impl MlpMaskDecoder {
fn new(
input_dim: usize,
hidd... | candle/candle-transformers/src/models/segment_anything/mask_decoder.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/mask_decoder.rs",
"repo_id": "candle",
"token_count": 4213
} | 55 |
//! 2D UNet Denoising Models
//!
//! The 2D Unet models take as input a noisy sample and the current diffusion
//! timestep and return a denoised version of the input.
use super::embeddings::{TimestepEmbedding, Timesteps};
use super::unet_2d_blocks::*;
use crate::models::with_tracing::{conv2d, Conv2d};
use candle::{Res... | candle/candle-transformers/src/models/stable_diffusion/unet_2d.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/unet_2d.rs",
"repo_id": "candle",
"token_count": 8419
} | 56 |
use candle::{Module, Result, Tensor};
use candle_nn::VarBuilder;
#[derive(Debug, Clone)]
pub struct Embedding {
inner: candle_nn::Embedding,
span: tracing::Span,
}
impl Embedding {
pub fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
let inner = candle_nn::embedding(d1, d2, vb)?;
... | candle/candle-transformers/src/models/with_tracing.rs/0 | {
"file_path": "candle/candle-transformers/src/models/with_tracing.rs",
"repo_id": "candle",
"token_count": 2381
} | 57 |
use candle::{Device, Result, Tensor};
use candle_transformers::generation::LogitsProcessor;
#[test]
fn sample_with_zero_temperature() -> Result<()> {
let mut logits_process = LogitsProcessor::new(1337, None, None);
let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
let token = logits_process.s... | candle/candle-transformers/tests/generation_tests.rs/0 | {
"file_path": "candle/candle-transformers/tests/generation_tests.rs",
"repo_id": "candle",
"token_count": 806
} | 58 |
use wasm_bindgen::prelude::*;
pub mod token_output_stream;
#[wasm_bindgen]
extern "C" {
// Use `js_namespace` here to bind `console.log(..)` instead of just
// `log(..)`
#[wasm_bindgen(js_namespace = console)]
pub fn log(s: &str);
}
#[macro_export]
macro_rules! console_log {
// Note that this is u... | candle/candle-wasm-examples/blip/src/lib.rs/0 | {
"file_path": "candle/candle-wasm-examples/blip/src/lib.rs",
"repo_id": "candle",
"token_count": 192
} | 59 |
cargo build --target wasm32-unknown-unknown --release
wasm-bindgen ../../target/wasm32-unknown-unknown/release/m.wasm --out-dir build --target web
| candle/candle-wasm-examples/segment-anything/build-lib.sh/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/build-lib.sh",
"repo_id": "candle",
"token_count": 48
} | 60 |
## Running Whisper Examples
Here, we provide two examples of how to run Whisper using a Candle-compiled WASM binary and runtimes.
### Pure Rust UI
To build and test the UI made in Rust you will need [Trunk](https://trunkrs.dev/#install)
From the `candle-wasm-examples/whisper` directory run:
Download assets:
```bas... | candle/candle-wasm-examples/whisper/README.md/0 | {
"file_path": "candle/candle-wasm-examples/whisper/README.md",
"repo_id": "candle",
"token_count": 1023
} | 61 |
{
"moz:firefoxOptions": {
"prefs": {
"media.navigator.streams.fake": true,
"media.navigator.permission.disabled": true
},
"args": []
},
"goog:chromeOptions": {
"args": [
"--use-fake-device-for-media-stream",
"--use-fake-ui-for-media-stream"
]
}
}
| candle/candle-wasm-tests/webdriver.json/0 | {
"file_path": "candle/candle-wasm-tests/webdriver.json",
"repo_id": "candle",
"token_count": 143
} | 62 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
SPEED_TEST_N_EXAMPLES = 50_000
SMALL_TEST = 5_000
RESULTS_BASEPATH, RESULTS_FILENAME = os.path.split(__file__)
RESULTS_FILE_PATH = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ... | datasets/benchmarks/benchmark_iterating.py/0 | {
"file_path": "datasets/benchmarks/benchmark_iterating.py",
"repo_id": "datasets",
"token_count": 1697
} | 63 |
# Dataset features
[`Features`] defines the internal structure of a dataset. It is used to specify the underlying serialization format. What's more interesting to you though is that [`Features`] contains high-level information about everything from the column names and types, to the [`ClassLabel`]. You can think of [`... | datasets/docs/source/about_dataset_features.mdx/0 | {
"file_path": "datasets/docs/source/about_dataset_features.mdx",
"repo_id": "datasets",
"token_count": 2349
} | 64 |
# Overview
The how-to guides offer a more comprehensive overview of all the tools 🤗 Datasets offers and how to use them. This will help you tackle messier real-world datasets where you may need to manipulate the dataset structure or content to get it ready for training.
The guides assume you are familiar and comfort... | datasets/docs/source/how_to.md/0 | {
"file_path": "datasets/docs/source/how_to.md",
"repo_id": "datasets",
"token_count": 471
} | 65 |
# Main classes
## DatasetInfo
[[autodoc]] datasets.DatasetInfo
## Dataset
The base class [`Dataset`] implements a Dataset backed by an Apache Arrow table.
[[autodoc]] datasets.Dataset
- add_column
- add_item
- from_file
- from_buffer
- from_pandas
- from_dict
- from_generator
- dat... | datasets/docs/source/package_reference/main_classes.mdx/0 | {
"file_path": "datasets/docs/source/package_reference/main_classes.mdx",
"repo_id": "datasets",
"token_count": 1897
} | 66 |
# Use with Pandas
This document is a quick introduction to using `datasets` with Pandas, with a particular focus on how to process
datasets using Pandas functions, and how to convert a dataset to Pandas or from Pandas.
This is particularly useful as it allows fast operations, since `datasets` uses PyArrow under the h... | datasets/docs/source/use_with_pandas.mdx/0 | {
"file_path": "datasets/docs/source/use_with_pandas.mdx",
"repo_id": "datasets",
"token_count": 1010
} | 67 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interle... | datasets/src/datasets/combine.py/0 | {
"file_path": "datasets/src/datasets/combine.py",
"repo_id": "datasets",
"token_count": 4604
} | 68 |
# SPDX-License-Identifier: Apache-2.0
# Copyright 2023 The HuggingFace Authors.
from typing import Any, Dict, List, Optional, Union
from huggingface_hub import HfFileSystem
from . import config
from .table import CastError
from .utils.track import TrackedIterableFromGenerator, tracked_list, tracked_str
class Datase... | datasets/src/datasets/exceptions.py/0 | {
"file_path": "datasets/src/datasets/exceptions.py",
"repo_id": "datasets",
"token_count": 1552
} | 69 |
# Copyright 2020 The HuggingFace Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | datasets/src/datasets/formatting/torch_formatter.py/0 | {
"file_path": "datasets/src/datasets/formatting/torch_formatter.py",
"repo_id": "datasets",
"token_count": 2062
} | 70 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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
#
# U... | datasets/src/datasets/naming.py/0 | {
"file_path": "datasets/src/datasets/naming.py",
"repo_id": "datasets",
"token_count": 1179
} | 71 |
import io
import json
import re
from itertools import islice
from typing import Any, Callable, Dict, List
import fsspec
import numpy as np
import pyarrow as pa
import datasets
from datasets.features.features import cast_to_python_objects
from datasets.utils.file_utils import SINGLE_FILE_COMPRESSION_EXTENSION_TO_PROTO... | datasets/src/datasets/packaged_modules/webdataset/webdataset.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/webdataset/webdataset.py",
"repo_id": "datasets",
"token_count": 4913
} | 72 |
import bz2
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from ._filelock import FileLock
from .logging import get_logger
... | datasets/src/datasets/utils/extract.py/0 | {
"file_path": "datasets/src/datasets/utils/extract.py",
"repo_id": "datasets",
"token_count": 5809
} | 73 |
import numpy as np
def approximate_mode(class_counts, n_draws, rng):
"""Computes approximate mode of multivariate hypergeometric.
This is an approximation to the mode of the multivariate
hypergeometric given by class_counts and n_draws.
It shouldn't be off by more than one.
It is the mostly likely... | datasets/src/datasets/utils/stratify.py/0 | {
"file_path": "datasets/src/datasets/utils/stratify.py",
"repo_id": "datasets",
"token_count": 1670
} | 74 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
NUM_SHARDS = 4
NUM_ITEMS_PER_SHARD = 3
class FailedTestError(RuntimeError):
pass
def gen(shards: List[str]):
... | datasets/tests/distributed_scripts/run_torch_distributed.py/0 | {
"file_path": "datasets/tests/distributed_scripts/run_torch_distributed.py",
"repo_id": "datasets",
"token_count": 617
} | 75 |
import contextlib
import csv
import json
import os
import sqlite3
import tarfile
import textwrap
import zipfile
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
# dataset + arrow_file
@pytest.fixture(scope="session")
def dataset():
n = ... | datasets/tests/fixtures/files.py/0 | {
"file_path": "datasets/tests/fixtures/files.py",
"repo_id": "datasets",
"token_count": 8339
} | 76 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.csv.csv import Csv, CsvConfig
from ..utils import require_pil
@pytest.fixture
def... | datasets/tests/packaged_modules/test_csv.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_csv.py",
"repo_id": "datasets",
"token_count": 1880
} | 77 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class DatasetListTest(TestCase):
def _create_example_records(self):
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"}... | datasets/tests/test_dataset_list.py/0 | {
"file_path": "datasets/tests/test_dataset_list.py",
"repo_id": "datasets",
"token_count": 875
} | 78 |
import importlib
import os
import pickle
import shutil
import tempfile
import time
from hashlib import sha256
from multiprocessing import Pool
from pathlib import Path
from unittest import TestCase
from unittest.mock import patch
import dill
import pyarrow as pa
import pytest
import requests
import datasets
from data... | datasets/tests/test_load.py/0 | {
"file_path": "datasets/tests/test_load.py",
"repo_id": "datasets",
"token_count": 35807
} | 79 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/CONTRIBUTING.md/0 | {
"file_path": "diffusers/CONTRIBUTING.md",
"repo_id": "diffusers",
"token_count": 9937
} | 80 |
import glob
import subprocess
import sys
from typing import List
sys.path.append(".")
from benchmark_text_to_image import ALL_T2I_CKPTS # noqa: E402
PATTERN = "benchmark_*.py"
class SubprocessCallException(Exception):
pass
# Taken from `test_examples_utils.py`
def run_command(command: List[str], return_std... | diffusers/benchmarks/run_all.py/0 | {
"file_path": "diffusers/benchmarks/run_all.py",
"repo_id": "diffusers",
"token_count": 1527
} | 81 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/advanced_inference/outpaint.md/0 | {
"file_path": "diffusers/docs/source/en/advanced_inference/outpaint.md",
"repo_id": "diffusers",
"token_count": 3145
} | 82 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/models/controlnet.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/controlnet.md",
"repo_id": "diffusers",
"token_count": 782
} | 83 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/models/overview.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/overview.md",
"repo_id": "diffusers",
"token_count": 336
} | 84 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/normalization.md/0 | {
"file_path": "diffusers/docs/source/en/api/normalization.md",
"repo_id": "diffusers",
"token_count": 578
} | 85 |
<!--Copyright 2024 The HuggingFace Team, The Black Forest 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 a... | diffusers/docs/source/en/api/pipelines/control_flux_inpaint.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/control_flux_inpaint.md",
"repo_id": "diffusers",
"token_count": 1354
} | 86 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/optimization/tome.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/tome.md",
"repo_id": "diffusers",
"token_count": 3385
} | 87 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/training/distributed_inference.md/0 | {
"file_path": "diffusers/docs/source/en/training/distributed_inference.md",
"repo_id": "diffusers",
"token_count": 3310
} | 88 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/tutorials/inference_with_big_models.md/0 | {
"file_path": "diffusers/docs/source/en/tutorials/inference_with_big_models.md",
"repo_id": "diffusers",
"token_count": 1990
} | 89 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/inference_with_tcd_lora.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/inference_with_tcd_lora.md",
"repo_id": "diffusers",
"token_count": 6011
} | 90 |
<!--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
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<!--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
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<!--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
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<!--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
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<!--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
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<!--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
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# 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
#
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"repo_id": "diffusers",
"token_count": 28144
} | 97 |
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