text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | transformers/utils/check_tf_ops.py/0 | {
"file_path": "transformers/utils/check_tf_ops.py",
"repo_id": "transformers",
"token_count": 1302
} | 412 |
## w/ and w/o gradient accumulation
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --exp_name ppo_step_grad_accu --mini_batch_size 1 --gradient_accumulation_steps 128 --log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per... | trl/benchmark/benchmark_level3.sh/0 | {
"file_path": "trl/benchmark/benchmark_level3.sh",
"repo_id": "trl",
"token_count": 689
} | 413 |
# DPO Trainer
TRL supports the DPO Trainer for training language models from preference data, as described in the paper [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/abs/2305.18290) by Rafailov et al., 2023. For a full example have a look at [`examples/scripts/dpo.... | trl/docs/source/dpo_trainer.mdx/0 | {
"file_path": "trl/docs/source/dpo_trainer.mdx",
"repo_id": "trl",
"token_count": 3648
} | 414 |
# Text Environments
Text environments provide a learning ground for language agents. It allows a language model to use tools to accomplish a task such as using a Python interpreter to answer math questions or using a search index for trivia questions. Having access to tools allows language models to solve tasks that w... | trl/docs/source/text_environments.md/0 | {
"file_path": "trl/docs/source/text_environments.md",
"repo_id": "trl",
"token_count": 2826
} | 415 |
# RLHF pipeline for the creation of StackLLaMa: a Stack exchange llama-7b model.
There were three main steps to the training process:
1. Supervised fine-tuning of the base llama-7b model to create llama-7b-se:
- `torchrun --nnodes 1 --nproc_per_node 8 examples/research_projects/stack_llama/scripts/supervised_finet... | trl/examples/research_projects/stack_llama/scripts/README.md/0 | {
"file_path": "trl/examples/research_projects/stack_llama/scripts/README.md",
"repo_id": "trl",
"token_count": 696
} | 416 |
# coding=utf-8
# Copyright 2023 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... | trl/examples/scripts/dpo.py/0 | {
"file_path": "trl/examples/scripts/dpo.py",
"repo_id": "trl",
"token_count": 2629
} | 417 |
# 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... | trl/trl/trainer/base.py/0 | {
"file_path": "trl/trl/trainer/base.py",
"repo_id": "trl",
"token_count": 538
} | 418 |
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level o... | accelerate/CODE_OF_CONDUCT.md/0 | {
"file_path": "accelerate/CODE_OF_CONDUCT.md",
"repo_id": "accelerate",
"token_count": 1107
} | 0 |
<!--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/notebook.md/0 | {
"file_path": "accelerate/docs/source/basic_tutorials/notebook.md",
"repo_id": "accelerate",
"token_count": 5538
} | 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/usage_guides/fsdp.md/0 | {
"file_path": "accelerate/docs/source/usage_guides/fsdp.md",
"repo_id": "accelerate",
"token_count": 2785
} | 2 |
{
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"weight_decay": "auto"... | accelerate/examples/deepspeed_config_templates/zero_stage3_config.json/0 | {
"file_path": "accelerate/examples/deepspeed_config_templates/zero_stage3_config.json",
"repo_id": "accelerate",
"token_count": 657
} | 3 |
# 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/accelerator.py/0 | {
"file_path": "accelerate/src/accelerate/accelerator.py",
"repo_id": "accelerate",
"token_count": 61941
} | 4 |
from .selection_menu import BulletMenu
| accelerate/src/accelerate/commands/menu/__init__.py/0 | {
"file_path": "accelerate/src/accelerate/commands/menu/__init__.py",
"repo_id": "accelerate",
"token_count": 9
} | 5 |
# 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": 21388
} | 6 |
# 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/test_utils/training.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/training.py",
"repo_id": "accelerate",
"token_count": 1572
} | 7 |
# 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/other.py/0 | {
"file_path": "accelerate/src/accelerate/utils/other.py",
"repo_id": "accelerate",
"token_count": 4102
} | 8 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class MockLaunchConfig(SageMakerConfig):
compute_environment = ... | accelerate/tests/test_sagemaker.py/0 | {
"file_path": "accelerate/tests/test_sagemaker.py",
"repo_id": "accelerate",
"token_count": 851
} | 9 |
# Welcome to the RLHF Handbook!
Stay tuned for more details 🤗 | alignment-handbook/chapters/en/chapter0/introduction.mdx/0 | {
"file_path": "alignment-handbook/chapters/en/chapter0/introduction.mdx",
"repo_id": "alignment-handbook",
"token_count": 18
} | 10 |
# Model arguments
model_name_or_path: mistralai/Mistral-7B-v0.1
model_revision: main
torch_dtype: float16
# LoRA arguments
load_in_4bit: true
use_peft: true
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
# Data training arguments... | alignment-handbook/recipes/zephyr-7b-beta/sft/config_qlora.yaml/0 | {
"file_path": "alignment-handbook/recipes/zephyr-7b-beta/sft/config_qlora.yaml",
"repo_id": "alignment-handbook",
"token_count": 646
} | 11 |
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | alignment-handbook/tests/test_model_utils.py/0 | {
"file_path": "alignment-handbook/tests/test_model_utils.py",
"repo_id": "alignment-handbook",
"token_count": 1782
} | 12 |
# Summary
[Introduction](README.md)
# User Guide
- [Installation](guide/installation.md)
- [Hello World - MNIST](guide/hello_world.md)
- [PyTorch cheatsheet](guide/cheatsheet.md)
# Reference Guide
- [Running a model](inference/inference.md)
- [Using the hub](inference/hub.md)
- [Error management](error_manage.... | candle/candle-book/src/SUMMARY.md/0 | {
"file_path": "candle/candle-book/src/SUMMARY.md",
"repo_id": "candle",
"token_count": 274
} | 13 |
# Writing a custom kernel
| candle/candle-book/src/inference/cuda/writing.md/0 | {
"file_path": "candle/candle-book/src/inference/cuda/writing.md",
"repo_id": "candle",
"token_count": 6
} | 14 |
pub(crate) mod affine;
pub(crate) mod matmul;
pub(crate) mod random;
pub(crate) mod where_cond;
use candle_core::{Device, Result};
pub(crate) trait BenchDevice {
fn sync(&self) -> Result<()>;
fn bench_name<S: Into<String>>(&self, name: S) -> String;
}
impl BenchDevice for Device {
fn sync(&self) -> Resu... | candle/candle-core/benches/benchmarks/mod.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/mod.rs",
"repo_id": "candle",
"token_count": 1019
} | 15 |
use super::Cpu;
#[cfg(target_arch = "arm")]
use core::arch::arm::*;
#[cfg(target_arch = "aarch64")]
use core::arch::aarch64::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl CurrentCpu {
#[cfg(target_arch = "aarch64")]
unsafe fn reduce_one(x: floa... | candle/candle-core/src/cpu/neon.rs/0 | {
"file_path": "candle/candle-core/src/cpu/neon.rs",
"repo_id": "candle",
"token_count": 897
} | 16 |
//! Numpy support for tensors.
//!
//! The spec for the npy format can be found in
//! [npy-format](https://docs.scipy.org/doc/numpy-1.14.2/neps/npy-format.html).
//! The functions from this module can be used to read tensors from npy/npz files
//! or write tensors to these files. A npy file contains a single tensor (u... | candle/candle-core/src/npy.rs/0 | {
"file_path": "candle/candle-core/src/npy.rs",
"repo_id": "candle",
"token_count": 8717
} | 17 |
use crate::Layout;
/// An iterator over offset position for items of an N-dimensional arrays stored in a
/// flat buffer using some potential strides.
#[derive(Debug)]
pub struct StridedIndex<'a> {
next_storage_index: Option<usize>,
multi_index: Vec<usize>,
dims: &'a [usize],
stride: &'a [usize],
}
im... | candle/candle-core/src/strided_index.rs/0 | {
"file_path": "candle/candle-core/src/strided_index.rs",
"repo_id": "candle",
"token_count": 1148
} | 18 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::bert::{BertModel, Config, HiddenAct, DTYPE};
use anyhow::{Error as E, Result};
use candle::Tensor;
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::sync::Api, ... | candle/candle-examples/examples/bert/main.rs/0 | {
"file_path": "candle/candle-examples/examples/bert/main.rs",
"repo_id": "candle",
"token_count": 3527
} | 19 |
// TODO: Add an offline mode.
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use clap::Parser;
use h... | candle/candle-examples/examples/falcon/main.rs/0 | {
"file_path": "candle/candle-examples/examples/falcon/main.rs",
"repo_id": "candle",
"token_count": 2723
} | 20 |
# candle-mixtral: 8x7b LLM using a sparse mixture of experts.
Mixtral-8x7B-v0.1 is a pretrained generative LLM with 56 billion parameters.
- [Blog post](https://mistral.ai/news/mixtral-of-experts/) from Mistral announcing the model release.
- [Model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the Hu... | candle/candle-examples/examples/mixtral/README.md/0 | {
"file_path": "candle/candle-examples/examples/mixtral/README.md",
"repo_id": "candle",
"token_count": 322
} | 21 |
# candle-quantized-llama: Fast Inference of quantized LLaMA models
This example provides a quantized LLaMA model similar to
[llama.cpp](https://github.com/ggerganov/llama.cpp). This is based on candle
built-in quantization methods. Supported features include:
- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quan... | candle/candle-examples/examples/quantized/README.md/0 | {
"file_path": "candle/candle-examples/examples/quantized/README.md",
"repo_id": "candle",
"token_count": 820
} | 22 |
#[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::resnet;
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
#[val... | candle/candle-examples/examples/resnet/main.rs/0 | {
"file_path": "candle/candle-examples/examples/resnet/main.rs",
"repo_id": "candle",
"token_count": 1281
} | 23 |
# candle-trocr
`TrOCR` is a transformer OCR Model. In this example it is used to
transcribe image text. See the associated [model
card](https://huggingface.co/microsoft/trocr-base-printed) for details on
the model itself.
## Running an example
```bash
cargo run --example trocr --release -- --which base --cpu --imag... | candle/candle-examples/examples/trocr/readme.md/0 | {
"file_path": "candle/candle-examples/examples/trocr/readme.md",
"repo_id": "candle",
"token_count": 146
} | 24 |
def remove_prefix(text, prefix):
return text[text.startswith(prefix) and len(prefix):]
nps = {}
for k, v in model.state_dict().items():
k = remove_prefix(k, 'module_list.')
nps[k] = v.detach().numpy()
np.savez('yolo-v3.ot', **nps)
| candle/candle-examples/examples/yolo-v3/extract-weights.py/0 | {
"file_path": "candle/candle-examples/examples/yolo-v3/extract-weights.py",
"repo_id": "candle",
"token_count": 98
} | 25 |
// Build script to run nvcc and generate the C glue code for launching the flash-attention kernel.
// The cuda build time is very long so one can set the CANDLE_FLASH_ATTN_BUILD_DIR environment
// variable in order to cache the compiled artifacts and avoid recompiling too often.
use anyhow::{Context, Result};
use std::... | candle/candle-flash-attn/build.rs/0 | {
"file_path": "candle/candle-flash-attn/build.rs",
"repo_id": "candle",
"token_count": 1604
} | 26 |
[package]
name = "candle-kernels"
version = "0.3.3"
edition = "2021"
description = "CUDA kernels for Candle"
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[dependencies]
[build-dependencies]
bindgen_cuda =... | candle/candle-kernels/Cargo.toml/0 | {
"file_path": "candle/candle-kernels/Cargo.toml",
"repo_id": "candle",
"token_count": 126
} | 27 |
[package]
name = "candle-metal-kernels"
version = "0.3.3"
edition = "2021"
description = "Metal kernels for Candle"
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[dependencies]
metal = { version = "0.27.0"... | candle/candle-metal-kernels/Cargo.toml/0 | {
"file_path": "candle/candle-metal-kernels/Cargo.toml",
"repo_id": "candle",
"token_count": 218
} | 28 |
use candle_metal_kernels::{binary, call_binary_contiguous, call_binary_strided, Kernels};
use half::{bf16, f16};
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();
... | candle/candle-metal-kernels/tmp/binary.rs/0 | {
"file_path": "candle/candle-metal-kernels/tmp/binary.rs",
"repo_id": "candle",
"token_count": 3149
} | 29 |
pub mod activation;
pub mod batch_norm;
pub mod conv;
pub mod embedding;
pub mod encoding;
pub mod func;
pub mod group_norm;
pub mod init;
pub mod layer_norm;
pub mod linear;
pub mod loss;
pub mod ops;
pub mod optim;
pub mod rnn;
pub mod sequential;
pub mod var_builder;
pub mod var_map;
pub use activation::{prelu, Act... | candle/candle-nn/src/lib.rs/0 | {
"file_path": "candle/candle-nn/src/lib.rs",
"repo_id": "candle",
"token_count": 421
} | 30 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{test_utils::to_vec2_round, DType, Device, Result, Tensor};
use candle_nn::RNN;
/* The following test can be verified against PyTorch using the following snippet.
import torch
from torch import... | candle/candle-nn/tests/rnn.rs/0 | {
"file_path": "candle/candle-nn/tests/rnn.rs",
"repo_id": "candle",
"token_count": 2010
} | 31 |
# 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
class bf16(DType):
pass
@staticmethod
def cat(tensors: List[Tensor], dim: int) -> Tensor:
"""
Concatenat... | candle/candle-pyo3/py_src/candle/__init__.pyi/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/__init__.pyi",
"repo_id": "candle",
"token_count": 5785
} | 32 |
# 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 cuda_is_available() -> bool:
"""
Returns true if ... | candle/candle-pyo3/py_src/candle/utils/__init__.pyi/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/utils/__init__.pyi",
"repo_id": "candle",
"token_count": 712
} | 33 |
import candle
from candle import Tensor, QTensor
from candle.utils import load_safetensors, save_gguf, load_gguf, save_safetensors
from pathlib import Path
TEST_DIR = Path(__file__).parent.parent / "_workdir"
TEST_DIR.mkdir(exist_ok=True)
def test_can_roundtrip_safetensors():
tensors = {
"a": candle.rand... | candle/candle-pyo3/tests/native/test_utils.py/0 | {
"file_path": "candle/candle-pyo3/tests/native/test_utils.py",
"repo_id": "candle",
"token_count": 774
} | 34 |
use crate::quantized_nn::{layer_norm, linear, Linear};
pub use crate::quantized_var_builder::VarBuilder;
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::Activation;
pub use crate::models::mixformer::Config;
const MAX_SEQ_LEN: usize = 4096;
#[derive(Debug, Clone)]
struct Embedding {
... | candle/candle-transformers/src/models/quantized_mixformer.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_mixformer.rs",
"repo_id": "candle",
"token_count": 5892
} | 35 |
use super::schedulers::{betas_for_alpha_bar, BetaSchedule, PredictionType};
use candle::{Result, Tensor};
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DDPMVarianceType {
FixedSmall,
FixedSmallLog,
FixedLarge,
FixedLargeLog,
Learned,
}
impl Default for DDPMVarianceType {
fn default() -> Self... | candle/candle-transformers/src/models/stable_diffusion/ddpm.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/ddpm.rs",
"repo_id": "candle",
"token_count": 3662
} | 36 |
pub mod audio;
pub mod model;
pub mod quantized_model;
use serde::Deserialize;
// The names in comments correspond to the original implementation:
// https://github.com/openai/whisper/blob/f572f2161ba831bae131364c3bffdead7af6d210/whisper/model.py#L17
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct Config {... | candle/candle-transformers/src/models/whisper/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/whisper/mod.rs",
"repo_id": "candle",
"token_count": 812
} | 37 |
use candle::quantized::QTensor;
use candle::{Device, Result, Shape};
use std::sync::Arc;
// VarBuilder specialized for QTensors
pub struct VarBuilder {
data: Arc<std::collections::HashMap<String, Arc<QTensor>>>,
path: Vec<String>,
device: Device,
}
impl VarBuilder {
pub fn from_gguf<P: AsRef<std::path... | candle/candle-transformers/src/quantized_var_builder.rs/0 | {
"file_path": "candle/candle-transformers/src/quantized_var_builder.rs",
"repo_id": "candle",
"token_count": 1550
} | 38 |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use candle_transformers::models::blip;
use candle_transformers::models::quantized_blip;
use candle_wasm_example_blip::console_log;
use candle_wasm_example_blip::token_output_stream::TokenOutputStream;
u... | candle/candle-wasm-examples/blip/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/blip/src/bin/m.rs",
"repo_id": "candle",
"token_count": 2699
} | 39 |
//load Candle Bert Module wasm module
let init, ModelConditionalGeneration;
async function fetchArrayBuffer(url) {
const cacheName = "t5-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.match(url);
if (cachedResponse) {
const data = await cachedResponse.arrayBuf... | candle/candle-wasm-examples/t5/T5ModelConditionalGeneration.js/0 | {
"file_path": "candle/candle-wasm-examples/t5/T5ModelConditionalGeneration.js",
"repo_id": "candle",
"token_count": 980
} | 40 |
fn main() {
wasm_logger::init(wasm_logger::Config::new(log::Level::Trace));
yew::Renderer::<candle_wasm_example_whisper::App>::new().render();
}
| candle/candle-wasm-examples/whisper/src/bin/app.rs/0 | {
"file_path": "candle/candle-wasm-examples/whisper/src/bin/app.rs",
"repo_id": "candle",
"token_count": 67
} | 41 |
module.exports = {
root: true,
parser: "@typescript-eslint/parser",
extends: [
"eslint:recommended",
"plugin:@typescript-eslint/recommended",
"plugin:svelte/recommended",
"prettier",
],
plugins: ["@typescript-eslint"],
ignorePatterns: ["*.cjs"],
overrides: [
{
files: ["*.svelte"],
parser: "svelte... | chat-ui/.eslintrc.cjs/0 | {
"file_path": "chat-ui/.eslintrc.cjs",
"repo_id": "chat-ui",
"token_count": 419
} | 42 |
/// <reference types="@sveltejs/kit" />
/// <reference types="unplugin-icons/types/svelte" />
import type { User } from "$lib/types/User";
// See https://kit.svelte.dev/docs/types#app
// for information about these interfaces
declare global {
namespace App {
// interface Error {}
interface Locals {
sessionId:... | chat-ui/src/app.d.ts/0 | {
"file_path": "chat-ui/src/app.d.ts",
"repo_id": "chat-ui",
"token_count": 145
} | 43 |
<script lang="ts">
import { base } from "$app/paths";
import { page } from "$app/stores";
import { createEventDispatcher } from "svelte";
import CarbonCheckmark from "~icons/carbon/checkmark";
import CarbonTrashCan from "~icons/carbon/trash-can";
import CarbonClose from "~icons/carbon/close";
import CarbonEdit ... | chat-ui/src/lib/components/NavConversationItem.svelte/0 | {
"file_path": "chat-ui/src/lib/components/NavConversationItem.svelte",
"repo_id": "chat-ui",
"token_count": 1309
} | 44 |
<script lang="ts">
import { marked } from "marked";
import markedKatex from "marked-katex-extension";
import type { Message } from "$lib/types/Message";
import { afterUpdate, createEventDispatcher } from "svelte";
import { deepestChild } from "$lib/utils/deepestChild";
import { page } from "$app/stores";
import... | chat-ui/src/lib/components/chat/ChatMessage.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/ChatMessage.svelte",
"repo_id": "chat-ui",
"token_count": 4251
} | 45 |
import { z } from "zod";
import {
embeddingEndpointTei,
embeddingEndpointTeiParametersSchema,
} from "./tei/embeddingEndpoints";
import {
embeddingEndpointTransformersJS,
embeddingEndpointTransformersJSParametersSchema,
} from "./transformersjs/embeddingEndpoints";
// parameters passed when generating text
interfa... | chat-ui/src/lib/server/embeddingEndpoints/embeddingEndpoints.ts/0 | {
"file_path": "chat-ui/src/lib/server/embeddingEndpoints/embeddingEndpoints.ts",
"repo_id": "chat-ui",
"token_count": 413
} | 46 |
import { dot } from "@xenova/transformers";
import type { EmbeddingBackendModel } from "$lib/server/embeddingModels";
import type { Embedding } from "$lib/server/embeddingEndpoints/embeddingEndpoints";
// see here: https://github.com/nmslib/hnswlib/blob/359b2ba87358224963986f709e593d799064ace6/README.md?plain=1#L34
fu... | chat-ui/src/lib/server/sentenceSimilarity.ts/0 | {
"file_path": "chat-ui/src/lib/server/sentenceSimilarity.ts",
"repo_id": "chat-ui",
"token_count": 503
} | 47 |
import type { ObjectId } from "mongodb";
import type { User } from "./User";
import type { Timestamps } from "./Timestamps";
export interface Assistant extends Timestamps {
_id: ObjectId;
createdById: User["_id"] | string; // user id or session
createdByName?: User["username"];
avatar?: string;
name: string;
des... | chat-ui/src/lib/types/Assistant.ts/0 | {
"file_path": "chat-ui/src/lib/types/Assistant.ts",
"repo_id": "chat-ui",
"token_count": 145
} | 48 |
export interface GAEvent {
hitType: "event";
eventCategory: string;
eventAction: string;
eventLabel?: string;
eventValue?: number;
}
// Send a Google Analytics event
export function sendAnalyticsEvent({
eventCategory,
eventAction,
eventLabel,
eventValue,
}: Omit<GAEvent, "hitType">): void {
// Mandatory fiel... | chat-ui/src/lib/utils/analytics.ts/0 | {
"file_path": "chat-ui/src/lib/utils/analytics.ts",
"repo_id": "chat-ui",
"token_count": 313
} | 49 |
import type { Message } from "$lib/types/Message";
import type { LegacyParamatersTemplateInput } from "$lib/types/Template";
import Handlebars from "handlebars";
Handlebars.registerHelper("ifUser", function (this: Pick<Message, "from" | "content">, options) {
if (this.from == "user") return options.fn(this);
});
Han... | chat-ui/src/lib/utils/template.ts/0 | {
"file_path": "chat-ui/src/lib/utils/template.ts",
"repo_id": "chat-ui",
"token_count": 290
} | 50 |
<script lang="ts">
import type { PageData } from "./$types";
import { PUBLIC_APP_ASSETS, PUBLIC_ORIGIN } from "$env/static/public";
import { isHuggingChat } from "$lib/utils/isHuggingChat";
import { goto } from "$app/navigation";
import { base } from "$app/paths";
import { page } from "$app/stores";
import Ca... | chat-ui/src/routes/assistants/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/assistants/+page.svelte",
"repo_id": "chat-ui",
"token_count": 2000
} | 51 |
<script lang="ts">
import { marked } from "marked";
import privacy from "../../../PRIVACY.md?raw";
</script>
<div class="overflow-auto p-6">
<div class="prose mx-auto px-4 pb-24 pt-6 dark:prose-invert md:pt-12">
<!-- eslint-disable-next-line svelte/no-at-html-tags -->
{@html marked(privacy, { gfm: true })}
</d... | chat-ui/src/routes/privacy/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/privacy/+page.svelte",
"repo_id": "chat-ui",
"token_count": 141
} | 52 |
@import "highlight.js/styles/atom-one-dark";
| chat-ui/src/styles/highlight-js.css/0 | {
"file_path": "chat-ui/src/styles/highlight-js.css",
"repo_id": "chat-ui",
"token_count": 17
} | 53 |
const defaultTheme = require("tailwindcss/defaultTheme");
const colors = require("tailwindcss/colors");
import dotenv from "dotenv";
dotenv.config({ path: "./.env" });
/** @type {import('tailwindcss').Config} */
export default {
darkMode: "class",
content: ["./src/**/*.{html,js,svelte,ts}"],
theme: {
extend: {
... | chat-ui/tailwind.config.cjs/0 | {
"file_path": "chat-ui/tailwind.config.cjs",
"repo_id": "chat-ui",
"token_count": 276
} | 54 |
repos:
- repo: https://github.com/charliermarsh/ruff-pre-commit # https://github.com/charliermarsh/ruff#usage
rev: 'v0.1.5'
hooks:
# Run the linter.
- id: ruff
args: [ --fix ]
# Run the formatter.
- id: ruff-format
| datasets/.pre-commit-config.yaml/0 | {
"file_path": "datasets/.pre-commit-config.yaml",
"repo_id": "datasets",
"token_count": 122
} | 55 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
SPEED_TEST_N_EXAMPLES = 500_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_map_filter.py/0 | {
"file_path": "datasets/benchmarks/benchmark_map_filter.py",
"repo_id": "datasets",
"token_count": 996
} | 56 |
# Build and load
Nearly every deep learning workflow begins with loading a dataset, which makes it one of the most important steps. With 🤗 Datasets, there are more than 900 datasets available to help you get started with your NLP task. All you have to do is call: [`load_dataset`] to take your first step. This functio... | datasets/docs/source/about_dataset_load.mdx/0 | {
"file_path": "datasets/docs/source/about_dataset_load.mdx",
"repo_id": "datasets",
"token_count": 2537
} | 57 |
# 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": 469
} | 58 |
# Builder classes
## Builders
🤗 Datasets relies on two main classes during the dataset building process: [`DatasetBuilder`] and [`BuilderConfig`].
[[autodoc]] datasets.DatasetBuilder
[[autodoc]] datasets.GeneratorBasedBuilder
[[autodoc]] datasets.BeamBasedBuilder
[[autodoc]] datasets.ArrowBasedBuilder
[[autodoc... | datasets/docs/source/package_reference/builder_classes.mdx/0 | {
"file_path": "datasets/docs/source/package_reference/builder_classes.mdx",
"repo_id": "datasets",
"token_count": 253
} | 59 |
# Preprocess
In addition to loading datasets, 🤗 Datasets other main goal is to offer a diverse set of preprocessing functions to get a dataset into an appropriate format for training with your machine learning framework.
There are many possible ways to preprocess a dataset, and it all depends on your specific datas... | datasets/docs/source/use_dataset.mdx/0 | {
"file_path": "datasets/docs/source/use_dataset.mdx",
"repo_id": "datasets",
"token_count": 3252
} | 60 |
# Metric Card for chrF(++)
## Metric Description
ChrF and ChrF++ are two MT evaluation metrics that use the F-score statistic for character n-gram matches. ChrF++ additionally includes word n-grams, which correlate more strongly with direct assessment. We use the implementation that is already present in sacrebleu.
... | datasets/metrics/chrf/README.md/0 | {
"file_path": "datasets/metrics/chrf/README.md",
"repo_id": "datasets",
"token_count": 2254
} | 61 |
# Metric Card for F1
## Metric Description
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
## How to Use
At minimum, this metric requires predictions and references as input
```python
>>> f1_metric = dataset... | datasets/metrics/f1/README.md/0 | {
"file_path": "datasets/metrics/f1/README.md",
"repo_id": "datasets",
"token_count": 1624
} | 62 |
# Metric Card for MAUVE
## Metric description
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure. It summarizes both Type I and Type II errors measured softly using [Kullback–Leibler (KL) divergences](https://en.wikip... | datasets/metrics/mauve/README.md/0 | {
"file_path": "datasets/metrics/mauve/README.md",
"repo_id": "datasets",
"token_count": 1650
} | 63 |
# Metric Card for ROC AUC
## Metric Description
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting... | datasets/metrics/roc_auc/README.md/0 | {
"file_path": "datasets/metrics/roc_auc/README.md",
"repo_id": "datasets",
"token_count": 3273
} | 64 |
"""Official evaluation script for SQuAD version 2.0.
In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable... | datasets/metrics/squad_v2/evaluate.py/0 | {
"file_path": "datasets/metrics/squad_v2/evaluate.py",
"repo_id": "datasets",
"token_count": 5443
} | 65 |
<!---
Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or ... | datasets/notebooks/README.md/0 | {
"file_path": "datasets/notebooks/README.md",
"repo_id": "datasets",
"token_count": 534
} | 66 |
import importlib
import importlib.metadata
import logging
import os
import platform
from pathlib import Path
from typing import Optional
from packaging import version
logger = logging.getLogger(__name__.split(".", 1)[0]) # to avoid circular import from .utils.logging
# Datasets
S3_DATASETS_BUCKET_PREFIX = "https:/... | datasets/src/datasets/config.py/0 | {
"file_path": "datasets/src/datasets/config.py",
"repo_id": "datasets",
"token_count": 4310
} | 67 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class BaseCompressedFileFileSystem(AbstractArchiveFileSystem):
"""Read contents of compressed file as a filesystem with one file inside."""
root_marker = ""
... | datasets/src/datasets/filesystems/compression.py/0 | {
"file_path": "datasets/src/datasets/filesystems/compression.py",
"repo_id": "datasets",
"token_count": 2608
} | 68 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MO... | datasets/src/datasets/io/parquet.py/0 | {
"file_path": "datasets/src/datasets/io/parquet.py",
"repo_id": "datasets",
"token_count": 2566
} | 69 |
import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar
from ..features import Features
T = TypeVar("T", bound="TaskTemplate")
@dataclass(frozen=True)
class TaskTemplate(abc.ABC):
# `task` is not a ClassVar since we want it to be part of the ... | datasets/src/datasets/tasks/base.py/0 | {
"file_path": "datasets/src/datasets/tasks/base.py",
"repo_id": "datasets",
"token_count": 417
} | 70 |
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import copy
import io
import json
import multiprocessing
import os
import posixpath
import re
import shutil
import sys
import time
import ... | datasets/src/datasets/utils/file_utils.py/0 | {
"file_path": "datasets/src/datasets/utils/file_utils.py",
"repo_id": "datasets",
"token_count": 11169
} | 71 |
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": 1674
} | 72 |
import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... | datasets/tests/conftest.py/0 | {
"file_path": "datasets/tests/conftest.py",
"repo_id": "datasets",
"token_count": 957
} | 73 |
import posixpath
from pathlib import Path
from unittest.mock import patch
import pytest
from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path
from fsspec.registry import _registry as _fsspec_registry
class MockFileSystem(AbstractFileSystem):
protocol = "mock"
def __ini... | datasets/tests/fixtures/fsspec.py/0 | {
"file_path": "datasets/tests/fixtures/fsspec.py",
"repo_id": "datasets",
"token_count": 1757
} | 74 |
import shutil
import textwrap
import numpy as np
import pytest
from datasets import ClassLabel, Features, Image, Value
from datasets.data_files import DataFilesDict, get_data_patterns
from datasets.download.streaming_download_manager import StreamingDownloadManager
from datasets.packaged_modules.imagefolder.imagefold... | datasets/tests/packaged_modules/test_imagefolder.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_imagefolder.py",
"repo_id": "datasets",
"token_count": 8692
} | 75 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
Bzip2Extractor,
Extractor,
GzipExtractor,
Lz4Extractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lz4, require_py7zr, require_zstandard
@pyte... | datasets/tests/test_extract.py/0 | {
"file_path": "datasets/tests/test_extract.py",
"repo_id": "datasets",
"token_count": 2984
} | 76 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def test_offline_with_timeout():
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT):
with pytest.raises(Reques... | datasets/tests/test_offline_util.py/0 | {
"file_path": "datasets/tests/test_offline_util.py",
"repo_id": "datasets",
"token_count": 382
} | 77 |
<jupyter_start><jupyter_text>Unit 8: Proximal Policy Gradient (PPO) with PyTorch 🤖In this notebook, you'll learn to **code your PPO agent from scratch with PyTorch using CleanRL implementation as model**.To test its robustness, we're going to train it in:- [LunarLander-v2 🚀](https://www.gymlibrary.dev/environments/bo... | deep-rl-class/notebooks/unit8/unit8_part1.ipynb/0 | {
"file_path": "deep-rl-class/notebooks/unit8/unit8_part1.ipynb",
"repo_id": "deep-rl-class",
"token_count": 15492
} | 78 |
# Quiz [[quiz]]
The best way to learn and [to avoid the illusion of competence](https://www.coursera.org/lecture/learning-how-to-learn/illusions-of-competence-BuFzf) **is to test yourself.** This will help you to find **where you need to reinforce your knowledge**.
### Q1: What is Reinforcement Learning?
<details>
<... | deep-rl-class/units/en/unit1/quiz.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit1/quiz.mdx",
"repo_id": "deep-rl-class",
"token_count": 1866
} | 79 |
# Q-Learning Recap [[q-learning-recap]]
*Q-Learning* **is the RL algorithm that** :
- Trains a *Q-function*, an **action-value function** encoded, in internal memory, by a *Q-table* **containing all the state-action pair values.**
- Given a state and action, our Q-function **will search its Q-table for the correspo... | deep-rl-class/units/en/unit2/q-learning-recap.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit2/q-learning-recap.mdx",
"repo_id": "deep-rl-class",
"token_count": 505
} | 80 |
# Conclusion
**Congrats on finishing this unit**! There was a lot of information.
And congrats on finishing the tutorial. You've just coded your first Deep Reinforcement Learning agent from scratch using PyTorch and shared it on the Hub 🥳.
Don't hesitate to iterate on this unit **by improving the implementation for... | deep-rl-class/units/en/unit4/conclusion.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit4/conclusion.mdx",
"repo_id": "deep-rl-class",
"token_count": 250
} | 81 |
# The SnowballTarget Environment
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget.gif" alt="SnowballTarget"/>
SnowballTarget is an environment we created at Hugging Face using assets from [Kay Lousberg](https://kaylousberg.com/). We have an option... | deep-rl-class/units/en/unit5/snowball-target.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit5/snowball-target.mdx",
"repo_id": "deep-rl-class",
"token_count": 1019
} | 82 |
# Additional Readings [[additional-readings]]
These are **optional readings** if you want to go deeper.
## PPO Explained
- [Towards Delivering a Coherent Self-Contained Explanation of Proximal Policy Optimization by Daniel Bick](https://fse.studenttheses.ub.rug.nl/25709/1/mAI_2021_BickD.pdf)
- [What is the way to un... | deep-rl-class/units/en/unit8/additional-readings.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit8/additional-readings.mdx",
"repo_id": "deep-rl-class",
"token_count": 418
} | 83 |
# Introduction [[introduction]]
One of the most critical tasks in Deep Reinforcement Learning is to **find a good set of training hyperparameters**.
<img src="https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png" alt="Optuna Logo"/>
[Optuna](https://optuna.org/) is a library that helps y... | deep-rl-class/units/en/unitbonus2/introduction.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus2/introduction.mdx",
"repo_id": "deep-rl-class",
"token_count": 156
} | 84 |
import argparse
import sys
sys.path.append(".")
from base_classes import T2IAdapterBenchmark, T2IAdapterSDXLBenchmark # noqa: E402
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt",
type=str,
default="TencentARC/t2iadapter_canny_sd14v1",
... | diffusers/benchmarks/benchmark_t2i_adapter.py/0 | {
"file_path": "diffusers/benchmarks/benchmark_t2i_adapter.py",
"repo_id": "diffusers",
"token_count": 393
} | 85 |
# docstyle-ignore
INSTALL_CONTENT = """
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
"""
notebook_first_... | diffusers/docs/source/_config.py/0 | {
"file_path": "diffusers/docs/source/_config.py",
"repo_id": "diffusers",
"token_count": 102
} | 86 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/models/autoencoderkl.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/autoencoderkl.md",
"repo_id": "diffusers",
"token_count": 785
} | 87 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/optimization/onnx.md/0 | {
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"repo_id": "diffusers",
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/training/kandinsky.md/0 | {
"file_path": "diffusers/docs/source/en/training/kandinsky.md",
"repo_id": "diffusers",
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/conditional_image_generation.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/conditional_image_generation.md",
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} | 91 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/loading.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/loading.md",
"repo_id": "diffusers",
"token_count": 7192
} | 92 |
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