text stringlengths 7 323k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 457 |
|---|---|---|---|
# Builds GPU docker image of PyTorch specifically
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.9
# Install apt libs
RUN apt-get update && \
apt-get install -y curl git wget &&... | accelerate/docker/accelerate-gpu/Dockerfile/0 | {
"file_path": "accelerate/docker/accelerate-gpu/Dockerfile",
"repo_id": "accelerate",
"token_count": 539
} | 0 |
<!--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... | accelerate/docs/source/concept_guides/low_precision_training.md/0 | {
"file_path": "accelerate/docs/source/concept_guides/low_precision_training.md",
"repo_id": "accelerate",
"token_count": 1466
} | 1 |
<!--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 agreed... | accelerate/docs/source/package_reference/state.md/0 | {
"file_path": "accelerate/docs/source/package_reference/state.md",
"repo_id": "accelerate",
"token_count": 291
} | 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": 2030
} | 3 |
#!/usr/bin/env python
# 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
#
# U... | accelerate/examples/by_feature/megatron_lm_gpt_pretraining.py/0 | {
"file_path": "accelerate/examples/by_feature/megatron_lm_gpt_pretraining.py",
"repo_id": "accelerate",
"token_count": 12432
} | 4 |
# 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/big_modeling.py/0 | {
"file_path": "accelerate/src/accelerate/big_modeling.py",
"repo_id": "accelerate",
"token_count": 10832
} | 5 |
# 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/cursor.py/0 | {
"file_path": "accelerate/src/accelerate/commands/menu/cursor.py",
"repo_id": "accelerate",
"token_count": 763
} | 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/src/accelerate/scheduler.py/0 | {
"file_path": "accelerate/src/accelerate/scheduler.py",
"repo_id": "accelerate",
"token_count": 1577
} | 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/test_utils/scripts/test_sync.py/0 | {
"file_path": "accelerate/src/accelerate/test_utils/scripts/test_sync.py",
"repo_id": "accelerate",
"token_count": 7294
} | 8 |
# 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/offload.py/0 | {
"file_path": "accelerate/src/accelerate/utils/offload.py",
"repo_id": "accelerate",
"token_count": 3177
} | 9 |
compute_environment: LOCAL_MACHINE
deepspeed_config: {}
distributed_type: 'NO'
fsdp_config: {}
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
mixed_precision: 'no'
num_machines: 1
num_processes: 1
use_cpu: false | accelerate/tests/test_configs/0_11_0.yaml/0 | {
"file_path": "accelerate/tests/test_configs/0_11_0.yaml",
"repo_id": "accelerate",
"token_count": 95
} | 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/tests/test_offload.py/0 | {
"file_path": "accelerate/tests/test_offload.py",
"repo_id": "accelerate",
"token_count": 1981
} | 11 |
.PHONY: style quality
# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
export PYTHONPATH = src
check_dirs := src tests scripts
style:
black --line-length 119 --target-version py310 $(check_dirs) setup.py
isort $(check_dirs) setup.py
quality:
black --check --line... | alignment-handbook/Makefile/0 | {
"file_path": "alignment-handbook/Makefile",
"repo_id": "alignment-handbook",
"token_count": 363
} | 12 |
# Instructions to Replicate Zephyr-7b-β
As described in the Zephyr [technical report](https://huggingface.co/papers/2310.16944), training this model proceeds in two steps:
1. Apply SFT to fine-tune Mistral 7B on a filtered version of the UltraChat dataset ([link](https://huggingface.co/datasets/HuggingFaceH4/ultrach... | alignment-handbook/recipes/zephyr-7b-beta/README.md/0 | {
"file_path": "alignment-handbook/recipes/zephyr-7b-beta/README.md",
"repo_id": "alignment-handbook",
"token_count": 888
} | 13 |
# 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/src/alignment/model_utils.py/0 | {
"file_path": "alignment-handbook/src/alignment/model_utils.py",
"repo_id": "alignment-handbook",
"token_count": 1693
} | 14 |
# Porting a custom kernel
| candle/candle-book/src/cuda/porting.md/0 | {
"file_path": "candle/candle-book/src/cuda/porting.md",
"repo_id": "candle",
"token_count": 7
} | 15 |
# Simplified
## How its works
This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
Basic moments:
1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
2. The inpu... | candle/candle-book/src/training/simplified.md/0 | {
"file_path": "candle/candle-book/src/training/simplified.md",
"repo_id": "candle",
"token_count": 530
} | 16 |
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Layout, Result, Shape};
pub trait BackendStorage: Sized {
type Device: BackendDevice;
fn try_clone(&self, _: &Layout) -> Result<Self>;
fn dtype(&self) -> DType;
fn device(&self) -> &Self::Device;
// Maybe this... | candle/candle-core/src/backend.rs/0 | {
"file_path": "candle/candle-core/src/backend.rs",
"repo_id": "candle",
"token_count": 1732
} | 17 |
#![allow(dead_code)]
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
#[derive(Debug, Clone)]
pub struct CudaDevice;
#[derive(Debug)]
pub struct CudaStorage;
macro_rules! fail {
() => {
unimplemented!("cuda support has not been enabled, ... | candle/candle-core/src/dummy_cuda_backend.rs/0 | {
"file_path": "candle/candle-core/src/dummy_cuda_backend.rs",
"repo_id": "candle",
"token_count": 2634
} | 18 |
//! Support for the GGUF file format.
//!
//! Spec: https://github.com/philpax/ggml/blob/gguf-spec/docs/gguf.md
use super::{GgmlDType, QTensor};
use crate::{Device, Result};
use byteorder::{LittleEndian, ReadBytesExt, WriteBytesExt};
use std::collections::HashMap;
pub const DEFAULT_ALIGNMENT: u64 = 32;
#[derive(Debu... | candle/candle-core/src/quantized/gguf_file.rs/0 | {
"file_path": "candle/candle-core/src/quantized/gguf_file.rs",
"repo_id": "candle",
"token_count": 9397
} | 19 |
use anyhow::Result;
use candle_core::{test_device, test_utils, Device, IndexOp, Tensor};
/* This test is based on the following script.
import torch
torch.manual_seed(4242)
t = torch.randn((1, 4, 5))
w = torch.randn((2, 4, 3))
print(t.flatten())
print(w.flatten())
res = torch.nn.functional.conv1d(t, w)
print(res.flat... | candle/candle-core/tests/conv_tests.rs/0 | {
"file_path": "candle/candle-core/tests/conv_tests.rs",
"repo_id": "candle",
"token_count": 14891
} | 20 |
#[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
} | 21 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::distilbert::{Config, DistilBertModel, DTYPE};
use anyhow::{Error as E, Result};
use candle::{Device, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::... | candle/candle-examples/examples/distilbert/main.rs/0 | {
"file_path": "candle/candle-examples/examples/distilbert/main.rs",
"repo_id": "candle",
"token_count": 1939
} | 22 |
// An implementation of LLaMA https://github.com/facebookresearch/llama
//
// This is based on nanoGPT in a similar way to:
// https://github.com/Lightning-AI/lit-llama/blob/main/lit_llama/model.py
//
// The tokenizer config can be retrieved from:
// https://huggingface.co/hf-internal-testing/llama-tokenizer/raw/main/t... | candle/candle-examples/examples/llama_multiprocess/main.rs/0 | {
"file_path": "candle/candle-examples/examples/llama_multiprocess/main.rs",
"repo_id": "candle",
"token_count": 3470
} | 23 |
// This should reach 91.5% accuracy.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use rand::prelude::*;
use candle::{DType, Result, Tensor, D};
use candle_nn::{loss, ops, Conv2d, Linear, Module, ModuleT, Optimizer, VarB... | candle/candle-examples/examples/mnist-training/main.rs/0 | {
"file_path": "candle/candle-examples/examples/mnist-training/main.rs",
"repo_id": "candle",
"token_count": 4094
} | 24 |
# candle-reinforcement-learning
Reinforcement Learning examples for candle.
This has been tested with `gymnasium` version `0.29.1`. You can install the
Python package with:
```bash
pip install "gymnasium[accept-rom-license]"
```
In order to run the examples, use the following commands. Note the additional
`--package... | candle/candle-examples/examples/reinforcement-learning/README.md/0 | {
"file_path": "candle/candle-examples/examples/reinforcement-learning/README.md",
"repo_id": "candle",
"token_count": 198
} | 25 |
# candle-segformer
- [HuggingFace Segformer Model Card][segformer]
- [`mit-b0` - An encoder only pretrained model][encoder]
- [`segformer-b0-finetuned-ade-512-512` - A fine tuned model for segmentation][ade512]
## How to run the example
If you want you can use the example images from this [pull request][pr], downloa... | candle/candle-examples/examples/segformer/README.md/0 | {
"file_path": "candle/candle-examples/examples/segformer/README.md",
"repo_id": "candle",
"token_count": 357
} | 26 |
use candle::{IndexOp, Result, Tensor, D};
use tokenizers::Tokenizer;
const LANGUAGES: [(&str, &str); 99] = [
("en", "english"),
("zh", "chinese"),
("de", "german"),
("es", "spanish"),
("ru", "russian"),
("ko", "korean"),
("fr", "french"),
("ja", "japanese"),
("pt", "portuguese"),
... | candle/candle-examples/examples/whisper/multilingual.rs/0 | {
"file_path": "candle/candle-examples/examples/whisper/multilingual.rs",
"repo_id": "candle",
"token_count": 1846
} | 27 |
// Copyright (c) 2023, 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, 128>(Flash_fwd_params ¶ms, cudaStream_t stream) {
... | candle/candle-flash-attn/kernels/flash_fwd_hdim128_fp16_sm80.cu/0 | {
"file_path": "candle/candle-flash-attn/kernels/flash_fwd_hdim128_fp16_sm80.cu",
"repo_id": "candle",
"token_count": 135
} | 28 |
/******************************************************************************
* Copyright (c) 2023, Tri Dao.
******************************************************************************/
#pragma once
#include "static_switch.h"
#include "flash.h"
#include "flash_fwd_kernel.h"
template<typename Kernel_traits, bo... | candle/candle-flash-attn/kernels/flash_fwd_launch_template.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/flash_fwd_launch_template.h",
"repo_id": "candle",
"token_count": 7583
} | 29 |
#include "cuda_utils.cuh"
#include<stdint.h>
template <typename S, typename T>
__device__ void cast_(
const size_t numel,
const size_t num_dims,
const size_t *info,
const S *inp,
T *out
) {
const size_t *dims = info;
const size_t *strides = info + num_dims;
if (is_contiguous(num_dims, d... | candle/candle-kernels/src/cast.cu/0 | {
"file_path": "candle/candle-kernels/src/cast.cu",
"repo_id": "candle",
"token_count": 2161
} | 30 |
template <typename T>
METAL_FUNC void im2col(
constant size_t &dst_numel,
constant size_t &h_out,
constant size_t &w_out,
constant size_t &h_k,
constant size_t &w_k,
constant size_t &stride,
constant size_t &padding,
constant size_t &dilation,
constant size_t *src_dims,
constant ... | candle/candle-metal-kernels/src/conv.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/conv.metal",
"repo_id": "candle",
"token_count": 3054
} | 31 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, Device, Result, Tensor};
use candle_nn::{linear, AdamW, Linear, Module, Optimizer, ParamsAdamW, VarBuilder, VarMap};
fn gen_data() -> Result<(Tensor, Tensor)> {
// Generate some sam... | candle/candle-nn/examples/basic_optimizer.rs/0 | {
"file_path": "candle/candle-nn/examples/basic_optimizer.rs",
"repo_id": "candle",
"token_count": 595
} | 32 |
//! Recurrent Neural Networks
use candle::{DType, Device, IndexOp, Result, Tensor};
/// Trait for Recurrent Neural Networks.
#[allow(clippy::upper_case_acronyms)]
pub trait RNN {
type State: Clone;
/// A zero state from which the recurrent network is usually initialized.
fn zero_state(&self, batch_dim: us... | candle/candle-nn/src/rnn.rs/0 | {
"file_path": "candle/candle-nn/src/rnn.rs",
"repo_id": "candle",
"token_count": 4874
} | 33 |
use candle::Result;
use prost::Message;
pub mod onnx {
include!(concat!(env!("OUT_DIR"), "/onnx.rs"));
}
pub mod eval;
pub use eval::{dtype, simple_eval};
pub fn read_file<P: AsRef<std::path::Path>>(p: P) -> Result<onnx::ModelProto> {
let buf = std::fs::read(p)?;
onnx::ModelProto::decode(buf.as_slice()).... | candle/candle-onnx/src/lib.rs/0 | {
"file_path": "candle/candle-onnx/src/lib.rs",
"repo_id": "candle",
"token_count": 154
} | 34 |
from .module import Module
from .container import Sequential, ModuleList, ModuleDict
from .sparse import Embedding
from .normalization import LayerNorm
from .linear import Linear
| candle/candle-pyo3/py_src/candle/nn/__init__.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/__init__.py",
"repo_id": "candle",
"token_count": 43
} | 35 |
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(ob: &'source PyAny) -> PyResult<Self> {
if ob.is_none() {
return Err(PyErr::new... | candle/candle-pyo3/src/shape.rs/0 | {
"file_path": "candle/candle-pyo3/src/shape.rs",
"repo_id": "candle",
"token_count": 1646
} | 36 |
use super::with_tracing::{layer_norm, linear, LayerNorm, Linear};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{embedding, Embedding, Module, VarBuilder};
use serde::Deserialize;
pub const DTYPE: DType = DType::F32;
#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize)]
#[serde(rename_all = "lowerca... | candle/candle-transformers/src/models/bert.rs/0 | {
"file_path": "candle/candle-transformers/src/models/bert.rs",
"repo_id": "candle",
"token_count": 7941
} | 37 |
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::linear_no_bias as linear;
use candle_nn::{embedding, rms_norm, Embedding, Linear, Module, RmsNorm, VarBuilder};
use std::collections::HashMap;
#[derive(Debug, Clone)]
pub struct Config {
pub dim: usize, // transformer dimension
pub ... | candle/candle-transformers/src/models/llama2_c.rs/0 | {
"file_path": "candle/candle-transformers/src/models/llama2_c.rs",
"repo_id": "candle",
"token_count": 6423
} | 38 |
use super::llama2_c::{Cache, Config};
use crate::quantized_nn::{linear_no_bias as linear, Embedding, Linear, RmsNorm};
pub use crate::quantized_var_builder::VarBuilder;
use candle::{DType, IndexOp, Module, Result, Tensor, D};
fn silu(xs: &Tensor) -> Result<Tensor> {
xs / (xs.neg()?.exp()? + 1.0)?
}
#[derive(Debug... | candle/candle-transformers/src/models/quantized_llama2_c.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_llama2_c.rs",
"repo_id": "candle",
"token_count": 4375
} | 39 |
pub use crate::models::with_tracing::Linear;
use candle::{Result, Tensor};
use candle_nn::{Module, VarBuilder};
pub mod image_encoder;
pub mod mask_decoder;
pub mod prompt_encoder;
pub mod sam;
pub mod tiny_vit;
pub mod transformer;
pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Li... | candle/candle-transformers/src/models/segment_anything/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/mod.rs",
"repo_id": "candle",
"token_count": 1119
} | 40 |
use candle::{Device, Result, Tensor};
pub fn linspace(start: f64, stop: f64, steps: usize) -> Result<Tensor> {
if steps == 0 {
Tensor::from_vec(Vec::<f64>::new(), steps, &Device::Cpu)
} else if steps == 1 {
Tensor::from_vec(vec![start], steps, &Device::Cpu)
} else {
let delta = (sto... | candle/candle-transformers/src/models/stable_diffusion/utils.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/utils.rs",
"repo_id": "candle",
"token_count": 979
} | 41 |
use super::common::{AttnBlock, GlobalResponseNorm, LayerNormNoWeights, TimestepBlock, WLayerNorm};
use candle::{DType, Module, Result, Tensor, D};
use candle_nn::VarBuilder;
#[derive(Debug)]
pub struct ResBlockStageB {
depthwise: candle_nn::Conv2d,
norm: WLayerNorm,
channelwise_lin1: candle_nn::Linear,
... | candle/candle-transformers/src/models/wuerstchen/diffnext.rs/0 | {
"file_path": "candle/candle-transformers/src/models/wuerstchen/diffnext.rs",
"repo_id": "candle",
"token_count": 8148
} | 42 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Bert</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
@import u... | candle/candle-wasm-examples/bert/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/bert/lib-example.html",
"repo_id": "candle",
"token_count": 6066
} | 43 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Llama.c Rust/WASM</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
... | candle/candle-wasm-examples/llama2-c/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/lib-example.html",
"repo_id": "candle",
"token_count": 6089
} | 44 |
[package]
name = "candle-wasm-example-sam"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
[dependencies]
candle = { workspace = true }
candle-nn = { workspace = true }
candle-trans... | candle/candle-wasm-examples/segment-anything/Cargo.toml/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/Cargo.toml",
"repo_id": "candle",
"token_count": 264
} | 45 |
export async function extractEmbeddings(
worker,
weightsURL,
tokenizerURL,
configURL,
modelID,
sentences,
updateStatus,
normalize_embeddings = true
) {
return new Promise((resolve, reject) => {
worker.postMessage({
weightsURL,
tokenizerURL,
configURL,
modelID,
sentenc... | candle/candle-wasm-examples/t5/utils.js/0 | {
"file_path": "candle/candle-wasm-examples/t5/utils.js",
"repo_id": "candle",
"token_count": 2339
} | 46 |
[package]
name = "candle-wasm-example-yolo"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
[dependencies]
candle = { workspace = true }
candle-nn = { workspace = true }
num-traits ... | candle/candle-wasm-examples/yolo/Cargo.toml/0 | {
"file_path": "candle/candle-wasm-examples/yolo/Cargo.toml",
"repo_id": "candle",
"token_count": 463
} | 47 |
pub fn add(left: usize, right: usize) -> usize {
left + right
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn it_works() {
let result = add(2, 2);
assert_eq!(result, 4);
}
}
| candle/candle-wasm-tests/src/lib.rs/0 | {
"file_path": "candle/candle-wasm-tests/src/lib.rs",
"repo_id": "candle",
"token_count": 108
} | 48 |
## Privacy
> Last updated: October 4, 2023
Users of HuggingChat are authenticated through their HF user account.
By default, your conversations may be shared with the respective models' authors to improve their training data and model over time. Model authors are the custodians of the data collected by their model, ... | chat-ui/PRIVACY.md/0 | {
"file_path": "chat-ui/PRIVACY.md",
"repo_id": "chat-ui",
"token_count": 762
} | 49 |
import type { EndpointParameters } from "./server/endpoints/endpoints";
import type { BackendModel } from "./server/models";
type buildPromptOptions = Pick<EndpointParameters, "messages" | "preprompt" | "continueMessage"> & {
model: BackendModel;
};
export async function buildPrompt({
messages,
model,
preprompt,
... | chat-ui/src/lib/buildPrompt.ts/0 | {
"file_path": "chat-ui/src/lib/buildPrompt.ts",
"repo_id": "chat-ui",
"token_count": 327
} | 50 |
<script lang="ts">
import CarbonCaretLeft from "~icons/carbon/caret-left";
import CarbonCaretRight from "~icons/carbon/caret-right";
export let href: string;
export let direction: "next" | "previous";
export let isDisabled = false;
</script>
<a
class="flex items-center rounded-lg px-2.5 py-1 hover:bg-gray-50 da... | chat-ui/src/lib/components/PaginationArrow.svelte/0 | {
"file_path": "chat-ui/src/lib/components/PaginationArrow.svelte",
"repo_id": "chat-ui",
"token_count": 226
} | 51 |
<script lang="ts">
import { onDestroy } from "svelte";
import CarbonImage from "~icons/carbon/image";
// import EosIconsLoading from "~icons/eos-icons/loading";
export let files: File[];
let file_error_message = "";
let errorTimeout: ReturnType<typeof setTimeout>;
export let onDrag = false;
async function d... | chat-ui/src/lib/components/chat/FileDropzone.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/FileDropzone.svelte",
"repo_id": "chat-ui",
"token_count": 1232
} | 52 |
import { Issuer, BaseClient, type UserinfoResponse, TokenSet, custom } from "openid-client";
import { addHours, addWeeks } from "date-fns";
import {
COOKIE_NAME,
OPENID_CLIENT_ID,
OPENID_CLIENT_SECRET,
OPENID_PROVIDER_URL,
OPENID_SCOPES,
OPENID_TOLERANCE,
OPENID_RESOURCE,
OPENID_CONFIG,
} from "$env/static/priv... | chat-ui/src/lib/server/auth.ts/0 | {
"file_path": "chat-ui/src/lib/server/auth.ts",
"repo_id": "chat-ui",
"token_count": 1500
} | 53 |
import { smallModel } from "$lib/server/models";
import type { Conversation } from "$lib/types/Conversation";
export async function generateFromDefaultEndpoint({
messages,
preprompt,
}: {
messages: Omit<Conversation["messages"][0], "id">[];
preprompt?: string;
}): Promise<string> {
const endpoint = await smallMod... | chat-ui/src/lib/server/generateFromDefaultEndpoint.ts/0 | {
"file_path": "chat-ui/src/lib/server/generateFromDefaultEndpoint.ts",
"repo_id": "chat-ui",
"token_count": 289
} | 54 |
import { writable } from "svelte/store";
export const pendingMessage = writable<
| {
content: string;
files: File[];
}
| undefined
>();
| chat-ui/src/lib/stores/pendingMessage.ts/0 | {
"file_path": "chat-ui/src/lib/stores/pendingMessage.ts",
"repo_id": "chat-ui",
"token_count": 56
} | 55 |
import type { Timestamps } from "./Timestamps";
export interface Semaphore extends Timestamps {
key: string;
}
| chat-ui/src/lib/types/Semaphore.ts/0 | {
"file_path": "chat-ui/src/lib/types/Semaphore.ts",
"repo_id": "chat-ui",
"token_count": 35
} | 56 |
export function formatUserCount(userCount: number): string {
const userCountRanges: { min: number; max: number; label: string }[] = [
{ min: 0, max: 1, label: "1" },
{ min: 2, max: 9, label: "1-10" },
{ min: 10, max: 49, label: "10+" },
{ min: 50, max: 99, label: "50+" },
{ min: 100, max: 299, label: "100+" ... | chat-ui/src/lib/utils/formatUserCount.ts/0 | {
"file_path": "chat-ui/src/lib/utils/formatUserCount.ts",
"repo_id": "chat-ui",
"token_count": 308
} | 57 |
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { describe, expect, it } from "vitest";
import { insertLegacyConversation, insertSideBranchesConversation } from "./treeHelpers.spec";
import { addChildren } from "./addChildren";
import type { Message } from "$lib/types/Mes... | chat-ui/src/lib/utils/tree/addChildren.spec.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/addChildren.spec.ts",
"repo_id": "chat-ui",
"token_count": 1301
} | 58 |
import { json } from "@sveltejs/kit";
import type { ConversationStats } from "$lib/types/ConversationStats";
import { CONVERSATION_STATS_COLLECTION, collections } from "$lib/server/database.js";
// Triger like this:
// curl -X POST "http://localhost:5173/chat/admin/stats/compute" -H "Authorization: Bearer <ADMIN_API_S... | chat-ui/src/routes/admin/stats/compute/+server.ts/0 | {
"file_path": "chat-ui/src/routes/admin/stats/compute/+server.ts",
"repo_id": "chat-ui",
"token_count": 2379
} | 59 |
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { z } from "zod";
export async function POST({ params, request, locals }) {
const { score } = z
.object({
score: z.number().int()... | chat-ui/src/routes/conversation/[id]/message/[messageId]/vote/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/[id]/message/[messageId]/vote/+server.ts",
"repo_id": "chat-ui",
"token_count": 337
} | 60 |
<script lang="ts">
import { page } from "$app/stores";
import { base } from "$app/paths";
import { PUBLIC_ORIGIN } from "$env/static/public";
import type { BackendModel } from "$lib/server/models";
import { useSettingsStore } from "$lib/stores/settings";
import CopyToClipBoardBtn from "$lib/components/CopyToClipB... | chat-ui/src/routes/settings/(nav)/[...model]/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/[...model]/+page.svelte",
"repo_id": "chat-ui",
"token_count": 1517
} | 61 |
# How to add one new datasets
Add datasets directly to the 🤗 Hugging Face Hub!
You can share your dataset on https://huggingface.co/datasets directly using your account, see the documentation:
* [Create a dataset and upload files on the website](https://huggingface.co/docs/datasets/upload_dataset)
* [Advanced guide... | datasets/ADD_NEW_DATASET.md/0 | {
"file_path": "datasets/ADD_NEW_DATASET.md",
"repo_id": "datasets",
"token_count": 113
} | 62 |
# Differences between Dataset and IterableDataset
There are two types of dataset objects, a [`Dataset`] and an [`IterableDataset`].
Whichever type of dataset you choose to use or create depends on the size of the dataset.
In general, an [`IterableDataset`] is ideal for big datasets (think hundreds of GBs!) due to its ... | datasets/docs/source/about_mapstyle_vs_iterable.mdx/0 | {
"file_path": "datasets/docs/source/about_mapstyle_vs_iterable.mdx",
"repo_id": "datasets",
"token_count": 3261
} | 63 |
# Image classification
Image classification datasets are used to train a model to classify an entire image. There are a wide variety of applications enabled by these datasets such as identifying endangered wildlife species or screening for disease in medical images. This guide will show you how to apply transformation... | datasets/docs/source/image_classification.mdx/0 | {
"file_path": "datasets/docs/source/image_classification.mdx",
"repo_id": "datasets",
"token_count": 1043
} | 64 |
# 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": 1908
} | 65 |
# Use with PyTorch
This document is a quick introduction to using `datasets` with PyTorch, with a particular focus on how to get
`torch.Tensor` objects out of our datasets, and how to use a PyTorch `DataLoader` and a Hugging Face `Dataset`
with the best performance.
## Dataset format
By default, datasets return regu... | datasets/docs/source/use_with_pytorch.mdx/0 | {
"file_path": "datasets/docs/source/use_with_pytorch.mdx",
"repo_id": "datasets",
"token_count": 3104
} | 66 |
# Metric Card for Code Eval
## Metric description
The CodeEval metric estimates the pass@k metric for code synthesis.
It implements the evaluation harness for the HumanEval problem solving dataset described in the paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374).
## How... | datasets/metrics/code_eval/README.md/0 | {
"file_path": "datasets/metrics/code_eval/README.md",
"repo_id": "datasets",
"token_count": 1698
} | 67 |
# Metric Card for FrugalScore
## Metric Description
FrugalScore is a reference-based metric for Natural Language Generation (NLG) model evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
The ... | datasets/metrics/frugalscore/README.md/0 | {
"file_path": "datasets/metrics/frugalscore/README.md",
"repo_id": "datasets",
"token_count": 2127
} | 68 |
# Metric Card for Mean IoU
## Metric Description
IoU (Intersection over Union) is the area of overlap between the predicted segmentation and the ground truth divided by the area of union between the predicted segmentation and the ground truth.
For binary (two classes) or multi-class segmentation, the *mean IoU* o... | datasets/metrics/mean_iou/README.md/0 | {
"file_path": "datasets/metrics/mean_iou/README.md",
"repo_id": "datasets",
"token_count": 1803
} | 69 |
# Metric Card for ROUGE
## Metric Description
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing. The metrics compare an automatically produced summary or tra... | datasets/metrics/rouge/README.md/0 | {
"file_path": "datasets/metrics/rouge/README.md",
"repo_id": "datasets",
"token_count": 2244
} | 70 |
# Metric Card for SuperGLUE
## Metric description
This metric is used to compute the SuperGLUE evaluation metric associated to each of the subsets of the [SuperGLUE dataset](https://huggingface.co/datasets/super_glue).
SuperGLUE is a new benchmark styled after GLUE with a new set of more difficult language understan... | datasets/metrics/super_glue/README.md/0 | {
"file_path": "datasets/metrics/super_glue/README.md",
"repo_id": "datasets",
"token_count": 1767
} | 71 |
# Lint as: python3
"""HuggingFace/Datasets is an open library of datasets.
Note:
VERSION needs to be formatted following the MAJOR.MINOR.PATCH convention
(we need to follow this convention to be able to retrieve versioned scripts)
Simple check list for release from AllenNLP repo: https://github.com/allenai/all... | datasets/setup.py/0 | {
"file_path": "datasets/setup.py",
"repo_id": "datasets",
"token_count": 4161
} | 72 |
import contextlib
import copy
import fnmatch
import json
import math
import posixpath
import re
import warnings
from io import BytesIO
from pathlib import Path
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import fsspec
import numpy as np
from huggingface_hub import (
CommitInfo,
Co... | datasets/src/datasets/dataset_dict.py/0 | {
"file_path": "datasets/src/datasets/dataset_dict.py",
"repo_id": "datasets",
"token_count": 47125
} | 73 |
import inspect
import os
import random
import shutil
import tempfile
import weakref
from functools import wraps
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import xxhash
from . import config
from .naming import INVALID_WINDOWS_CHARACT... | datasets/src/datasets/fingerprint.py/0 | {
"file_path": "datasets/src/datasets/fingerprint.py",
"repo_id": "datasets",
"token_count": 8037
} | 74 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import tqdm as hf_tqdm
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlite3
i... | datasets/src/datasets/io/sql.py/0 | {
"file_path": "datasets/src/datasets/io/sql.py",
"repo_id": "datasets",
"token_count": 2040
} | 75 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=True)
class LanguageModeling(TaskTemplate):
task: str = field(default="language-modeling", metadata={"include_in_asdict_even_if_is_default": True})
... | datasets/src/datasets/tasks/language_modeling.py/0 | {
"file_path": "datasets/src/datasets/tasks/language_modeling.py",
"repo_id": "datasets",
"token_count": 195
} | 76 |
import time
from functools import partial
from huggingface_hub import HfApi, hf_hub_url
from huggingface_hub.hf_api import RepoFile
from packaging import version
from requests import ConnectionError, HTTPError
from .. import config
from . import logging
logger = logging.get_logger(__name__)
# Retry `preupload_lfs_... | datasets/src/datasets/utils/hub.py/0 | {
"file_path": "datasets/src/datasets/utils/hub.py",
"repo_id": "datasets",
"token_count": 1118
} | 77 |
"""Utility helpers to handle progress bars in `datasets`.
Example:
1. Use `datasets.utils.tqdm` as you would use `tqdm.tqdm` or `tqdm.auto.tqdm`.
2. To disable progress bars, either use `disable_progress_bars()` helper or set the
environment variable `HF_DATASETS_DISABLE_PROGRESS_BARS` to 1.
3. To r... | datasets/src/datasets/utils/tqdm.py/0 | {
"file_path": "datasets/src/datasets/utils/tqdm.py",
"repo_id": "datasets",
"token_count": 1662
} | 78 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _get_expected_row_ids_and_row_dicts_for_partition_order(df, par... | datasets/tests/packaged_modules/test_spark.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_spark.py",
"repo_id": "datasets",
"token_count": 2054
} | 79 |
import os
from datasets.utils._filelock import FileLock
def test_long_path(tmpdir):
filename = "a" * 1000 + ".lock"
lock1 = FileLock(str(tmpdir / filename))
assert lock1.lock_file.endswith(".lock")
assert not lock1.lock_file.endswith(filename)
assert len(os.path.basename(lock1.lock_file)) <= 255
| datasets/tests/test_filelock.py/0 | {
"file_path": "datasets/tests/test_filelock.py",
"repo_id": "datasets",
"token_count": 120
} | 80 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def test_patch_submodule():
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path impor... | datasets/tests/test_patching.py/0 | {
"file_path": "datasets/tests/test_patching.py",
"repo_id": "datasets",
"token_count": 2274
} | 81 |
# [The Hugging Face Deep Reinforcement Learning Course 🤗 (v2.0)](https://huggingface.co/deep-rl-course/unit0/introduction)
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/thumbnail.jpg" alt="Thumbnail"/>
If you like the course, don't hesitate to **⭐ star this ... | deep-rl-class/README.md/0 | {
"file_path": "deep-rl-class/README.md",
"repo_id": "deep-rl-class",
"token_count": 388
} | 82 |
# The certification process
The certification process is **completely free**:
- To get a *certificate of completion*: you need **to pass 80% of the assignments**.
- To get a *certificate of excellence*: you need **to pass 100% of the assignments**.
There's **no deadlines, the course is self-paced**.
<img src="http... | deep-rl-class/units/en/communication/certification.mdx/0 | {
"file_path": "deep-rl-class/units/en/communication/certification.mdx",
"repo_id": "deep-rl-class",
"token_count": 418
} | 83 |
# Type of tasks [[tasks]]
A task is an **instance** of a Reinforcement Learning problem. We can have two types of tasks: **episodic** and **continuing**.
## Episodic task [[episodic-task]]
In this case, we have a starting point and an ending point **(a terminal state). This creates an episode**: a list of States, Ac... | deep-rl-class/units/en/unit1/tasks.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit1/tasks.mdx",
"repo_id": "deep-rl-class",
"token_count": 436
} | 84 |
# Two types of value-based methods [[two-types-value-based-methods]]
In value-based methods, **we learn a value function** that **maps a state to the expected value of being at that state.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/vbm-1.jpg" alt="Value ... | deep-rl-class/units/en/unit2/two-types-value-based-methods.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit2/two-types-value-based-methods.mdx",
"repo_id": "deep-rl-class",
"token_count": 1727
} | 85 |
# Introduction [[introduction]]
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/thumbnail.png" alt="thumbnail"/>
In the last unit, we learned about Deep Q-Learning. In this value-based deep reinforcement learning algorithm, we **used a deep neural network to ... | deep-rl-class/units/en/unit4/introduction.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit4/introduction.mdx",
"repo_id": "deep-rl-class",
"token_count": 462
} | 86 |
# Conclusion [[conclusion]]
Congrats on finishing this unit and the tutorial. You've just trained your first virtual robots 🥳.
**Take time to grasp the material before continuing**. You can also look at the additional reading materials we provided in the *additional reading* section.
Finally, we would love **to hea... | deep-rl-class/units/en/unit6/conclusion.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit6/conclusion.mdx",
"repo_id": "deep-rl-class",
"token_count": 145
} | 87 |
# Conclusion [[Conclusion]]
That’s all for today. Congrats on finishing this unit and the tutorial!
The best way to learn is to practice and try stuff. **Why not improve the implementation to handle frames as input?**.
See you on second part of this Unit 🔥
## Keep Learning, Stay awesome 🤗
| deep-rl-class/units/en/unit8/conclusion.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit8/conclusion.mdx",
"repo_id": "deep-rl-class",
"token_count": 78
} | 88 |
# Decision Transformers
The Decision Transformer model was introduced by ["Decision Transformer: Reinforcement Learning via Sequence Modeling” by Chen L. et al](https://arxiv.org/abs/2106.01345). It abstracts Reinforcement Learning as a conditional-sequence modeling problem.
The main idea is that instead of training ... | deep-rl-class/units/en/unitbonus3/decision-transformers.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus3/decision-transformers.mdx",
"repo_id": "deep-rl-class",
"token_count": 543
} | 89 |
cff-version: 1.2.0
title: 'Diffusers: State-of-the-art diffusion models'
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Patrick
family-names: von Platen
- given-names: Suraj
family-names: Patil
- given-names: Anton
fam... | diffusers/CITATION.cff/0 | {
"file_path": "diffusers/CITATION.cff",
"repo_id": "diffusers",
"token_count": 369
} | 90 |
import argparse
import sys
sys.path.append(".")
from base_classes import TextToImageBenchmark, TurboTextToImageBenchmark # noqa: E402
ALL_T2I_CKPTS = [
"runwayml/stable-diffusion-v1-5",
"segmind/SSD-1B",
"stabilityai/stable-diffusion-xl-base-1.0",
"kandinsky-community/kandinsky-2-2-decoder",
"w... | diffusers/benchmarks/benchmark_text_to_image.py/0 | {
"file_path": "diffusers/benchmarks/benchmark_text_to_image.py",
"repo_id": "diffusers",
"token_count": 480
} | 91 |
<!--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": 770
} | 92 |
<!--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 to... | diffusers/docs/source/en/api/pipelines/kandinsky3.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/kandinsky3.md",
"repo_id": "diffusers",
"token_count": 766
} | 93 |
<!--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/pipelines/stable_diffusion/depth2img.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md",
"repo_id": "diffusers",
"token_count": 502
} | 94 |
<!--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/mps.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/mps.md",
"repo_id": "diffusers",
"token_count": 1061
} | 95 |
<!--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/instructpix2pix.md/0 | {
"file_path": "diffusers/docs/source/en/training/instructpix2pix.md",
"repo_id": "diffusers",
"token_count": 4160
} | 96 |
<!--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/callback.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/callback.md",
"repo_id": "diffusers",
"token_count": 2993
} | 97 |
<!--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/ip_adapter.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/ip_adapter.md",
"repo_id": "diffusers",
"token_count": 9496
} | 98 |
<!--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/stable_diffusion_jax_how_to.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md",
"repo_id": "diffusers",
"token_count": 3095
} | 99 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.