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# 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/tracking.py/0 | {
"file_path": "accelerate/src/accelerate/tracking.py",
"repo_id": "accelerate",
"token_count": 17060
} | 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/utils/random.py/0 | {
"file_path": "accelerate/src/accelerate/utils/random.py",
"repo_id": "accelerate",
"token_count": 1885
} | 7 |
- title: Unit 0. Welcome to the RLHF Handbook!
sections:
- local: chapter0/introduction
title: What is this about? | alignment-handbook/chapters/en/_toctree.yml/0 | {
"file_path": "alignment-handbook/chapters/en/_toctree.yml",
"repo_id": "alignment-handbook",
"token_count": 38
} | 8 |
#!/bin/bash
# Define an array containing the base configs we wish to fine tune
configs=("zephyr" "openhermes")
# Define an array of loss types
loss_types=("sigmoid" "kto_pair" "ipo")
# Define an array of beta values
betas=("0.01" "0.1" "0.2" "0.3" "0.4" "0.5" "0.6" "0.7" "0.8" "0.9")
# Outer loop for loss types
for co... | alignment-handbook/recipes/pref_align_scan/launch_scan.sh/0 | {
"file_path": "alignment-handbook/recipes/pref_align_scan/launch_scan.sh",
"repo_id": "alignment-handbook",
"token_count": 430
} | 9 |
[isort]
default_section = FIRSTPARTY
ensure_newline_before_comments = True
force_grid_wrap = 0
include_trailing_comma = True
known_first_party = alignment
known_third_party =
transformers
datasets
fugashi
git
h5py
matplotlib
nltk
numpy
packaging
pandas
psutil
pytest
r... | alignment-handbook/setup.cfg/0 | {
"file_path": "alignment-handbook/setup.cfg",
"repo_id": "alignment-handbook",
"token_count": 297
} | 10 |
# Using MKL
| candle/candle-book/src/advanced/mkl.md/0 | {
"file_path": "candle/candle-book/src/advanced/mkl.md",
"repo_id": "candle",
"token_count": 5
} | 11 |
# Using the hub
Install the [`hf-hub`](https://github.com/huggingface/hf-hub) crate:
```bash
cargo add hf-hub
```
Then let's start by downloading the [model file](https://huggingface.co/bert-base-uncased/tree/main).
```rust
# extern crate candle_core;
# extern crate hf_hub;
use hf_hub::api::sync::Api;
use candle_c... | candle/candle-book/src/inference/hub.md/0 | {
"file_path": "candle/candle-book/src/inference/hub.md",
"repo_id": "candle",
"token_count": 1098
} | 12 |
use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{DType, Device, Tensor};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn rand_uniform(a: &Tensor) {
a.rand_like(-1.0, 123.0).unwrap();
}
fn rand_normal(a: &Tensor) {
a.randn_like(100.0, 15... | candle/candle-core/benches/benchmarks/random.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/random.rs",
"repo_id": "candle",
"token_count": 812
} | 13 |
use super::Cpu;
use core::arch::wasm32::*;
pub struct CurrentCpu {}
const STEP: usize = 16;
const EPR: usize = 4;
const ARR: usize = STEP / EPR;
impl Cpu<ARR> for CurrentCpu {
type Unit = v128;
type Array = [v128; ARR];
const STEP: usize = STEP;
const EPR: usize = EPR;
fn n() -> usize {
... | candle/candle-core/src/cpu/simd128.rs/0 | {
"file_path": "candle/candle-core/src/cpu/simd128.rs",
"repo_id": "candle",
"token_count": 839
} | 14 |
#![allow(clippy::redundant_closure_call)]
use crate::{CpuStorage, CudaStorage, Layout, MetalStorage, Result, Shape, Tensor};
use half::{bf16, f16};
use num_traits::float::Float;
#[derive(Clone, Copy, PartialEq, Eq)]
pub enum CmpOp {
Eq,
Ne,
Le,
Ge,
Lt,
Gt,
}
#[derive(Debug, Clone, Copy, Partia... | candle/candle-core/src/op.rs/0 | {
"file_path": "candle/candle-core/src/op.rs",
"repo_id": "candle",
"token_count": 15013
} | 15 |
//! The shape of a tensor is a tuple with the size of each of its dimensions.
#![allow(clippy::redundant_closure_call)]
use crate::{Error, Result};
#[derive(Clone, PartialEq, Eq)]
pub struct Shape(Vec<usize>);
pub const SCALAR: Shape = Shape(vec![]);
impl std::fmt::Debug for Shape {
fn fmt(&self, f: &mut std::fm... | candle/candle-core/src/shape.rs/0 | {
"file_path": "candle/candle-core/src/shape.rs",
"repo_id": "candle",
"token_count": 9806
} | 16 |
use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
// https://github.com/huggingface/candle/issues/364
fn avg_pool2d(dev: &Device) -> Result<()> {
let data: Vec<f32> = vec![
1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
];
let t = Tensor::from_vec(data, (... | candle/candle-core/tests/pool_tests.rs/0 | {
"file_path": "candle/candle-core/tests/pool_tests.rs",
"repo_id": "candle",
"token_count": 2112
} | 17 |
//! Helper functions for the tinystories dataset. This uses the pre-tokenized version as generated
//! by the tools from https://github.com/karpathy/llama2.c
use candle::{Device, Result, Tensor};
pub struct Dataset {
valid_tokens: Vec<memmap2::Mmap>,
train_tokens: Vec<memmap2::Mmap>,
}
fn mmap_file(p: &std::p... | candle/candle-datasets/src/nlp/tinystories.rs/0 | {
"file_path": "candle/candle-datasets/src/nlp/tinystories.rs",
"repo_id": "candle",
"token_count": 2097
} | 18 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::convnext;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
At... | candle/candle-examples/examples/convnext/main.rs/0 | {
"file_path": "candle/candle-examples/examples/convnext/main.rs",
"repo_id": "candle",
"token_count": 1926
} | 19 |
# candle-falcon
Falcon is a general large language model.
| candle/candle-examples/examples/falcon/README.md/0 | {
"file_path": "candle/candle-examples/examples/falcon/README.md",
"repo_id": "candle",
"token_count": 17
} | 20 |
# candle-marian-mt
`marian-mt` is a neural machine translation model. In this example it is used to
translate text from French to English. See the associated [model
card](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-fr-en) for details on
the model itself.
## Running an example
```bash
cargo run --example maria... | candle/candle-examples/examples/marian-mt/README.md/0 | {
"file_path": "candle/candle-examples/examples/marian-mt/README.md",
"repo_id": "candle",
"token_count": 497
} | 21 |
use anyhow::Result;
use candle::{Device, Tensor};
use clap::{Parser, Subcommand};
#[derive(Subcommand, Debug, Clone)]
enum Command {
Print {
#[arg(long)]
file: String,
},
SimpleEval {
#[arg(long)]
file: String,
},
}
#[derive(Parser, Debug)]
#[command(author, version, a... | candle/candle-examples/examples/onnx_basics.rs/0 | {
"file_path": "candle/candle-examples/examples/onnx_basics.rs",
"repo_id": "candle",
"token_count": 2016
} | 22 |
#![allow(unused)]
//! Vectorized version of the gym environment.
use candle::{DType, Device, Result, Tensor};
use pyo3::prelude::*;
use pyo3::types::PyDict;
#[derive(Debug)]
pub struct Step {
pub obs: Tensor,
pub reward: Tensor,
pub is_done: Tensor,
}
pub struct VecGymEnv {
env: PyObject,
action_s... | candle/candle-examples/examples/reinforcement-learning/vec_gym_env.rs/0 | {
"file_path": "candle/candle-examples/examples/reinforcement-learning/vec_gym_env.rs",
"repo_id": "candle",
"token_count": 1563
} | 23 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, IndexOp, D};
use candle_nn::{ModuleT, VarBuilder};
use candle_transformers::models::vgg::{Models, Vgg};
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Whic... | candle/candle-examples/examples/vgg/main.rs/0 | {
"file_path": "candle/candle-examples/examples/vgg/main.rs",
"repo_id": "candle",
"token_count": 967
} | 24 |
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{batch_norm, conv2d, conv2d_no_bias, Func, Module, VarBuilder};
use std::collections::BTreeMap;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
#[derive(Debug)]
struct Block {
block_type: String,
parameters: BTreeMa... | candle/candle-examples/examples/yolo-v3/darknet.rs/0 | {
"file_path": "candle/candle-examples/examples/yolo-v3/darknet.rs",
"repo_id": "candle",
"token_count": 5403
} | 25 |
use candle::Result;
/// This is a wrapper around a tokenizer to ensure that tokens can be returned to the user in a
/// streaming way rather than having to wait for the full decoding.
pub struct TokenOutputStream {
tokenizer: tokenizers::Tokenizer,
tokens: Vec<u32>,
prev_index: usize,
current_index: us... | candle/candle-examples/src/token_output_stream.rs/0 | {
"file_path": "candle/candle-examples/src/token_output_stream.rs",
"repo_id": "candle",
"token_count": 1295
} | 26 |
/******************************************************************************
* Copyright (c) 2023, Tri Dao.
******************************************************************************/
#pragma once
#include <assert.h>
#include <stdint.h>
#include <stdlib.h>
#include <cuda_fp16.h>
#if defined(__CUDA_ARCH__) ... | candle/candle-flash-attn/kernels/utils.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/utils.h",
"repo_id": "candle",
"token_count": 6965
} | 27 |
pub const AFFINE: &str = include_str!(concat!(env!("OUT_DIR"), "/affine.ptx"));
pub const BINARY: &str = include_str!(concat!(env!("OUT_DIR"), "/binary.ptx"));
pub const CAST: &str = include_str!(concat!(env!("OUT_DIR"), "/cast.ptx"));
pub const CONV: &str = include_str!(concat!(env!("OUT_DIR"), "/conv.ptx"));
pub cons... | candle/candle-kernels/src/lib.rs/0 | {
"file_path": "candle/candle-kernels/src/lib.rs",
"repo_id": "candle",
"token_count": 333
} | 28 |
#include <metal_stdlib>
using namespace metal;
#define MAX(x, y) ((x) > (y) ? (x) : (y))
#define MIN(x, y) ((x) < (y) ? (x) : (y))
METAL_FUNC uint get_strided_index(
uint idx,
constant size_t &num_dims,
constant size_t *dims,
constant size_t *strides
) {
uint strided_i = 0;
for (uint d = 0; d ... | candle/candle-metal-kernels/src/reduce.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/reduce.metal",
"repo_id": "candle",
"token_count": 8032
} | 29 |
//! Encoding Utilities. (e.g., one-hot/cold encoding)
use candle::{bail, DType, Result, Tensor, WithDType};
/// One-hot/cold encoding.
///
/// Given an input tensor of indices, this function returns a tensor of the same shape as the input
/// tensor with an additional dimension of the given depth size. The values in ... | candle/candle-nn/src/encoding.rs/0 | {
"file_path": "candle/candle-nn/src/encoding.rs",
"repo_id": "candle",
"token_count": 2025
} | 30 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use candle::{test_utils, Device, Tensor};
use candle_nn::{LayerNorm, Module};
#[test]
fn layer_norm() -> Result<()> {
let device = &Device::Cpu;
let w = Tensor::new(&[3f32], dev... | candle/candle-nn/tests/layer_norm.rs/0 | {
"file_path": "candle/candle-nn/tests/layer_norm.rs",
"repo_id": "candle",
"token_count": 733
} | 31 |
# Generated content DO NOT EDIT
from .. import onnx
ONNXModel = onnx.ONNXModel
ONNXTensorDescription = onnx.ONNXTensorDescription
| candle/candle-pyo3/py_src/candle/onnx/__init__.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/onnx/__init__.py",
"repo_id": "candle",
"token_count": 46
} | 32 |
import candle
from candle import Tensor
from candle.nn import Linear
def test_linear_layer_can_be_constructed():
linear = Linear(10, 10)
assert linear is not None
def test_linear_layer_can_forward_a_singular_input():
linear = Linear(384, 1536)
input_tensor = candle.randn((8, 384))
output = linea... | candle/candle-pyo3/tests/bindings/test_linear.py/0 | {
"file_path": "candle/candle-pyo3/tests/bindings/test_linear.py",
"repo_id": "candle",
"token_count": 431
} | 33 |
//! ConvNeXt implementation.
//!
//! See "A ConvNet for the 2020s" Liu et al. 2022
//! <https://arxiv.org/abs/2201.03545>
//! and
//! "ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders" Woo et al. 2023
//! <https://arxiv.org/abs/2301.00808>
//! Original code:
//! https://github.com/facebookresear... | candle/candle-transformers/src/models/convnext.rs/0 | {
"file_path": "candle/candle-transformers/src/models/convnext.rs",
"repo_id": "candle",
"token_count": 4881
} | 34 |
//! Attention Based Building Blocks
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
#[derive(Debug)]
struct GeGlu {
proj: nn::Linear,
span: tracing::Span,
}
impl GeGlu {
fn new(vs: nn::VarBuilder, dim_in: usize, dim_out: usize) -> Result<Self> {
let pro... | candle/candle-transformers/src/models/stable_diffusion/attention.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/attention.rs",
"repo_id": "candle",
"token_count": 9413
} | 35 |
use crate::models::vit::{Config, Embeddings, Encoder};
use candle::{DType, Result, Tensor};
use candle_nn::{
embedding, layer_norm, linear_no_bias, Embedding, LayerNorm, Linear, Module, VarBuilder,
};
fn default_tie_word_embeddings() -> bool {
true
}
fn default_use_learned_position_embeddings() -> bool {
t... | candle/candle-transformers/src/models/trocr.rs/0 | {
"file_path": "candle/candle-transformers/src/models/trocr.rs",
"repo_id": "candle",
"token_count": 8465
} | 36 |
/// A bounding box around an object.
#[derive(Debug, Clone)]
pub struct Bbox<D> {
pub xmin: f32,
pub ymin: f32,
pub xmax: f32,
pub ymax: f32,
pub confidence: f32,
pub data: D,
}
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct KeyPoint {
pub x: f32,
pub y: f32,
pub mask: f32,
}
... | candle/candle-transformers/src/object_detection.rs/0 | {
"file_path": "candle/candle-transformers/src/object_detection.rs",
"repo_id": "candle",
"token_count": 894
} | 37 |
use yew_agent::PublicWorker;
fn main() {
console_error_panic_hook::set_once();
candle_wasm_example_llama2::Worker::register();
}
| candle/candle-wasm-examples/llama2-c/src/bin/worker.rs/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/src/bin/worker.rs",
"repo_id": "candle",
"token_count": 54
} | 38 |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_wasm_example_sam as sam;
use wasm_bindgen::prelude::*;
struct Embeddings {
original_width: u32,
original_height: u32,
width: u32,
height: u32,
data: Tensor,
}
#[wasm_bindgen]
pub struct Model {
sam: sam::Sam,
embedd... | candle/candle-wasm-examples/segment-anything/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/src/bin/m.rs",
"repo_id": "candle",
"token_count": 2400
} | 39 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Whisper 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/whisper/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/whisper/lib-example.html",
"repo_id": "candle",
"token_count": 6488
} | 40 |
use crate::console_log;
use crate::worker::{ModelData, RunData, Worker, WorkerInput, WorkerOutput};
use wasm_bindgen::prelude::*;
use wasm_bindgen_futures::JsFuture;
use yew::{html, Component, Context, Html};
use yew_agent::{Bridge, Bridged};
async fn fetch_url(url: &str) -> Result<Vec<u8>, JsValue> {
use web_sys:... | candle/candle-wasm-examples/yolo/src/app.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/app.rs",
"repo_id": "candle",
"token_count": 5971
} | 41 |
# Use .env.local to change these variables
# DO NOT EDIT THIS FILE WITH SENSITIVE DATA
MONGODB_URL=#your mongodb URL here
MONGODB_DB_NAME=chat-ui
MONGODB_DIRECT_CONNECTION=false
COOKIE_NAME=hf-chat
HF_TOKEN=#hf_<token> from https://huggingface.co/settings/token
HF_API_ROOT=https://api-inference.huggingface.co/models
... | chat-ui/.env/0 | {
"file_path": "chat-ui/.env",
"repo_id": "chat-ui",
"token_count": 2343
} | 42 |
{
"name": "chat-ui",
"version": "0.7.0",
"private": true,
"packageManager": "npm@9.5.0",
"scripts": {
"dev": "vite dev",
"build": "vite build",
"preview": "vite preview",
"check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json",
"check:watch": "svelte-kit sync && svelte-check --tsconfig ./ts... | chat-ui/package.json/0 | {
"file_path": "chat-ui/package.json",
"repo_id": "chat-ui",
"token_count": 1484
} | 43 |
<script lang="ts">
import { onDestroy } from "svelte";
import IconCopy from "./icons/IconCopy.svelte";
import Tooltip from "./Tooltip.svelte";
export let classNames = "";
export let value: string;
let isSuccess = false;
let timeout: ReturnType<typeof setTimeout>;
const handleClick = async () => {
// write... | chat-ui/src/lib/components/CopyToClipBoardBtn.svelte/0 | {
"file_path": "chat-ui/src/lib/components/CopyToClipBoardBtn.svelte",
"repo_id": "chat-ui",
"token_count": 433
} | 44 |
<script lang="ts">
export let checked: boolean;
export let name: string;
</script>
<input bind:checked type="checkbox" {name} class="peer pointer-events-none absolute opacity-0" />
<div
aria-checked={checked}
aria-roledescription="switch"
aria-label="switch"
role="switch"
tabindex="0"
class="relative inline-fl... | chat-ui/src/lib/components/Switch.svelte/0 | {
"file_path": "chat-ui/src/lib/components/Switch.svelte",
"repo_id": "chat-ui",
"token_count": 239
} | 45 |
<script lang="ts">
export let classNames = "";
</script>
<div class={"inline-flex h-8 flex-none items-center gap-1 " + classNames}>
<div
class="h-1 w-1 flex-none animate-bounce rounded-full bg-gray-500 dark:bg-gray-400"
style="animation-delay: 0.25s;"
/>
<div
class="h-1 w-1 flex-none animate-bounce rounded-f... | chat-ui/src/lib/components/icons/IconLoading.svelte/0 | {
"file_path": "chat-ui/src/lib/components/icons/IconLoading.svelte",
"repo_id": "chat-ui",
"token_count": 223
} | 46 |
import { z } from "zod";
import type { EmbeddingEndpoint } from "../embeddingEndpoints";
import type { Tensor, Pipeline } from "@xenova/transformers";
import { pipeline } from "@xenova/transformers";
export const embeddingEndpointTransformersJSParametersSchema = z.object({
weight: z.number().int().positive().default(... | chat-ui/src/lib/server/embeddingEndpoints/transformersjs/embeddingEndpoints.ts/0 | {
"file_path": "chat-ui/src/lib/server/embeddingEndpoints/transformersjs/embeddingEndpoints.ts",
"repo_id": "chat-ui",
"token_count": 483
} | 47 |
import {
HF_TOKEN,
HF_API_ROOT,
MODELS,
OLD_MODELS,
TASK_MODEL,
HF_ACCESS_TOKEN,
} from "$env/static/private";
import type { ChatTemplateInput } from "$lib/types/Template";
import { compileTemplate } from "$lib/utils/template";
import { z } from "zod";
import endpoints, { endpointSchema, type Endpoint } from "./e... | chat-ui/src/lib/server/models.ts/0 | {
"file_path": "chat-ui/src/lib/server/models.ts",
"repo_id": "chat-ui",
"token_count": 2084
} | 48 |
import { browser } from "$app/environment";
import { invalidate } from "$app/navigation";
import { base } from "$app/paths";
import { UrlDependency } from "$lib/types/UrlDependency";
import type { ObjectId } from "mongodb";
import { getContext, setContext } from "svelte";
import { type Writable, writable, get } from "s... | chat-ui/src/lib/stores/settings.ts/0 | {
"file_path": "chat-ui/src/lib/stores/settings.ts",
"repo_id": "chat-ui",
"token_count": 983
} | 49 |
import type { ObjectId } from "bson";
import type { Timestamps } from "./Timestamps";
import type { User } from "./User";
export interface Session extends Timestamps {
_id: ObjectId;
sessionId: string;
userId: User["_id"];
userAgent?: string;
ip?: string;
expiresAt: Date;
}
| chat-ui/src/lib/types/Session.ts/0 | {
"file_path": "chat-ui/src/lib/types/Session.ts",
"repo_id": "chat-ui",
"token_count": 97
} | 50 |
export function getHref(
url: URL | string,
modifications: {
newKeys?: Record<string, string | undefined | null>;
existingKeys?: { behaviour: "delete_except" | "delete"; keys: string[] };
}
) {
const newUrl = new URL(url);
const { newKeys, existingKeys } = modifications;
// exsiting keys logic
if (existingK... | chat-ui/src/lib/utils/getHref.ts/0 | {
"file_path": "chat-ui/src/lib/utils/getHref.ts",
"repo_id": "chat-ui",
"token_count": 373
} | 51 |
import { collections } from "$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 "$lib/types/Mes... | chat-ui/src/lib/utils/tree/addChildren.spec.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/addChildren.spec.ts",
"repo_id": "chat-ui",
"token_count": 1301
} | 52 |
import { json } from "@sveltejs/kit";
import type { ConversationStats } from "$lib/types/ConversationStats";
import { CONVERSATION_STATS_COLLECTION, collections } from "$lib/server/database.js";
// Triger like this:
// curl -X POST "http://localhost:5173/chat/admin/stats/compute" -H "Authorization: Bearer <ADMIN_API_S... | chat-ui/src/routes/admin/stats/compute/+server.ts/0 | {
"file_path": "chat-ui/src/routes/admin/stats/compute/+server.ts",
"repo_id": "chat-ui",
"token_count": 2379
} | 53 |
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { z } from "zod";
export async function POST({ params, request, locals }) {
const { score } = z
.object({
score: z.number().int()... | chat-ui/src/routes/conversation/[id]/message/[messageId]/vote/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/[id]/message/[messageId]/vote/+server.ts",
"repo_id": "chat-ui",
"token_count": 337
} | 54 |
import { redirect } from "@sveltejs/kit";
export const load = async ({ params }) => {
throw redirect(302, "../conversation/" + params.id);
};
| chat-ui/src/routes/r/[id]/+page.ts/0 | {
"file_path": "chat-ui/src/routes/r/[id]/+page.ts",
"repo_id": "chat-ui",
"token_count": 46
} | 55 |
<script lang="ts">
import { base } from "$app/paths";
import { clickOutside } from "$lib/actions/clickOutside";
import { afterNavigate, goto } from "$app/navigation";
import { useSettingsStore } from "$lib/stores/settings";
import CarbonCheckmark from "~icons/carbon/checkmark";
import { fade, fly } from "svelte/... | chat-ui/src/routes/settings/+layout.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/+layout.svelte",
"repo_id": "chat-ui",
"token_count": 513
} | 56 |
import adapter from "@sveltejs/adapter-node";
import { vitePreprocess } from "@sveltejs/kit/vite";
import dotenv from "dotenv";
dotenv.config({ path: "./.env.local" });
dotenv.config({ path: "./.env" });
process.env.PUBLIC_VERSION = process.env.npm_package_version;
/** @type {import('@sveltejs/kit').Config} */
const... | chat-ui/svelte.config.js/0 | {
"file_path": "chat-ui/svelte.config.js",
"repo_id": "chat-ui",
"token_count": 253
} | 57 |
import json
import os
import tempfile
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", ".json"))
@get_d... | datasets/benchmarks/benchmark_indices_mapping.py/0 | {
"file_path": "datasets/benchmarks/benchmark_indices_mapping.py",
"repo_id": "datasets",
"token_count": 677
} | 58 |
# The cache
The cache is one of the reasons why 🤗 Datasets is so efficient. It stores previously downloaded and processed datasets so when you need to use them again, they are reloaded directly from the cache. This avoids having to download a dataset all over again, or reapplying processing functions. Even after you ... | datasets/docs/source/about_cache.mdx/0 | {
"file_path": "datasets/docs/source/about_cache.mdx",
"repo_id": "datasets",
"token_count": 909
} | 59 |
# Search index
[FAISS](https://github.com/facebookresearch/faiss) and [Elasticsearch](https://www.elastic.co/elasticsearch/) enables searching for examples in a dataset. This can be useful when you want to retrieve specific examples from a dataset that are relevant to your NLP task. For example, if you are working on ... | datasets/docs/source/faiss_es.mdx/0 | {
"file_path": "datasets/docs/source/faiss_es.mdx",
"repo_id": "datasets",
"token_count": 1830
} | 60 |
# Process text data
This guide shows specific methods for processing text datasets. Learn how to:
- Tokenize a dataset with [`~Dataset.map`].
- Align dataset labels with label ids for NLI datasets.
For a guide on how to process any type of dataset, take a look at the <a class="underline decoration-sky-400 decoration... | datasets/docs/source/nlp_process.mdx/0 | {
"file_path": "datasets/docs/source/nlp_process.mdx",
"repo_id": "datasets",
"token_count": 1109
} | 61 |
# Overview
Welcome to the 🤗 Datasets tutorials! These beginner-friendly tutorials will guide you through the fundamentals of working with 🤗 Datasets. You'll load and prepare a dataset for training with your machine learning framework of choice. Along the way, you'll learn how to load different dataset configurations... | datasets/docs/source/tutorial.md/0 | {
"file_path": "datasets/docs/source/tutorial.md",
"repo_id": "datasets",
"token_count": 311
} | 62 |
# Copyright 2021 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | datasets/metrics/cer/cer.py/0 | {
"file_path": "datasets/metrics/cer/cer.py",
"repo_id": "datasets",
"token_count": 2133
} | 63 |
# Metric Card for Exact Match
## Metric Description
A given predicted string's exact match score is 1 if it is the exact same as its reference string, and is 0 otherwise.
- **Example 1**: The exact match score of prediction "Happy Birthday!" is 0, given its reference is "Happy New Year!".
- **Example 2**: The exact ... | datasets/metrics/exact_match/README.md/0 | {
"file_path": "datasets/metrics/exact_match/README.md",
"repo_id": "datasets",
"token_count": 1508
} | 64 |
# Metric Card for Matthews Correlation Coefficient
## Metric Description
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure wh... | datasets/metrics/matthews_correlation/README.md/0 | {
"file_path": "datasets/metrics/matthews_correlation/README.md",
"repo_id": "datasets",
"token_count": 1251
} | 65 |
# Metric Card for Recall
## Metric Description
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the number of true positives and FN is the number of false negatives.
## How to Use
At mini... | datasets/metrics/recall/README.md/0 | {
"file_path": "datasets/metrics/recall/README.md",
"repo_id": "datasets",
"token_count": 1704
} | 66 |
# Copyright 2020 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | datasets/metrics/squad/squad.py/0 | {
"file_path": "datasets/metrics/squad/squad.py",
"repo_id": "datasets",
"token_count": 1933
} | 67 |
# Copyright 2022 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | datasets/metrics/xtreme_s/xtreme_s.py/0 | {
"file_path": "datasets/metrics/xtreme_s/xtreme_s.py",
"repo_id": "datasets",
"token_count": 4467
} | 68 |
import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List
from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datas... | datasets/src/datasets/commands/run_beam.py/0 | {
"file_path": "datasets/src/datasets/commands/run_beam.py",
"repo_id": "datasets",
"token_count": 3238
} | 69 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`FeatureConnector` for translations with fixed languages per example.
Here for ... | datasets/src/datasets/features/translation.py/0 | {
"file_path": "datasets/src/datasets/features/translation.py",
"repo_id": "datasets",
"token_count": 1680
} | 70 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import tqdm as hf_tqdm
from ..utils.typing import NestedDataStructureLike, PathL... | datasets/src/datasets/io/csv.py/0 | {
"file_path": "datasets/src/datasets/io/csv.py",
"repo_id": "datasets",
"token_count": 2556
} | 71 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
... | datasets/src/datasets/packaged_modules/audiofolder/audiofolder.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/audiofolder/audiofolder.py",
"repo_id": "datasets",
"token_count": 618
} | 72 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... | datasets/src/datasets/packaged_modules/parquet/parquet.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/parquet/parquet.py",
"repo_id": "datasets",
"token_count": 2193
} | 73 |
from typing import Optional
from ..utils.logging import get_logger
from .audio_classification import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModel... | datasets/src/datasets/tasks/__init__.py/0 | {
"file_path": "datasets/src/datasets/tasks/__init__.py",
"repo_id": "datasets",
"token_count": 506
} | 74 |
# deprecated, please use datasets.download.download_manager
| datasets/src/datasets/utils/download_manager.py/0 | {
"file_path": "datasets/src/datasets/utils/download_manager.py",
"repo_id": "datasets",
"token_count": 13
} | 75 |
name: "" # Filename comes here
allow_empty: false
allow_empty_text: true
subsections:
- name: "Dataset Card for X" # First-level markdown heading
allow_empty: false
allow_empty_text: true
subsections:
- name: "Table of Contents"
allow_empty: false
allow_empty_text: false
subs... | datasets/src/datasets/utils/resources/readme_structure.yaml/0 | {
"file_path": "datasets/src/datasets/utils/resources/readme_structure.yaml",
"repo_id": "datasets",
"token_count": 1924
} | 76 |
import os
import tarfile
import warnings
from io import BytesIO
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from datasets import Dataset, Features, Image, Sequence, Value, concatenate_datasets, load_dataset
from datasets.features.image import encode_np_array, image_to_bytes
from ..utils... | datasets/tests/features/test_image.py/0 | {
"file_path": "datasets/tests/features/test_image.py",
"repo_id": "datasets",
"token_count": 11815
} | 77 |
from pathlib import Path
import pytest
from datasets import load_dataset
from datasets.packaged_modules.cache.cache import Cache
SAMPLE_DATASET_TWO_CONFIG_IN_METADATA = "hf-internal-testing/audiofolder_two_configs_in_metadata"
def test_cache(text_dir: Path):
ds = load_dataset(str(text_dir))
hash = Path(ds... | datasets/tests/packaged_modules/test_cache.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_cache.py",
"repo_id": "datasets",
"token_count": 1243
} | 78 |
import os
import sys
from pathlib import Path
import pytest
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def test_split_dataset_by_node_map_style():
full_ds = Dataset.f... | datasets/tests/test_distributed.py/0 | {
"file_path": "datasets/tests/test_distributed.py",
"repo_id": "datasets",
"token_count": 1926
} | 79 |
import re
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import yaml
from huggingface_hub import DatasetCard, DatasetCardData
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.info import DatasetInfo
from datasets.utils.metadata import MetadataConfigs
def _dedent(st... | datasets/tests/test_metadata_util.py/0 | {
"file_path": "datasets/tests/test_metadata_util.py",
"repo_id": "datasets",
"token_count": 5453
} | 80 |
import pytest
from datasets.utils.version import Version
@pytest.mark.parametrize(
"other, expected_equality",
[
(Version("1.0.0"), True),
("1.0.0", True),
(Version("2.0.0"), False),
("2.0.0", False),
("1", False),
("a", False),
(1, False),
(Non... | datasets/tests/test_version.py/0 | {
"file_path": "datasets/tests/test_version.py",
"repo_id": "datasets",
"token_count": 254
} | 81 |
# Train your first Deep Reinforcement Learning Agent 🤖 [[hands-on]]
<CourseFloatingBanner classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit1/unit1.ipynb"}
]}
... | deep-rl-class/units/en/unit1/hands-on.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit1/hands-on.mdx",
"repo_id": "deep-rl-class",
"token_count": 9469
} | 82 |
# Mid-way Recap [[mid-way-recap]]
Before diving into Q-Learning, let's summarize what we've just learned.
We have two types of value-based functions:
- State-value function: outputs the expected return if **the agent starts at a given state and acts according to the policy forever after.**
- Action-value function: o... | deep-rl-class/units/en/unit2/mid-way-recap.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit2/mid-way-recap.mdx",
"repo_id": "deep-rl-class",
"token_count": 317
} | 83 |
# Additional Readings
These are **optional readings** if you want to go deeper.
## Introduction to Policy Optimization
- [Part 3: Intro to Policy Optimization - Spinning Up documentation](https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html)
## Policy Gradient
- [https://johnwlambert.github.io/polic... | deep-rl-class/units/en/unit4/additional-readings.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit4/additional-readings.mdx",
"repo_id": "deep-rl-class",
"token_count": 281
} | 84 |
# The Pyramid environment
The goal in this environment is to train our agent to **get the gold brick on the top of the Pyramid. To do that, it needs to press a button to spawn a Pyramid, navigate to the Pyramid, knock it over, and move to the gold brick at the top**.
<img src="https://huggingface.co/datasets/huggingf... | deep-rl-class/units/en/unit5/pyramids.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit5/pyramids.mdx",
"repo_id": "deep-rl-class",
"token_count": 645
} | 85 |
# 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: Chose the option which fits better when comparing di... | deep-rl-class/units/en/unit7/quiz.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit7/quiz.mdx",
"repo_id": "deep-rl-class",
"token_count": 1361
} | 86 |
# Let's train and play with Huggy 🐶 [[train]]
<CourseFloatingBanner classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/bonus-unit1/bonus-unit1.ipynb"}
... | deep-rl-class/units/en/unitbonus1/train.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus1/train.mdx",
"repo_id": "deep-rl-class",
"token_count": 4009
} | 87 |
# Student Works
Since the launch of the Deep Reinforcement Learning Course, **many students have created amazing projects that you should check out and consider participating in**.
If you've created an interesting project, don't hesitate to [add it to this list by opening a pull request on the GitHub repository](http... | deep-rl-class/units/en/unitbonus3/student-works.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus3/student-works.mdx",
"repo_id": "deep-rl-class",
"token_count": 629
} | 88 |
import os
import sys
import torch
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ControlNetModel,
LCMScheduler,
StableDiffusionAdapterPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionXLAdapterPipeline,
StableDiff... | diffusers/benchmarks/base_classes.py/0 | {
"file_path": "diffusers/benchmarks/base_classes.py",
"repo_id": "diffusers",
"token_count": 5600
} | 89 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/loaders/textual_inversion.md/0 | {
"file_path": "diffusers/docs/source/en/api/loaders/textual_inversion.md",
"repo_id": "diffusers",
"token_count": 340
} | 90 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/api/pipelines/ddim.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/ddim.md",
"repo_id": "diffusers",
"token_count": 477
} | 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/pipelines/stable_diffusion/overview.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md",
"repo_id": "diffusers",
"token_count": 4759
} | 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... | diffusers/docs/source/en/optimization/xformers.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/xformers.md",
"repo_id": "diffusers",
"token_count": 447
} | 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/training/t2i_adapters.md/0 | {
"file_path": "diffusers/docs/source/en/training/t2i_adapters.md",
"repo_id": "diffusers",
"token_count": 3502
} | 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/using-diffusers/custom_pipeline_examples.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/custom_pipeline_examples.md",
"repo_id": "diffusers",
"token_count": 1896
} | 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/using-diffusers/merge_loras.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/merge_loras.md",
"repo_id": "diffusers",
"token_count": 4459
} | 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/using_safetensors.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/using_safetensors.md",
"repo_id": "diffusers",
"token_count": 1535
} | 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/ko/optimization/fp16.md/0 | {
"file_path": "diffusers/docs/source/ko/optimization/fp16.md",
"repo_id": "diffusers",
"token_count": 10776
} | 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/ko/training/dreambooth.md/0 | {
"file_path": "diffusers/docs/source/ko/training/dreambooth.md",
"repo_id": "diffusers",
"token_count": 11697
} | 99 |
<!--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/ko/using-diffusers/img2img.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/img2img.md",
"repo_id": "diffusers",
"token_count": 2084
} | 100 |
<!--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/pt/index.md/0 | {
"file_path": "diffusers/docs/source/pt/index.md",
"repo_id": "diffusers",
"token_count": 1653
} | 101 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm im... | diffusers/examples/community/bit_diffusion.py/0 | {
"file_path": "diffusers/examples/community/bit_diffusion.py",
"repo_id": "diffusers",
"token_count": 4362
} | 102 |
# Copyright 2024 Stanford University Team and 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
#
#... | diffusers/examples/community/latent_consistency_img2img.py/0 | {
"file_path": "diffusers/examples/community/latent_consistency_img2img.py",
"repo_id": "diffusers",
"token_count": 16142
} | 103 |
import inspect
import os
import random
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers.image_processor imp... | diffusers/examples/community/pipeline_demofusion_sdxl.py/0 | {
"file_path": "diffusers/examples/community/pipeline_demofusion_sdxl.py",
"repo_id": "diffusers",
"token_count": 34622
} | 104 |
# 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 applicabl... | diffusers/examples/community/sd_text2img_k_diffusion.py/0 | {
"file_path": "diffusers/examples/community/sd_text2img_k_diffusion.py",
"repo_id": "diffusers",
"token_count": 8539
} | 105 |
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