text stringlengths 7 328k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 471 |
|---|---|---|---|
use crate::{CpuStorage, Device, Result, Shape, Storage, Tensor};
use k_quants::*;
use std::borrow::Cow;
#[cfg(target_feature = "avx")]
pub mod avx;
mod dummy_cuda;
mod dummy_metal;
pub mod ggml_file;
pub mod gguf_file;
pub mod k_quants;
#[cfg(feature = "metal")]
pub mod metal;
#[cfg(not(feature = "metal"))]
mod metal ... | candle/candle-core/src/quantized/mod.rs/0 | {
"file_path": "candle/candle-core/src/quantized/mod.rs",
"repo_id": "candle",
"token_count": 8178
} | 15 |
use anyhow::Result;
use candle_core::{DType, Device::Cpu, Tensor};
#[test]
fn display_scalar() -> Result<()> {
let t = Tensor::new(1234u32, &Cpu)?;
let s = format!("{t}");
assert_eq!(&s, "[1234]\nTensor[[], u32]");
let t = t.to_dtype(DType::F32)?.neg()?;
let s = format!("{}", (&t / 10.0)?);
ass... | candle/candle-core/tests/display_tests.rs/0 | {
"file_path": "candle/candle-core/tests/display_tests.rs",
"repo_id": "candle",
"token_count": 1395
} | 16 |
# candle-starcoder: code generation model
[StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
model specialized to code generation. The initial model was trained on 80
programming languages.
## Running some example
```bash
cargo run --example bigcode --release -- --prompt "fn fact(n: u64) -... | candle/candle-examples/examples/bigcode/README.md/0 | {
"file_path": "candle/candle-examples/examples/bigcode/README.md",
"repo_id": "candle",
"token_count": 180
} | 17 |
# candle-distilbert
DistilBert is a distiled version of the Bert model.
## Sentence embeddings
DistilBert is used to compute the sentence embeddings for a prompt. The model weights
are downloaded from the hub on the first run.
```bash
cargo run --example distilbert --release -- --prompt "Here is a test sentence"
>... | candle/candle-examples/examples/distilbert/README.md/0 | {
"file_path": "candle/candle-examples/examples/distilbert/README.md",
"repo_id": "candle",
"token_count": 367
} | 18 |
// https://github.com/karpathy/llama2.c
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::llama2_c as model;
use candle_transformers::models::llama2_c_weights as weights;
use candle_transformers::models::quantized_llama2_c... | candle/candle-examples/examples/llama2-c/main.rs/0 | {
"file_path": "candle/candle-examples/examples/llama2-c/main.rs",
"repo_id": "candle",
"token_count": 6004
} | 19 |
# candle-mixtral: 8x7b LLM using a sparse mixture of experts.
Mixtral-8x7B-v0.1 is a pretrained generative LLM with 56 billion parameters.
- [Blog post](https://mistral.ai/news/mixtral-of-experts/) from Mistral announcing the model release.
- [Model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the Hu... | candle/candle-examples/examples/mixtral/README.md/0 | {
"file_path": "candle/candle-examples/examples/mixtral/README.md",
"repo_id": "candle",
"token_count": 322
} | 20 |
# candle-quantized-llama: Fast Inference of quantized LLaMA models
This example provides a quantized LLaMA model similar to
[llama.cpp](https://github.com/ggerganov/llama.cpp). This is based on candle
built-in quantization methods. Supported features include:
- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quan... | candle/candle-examples/examples/quantized/README.md/0 | {
"file_path": "candle/candle-examples/examples/quantized/README.md",
"repo_id": "candle",
"token_count": 820
} | 21 |
#[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::repvgg;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
A0,
... | candle/candle-examples/examples/repvgg/main.rs/0 | {
"file_path": "candle/candle-examples/examples/repvgg/main.rs",
"repo_id": "candle",
"token_count": 1525
} | 22 |
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::stable_diffusion;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Module, Tensor, D};
use clap::Parser;
use tokenizers::Tokenizer;
#[derive(Parser)]... | candle/candle-examples/examples/stable-diffusion/main.rs/0 | {
"file_path": "candle/candle-examples/examples/stable-diffusion/main.rs",
"repo_id": "candle",
"token_count": 9839
} | 23 |
# candle-yolo-v8: Object Detection and Pose Estimation
This is a port of [Ultralytics
YOLOv8](https://github.com/ultralytics/ultralytics). The implementation is based
on the [tinygrad
version](https://github.com/tinygrad/tinygrad/blob/master/examples/yolov8.py)
and on the model architecture described in this
[issue](h... | candle/candle-examples/examples/yolo-v8/README.md/0 | {
"file_path": "candle/candle-examples/examples/yolo-v8/README.md",
"repo_id": "candle",
"token_count": 562
} | 24 |
// Build script to run nvcc and generate the C glue code for launching the flash-attention kernel.
// The cuda build time is very long so one can set the CANDLE_FLASH_ATTN_BUILD_DIR environment
// variable in order to cache the compiled artifacts and avoid recompiling too often.
use anyhow::{Context, Result};
use std::... | candle/candle-flash-attn/build.rs/0 | {
"file_path": "candle/candle-flash-attn/build.rs",
"repo_id": "candle",
"token_count": 1604
} | 25 |
[package]
name = "candle-kernels"
version = "0.5.0"
edition = "2021"
description = "CUDA kernels for Candle"
repository = "https://github.com/huggingface/candle"
keywords = ["blas", "tensor", "machine-learning"]
categories = ["science"]
license = "MIT OR Apache-2.0"
[dependencies]
[build-dependencies]
bindgen_cuda =... | candle/candle-kernels/Cargo.toml/0 | {
"file_path": "candle/candle-kernels/Cargo.toml",
"repo_id": "candle",
"token_count": 126
} | 26 |
#define _USE_MATH_DEFINES
#include<math.h>
#include<stdint.h>
#include "cuda_utils.cuh"
#define UNARY_OP(TYPENAME, FN_NAME, FUNC) \
extern "C" __global__ void FN_NAME( \
const size_t numel, \
const size_t num_dims, \
const size_t *info, \
const TYPENAME *inp, \
TYPENAME *out \
) { \
const size_... | candle/candle-kernels/src/unary.cu/0 | {
"file_path": "candle/candle-kernels/src/unary.cu",
"repo_id": "candle",
"token_count": 3522
} | 27 |
use metal::{Buffer, ComputeCommandEncoderRef, ComputePipelineState, MTLSize};
use std::ffi::c_void;
/// Most kernels apply similarly across the tensors
/// This creates a strategy that uses the maximum amount of threads per threadgroup (capped at the
/// actual total buffer length).
/// Then kernels can just do their ... | candle/candle-metal-kernels/src/utils.rs/0 | {
"file_path": "candle/candle-metal-kernels/src/utils.rs",
"repo_id": "candle",
"token_count": 2129
} | 28 |
//! Embedding Layer.
use candle::{Result, Tensor};
#[derive(Clone, Debug)]
pub struct Embedding {
embeddings: Tensor,
hidden_size: usize,
}
impl Embedding {
pub fn new(embeddings: Tensor, hidden_size: usize) -> Self {
Self {
embeddings,
hidden_size,
}
}
pub... | candle/candle-nn/src/embedding.rs/0 | {
"file_path": "candle/candle-nn/src/embedding.rs",
"repo_id": "candle",
"token_count": 571
} | 29 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use candle::{test_utils, DType, Device, Tensor};
use candle_nn::BatchNorm;
/* The test below has been generated using the following PyTorch code:
import torch
torch.manual_seed(19551105... | candle/candle-nn/tests/batch_norm.rs/0 | {
"file_path": "candle/candle-nn/tests/batch_norm.rs",
"repo_id": "candle",
"token_count": 2474
} | 30 |
[package]
name = "candle-pyo3"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[lib]
name = "candle"
crate-type = ["cdylib"]
[dependencies]
accelerate-src = { ... | candle/candle-pyo3/Cargo.toml/0 | {
"file_path": "candle/candle-pyo3/Cargo.toml",
"repo_id": "candle",
"token_count": 315
} | 31 |
from candle import Tensor, QTensor, DType
from typing import (
Dict,
Tuple,
Any,
Optional,
Union,
Iterator,
Set,
overload,
Mapping,
TypeVar,
List,
)
from collections import OrderedDict, namedtuple
TensorLike = Union[Tensor, QTensor]
T = TypeVar("T", bound="Module")
class _... | candle/candle-pyo3/py_src/candle/nn/module.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/module.py",
"repo_id": "candle",
"token_count": 12028
} | 32 |
import candle
print(f"mkl: {candle.utils.has_mkl()}")
print(f"accelerate: {candle.utils.has_accelerate()}")
print(f"num-threads: {candle.utils.get_num_threads()}")
print(f"cuda: {candle.utils.cuda_is_available()}")
t = candle.Tensor(42.0)
print(t)
print(t.shape, t.rank, t.device)
print(t + t)
t = can... | candle/candle-pyo3/test.py/0 | {
"file_path": "candle/candle-pyo3/test.py",
"repo_id": "candle",
"token_count": 340
} | 33 |
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": 6478
} | 34 |
use std::collections::HashMap;
use crate::quantized_nn::RmsNorm;
use candle::quantized::QTensor;
use candle::quantized::{ggml_file, gguf_file};
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{Embedding, Module};
pub const MAX_SEQ_LEN: usize = 4096;
// QMatMul wrapper adding some tracing.
#[deri... | candle/candle-transformers/src/models/quantized_llama.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_llama.rs",
"repo_id": "candle",
"token_count": 11306
} | 35 |
//! ResNet Building Blocks
//!
//! Some Residual Network blocks used in UNet models.
//!
//! Denoising Diffusion Implicit Models, K. He and al, 2015.
//! https://arxiv.org/abs/1512.03385
use crate::models::with_tracing::{conv2d, Conv2d};
use candle::{Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
/// ... | candle/candle-transformers/src/models/stable_diffusion/resnet.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/resnet.rs",
"repo_id": "candle",
"token_count": 2284
} | 36 |
use candle::{Module, Result, Tensor};
use candle_nn::VarBuilder;
#[derive(Debug, Clone)]
pub struct Embedding {
inner: candle_nn::Embedding,
span: tracing::Span,
}
impl Embedding {
pub fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
let inner = candle_nn::embedding(d1, d2, vb)?;
... | candle/candle-transformers/src/models/with_tracing.rs/0 | {
"file_path": "candle/candle-transformers/src/models/with_tracing.rs",
"repo_id": "candle",
"token_count": 2315
} | 37 |
[package]
name = "candle-wasm-example-bert"
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-tran... | candle/candle-wasm-examples/bert/Cargo.toml/0 | {
"file_path": "candle/candle-wasm-examples/bert/Cargo.toml",
"repo_id": "candle",
"token_count": 304
} | 38 |
[package]
name = "candle-wasm-example-llama2"
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-tr... | candle/candle-wasm-examples/llama2-c/Cargo.toml/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/Cargo.toml",
"repo_id": "candle",
"token_count": 434
} | 39 |
import snarkdown from "https://cdn.skypack.dev/snarkdown";
import hljs from "https://cdn.skypack.dev/highlight.js";
// models base url
const MODELS = {
moondream2_q4k: {
base_url:
"https://huggingface.co/santiagomed/candle-moondream/resolve/main/",
model: "model-q4_0.gguf",
tokenizer: "tokenizer.jso... | candle/candle-wasm-examples/moondream/code.js/0 | {
"file_path": "candle/candle-wasm-examples/moondream/code.js",
"repo_id": "candle",
"token_count": 2873
} | 40 |
//load the candle SAM Model wasm module
import init, { Model } from "./build/m.js";
async function fetchArrayBuffer(url, cacheModel = true) {
if (!cacheModel)
return new Uint8Array(await (await fetch(url)).arrayBuffer());
const cacheName = "sam-candle-cache";
const cache = await caches.open(cacheName);
con... | candle/candle-wasm-examples/segment-anything/samWorker.js/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/samWorker.js",
"repo_id": "candle",
"token_count": 1747
} | 41 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<title>Welcome to Candle!</title>
<link data-trunk rel="copy-file" href="mel_filters.safetensors" />
<!-- samples -->
<link data-trunk rel="copy-dir" href="audios" />
<!-- tiny.en -->
<link data-trunk rel="copy-dir" href="whi... | candle/candle-wasm-examples/whisper/index.html/0 | {
"file_path": "candle/candle-wasm-examples/whisper/index.html",
"repo_id": "candle",
"token_count": 523
} | 42 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle YOLOv8 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/yolo/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/yolo/lib-example.html",
"repo_id": "candle",
"token_count": 9649
} | 43 |
use candle::quantized::{gguf_file, GgmlDType, QTensor};
use candle::{Device, Result};
use clap::{Parser, Subcommand, ValueEnum};
use rayon::prelude::*;
#[derive(ValueEnum, Debug, Clone)]
enum QuantizationMode {
/// The default quantization includes all 2d tensors, except the output tensor which always
/// uses... | candle/tensor-tools/src/main.rs/0 | {
"file_path": "candle/tensor-tools/src/main.rs",
"repo_id": "candle",
"token_count": 8827
} | 44 |
---
title: chat-ui
emoji: 🔥
colorFrom: purple
colorTo: purple
sdk: docker
pinned: false
license: apache-2.0
base_path: /chat
app_port: 3000
failure_strategy: rollback
---
# Chat UI
A c... | chat-ui/README.md/0 | {
"file_path": "chat-ui/README.md",
"repo_id": "chat-ui",
"token_count": 10432
} | 45 |
<script lang="ts">
export let title = "";
export let classNames = "";
</script>
<div class="flex items-center rounded-xl bg-gray-100 p-1 text-sm dark:bg-gray-800 {classNames}">
<span
class="mr-2 inline-flex items-center rounded-lg bg-gradient-to-br from-primary-300 px-2 py-1 text-xxs font-medium uppercase leading... | chat-ui/src/lib/components/AnnouncementBanner.svelte/0 | {
"file_path": "chat-ui/src/lib/components/AnnouncementBanner.svelte",
"repo_id": "chat-ui",
"token_count": 184
} | 46 |
<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
} | 47 |
<script lang="ts">
import type { Message } from "$lib/types/Message";
import { createEventDispatcher, onDestroy, tick } from "svelte";
import CarbonSendAltFilled from "~icons/carbon/send-alt-filled";
import CarbonExport from "~icons/carbon/export";
import CarbonStopFilledAlt from "~icons/carbon/stop-filled-alt";
... | chat-ui/src/lib/components/chat/ChatWindow.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/ChatWindow.svelte",
"repo_id": "chat-ui",
"token_count": 5436
} | 48 |
// Shouldn't be needed if we dove into sveltekit internals, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
import { setTimeout } from "node:timers/promises";
import { collections } from "./database";
let closed = false;
process.on("SIGINT", () => {
closed = true;
});
export let abortedGe... | chat-ui/src/lib/server/abortedGenerations.ts/0 | {
"file_path": "chat-ui/src/lib/server/abortedGenerations.ts",
"repo_id": "chat-ui",
"token_count": 267
} | 49 |
import { z } from "zod";
import { openAICompletionToTextGenerationStream } from "./openAICompletionToTextGenerationStream";
import { openAIChatToTextGenerationStream } from "./openAIChatToTextGenerationStream";
import { buildPrompt } from "$lib/buildPrompt";
import { OPENAI_API_KEY } from "$env/static/private";
import ... | chat-ui/src/lib/server/endpoints/openai/endpointOai.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/openai/endpointOai.ts",
"repo_id": "chat-ui",
"token_count": 1122
} | 50 |
import { searchWeb } from "$lib/server/websearch/searchWeb";
import { generateQuery } from "$lib/server/websearch/generateQuery";
import { parseWeb } from "$lib/server/websearch/parseWeb";
import { chunk } from "$lib/utils/chunk";
import { findSimilarSentences } from "$lib/server/sentenceSimilarity";
import { getWebSea... | chat-ui/src/lib/server/websearch/runWebSearch.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/runWebSearch.ts",
"repo_id": "chat-ui",
"token_count": 2217
} | 51 |
export interface ConvSidebar {
id: string;
title: string;
updatedAt: Date;
model?: string;
assistantId?: string;
avatarHash?: string;
}
| chat-ui/src/lib/types/ConvSidebar.ts/0 | {
"file_path": "chat-ui/src/lib/types/ConvSidebar.ts",
"repo_id": "chat-ui",
"token_count": 50
} | 52 |
import type { ObjectId } from "mongodb";
import type { Timestamps } from "./Timestamps";
export interface User extends Timestamps {
_id: ObjectId;
username?: string;
name: string;
email?: string;
avatarUrl: string | undefined;
hfUserId: string;
}
| chat-ui/src/lib/types/User.ts/0 | {
"file_path": "chat-ui/src/lib/types/User.ts",
"repo_id": "chat-ui",
"token_count": 85
} | 53 |
import * as fs from "fs";
import { setGlobalDispatcher, Agent } from "undici";
/**
* Load client certificates for mutual TLS authentication. This function must be called before any HTTP requests are made.
* This is a global setting that affects all HTTP requests made by the application using the native fetch API.
*... | chat-ui/src/lib/utils/loadClientCerts.ts/0 | {
"file_path": "chat-ui/src/lib/utils/loadClientCerts.ts",
"repo_id": "chat-ui",
"token_count": 551
} | 54 |
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { describe, expect, it } from "vitest";
import {
insertLegacyConversation,
insertLinearBranchConversation,
insertSideBranchesConversation,
} from "./treeHelpers.spec";
import { buildSubtree } from "./buildSubtree";
descr... | chat-ui/src/lib/utils/tree/buildSubtree.spec.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/buildSubtree.spec.ts",
"repo_id": "chat-ui",
"token_count": 1375
} | 55 |
import { models } from "$lib/server/models";
export async function GET() {
const res = models.map((model) => ({
id: model.id,
name: model.name,
websiteUrl: model.websiteUrl,
modelUrl: model.modelUrl,
tokenizer: model.tokenizer,
datasetName: model.datasetName,
datasetUrl: model.datasetUrl,
displayName:... | chat-ui/src/routes/api/models/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/models/+server.ts",
"repo_id": "chat-ui",
"token_count": 217
} | 56 |
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import type { SharedConversation } from "$lib/types/SharedConversation";
import { getShareUrl } from "$lib/utils/getShareUrl";
import { hashConv } from "$lib/utils/hashConv";
import { error } from "@sveltejs/kit";
impo... | chat-ui/src/routes/conversation/[id]/share/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/[id]/share/+server.ts",
"repo_id": "chat-ui",
"token_count": 761
} | 57 |
<script lang="ts">
import { createEventDispatcher } from "svelte";
import Modal from "$lib/components/Modal.svelte";
import CarbonClose from "~icons/carbon/close";
import CarbonTrashCan from "~icons/carbon/trash-can";
import CarbonArrowUpRight from "~icons/carbon/arrow-up-right";
import { enhance } from "$app/f... | chat-ui/src/routes/settings/(nav)/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/+page.svelte",
"repo_id": "chat-ui",
"token_count": 1373
} | 58 |
@import "./highlight-js.css";
@tailwind base;
@tailwind components;
@tailwind utilities;
@layer components {
.btn {
@apply inline-flex flex-shrink-0 cursor-pointer select-none items-center justify-center whitespace-nowrap outline-none transition-all focus:ring disabled:cursor-default;
}
}
@layer utilities {
.sc... | chat-ui/src/styles/main.css/0 | {
"file_path": "chat-ui/src/styles/main.css",
"repo_id": "chat-ui",
"token_count": 189
} | 59 |
{
"extends": "./.svelte-kit/tsconfig.json",
"compilerOptions": {
"allowJs": true,
"checkJs": true,
"esModuleInterop": true,
"forceConsistentCasingInFileNames": true,
"resolveJsonModule": true,
"skipLibCheck": true,
"sourceMap": true,
"strict": true,
"target": "ES2018"
}
// Path aliases are handled... | chat-ui/tsconfig.json/0 | {
"file_path": "chat-ui/tsconfig.json",
"repo_id": "chat-ui",
"token_count": 197
} | 60 |
repos:
- repo: https://github.com/charliermarsh/ruff-pre-commit # https://github.com/charliermarsh/ruff#usage
rev: 'v0.3.0'
hooks:
# Run the linter.
- id: ruff
args: [ --fix ]
# Run the formatter.
- id: ruff-format
| datasets/.pre-commit-config.yaml/0 | {
"file_path": "datasets/.pre-commit-config.yaml",
"repo_id": "datasets",
"token_count": 122
} | 61 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
SPEED_TEST_N_EXAMPLES = 500_000
RESULTS_BASEPATH, RESULTS_FILENAME = os.path.split(__file__)
RESULTS_FILE_PATH = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py... | datasets/benchmarks/benchmark_map_filter.py/0 | {
"file_path": "datasets/benchmarks/benchmark_map_filter.py",
"repo_id": "datasets",
"token_count": 996
} | 62 |
# Build and load
Nearly every deep learning workflow begins with loading a dataset, which makes it one of the most important steps. With 🤗 Datasets, there are more than 900 datasets available to help you get started with your NLP task. All you have to do is call: [`load_dataset`] to take your first step. This functio... | datasets/docs/source/about_dataset_load.mdx/0 | {
"file_path": "datasets/docs/source/about_dataset_load.mdx",
"repo_id": "datasets",
"token_count": 2537
} | 63 |
# Overview
The how-to guides offer a more comprehensive overview of all the tools 🤗 Datasets offers and how to use them. This will help you tackle messier real-world datasets where you may need to manipulate the dataset structure or content to get it ready for training.
The guides assume you are familiar and comfort... | datasets/docs/source/how_to.md/0 | {
"file_path": "datasets/docs/source/how_to.md",
"repo_id": "datasets",
"token_count": 469
} | 64 |
# Builder classes
## Builders
🤗 Datasets relies on two main classes during the dataset building process: [`DatasetBuilder`] and [`BuilderConfig`].
[[autodoc]] datasets.DatasetBuilder
[[autodoc]] datasets.GeneratorBasedBuilder
[[autodoc]] datasets.BeamBasedBuilder
[[autodoc]] datasets.ArrowBasedBuilder
[[autodoc... | datasets/docs/source/package_reference/builder_classes.mdx/0 | {
"file_path": "datasets/docs/source/package_reference/builder_classes.mdx",
"repo_id": "datasets",
"token_count": 253
} | 65 |
# Preprocess
In addition to loading datasets, 🤗 Datasets other main goal is to offer a diverse set of preprocessing functions to get a dataset into an appropriate format for training with your machine learning framework.
There are many possible ways to preprocess a dataset, and it all depends on your specific datas... | datasets/docs/source/use_dataset.mdx/0 | {
"file_path": "datasets/docs/source/use_dataset.mdx",
"repo_id": "datasets",
"token_count": 3367
} | 66 |
# Metric Card for chrF(++)
## Metric Description
ChrF and ChrF++ are two MT evaluation metrics that use the F-score statistic for character n-gram matches. ChrF++ additionally includes word n-grams, which correlate more strongly with direct assessment. We use the implementation that is already present in sacrebleu.
... | datasets/metrics/chrf/README.md/0 | {
"file_path": "datasets/metrics/chrf/README.md",
"repo_id": "datasets",
"token_count": 2254
} | 67 |
# Metric Card for F1
## Metric Description
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
## How to Use
At minimum, this metric requires predictions and references as input
```python
>>> f1_metric = dataset... | datasets/metrics/f1/README.md/0 | {
"file_path": "datasets/metrics/f1/README.md",
"repo_id": "datasets",
"token_count": 1624
} | 68 |
# Metric Card for MAUVE
## Metric description
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure. It summarizes both Type I and Type II errors measured softly using [Kullback–Leibler (KL) divergences](https://en.wikip... | datasets/metrics/mauve/README.md/0 | {
"file_path": "datasets/metrics/mauve/README.md",
"repo_id": "datasets",
"token_count": 1650
} | 69 |
# Metric Card for ROC AUC
## Metric Description
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting... | datasets/metrics/roc_auc/README.md/0 | {
"file_path": "datasets/metrics/roc_auc/README.md",
"repo_id": "datasets",
"token_count": 3273
} | 70 |
"""Official evaluation script for SQuAD version 2.0.
In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable... | datasets/metrics/squad_v2/evaluate.py/0 | {
"file_path": "datasets/metrics/squad_v2/evaluate.py",
"repo_id": "datasets",
"token_count": 5444
} | 71 |
<!---
Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or ... | datasets/notebooks/README.md/0 | {
"file_path": "datasets/notebooks/README.md",
"repo_id": "datasets",
"token_count": 534
} | 72 |
import importlib
import importlib.metadata
import logging
import os
import platform
from pathlib import Path
from typing import Optional
from packaging import version
logger = logging.getLogger(__name__.split(".", 1)[0]) # to avoid circular import from .utils.logging
# Datasets
S3_DATASETS_BUCKET_PREFIX = "https:/... | datasets/src/datasets/config.py/0 | {
"file_path": "datasets/src/datasets/config.py",
"repo_id": "datasets",
"token_count": 4439
} | 73 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class BaseCompressedFileFileSystem(AbstractArchiveFileSystem):
"""Read contents of compressed file as a filesystem with one file inside."""
root_marker = ""
... | datasets/src/datasets/filesystems/compression.py/0 | {
"file_path": "datasets/src/datasets/filesystems/compression.py",
"repo_id": "datasets",
"token_count": 2608
} | 74 |
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.json.json import Json
from ..utils import tqdm as hf_tqdm
from ..utils.typing import NestedDataStructureLike, Pa... | datasets/src/datasets/io/json.py/0 | {
"file_path": "datasets/src/datasets/io/json.py",
"repo_id": "datasets",
"token_count": 3086
} | 75 |
import glob
import json
import os
import shutil
import time
import warnings
from pathlib import Path
from typing import List, Optional, Tuple, Union
import pyarrow as pa
import datasets
import datasets.config
import datasets.data_files
from datasets.naming import filenames_for_dataset_split
logger = datasets.utils.... | datasets/src/datasets/packaged_modules/cache/cache.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/cache/cache.py",
"repo_id": "datasets",
"token_count": 4032
} | 76 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from dataset... | datasets/src/datasets/packaged_modules/spark/spark.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/spark/spark.py",
"repo_id": "datasets",
"token_count": 6664
} | 77 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=True)
class AutomaticSpeechRecognition(TaskTemplate):
task: str = field(default="automatic-speech-recognition", metadata={"include_... | datasets/src/datasets/tasks/automatic_speech_recognition.py/0 | {
"file_path": "datasets/src/datasets/tasks/automatic_speech_recognition.py",
"repo_id": "datasets",
"token_count": 459
} | 78 |
import bz2
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from ._filelock import FileLock
from .logging import get_logger
... | datasets/src/datasets/utils/extract.py/0 | {
"file_path": "datasets/src/datasets/utils/extract.py",
"repo_id": "datasets",
"token_count": 6394
} | 79 |
from typing import List
import numpy as np
def _number_of_shards_in_gen_kwargs(gen_kwargs: dict) -> int:
"""Return the number of possible shards according to the input gen_kwargs"""
# Having lists of different sizes makes sharding ambigious, raise an error in this case
# until we decide how to define sha... | datasets/src/datasets/utils/sharding.py/0 | {
"file_path": "datasets/src/datasets/utils/sharding.py",
"repo_id": "datasets",
"token_count": 1742
} | 80 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_TestCommandArgs = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",... | datasets/tests/commands/test_test.py/0 | {
"file_path": "datasets/tests/commands/test_test.py",
"repo_id": "datasets",
"token_count": 1511
} | 81 |
import contextlib
import csv
import json
import os
import sqlite3
import tarfile
import textwrap
import zipfile
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
# dataset + arrow_file
@pytest.fixture(scope="session")
def dataset():
n = ... | datasets/tests/fixtures/files.py/0 | {
"file_path": "datasets/tests/fixtures/files.py",
"repo_id": "datasets",
"token_count": 8208
} | 82 |
import importlib
import shutil
import textwrap
import pytest
from datasets import ClassLabel, DownloadManager, Features, Value
from datasets.data_files import DataFilesDict, get_data_patterns
from datasets.download.streaming_download_manager import StreamingDownloadManager
from datasets.packaged_modules.folder_based_... | datasets/tests/packaged_modules/test_folder_based_builder.py/0 | {
"file_path": "datasets/tests/packaged_modules/test_folder_based_builder.py",
"repo_id": "datasets",
"token_count": 8915
} | 83 |
import unittest
import warnings
from datasets.utils import experimental
@experimental
def dummy_function():
return "success"
class TestExperimentalFlag(unittest.TestCase):
def test_experimental_warning(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always"... | datasets/tests/test_experimental.py/0 | {
"file_path": "datasets/tests/test_experimental.py",
"repo_id": "datasets",
"token_count": 152
} | 84 |
# Copyright 2020 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | datasets/tests/test_metric_common.py/0 | {
"file_path": "datasets/tests/test_metric_common.py",
"repo_id": "datasets",
"token_count": 3144
} | 85 |
import asyncio
import importlib.metadata
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch... | datasets/tests/utils.py/0 | {
"file_path": "datasets/tests/utils.py",
"repo_id": "datasets",
"token_count": 6171
} | 86 |
<jupyter_start><jupyter_text>Unit 8: Proximal Policy Gradient (PPO) with PyTorch 🤖In this notebook, you'll learn to **code your PPO agent from scratch with PyTorch using CleanRL implementation as model**.To test its robustness, we're going to train it in:- [LunarLander-v2 🚀](https://www.gymlibrary.dev/environments/bo... | deep-rl-class/notebooks/unit8/unit8_part1.ipynb/0 | {
"file_path": "deep-rl-class/notebooks/unit8/unit8_part1.ipynb",
"repo_id": "deep-rl-class",
"token_count": 15492
} | 87 |
# Quiz [[quiz]]
The best way to learn and [to avoid the illusion of competence](https://www.coursera.org/lecture/learning-how-to-learn/illusions-of-competence-BuFzf) **is to test yourself.** This will help you to find **where you need to reinforce your knowledge**.
### Q1: What is Reinforcement Learning?
<details>
<... | deep-rl-class/units/en/unit1/quiz.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit1/quiz.mdx",
"repo_id": "deep-rl-class",
"token_count": 1866
} | 88 |
# Q-Learning Recap [[q-learning-recap]]
*Q-Learning* **is the RL algorithm that** :
- Trains a *Q-function*, an **action-value function** encoded, in internal memory, by a *Q-table* **containing all the state-action pair values.**
- Given a state and action, our Q-function **will search its Q-table for the correspo... | deep-rl-class/units/en/unit2/q-learning-recap.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit2/q-learning-recap.mdx",
"repo_id": "deep-rl-class",
"token_count": 505
} | 89 |
# Conclusion
**Congrats on finishing this unit**! There was a lot of information.
And congrats on finishing the tutorial. You've just coded your first Deep Reinforcement Learning agent from scratch using PyTorch and shared it on the Hub 🥳.
Don't hesitate to iterate on this unit **by improving the implementation for... | deep-rl-class/units/en/unit4/conclusion.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit4/conclusion.mdx",
"repo_id": "deep-rl-class",
"token_count": 250
} | 90 |
# The SnowballTarget Environment
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget.gif" alt="SnowballTarget"/>
SnowballTarget is an environment we created at Hugging Face using assets from [Kay Lousberg](https://kaylousberg.com/). We have an option... | deep-rl-class/units/en/unit5/snowball-target.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit5/snowball-target.mdx",
"repo_id": "deep-rl-class",
"token_count": 1019
} | 91 |
# Additional Readings [[additional-readings]]
These are **optional readings** if you want to go deeper.
## PPO Explained
- [Towards Delivering a Coherent Self-Contained Explanation of Proximal Policy Optimization by Daniel Bick](https://fse.studenttheses.ub.rug.nl/25709/1/mAI_2021_BickD.pdf)
- [What is the way to un... | deep-rl-class/units/en/unit8/additional-readings.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit8/additional-readings.mdx",
"repo_id": "deep-rl-class",
"token_count": 418
} | 92 |
# Introduction [[introduction]]
One of the most critical tasks in Deep Reinforcement Learning is to **find a good set of training hyperparameters**.
<img src="https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png" alt="Optuna Logo"/>
[Optuna](https://optuna.org/) is a library that helps y... | deep-rl-class/units/en/unitbonus2/introduction.mdx/0 | {
"file_path": "deep-rl-class/units/en/unitbonus2/introduction.mdx",
"repo_id": "deep-rl-class",
"token_count": 156
} | 93 |
import argparse
import sys
sys.path.append(".")
from base_classes import IPAdapterTextToImageBenchmark # noqa: E402
IP_ADAPTER_CKPTS = {
"runwayml/stable-diffusion-v1-5": ("h94/IP-Adapter", "ip-adapter_sd15.bin"),
"stabilityai/stable-diffusion-xl-base-1.0": ("h94/IP-Adapter", "ip-adapter_sdxl.bin"),
}
if... | diffusers/benchmarks/benchmark_ip_adapters.py/0 | {
"file_path": "diffusers/benchmarks/benchmark_ip_adapters.py",
"repo_id": "diffusers",
"token_count": 434
} | 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/api/logging.md/0 | {
"file_path": "diffusers/docs/source/en/api/logging.md",
"repo_id": "diffusers",
"token_count": 1351
} | 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/api/pipelines/deepfloyd_if.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/deepfloyd_if.md",
"repo_id": "diffusers",
"token_count": 6743
} | 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/api/pipelines/stable_diffusion/stable_diffusion_2.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md",
"repo_id": "diffusers",
"token_count": 2283
} | 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/stable_diffusion.md/0 | {
"file_path": "diffusers/docs/source/en/stable_diffusion.md",
"repo_id": "diffusers",
"token_count": 3962
} | 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 agree... | diffusers/docs/source/en/training/text_inversion.md/0 | {
"file_path": "diffusers/docs/source/en/training/text_inversion.md",
"repo_id": "diffusers",
"token_count": 4383
} | 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/en/using-diffusers/depth2img.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/depth2img.md",
"repo_id": "diffusers",
"token_count": 878
} | 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/en/using-diffusers/other-modalities.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/other-modalities.md",
"repo_id": "diffusers",
"token_count": 333
} | 101 |
<!--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/write_own_pipeline.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/write_own_pipeline.md",
"repo_id": "diffusers",
"token_count": 4145
} | 102 |
<!--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/mps.md/0 | {
"file_path": "diffusers/docs/source/ko/optimization/mps.md",
"repo_id": "diffusers",
"token_count": 2532
} | 103 |
<!--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/lora.md/0 | {
"file_path": "diffusers/docs/source/ko/training/lora.md",
"repo_id": "diffusers",
"token_count": 4733
} | 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 applicable law or agreed... | diffusers/docs/source/ko/using-diffusers/loading.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/loading.md",
"repo_id": "diffusers",
"token_count": 14650
} | 105 |
<!--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/quicktour.md/0 | {
"file_path": "diffusers/docs/source/pt/quicktour.md",
"repo_id": "diffusers",
"token_count": 6766
} | 106 |
import glob
import os
from typing import Dict, List, Union
import safetensors.torch
import torch
from huggingface_hub import snapshot_download
from huggingface_hub.utils import validate_hf_hub_args
from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_... | diffusers/examples/community/checkpoint_merger.py/0 | {
"file_path": "diffusers/examples/community/checkpoint_merger.py",
"repo_id": "diffusers",
"token_count": 6170
} | 107 |
# 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
} | 108 |
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": 34621
} | 109 |
import argparse
import inspect
import os
import time
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from PIL import Image
from transformers import CLIPTokenizer
from diffusers import OnnxRuntimeModel, StableDiffusionImg2ImgPipeline, UniPC... | diffusers/examples/community/run_onnx_controlnet.py/0 | {
"file_path": "diffusers/examples/community/run_onnx_controlnet.py",
"repo_id": "diffusers",
"token_count": 19745
} | 110 |
#
# Copyright 2024 The HuggingFace Inc. team.
# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Licens... | diffusers/examples/community/stable_diffusion_tensorrt_img2img.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_tensorrt_img2img.py",
"repo_id": "diffusers",
"token_count": 19790
} | 111 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The LCM team and 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.apach... | diffusers/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py/0 | {
"file_path": "diffusers/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py",
"repo_id": "diffusers",
"token_count": 27478
} | 112 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/examples/custom_diffusion/test_custom_diffusion.py/0 | {
"file_path": "diffusers/examples/custom_diffusion/test_custom_diffusion.py",
"repo_id": "diffusers",
"token_count": 2234
} | 113 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| diffusers/examples/inference/inpainting.py/0 | {
"file_path": "diffusers/examples/inference/inpainting.py",
"repo_id": "diffusers",
"token_count": 89
} | 114 |
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