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# DPO pipeline for the creation of StackLlaMa 2: a Stack exchange llama-v2-7b model
## Prerequisites
Install all the dependencies in the `requirements.txt`:
```
$ pip install -U -r requirements.txt
```
Since we will use `accelerate` for training, make sure to run:
```
$ accelerate config
```
## Training
There wer... | trl/examples/research_projects/stack_llama_2/scripts/README.md/0 | {
"file_path": "trl/examples/research_projects/stack_llama_2/scripts/README.md",
"repo_id": "trl",
"token_count": 880
} | 551 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | trl/examples/scripts/sft_video_llm.py/0 | {
"file_path": "trl/examples/scripts/sft_video_llm.py",
"repo_id": "trl",
"token_count": 3427
} | 552 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | trl/tests/test_core.py/0 | {
"file_path": "trl/tests/test_core.py",
"repo_id": "trl",
"token_count": 673
} | 553 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | trl/tests/test_peft_models.py/0 | {
"file_path": "trl/tests/test_peft_models.py",
"repo_id": "trl",
"token_count": 3884
} | 554 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | trl/trl/models/__init__.py/0 | {
"file_path": "trl/trl/models/__init__.py",
"repo_id": "trl",
"token_count": 1015
} | 555 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | trl/trl/scripts/kto.py/0 | {
"file_path": "trl/trl/scripts/kto.py",
"repo_id": "trl",
"token_count": 1996
} | 556 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | trl/trl/trainer/dpo_trainer.py/0 | {
"file_path": "trl/trl/trainer/dpo_trainer.py",
"repo_id": "trl",
"token_count": 45902
} | 557 |
# Conclusion [[conclusion]]
Congratulations on finishing this first Bonus Unit 🥳
You've just **mastered understanding function-calling and how to fine-tune your model to do function-calling**!
If we have one piece of advice now, it’s to try to **fine-tune different models**. The **best way to learn is by trying.**
... | agents-course/units/en/bonus-unit1/conclusion.mdx/0 | {
"file_path": "agents-course/units/en/bonus-unit1/conclusion.mdx",
"repo_id": "agents-course",
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} | 0 |
# Welcome to the 🤗 AI Agents Course [[introduction]]
<figure>
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit0/thumbnail.jpg" alt="AI Agents Course thumbnail" width="100%"/>
<figcaption>The background of the image was generated using <a href="https://scenario.com/">Scenario.... | agents-course/units/en/unit0/introduction.mdx/0 | {
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"repo_id": "agents-course",
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} | 1 |
# What is an Agent?
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-no-check.jpg" alt="Unit 1 planning"/>
By the end of this section, you'll feel comfortable with the concept of agents and their various applications in AI.
To explain what an Agent is, let's star... | agents-course/units/en/unit1/what-are-agents.mdx/0 | {
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"repo_id": "agents-course",
"token_count": 2301
} | 2 |
# Small Quiz (ungraded) [[quiz1]]
So far we've discussed the key components and tools used in LlamaIndex.
It's time to make a short quiz, since **testing yourself** is 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).
... | agents-course/units/en/unit2/llama-index/quiz1.mdx/0 | {
"file_path": "agents-course/units/en/unit2/llama-index/quiz1.mdx",
"repo_id": "agents-course",
"token_count": 947
} | 3 |
# Introducción
Bienvenido a esta primera **Unidad Bonus**, donde aprenderás a **hacer fine-tuning de un Modelo de Lenguaje Grande (LLM) para llamadas a funciones**.
En términos de LLMs, la... | agents-course/units/es/bonus-unit1/introduction.mdx/0 | {
"file_path": "agents-course/units/es/bonus-unit1/introduction.mdx",
"repo_id": "agents-course",
"token_count": 1189
} | 4 |
# Tabla de Contenidos
Puedes acceder a la Unidad 1 en hf.co/learn 👉 <a href="https://hf.co/learn/agents-course/unit1/introduction">aquí</a>
<!--
| Título | Descripción |
|-------|-------------|
| [Definición de un Agente](1_definition_of_an_agent.md) | Ejemplo general de lo que pueden hacer los agentes sin jerga té... | agents-course/units/es/unit1/README.md/0 | {
"file_path": "agents-course/units/es/unit1/README.md",
"repo_id": "agents-course",
"token_count": 629
} | 5 |
# Introducción a los Frameworks de Agentes
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/thumbnail.jpg" alt="Thumbnail"/>
Bienvenido/a a esta segunda unidad, donde **exploraremos diferentes frameworks de agentes** que pueden ser utilizados para construir poderosas aplicac... | agents-course/units/es/unit2/introduction.mdx/0 | {
"file_path": "agents-course/units/es/unit2/introduction.mdx",
"repo_id": "agents-course",
"token_count": 867
} | 6 |
# Uso de Herramientas en LlamaIndex
**Definir un conjunto claro de herramientas es crucial para el rendimiento.** Como discutimos en [unidad 1](../../unit1/tools), las interfaces de herramientas claras son más fáciles de usar para los LLM.
Al igual que una interfaz de API para ingenieros humanos, pueden obtener m s de... | agents-course/units/es/unit2/llama-index/tools.mdx/0 | {
"file_path": "agents-course/units/es/unit2/llama-index/tools.mdx",
"repo_id": "agents-course",
"token_count": 2510
} | 7 |
# Generación Aumentada por Recuperación con Agentes (RAG Agéntico)
En esta unidad, veremos cómo podemos usar RAG Agéntico para ayudar a Alfred a prepararse para la increíble gala.
<Tip>Sabemos que ya hemos discutido la Generación Aumentada por Recuperación (RAG) y RAG agéntico en la unidad anterior, así que siéntete ... | agents-course/units/es/unit3/agentic-rag/agentic-rag.mdx/0 | {
"file_path": "agents-course/units/es/unit3/agentic-rag/agentic-rag.mdx",
"repo_id": "agents-course",
"token_count": 743
} | 8 |
# Qu'est-ce que l'appel de fonctions ?
Tout comme les outils, l'appel de fonctions (*function-calling*) est une **façon pour un LLM de prendre des actions basé sur son environnement**. Cependant, la capacité d'appel de fonctions **est apprise par le modèle**, et repose **moins sur le *prompting* que d'autres technique... | agents-course/units/fr/bonus-unit1/what-is-function-calling.mdx/0 | {
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"repo_id": "agents-course",
"token_count": 1580
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# Actions : permettre à l'agent d'interagir avec son environnement
<Tip>
Dans cette section, nous explorons les étapes concrètes qu'un agent entreprend pour interagir avec son environnement.
Nous aborderons la manière dont les actions sont représentées (en utilisant du JSON ou du code), l'importance de l'approche <i>... | agents-course/units/fr/unit1/actions.mdx/0 | {
"file_path": "agents-course/units/fr/unit1/actions.mdx",
"repo_id": "agents-course",
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# Les composants de base de LangGraph
Pour créer des applications avec LangGraph, vous devez comprendre ses composants principaux. Explorons les blocs fondamentaux qui constituent une application LangGraph.
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/LangGraph/Building_... | agents-course/units/fr/unit2/langgraph/building_blocks.mdx/0 | {
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"repo_id": "agents-course",
"token_count": 1552
} | 11 |
# Créer des *workflows* agentiques dans LlamaIndex
Un *workflow* dans LlamaIndex fournit un moyen structuré d'organiser votre code en étapes séquentielles et gérables.
Un tel *workflow* est créé en définissant des `Steps` qui sont déclenchés par des `Events`, et qui émettent eux-mêmes des `Events` pour déclencher d'a... | agents-course/units/fr/unit2/llama-index/workflows.mdx/0 | {
"file_path": "agents-course/units/fr/unit2/llama-index/workflows.mdx",
"repo_id": "agents-course",
"token_count": 3892
} | 12 |
# Conclusion
Dans cette unité, nous avons appris comment créer un système de RAG agentique pour aider Alfred, notre sympathique agent, à préparer et gérer un gala exceptionnel.
La combinaison du RAG avec les capacités agentiques démontre à quel point les assistants IA peuvent devenir puissants quand ils ont :
- Accès... | agents-course/units/fr/unit3/agentic-rag/conclusion.mdx/0 | {
"file_path": "agents-course/units/fr/unit3/agentic-rag/conclusion.mdx",
"repo_id": "agents-course",
"token_count": 427
} | 13 |
# 사고-행동-관찰 주기를 통해 AI 에이전트 이해하기 [[understanding-ai-agents-through-the-thought-action-observation-cycle]]
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-check-3.jpg" alt="Unit 1 planning"/>
이전 섹션에서 우리는 다음 내용을 배웠습니다:
- **도구가 시스템 프롬프트에서 에이전트에 어떻게 제공되는지**.
- **AI 에이... | agents-course/units/ko/unit1/agent-steps-and-structure.mdx/0 | {
"file_path": "agents-course/units/ko/unit1/agent-steps-and-structure.mdx",
"repo_id": "agents-course",
"token_count": 6022
} | 14 |
# Давайте дообучим вашу модель для вызова функций
Теперь мы готовы к дообучению нашей первой модели для вызова функций 🔥.
## Как обучить нашу модель вызову функций?
> Ответ: Нам нужны **данные**.
Обучение модели можно разделить на 3 шага:
1. **Модель предварительно обучается на большом количестве данных**. Резуль... | agents-course/units/ru-RU/bonus-unit1/fine-tuning.mdx/0 | {
"file_path": "agents-course/units/ru-RU/bonus-unit1/fine-tuning.mdx",
"repo_id": "agents-course",
"token_count": 3680
} | 15 |
# Сообщения и Специальные Токены
Теперь, когда мы поняли, как работают LLM, давайте рассмотрим **как они структурируют свою генерацию с помощью шаблонов чата**.
Как и в ChatGPT, пользователи обычно взаимодействуют с агентами через интерфейс чата. Поэтому мы хотим понять, как LLM управляют чатами.
> **Q**: Но ... Ког... | agents-course/units/ru-RU/unit1/messages-and-special-tokens.mdx/0 | {
"file_path": "agents-course/units/ru-RU/unit1/messages-and-special-tokens.mdx",
"repo_id": "agents-course",
"token_count": 10402
} | 16 |
# (Bổ trợ) Discord 101 (nhập môn) [[discord-101]]
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit0/discord-etiquette.jpg" alt="Quy tắc ứng xử trên Discord" width="100%"/>
Hướng dẫn này giúp bạn làm quen với Discord - nền tảng chat miễn phí phổ biến trong cộng đồng gaming và ... | agents-course/units/vi/unit0/discord101.mdx/0 | {
"file_path": "agents-course/units/vi/unit0/discord101.mdx",
"repo_id": "agents-course",
"token_count": 1739
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# Agent là gì?
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-no-check.jpg" alt="Unit 1 planning"/>
Đến cuối phần này, các bạn sẽ hiểu rõ khái niệm Agent và các ứng dụng đa dạng của chúng trong AI.
Để giải thích Agent là gì, hãy bắt đầu với một phép so sánh.
#... | agents-course/units/vi/unit1/what-are-agents.mdx/0 | {
"file_path": "agents-course/units/vi/unit1/what-are-agents.mdx",
"repo_id": "agents-course",
"token_count": 5413
} | 18 |
# （选读） Discord 101 [[discord-101]]
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit0/discord-etiquette.jpg" alt="Discord 礼仪指南" width="100%"/>
本指南旨在帮助您快速上手 Discord ——这款在游戏与机器学习社区广受欢迎的自由聊天平台。
<a href="https://discord.gg/UrrTSsSyjb" target="_blank">点击此处</a> 加入**拥有逾10万成员**的 Hugg... | agents-course/units/zh-CN/unit0/discord101.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit0/discord101.mdx",
"repo_id": "agents-course",
"token_count": 1865
} | 19 |
# 使用 smolagents 创建我们的第一个智能体
在上一节中，我们学习了如何使用 Python 代码从头开始创建智能体，并且我们**看到了这个过程是多么繁琐**。幸运的是，许多智能体库通过**为你处理大量繁重的工作**来简化这项工作。
在本教程中，**你将创建你的第一个智能体**，它能够执行图像生成、网络搜索、时区检查等更多操作！
你还将把你的智能体**发布到 Hugging Face Space 上，以便与朋友和同事分享**。
让我们开始吧！
## 什么是 smolagents？
<img src="https://huggingface.co/datasets/agents-course/course-ima... | agents-course/units/zh-CN/unit1/tutorial.mdx/0 | {
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} | 20 |
# LlamaHub 简介
**LlamaHub 是一个包含数百个集成组件、智能体和工具的注册中心，这些资源均可用于LlamaIndex框架。**
在本课程中我们将使用多种集成组件，因此让我们首先了解LlamaHub及其如何助力开发。
接下来我们将学习如何查找和安装所需组件的依赖项。
## 安装
LlamaIndex的安装说明可通过结构清晰的**[LlamaHub官网](https:... | agents-course/units/zh-CN/unit2/llama-index/llama-hub.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit2/llama-index/llama-hub.mdx",
"repo_id": "agents-course",
"token_count": 967
} | 21 |
# 为什么选择 smolagents
在本模块中，我们将探讨使用 [smolagents](https://huggingface.co/docs/smolagents/en/index) 的优缺点，帮助您做出明智的决策，判断它是否是满足您需求的正确框架。
## 什么是 `smolagents`？
`smolagents` 是一个简单而强大的框架，用于构建 AI 智能体。它为 ... | agents-course/units/zh-CN/unit2/smolagents/why_use_smolagents.mdx/0 | {
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# Summary
[Introduction](README.md)
# User Guide
- [Installation](guide/installation.md)
- [Tutorial - MNIST](guide/mnist/intro.md)
- [Modeling](guide/mnist/modeling.md)
- [Training](guide/mnist/training.md)
- [Saving And Loading](guide/mnist/saving_loading.md)
- [PyTorch cheatsheet](guide/cheatsheet.md)
# Re... | candle/candle-book/src/SUMMARY.md/0 | {
"file_path": "candle/candle-book/src/SUMMARY.md",
"repo_id": "candle",
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# Candle MNIST Tutorial
## Saving and Loading Models
After training a model, it is useful to save and subsequently load the model parameters. In Candle, this functionality is managed through the `VarMap` data structure, with parameters stored on disk using the [safetensors](https://huggingface.co/docs/safetensors/ind... | candle/candle-book/src/guide/mnist/saving_loading.md/0 | {
"file_path": "candle/candle-book/src/guide/mnist/saving_loading.md",
"repo_id": "candle",
"token_count": 2293
} | 24 |
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::Result;
use candle_core::{Device, Tensor};
fn main() -> Result<()> {
// This requires the code to be run with MTL_CAPTURE_ENABLED=1
let device = Device::new_metal(0)?;
let metal_dev... | candle/candle-core/examples/metal_basics.rs/0 | {
"file_path": "candle/candle-core/examples/metal_basics.rs",
"repo_id": "candle",
"token_count": 346
} | 25 |
use crate::{DType, Layout};
/// cudarc related errors
#[derive(thiserror::Error, Debug)]
pub enum CudaError {
#[error(transparent)]
Cuda(#[from] cudarc::driver::DriverError),
#[error(transparent)]
Compiler(#[from] cudarc::nvrtc::CompileError),
#[error(transparent)]
Cublas(#[from] cudarc::cubl... | candle/candle-core/src/cuda_backend/error.rs/0 | {
"file_path": "candle/candle-core/src/cuda_backend/error.rs",
"repo_id": "candle",
"token_count": 750
} | 26 |
//! Numpy support for tensors.
//!
//! The spec for the npy format can be found in
//! [npy-format](https://docs.scipy.org/doc/numpy-1.14.2/neps/npy-format.html).
//! The functions from this module can be used to read tensors from npy/npz files
//! or write tensors to these files. A npy file contains a single tensor (u... | candle/candle-core/src/npy.rs/0 | {
"file_path": "candle/candle-core/src/npy.rs",
"repo_id": "candle",
"token_count": 9003
} | 27 |
//! TensorScalar Enum and Trait
//!
use crate::{DType, Result, Tensor, WithDType};
use float8::F8E4M3;
use half::{bf16, f16};
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum Scalar {
U8(u8),
U32(u32),
I64(i64),
BF16(bf16),
F16(f16),
F32(f32),
F64(f64),
F8E4M3(F8E4M3),
}
impl<T: WithD... | candle/candle-core/src/scalar.rs/0 | {
"file_path": "candle/candle-core/src/scalar.rs",
"repo_id": "candle",
"token_count": 1552
} | 28 |
use anyhow::Result;
use candle_core::{Device, IndexOp, Tensor};
#[test]
fn integer_index() -> Result<()> {
let dev = Device::Cpu;
let tensor = Tensor::arange(0u32, 2 * 3, &dev)?.reshape((2, 3))?;
let result = tensor.i(1)?;
assert_eq!(result.dims(), &[3]);
assert_eq!(result.to_vec1::<u32>()?, &[3, ... | candle/candle-core/tests/indexing_tests.rs/0 | {
"file_path": "candle/candle-core/tests/indexing_tests.rs",
"repo_id": "candle",
"token_count": 1994
} | 29 |
use candle::{Result, Tensor};
pub struct Batcher<I> {
inner: I,
batch_size: usize,
return_last_incomplete_batch: bool,
}
impl<I> Batcher<I> {
fn new(inner: I) -> Self {
Self {
inner,
batch_size: 16,
return_last_incomplete_batch: false,
}
}
p... | candle/candle-datasets/src/batcher.rs/0 | {
"file_path": "candle/candle-datasets/src/batcher.rs",
"repo_id": "candle",
"token_count": 2708
} | 30 |
# candle-bert
Bert is a general large language model. In this example it can be used for two
different tasks:
- Compute sentence embeddings for a prompt.
- Compute similarities between a set of sentences.
## Sentence embeddings
Bert is used to compute the sentence embeddings for a prompt. The model weights
are down... | candle/candle-examples/examples/bert/README.md/0 | {
"file_path": "candle/candle-examples/examples/bert/README.md",
"repo_id": "candle",
"token_count": 1564
} | 31 |
# candle-convmixer
A lightweight CNN architecture that processes image patches similar to a vision transformer, with separate spatial and channel convolutions.
ConvMixer from [Patches Are All You Need?](https://arxiv.org/pdf/2201.09792) and [ConvMixer](https://github.com/locuslab/convmixer).
## Running an example
... | candle/candle-examples/examples/convmixer/README.md/0 | {
"file_path": "candle/candle-examples/examples/convmixer/README.md",
"repo_id": "candle",
"token_count": 238
} | 32 |
# candle-dinov2
[Depth Anything V2] is a model for Monocular Depth Estimation (MDE, i.e. just using a single image) which
builds on the [DINOv2](https://github.com/facebookresearch/dinov2) vision transformer.
This example first instantiates the DINOv2 model and then proceeds to create DepthAnythingV2 and run it.
## ... | candle/candle-examples/examples/depth_anything_v2/README.md/0 | {
"file_path": "candle/candle-examples/examples/depth_anything_v2/README.md",
"repo_id": "candle",
"token_count": 168
} | 33 |
pub enum SeparatorStyle {
Two,
Mpt,
}
pub struct Conversation {
pub system: String,
pub roles: Vec<String>,
pub messages: Vec<(String, Option<String>)>,
pub offset: i32,
pub sep_style: SeparatorStyle,
pub sep: String,
pub sep2: Option<String>,
pub version: String,
}
impl Convers... | candle/candle-examples/examples/llava/conversation.rs/0 | {
"file_path": "candle/candle-examples/examples/llava/conversation.rs",
"repo_id": "candle",
"token_count": 1910
} | 34 |
# candle-moondream
[Moondream](https://github.com/vikhyat/moondream) is a computer-vision model can answer real-world questions about images. It's tiny by today's models, with only 1.6B parameters. That enables it to run on a variety of devices, including mobile phones and edge devices.
## Running some examples
First... | candle/candle-examples/examples/moondream/README.md/0 | {
"file_path": "candle/candle-examples/examples/moondream/README.md",
"repo_id": "candle",
"token_count": 383
} | 35 |
# PaliGemma
[HuggingFace Model Card](https://huggingface.co/google/paligemma-3b-pt-224) -
[Model Page](https://ai.google.dev/gemma/docs/paligemma)
```bash
cargo run --features cuda --release --example paligemma -- \
--prompt "caption fr" --image candle-examples/examples/yolo-v8/assets/bike.jpg
```
```
loaded ima... | candle/candle-examples/examples/paligemma/README.md/0 | {
"file_path": "candle/candle-examples/examples/paligemma/README.md",
"repo_id": "candle",
"token_count": 339
} | 36 |
use super::gym_env::{GymEnv, Step};
use candle::{DType, Device, Error, Module, Result, Tensor};
use candle_nn::{
linear, ops::log_softmax, ops::softmax, sequential::seq, Activation, AdamW, Optimizer,
ParamsAdamW, VarBuilder, VarMap,
};
use rand::{distr::Distribution, rngs::ThreadRng, Rng};
fn new_model(
in... | candle/candle-examples/examples/reinforcement-learning/policy_gradient.rs/0 | {
"file_path": "candle/candle-examples/examples/reinforcement-learning/policy_gradient.rs",
"repo_id": "candle",
"token_count": 2331
} | 37 |
use anyhow::{Ok, Result};
use candle_transformers::models::stable_diffusion::vae;
pub fn build_sd3_vae_autoencoder(vb: candle_nn::VarBuilder) -> Result<vae::AutoEncoderKL> {
let config = vae::AutoEncoderKLConfig {
block_out_channels: vec![128, 256, 512, 512],
layers_per_block: 2,
latent_cha... | candle/candle-examples/examples/stable-diffusion-3/vae.rs/0 | {
"file_path": "candle/candle-examples/examples/stable-diffusion-3/vae.rs",
"repo_id": "candle",
"token_count": 1772
} | 38 |
# candle-trocr
`TrOCR` is a transformer OCR Model. In this example it is used to
transcribe image text. See the associated [model
card](https://huggingface.co/microsoft/trocr-base-printed) for details on
the model itself.
Supported models include:
- `--which base`: small handwritten OCR model.
- `--which large`: lar... | candle/candle-examples/examples/trocr/readme.md/0 | {
"file_path": "candle/candle-examples/examples/trocr/readme.md",
"repo_id": "candle",
"token_count": 360
} | 39 |
// 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": 2265
} | 40 |
/******************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include <cute/tensor.hpp>
#include "utils.h"
////////////////////////////////////////////////////////////////... | candle/candle-flash-attn/kernels/rotary.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/rotary.h",
"repo_id": "candle",
"token_count": 5052
} | 41 |
#include "compatibility.cuh"
#include<stdint.h>
#include<cmath>
// TODO: This is often used to check that the data is contiguous so that
// kernels can be easily mapped. However this only returns true for row
// major, if all the inputs are column major, we could apply the fast path
// too (but we wouldn't if some of ... | candle/candle-kernels/src/cuda_utils.cuh/0 | {
"file_path": "candle/candle-kernels/src/cuda_utils.cuh",
"repo_id": "candle",
"token_count": 5289
} | 42 |
#include <metal_stdlib>
using namespace metal;
#define MAX(x, y) ((x) > (y) ? (x) : (y))
template <typename T>
METAL_FUNC void im2col(
constant size_t &dst_numel,
constant size_t &h_out,
constant size_t &w_out,
constant size_t &h_k,
constant size_t &w_k,
constant size_t &stride,
constant ... | candle/candle-metal-kernels/src/conv.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/conv.metal",
"repo_id": "candle",
"token_count": 8944
} | 43 |
#pragma once
#include <metal_stdlib>
using namespace metal;
METAL_FUNC uint nonzero(uint n) {
return n == 0 ? 1 : n;
}
template<uint N>
constexpr uint nonzero() {
return N == 0 ? 1 : N;
}
template<typename T>
constexpr ushort granularity() {
return nonzero<vec_elements<T>::value>();
}
METAL_FUNC uint ne... | candle/candle-metal-kernels/src/utils.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/utils.metal",
"repo_id": "candle",
"token_count": 453
} | 44 |
//! Batch Normalization.
//!
//! This layer applies Batch Normalization over a mini-batch of inputs as described in [`Batch
//! Normalization`]. The input is expected to have at least three dimensions.
//!
//! Note that this implementation is for inference only, there is no possibility to track the
//! running stats.
/... | candle/candle-nn/src/batch_norm.rs/0 | {
"file_path": "candle/candle-nn/src/batch_norm.rs",
"repo_id": "candle",
"token_count": 5325
} | 45 |
use candle::{Result, Tensor};
/// Sample according to the Gumbel-Softmax distribution.
pub fn gumbel_softmax<D: candle::shape::Dim>(
logits: &Tensor,
temperature: f64,
dim: D,
) -> Result<Tensor> {
if temperature <= 0.0 {
logits.argmax(dim)
} else {
// Cast to f32, doing the Gumbel ... | candle/candle-nn/src/sampling.rs/0 | {
"file_path": "candle/candle-nn/src/sampling.rs",
"repo_id": "candle",
"token_count": 357
} | 46 |
use std::io::Result;
fn main() -> Result<()> {
prost_build::compile_protos(&["src/onnx.proto3"], &["src/"])?;
Ok(())
}
| candle/candle-onnx/build.rs/0 | {
"file_path": "candle/candle-onnx/build.rs",
"repo_id": "candle",
"token_count": 60
} | 47 |
from dataclasses import dataclass
from typing import Optional
from candle.nn import Module, Embedding, LayerNorm, Linear, ModuleList
from candle import Tensor
import candle
import candle.functional as F
from typing import Tuple, Optional
@dataclass
class Config:
vocab_size: int = 30522
hidden_size: int = 768
... | candle/candle-pyo3/py_src/candle/models/bert.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/models/bert.py",
"repo_id": "candle",
"token_count": 3528
} | 48 |
# This example shows how the candle Python api can be used to replicate llama.cpp.
import sys
from typing import Dict, Tuple, Any
import candle
from candle.models.llama import QuantizedLlama
from candle import utils
MAX_SEQ_LEN = 4096
def gguf_rename(tensor_name: str):
if tensor_name == "token_embd.weight":
... | candle/candle-pyo3/quant-llama.py/0 | {
"file_path": "candle/candle-pyo3/quant-llama.py",
"repo_id": "candle",
"token_count": 1318
} | 49 |
# candle-transformers
| candle/candle-transformers/README.md/0 | {
"file_path": "candle/candle-transformers/README.md",
"repo_id": "candle",
"token_count": 6
} | 50 |
//! Falcon language model inference implementation
//!
//! See ["Falcon: a new approach to large language models"](https://huggingface.co/blog/falcon)
//!
//! Based on implementation from [Huggingface Transformers](https://github.com/huggingface/transformers/blob/main/src/transformers/models/falcon)
use candle::{DType... | candle/candle-transformers/src/models/falcon.rs/0 | {
"file_path": "candle/candle-transformers/src/models/falcon.rs",
"repo_id": "candle",
"token_count": 8880
} | 51 |
//! MixFormer (Microsoft's Phi Architecture)
//!
//! See "Textbooks Are All You Need II: phi-1.5 technical report", Lin et al. 2023
//! - [Arxiv](https://arxiv.org/abs/2309.05463)
//! - [Github](https://huggingface.co/microsoft/phi-1_5)
//!
use crate::models::with_tracing::{linear, Embedding as E, Linear};
/// MixForm... | candle/candle-transformers/src/models/mixformer.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mixformer.rs",
"repo_id": "candle",
"token_count": 8060
} | 52 |
use super::embedding::Model as EmbeddingModel;
use crate::models::{
mistral::Config,
with_tracing::{layer_norm, linear, linear_no_bias, LayerNorm, Linear},
};
use candle::{DType, Device, Result, Tensor, D};
use candle_nn::{ops::softmax_last_dim, LayerNormConfig, Module, VarBuilder};
// Geglu and feedforward fr... | candle/candle-transformers/src/models/nvembed_v2/model.rs/0 | {
"file_path": "candle/candle-transformers/src/models/nvembed_v2/model.rs",
"repo_id": "candle",
"token_count": 3730
} | 53 |
//! Quantized llama model implementation.
//!
//! This provides a quantized implementation of the llama language model architecture.
//! The model implements parameter efficient quantization for reduced memory usage
//! while maintaining model quality.
//!
//! Key characteristics:
//! - Transformer decoder architecture... | candle/candle-transformers/src/models/quantized_llama.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_llama.rs",
"repo_id": "candle",
"token_count": 11486
} | 54 |
//! Qwen2 model implementation with quantization support.
//!
//! Qwen2 is a large language model from Alibaba optimized for efficiency.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Streaming decode support
//! - Grouped query attention (GQA)
//! - RMS... | candle/candle-transformers/src/models/qwen2.rs/0 | {
"file_path": "candle/candle-transformers/src/models/qwen2.rs",
"repo_id": "candle",
"token_count": 6864
} | 55 |
use candle::{Result, Tensor};
use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
#[derive(Debug)]
struct Attention {
q_proj: Linear,
k_proj: Linear,
v_proj: Linear,
out_proj: Linear,
num_heads: usize,
}
impl Attention {
fn new(
embedding_dim: usize,
num_heads: ... | candle/candle-transformers/src/models/segment_anything/transformer.rs/0 | {
"file_path": "candle/candle-transformers/src/models/segment_anything/transformer.rs",
"repo_id": "candle",
"token_count": 3597
} | 56 |
#![allow(dead_code)]
//! # Variational Auto-Encoder (VAE) Models.
//!
//! Auto-encoder models compress their input to a usually smaller latent space
//! before expanding it back to its original shape. This results in the latent values
//! compressing the original information.
use super::unet_2d_blocks::{
DownEncode... | candle/candle-transformers/src/models/stable_diffusion/vae.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/vae.rs",
"repo_id": "candle",
"token_count": 6467
} | 57 |
use candle::{Module, Result, Tensor};
use candle_nn::VarBuilder;
#[derive(Debug, Clone)]
pub struct Embedding {
inner: candle_nn::Embedding,
span: tracing::Span,
}
impl Embedding {
pub fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
let inner = candle_nn::embedding(d1, d2, vb)?;
... | candle/candle-transformers/src/models/with_tracing.rs/0 | {
"file_path": "candle/candle-transformers/src/models/with_tracing.rs",
"repo_id": "candle",
"token_count": 2381
} | 58 |
use candle::{Device, Result, Tensor};
use candle_transformers::generation::LogitsProcessor;
#[test]
fn sample_with_zero_temperature() -> Result<()> {
let mut logits_process = LogitsProcessor::new(1337, None, None);
let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
let token = logits_process.s... | candle/candle-transformers/tests/generation_tests.rs/0 | {
"file_path": "candle/candle-transformers/tests/generation_tests.rs",
"repo_id": "candle",
"token_count": 1145
} | 59 |
## Running Whisper Examples
Here, we provide two examples of how to run Whisper using a Candle-compiled WASM binary and runtimes.
### Pure Rust UI
To build and test the UI made in Rust you will need [Trunk](https://trunkrs.dev/#install)
From the `candle-wasm-examples/whisper` directory run:
Download assets:
```bas... | candle/candle-wasm-examples/whisper/README.md/0 | {
"file_path": "candle/candle-wasm-examples/whisper/README.md",
"repo_id": "candle",
"token_count": 1023
} | 60 |
{
"moz:firefoxOptions": {
"prefs": {
"media.navigator.streams.fake": true,
"media.navigator.permission.disabled": true
},
"args": []
},
"goog:chromeOptions": {
"args": [
"--use-fake-device-for-media-stream",
"--use-fake-ui-for-media-stream"
]
}
}
| candle/candle-wasm-tests/webdriver.json/0 | {
"file_path": "candle/candle-wasm-tests/webdriver.json",
"repo_id": "candle",
"token_count": 143
} | 61 |
{{- define "name" -}}
{{- default $.Release.Name | trunc 63 | trimSuffix "-" -}}
{{- end -}}
{{- define "app.name" -}}
chat-ui
{{- end -}}
{{- define "labels.standard" -}}
release: {{ $.Release.Name | quote }}
heritage: {{ $.Release.Service | quote }}
chart: "{{ include "name" . }}"
app: "{{ include "app.name" . }}"
... | chat-ui/chart/templates/_helpers.tpl/0 | {
"file_path": "chat-ui/chart/templates/_helpers.tpl",
"repo_id": "chat-ui",
"token_count": 202
} | 62 |
# Multimodal
We currently support [IDEFICS](https://huggingface.co/blog/idefics) (hosted on [TGI](./providers/tgi)), OpenAI and Anthropic Claude 3 as multimodal models. You can enable it by setting `multimodal: true` in your `MODELS` configuration. For IDEFICS, you must have a [PRO HF Api token](https://huggingface.co... | chat-ui/docs/source/configuration/models/multimodal.md/0 | {
"file_path": "chat-ui/docs/source/configuration/models/multimodal.md",
"repo_id": "chat-ui",
"token_count": 439
} | 63 |
# Web Search
Chat UI features a powerful Web Search feature. A high level overview of how it works:
1. Generate an appropriate search query from the user prompt using the `TASK_MODEL`
2. Perform web search via an external provider (i.e. Serper) or via locally scrape Google results
3. Load each search result into play... | chat-ui/docs/source/configuration/web-search.md/0 | {
"file_path": "chat-ui/docs/source/configuration/web-search.md",
"repo_id": "chat-ui",
"token_count": 836
} | 64 |
<script lang="ts">
import CarbonContinue from "~icons/carbon/continue";
interface Props {
classNames?: string;
onClick?: () => void;
}
let { classNames = "", onClick }: Props = $props();
</script>
<button
type="button"
onclick={onClick}
class="btn flex h-8 rounded-lg border bg-white px-3 py-1 text-gray-50... | chat-ui/src/lib/components/ContinueBtn.svelte/0 | {
"file_path": "chat-ui/src/lib/components/ContinueBtn.svelte",
"repo_id": "chat-ui",
"token_count": 190
} | 65 |
<script lang="ts">
import { onMount, onDestroy } from "svelte";
interface Props {
children?: import("svelte").Snippet;
}
let { children }: Props = $props();
let el: HTMLElement | undefined = $state();
onMount(() => {
el?.ownerDocument.body.appendChild(el);
});
onDestroy(() => {
if (el?.parentNode) {
... | chat-ui/src/lib/components/Portal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/Portal.svelte",
"repo_id": "chat-ui",
"token_count": 179
} | 66 |
<script lang="ts">
import { createEventDispatcher } from "svelte";
import { base } from "$app/paths";
import { goto } from "$app/navigation";
import type { Model } from "$lib/types/Model";
import type { Assistant } from "$lib/types/Assistant";
import { useSettingsStore } from "$lib/stores/settings";
import { for... | chat-ui/src/lib/components/chat/AssistantIntroduction.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/AssistantIntroduction.svelte",
"repo_id": "chat-ui",
"token_count": 2991
} | 67 |
<script lang="ts">
interface Props {
classNames?: string;
}
let { classNames = "" }: Props = $props();
</script>
<svg
xmlns="http://www.w3.org/2000/svg"
width="1em"
height="1em"
class={classNames}
fill="none"
viewBox="0 0 26 23"
>
<path
fill="url(#a)"
d="M.93 10.65A10.17 10.17 0 0 1 11.11.48h4.67a9.45... | chat-ui/src/lib/components/icons/IconDazzled.svelte/0 | {
"file_path": "chat-ui/src/lib/components/icons/IconDazzled.svelte",
"repo_id": "chat-ui",
"token_count": 941
} | 68 |
import { afterEach, assert, beforeAll, describe, expect, it } from "vitest";
import { migrations } from "./routines";
import { acquireLock, isDBLocked, refreshLock, releaseLock } from "./lock";
import { Semaphores } from "$lib/types/Semaphore";
import { collections } from "$lib/server/database";
describe(
"migrations... | chat-ui/src/lib/migrations/migrations.spec.ts/0 | {
"file_path": "chat-ui/src/lib/migrations/migrations.spec.ts",
"repo_id": "chat-ui",
"token_count": 887
} | 69 |
import Elysia from "elysia";
import { authenticateRequest } from "../auth";
export const authPlugin = new Elysia({ name: "auth" }).derive(
{ as: "scoped" },
async ({
headers,
cookie,
}): Promise<{
locals: App.Locals;
}> => {
const auth = await authenticateRequest(
{ type: "elysia", value: headers },
... | chat-ui/src/lib/server/api/authPlugin.ts/0 | {
"file_path": "chat-ui/src/lib/server/api/authPlugin.ts",
"repo_id": "chat-ui",
"token_count": 211
} | 70 |
import { config } from "$lib/server/config";
import { z } from "zod";
import { sum } from "$lib/utils/sum";
import {
embeddingEndpoints,
embeddingEndpointSchema,
type EmbeddingEndpoint,
} from "$lib/server/embeddingEndpoints/embeddingEndpoints";
import { embeddingEndpointTransformersJS } from "$lib/server/embedding... | chat-ui/src/lib/server/embeddingModels.ts/0 | {
"file_path": "chat-ui/src/lib/server/embeddingModels.ts",
"repo_id": "chat-ui",
"token_count": 1114
} | 71 |
import { config } from "$lib/server/config";
import type {
Endpoint,
EndpointMessage,
TextGenerationStreamOutputWithToolsAndWebSources,
} from "../endpoints";
import { z } from "zod";
import {
createImageProcessorOptionsValidator,
makeImageProcessor,
type ImageProcessor,
} from "../images";
import { findRepoRoot ... | chat-ui/src/lib/server/endpoints/local/endpointLocal.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/local/endpointLocal.ts",
"repo_id": "chat-ui",
"token_count": 2768
} | 72 |
import { config } from "$lib/server/config";
import { generateFromDefaultEndpoint } from "$lib/server/generateFromDefaultEndpoint";
import { logger } from "$lib/server/logger";
import { MessageUpdateType, type MessageUpdate } from "$lib/types/MessageUpdate";
import type { Conversation } from "$lib/types/Conversation";
... | chat-ui/src/lib/server/textGeneration/title.ts/0 | {
"file_path": "chat-ui/src/lib/server/textGeneration/title.ts",
"repo_id": "chat-ui",
"token_count": 1028
} | 73 |
import type { SerializedHTMLElement } from "../scrape/types";
import { htmlElementToMarkdownElements, mergeAdjacentElements } from "./fromHtml";
import type { HeaderElement, MarkdownElement } from "./types";
import { MarkdownElementType } from "./types";
import { chunkElements } from "./utils/chunk";
/**
* Converts H... | chat-ui/src/lib/server/websearch/markdown/tree.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/markdown/tree.ts",
"repo_id": "chat-ui",
"token_count": 613
} | 74 |
import { config } from "$lib/server/config";
import type { WebSearchSource } from "$lib/types/WebSearch";
export default async function search(query: string): Promise<WebSearchSource[]> {
const params = {
q: query,
hl: "en",
gl: "us",
};
const response = await fetch("https://google.serper.dev/search", {
me... | chat-ui/src/lib/server/websearch/search/endpoints/serper.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/search/endpoints/serper.ts",
"repo_id": "chat-ui",
"token_count": 281
} | 75 |
import type { ObjectId } from "mongodb";
import type { User } from "./User";
import type { Timestamps } from "./Timestamps";
import type { ReviewStatus } from "./Review";
export interface Assistant extends Timestamps {
_id: ObjectId;
createdById: User["_id"] | string; // user id or session
createdByName?: User["use... | chat-ui/src/lib/types/Assistant.ts/0 | {
"file_path": "chat-ui/src/lib/types/Assistant.ts",
"repo_id": "chat-ui",
"token_count": 318
} | 76 |
import type { Conversation } from "./Conversation";
export type SharedConversation = Pick<
Conversation,
| "model"
| "embeddingModel"
| "title"
| "rootMessageId"
| "messages"
| "preprompt"
| "assistantId"
| "createdAt"
| "updatedAt"
> & {
_id: string;
hash: string;
};
| chat-ui/src/lib/types/SharedConversation.ts/0 | {
"file_path": "chat-ui/src/lib/types/SharedConversation.ts",
"repo_id": "chat-ui",
"token_count": 114
} | 77 |
export function getHref(
url: URL | string,
modifications: {
newKeys?: Record<string, string | undefined | null>;
existingKeys?: { behaviour: "delete_except" | "delete"; keys: string[] };
}
) {
const newUrl = new URL(url);
const { newKeys, existingKeys } = modifications;
// exsiting keys logic
if (existingK... | chat-ui/src/lib/utils/getHref.ts/0 | {
"file_path": "chat-ui/src/lib/utils/getHref.ts",
"repo_id": "chat-ui",
"token_count": 373
} | 78 |
import { browser } from "$app/environment";
import { isDesktop } from "./isDesktop";
export async function share(url: string, title: string, appendLeafId: boolean = false) {
if (!browser) return;
// Retrieve the leafId from localStorage
const leafId = localStorage.getItem("leafId");
if (appendLeafId && leafId) {... | chat-ui/src/lib/utils/share.ts/0 | {
"file_path": "chat-ui/src/lib/utils/share.ts",
"repo_id": "chat-ui",
"token_count": 331
} | 79 |
import { describe, expect, it } from "vitest";
import { isMessageId } from "./isMessageId";
import { v4 } from "uuid";
describe("isMessageId", () => {
it("should return true for a valid message id", () => {
expect(isMessageId(v4())).toBe(true);
});
it("should return false for an invalid message id", () => {
exp... | chat-ui/src/lib/utils/tree/isMessageId.spec.ts/0 | {
"file_path": "chat-ui/src/lib/utils/tree/isMessageId.spec.ts",
"repo_id": "chat-ui",
"token_count": 170
} | 80 |
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { defaultModel } from "$lib/server/models.js";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export async function POST({ params, locals }) {
const assistant = await collections.a... | chat-ui/src/routes/api/assistant/[id]/subscribe/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/assistant/[id]/subscribe/+server.ts",
"repo_id": "chat-ui",
"token_count": 607
} | 81 |
<script lang="ts">
import { page } from "$app/state";
import { base } from "$app/paths";
import { goto } from "$app/navigation";
import { onMount } from "svelte";
import { usePublicConfig } from "$lib/utils/PublicConfig.svelte";
const publicConfig = usePublicConfig();
import ChatWindow from "$lib/components/ch... | chat-ui/src/routes/assistant/[assistantId]/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/assistant/[assistantId]/+page.svelte",
"repo_id": "chat-ui",
"token_count": 983
} | 82 |
import { getOIDCAuthorizationUrl } from "$lib/server/auth";
import { base } from "$app/paths";
import { config } from "$lib/server/config";
export async function GET({ request, url, locals }) {
const referer = request.headers.get("referer");
let redirectURI = `${(referer ? new URL(referer) : url).origin}${base}/logi... | chat-ui/src/routes/login/+server.ts/0 | {
"file_path": "chat-ui/src/routes/login/+server.ts",
"repo_id": "chat-ui",
"token_count": 270
} | 83 |
<script lang="ts">
import { page } from "$app/state";
import { base } from "$app/paths";
import type { BackendModel } from "$lib/server/models";
import { useSettingsStore } from "$lib/stores/settings";
import CopyToClipBoardBtn from "$lib/components/CopyToClipBoardBtn.svelte";
import TokensCounter from "$lib/com... | chat-ui/src/routes/settings/(nav)/[...model]/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/[...model]/+page.svelte",
"repo_id": "chat-ui",
"token_count": 1894
} | 84 |
import { useAPIClient, handleResponse } from "$lib/APIClient";
export const load = async ({ params, fetch }) => {
const client = useAPIClient({ fetch });
const data = client
.tools({
id: params.toolId,
})
.get()
.then(handleResponse);
return { tool: await data };
};
| chat-ui/src/routes/tools/[toolId]/+layout.ts/0 | {
"file_path": "chat-ui/src/routes/tools/[toolId]/+layout.ts",
"repo_id": "chat-ui",
"token_count": 101
} | 85 |
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/datasets-logo-dark.svg">
<source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/d... | datasets/README.md/0 | {
"file_path": "datasets/README.md",
"repo_id": "datasets",
"token_count": 3640
} | 86 |
# docstyle-ignore
INSTALL_CONTENT = """
# Datasets installation
! pip install datasets transformers
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/datasets.git
"""
notebook_first_cells = [{"type": "code... | datasets/docs/source/_config.py/0 | {
"file_path": "datasets/docs/source/_config.py",
"repo_id": "datasets",
"token_count": 118
} | 87 |
# Create a dataset card
Each dataset should have a dataset card to promote responsible usage and inform users of any potential biases within the dataset.
This idea was inspired by the Model Cards proposed by [Mitchell, 2018](https://arxiv.org/abs/1810.03993).
Dataset cards help users understand a dataset's contents, t... | datasets/docs/source/dataset_card.mdx/0 | {
"file_path": "datasets/docs/source/dataset_card.mdx",
"repo_id": "datasets",
"token_count": 756
} | 88 |
# Load
Your data can be stored in various places; they can be on your local machine's disk, in a Github repository, and in in-memory data structures like Python dictionaries and Pandas DataFrames. Wherever a dataset is stored, 🤗 Datasets can help you load it.
This guide will show you how to load a dataset from:
- T... | datasets/docs/source/loading.mdx/0 | {
"file_path": "datasets/docs/source/loading.mdx",
"repo_id": "datasets",
"token_count": 5694
} | 89 |
# Troubleshooting
This guide aims to provide you the tools and knowledge required to navigate some common issues. If the suggestions listed
in this guide do not cover your such situation, please refer to the [Asking for Help](#asking-for-help) section to learn where to
find help with your specific issue.
## Issues w... | datasets/docs/source/troubleshoot.mdx/0 | {
"file_path": "datasets/docs/source/troubleshoot.mdx",
"repo_id": "datasets",
"token_count": 1455
} | 90 |
# Lint as: python3
"""HuggingFace/Datasets is an open library of datasets.
Note:
VERSION needs to be formatted following the MAJOR.MINOR.PATCH convention
Simple check list for release from AllenNLP repo: https://github.com/allenai/allennlp/blob/master/setup.py
Steps to make a release:
0. Prerequisites:
- Dep... | datasets/setup.py/0 | {
"file_path": "datasets/setup.py",
"repo_id": "datasets",
"token_count": 3826
} | 91 |
__all__ = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| datasets/src/datasets/download/__init__.py/0 | {
"file_path": "datasets/src/datasets/download/__init__.py",
"repo_id": "datasets",
"token_count": 77
} | 92 |
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