text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
|---|---|---|---|
[
{
"question": "Which of the following best describes a Large Language Model (LLM)?",
"answer_a": "A model specializing in language recognition",
"answer_b": "A massive neural network that understands and generates human language",
"answer_c": "A model exclusively used for language ... | agents-course/quiz/data/unit_1.json/0 | {
"file_path": "agents-course/quiz/data/unit_1.json",
"repo_id": "agents-course",
"token_count": 154
} | 0 |
# Build Your Own Pokémon Battle Agent
Now that you’ve explored the potential and limitations of Agentic AI in games, it’s time to get hands-on. In this section, you’ll **build your very own AI Agent to battle in Pokémon-style turn-based combat**, using everything you’ve learned throughout the course.
We’ll break the ... | agents-course/units/en/bonus-unit3/building_your_pokemon_agent.mdx/0 | {
"file_path": "agents-course/units/en/bonus-unit3/building_your_pokemon_agent.mdx",
"repo_id": "agents-course",
"token_count": 5276
} | 1 |
# Introduction to Agents
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/thumbnail.jpg" alt="Thumbnail"/>
Welcome to this first unit, where **you'll build a solid foundation in the fundamentals of AI Agents** including:
- **Understanding Agents**
- What is an Agent, an... | agents-course/units/en/unit1/introduction.mdx/0 | {
"file_path": "agents-course/units/en/unit1/introduction.mdx",
"repo_id": "agents-course",
"token_count": 530
} | 2 |
# Test Your Understanding of LangGraph
Let's test your understanding of `LangGraph` with a quick quiz! This will help reinforce the key concepts we've covered so far.
This is an optional quiz and it's not graded.
### Q1: What is the primary purpose of LangGraph?
Which statement best describes what LangGraph is desig... | agents-course/units/en/unit2/langgraph/quiz1.mdx/0 | {
"file_path": "agents-course/units/en/unit2/langgraph/quiz1.mdx",
"repo_id": "agents-course",
"token_count": 1169
} | 3 |
<CourseFloatingBanner
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/#fileId=https://huggingface.co/agents-course/notebooks/blob/main/unit2/smolagents/multiagent_notebook.ipynb"},
]}
askForHelpUrl="http://hf.co/join/discord" />
# Multi-A... | agents-course/units/en/unit2/smolagents/multi_agent_systems.mdx/0 | {
"file_path": "agents-course/units/en/unit2/smolagents/multi_agent_systems.mdx",
"repo_id": "agents-course",
"token_count": 9133
} | 4 |
# Conclusion
**Congratulations on finishing the Agents Course!**
Through perseverance and dedication, you’ve built a solid foundation in the world of AI Agents.
But finishing this course is **not the end of your journey**. It’s just the beginning: don’t hesitate to explore the next section where we share curated re... | agents-course/units/en/unit4/conclusion.mdx/0 | {
"file_path": "agents-course/units/en/unit4/conclusion.mdx",
"repo_id": "agents-course",
"token_count": 142
} | 5 |
# De LLMs a Agentes de IA
Aprendimos en la [primera unidad](https://huggingface.co/learn/agents-course/unit1/introduction) del curso que los Agentes de IA son capaces de planificar y tomar decisiones.
Y aunque los LLMs han permitido interacciones más naturales con los NPCs, la IA Agéntica va un paso más allá al permit... | agents-course/units/es/bonus-unit3/from-llm-to-agents.mdx/0 | {
"file_path": "agents-course/units/es/bonus-unit3/from-llm-to-agents.mdx",
"repo_id": "agents-course",
"token_count": 1073
} | 6 |
# Observar: Integrando Retroalimentación para Reflexionar y Adaptarse
Las observaciones son **cómo un Agente percibe las consecuencias de sus acciones**.
Proporcionan información crucial que alimenta el proceso de pensamiento del Agente y guía acciones futuras.
Son **señales del entorno**—ya sean datos de una API, m... | agents-course/units/es/unit1/observations.mdx/0 | {
"file_path": "agents-course/units/es/unit1/observations.mdx",
"repo_id": "agents-course",
"token_count": 1208
} | 7 |
# Índice de Contenidos
Este marco de trabajo de LlamaIndex es parte de la unidad 2 del curso. Puedes acceder a la unidad 2 sobre LlamaIndex en hf.co/learn <a href="https://hf.co/learn/agents-course/unit2/llama-index/introduction">aquí</a>
| Título | Descripción |
| --- | --- |
| [Introducción](introduction.mdx) | In... | agents-course/units/es/unit2/llama-index/README.md/0 | {
"file_path": "agents-course/units/es/unit2/llama-index/README.md",
"repo_id": "agents-course",
"token_count": 382
} | 8 |
# Pequeño Quiz (no calificado) [[quiz2]]
Es hora de poner a prueba tu comprensión de las secciones *Agentes de Código*, *Agentes de Llamada a Herramientas* y *Herramientas*. Este quiz es opcional y no está calificado.
---
### P1: ¿Cuál es la diferencia clave entre crear una herramienta con el decorador `@tool` versu... | agents-course/units/es/unit2/smolagents/quiz2.mdx/0 | {
"file_path": "agents-course/units/es/unit2/smolagents/quiz2.mdx",
"repo_id": "agents-course",
"token_count": 2768
} | 9 |
# Reclama tu Certificado 🎓
Si obtuviste una puntuación **superior al 30%, ¡felicitaciones! 👏 Ahora eres elegible para reclamar tu certificado oficial.**
Sigue los pasos a continuación para recibirlo:
1. Visita la [página del certificado](https://huggingface.co/spaces/agents-course/Unit4-Final-Certificate).
2. **In... | agents-course/units/es/unit4/get-your-certificate.mdx/0 | {
"file_path": "agents-course/units/es/unit4/get-your-certificate.mdx",
"repo_id": "agents-course",
"token_count": 387
} | 10 |
# Introduction
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/bonus-unit3/pokemon_thumbnail.png" alt="Bonus Unit 3 AI in Games"/>
🎶Je veux être le meilleur... 🎶
Bienvenue dans cette **unité bonus**, où vous explorerez l'intersection passionnante entre **les agents et les jeux... | agents-course/units/fr/bonus-unit3/introduction.mdx/0 | {
"file_path": "agents-course/units/fr/bonus-unit3/introduction.mdx",
"repo_id": "agents-course",
"token_count": 757
} | 11 |
# Quiz rapide 1 [[quiz1]]
---
### Q1 : Qu'est-ce qu'un agent ?
Laquelle des propositions suivantes décrit le mieux un agent en IA ?
<Question
choices={[
{
text: "Un système qui ne traite que du texte statique et n'interagit jamais avec son environnement.",
explain: "Un agent doit être capable de prendre une acti... | agents-course/units/fr/unit1/quiz1.mdx/0 | {
"file_path": "agents-course/units/fr/unit1/quiz1.mdx",
"repo_id": "agents-course",
"token_count": 2303
} | 12 |
# Utiliser les agents dans LlamaIndex
Vous vous souvenez d'Alfred, notre agent majordome serviable d'avant ? Eh bien, il va recevoir une mise à niveau !
Maintenant que nous comprenons les outils disponibles dans LlamaIndex, nous pouvons lui donner de nouvelles capacités pour mieux nous servir.
Mais avant de continuer... | agents-course/units/fr/unit2/llama-index/agents.mdx/0 | {
"file_path": "agents-course/units/fr/unit2/llama-index/agents.mdx",
"repo_id": "agents-course",
"token_count": 2938
} | 13 |
<CourseFloatingBanner
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/#fileId=https://huggingface.co/agents-course/notebooks/blob/main/fr/unit2/smolagents/retrieval_agents.ipynb"},
]}
askForHelpUrl="http://hf.co/join/discord" />
# Constru... | agents-course/units/fr/unit2/smolagents/retrieval_agents.mdx/0 | {
"file_path": "agents-course/units/fr/unit2/smolagents/retrieval_agents.mdx",
"repo_id": "agents-course",
"token_count": 3755
} | 14 |
# Introduction à l'unité finale [[introduction]]
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit4/thumbnail.jpg" alt="AI Agents Course thumbnail" width="100%"/>
Bienvenue dans l'unité finale du cours ! 🎉
Jusqu'à présent, vous avez **acquis de solides connaissances sur les ... | agents-course/units/fr/unit4/introduction.mdx/0 | {
"file_path": "agents-course/units/fr/unit4/introduction.mdx",
"repo_id": "agents-course",
"token_count": 648
} | 15 |
# 셀프 체크! (업데이트됨) [[quiz2]]
뭐라고요?! 또 퀴즈라고요? 우리도 알아요... 😅 하지만 걱정 마세요! 이 퀴즈는 **방금 배운 핵심 개념을 확실히 이해**하는 데 도움을 주기 위해 준비되었습니다.
이번 퀴즈에서는 대규모 언어 모델(LLM), 메시지 시스템, 도구(tool) 등 AI 에이전트를 이해하고 구축하는 데 필수적인 요소들을 다룹니다.
### Q1: AI 도구(tool)를 가장 잘 설명하는 것은 무엇인가요? [[q1-which-of-the-following-best-describes-an-ai-tool]]
<Question
ch... | agents-course/units/ko/unit1/quiz2.mdx/0 | {
"file_path": "agents-course/units/ko/unit1/quiz2.mdx",
"repo_id": "agents-course",
"token_count": 2638
} | 16 |
# Что такое LLM?
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-check-1.jpg" alt="Unit 1 planning"/>
В предыдущем разделе мы узнали, что каждый агент нуждается в ** AI Модели как в ядре**, и что LLM являются наиболее распространенным типом AI моделей использующи... | agents-course/units/ru-RU/unit1/what-are-llms.mdx/0 | {
"file_path": "agents-course/units/ru-RU/unit1/what-are-llms.mdx",
"repo_id": "agents-course",
"token_count": 11630
} | 17 |
# Giới thiệu về Agent
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/thumbnail.jpg" alt="Thumbnail"/>
Chào mừng bạn đến với chương đầu tiên, nơi **bạn sẽ xây dựng nền tảng vững chắc về nguyên lý cơ bản của AI agent** bao gồm:
- **Hiểu về Agent**
- Agent là gì và hoạt ... | agents-course/units/vi/unit1/introduction.mdx/0 | {
"file_path": "agents-course/units/vi/unit1/introduction.mdx",
"repo_id": "agents-course",
"token_count": 1317
} | 18 |
# 简介 (Introduction)
欢迎来到第一个**附加单元**，在这里你将学习如何**为函数调用 (function calling) 微调大语言模型 (Large Language Model, LLM)**。
在大语言模型领域，函数调用正在迅速成为一项*必须掌握*的技术。
这个想法是，不同于我们在第1单元中仅依赖基于提示的方法，函数调用在训练阶段就训练你的模型**采取行动和解释观察结果*... | agents-course/units/zh-CN/bonus-unit1/introduction.mdx/0 | {
"file_path": "agents-course/units/zh-CN/bonus-unit1/introduction.mdx",
"repo_id": "agents-course",
"token_count": 1875
} | 19 |
# 第一单元测验 (Unit 1 Quiz)
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-unit1sub4DONE.jpg" alt="Unit 1 planning"/>
恭喜你完成第一单元的学习！让我们测试一下你对目前所学关键概念的理解。
通过测验后，请继续下一部分领取你的证书。
祝你好运！
## 测验 (Quiz)
这是一个交互式测验。测验托管在 Hugging Face Hub 的空间中。你将通过一系列选择题来测试你对本单元所学关键概念的理解。完成测验... | agents-course/units/zh-CN/unit1/final-quiz.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit1/final-quiz.mdx",
"repo_id": "agents-course",
"token_count": 958
} | 20 |
# 欢迎来到 `LangGraph` 的世界
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/LangGraph/LangGraph.png" alt="Unit 2.3 缩略图"/>
欢迎来到学习旅程的下一站！在本章节中，您将学习如何使用 [`LangGraph`](https://github.com/langchain-ai/langgraph) 框架来构建应用程序，该框架能帮助您组织和编排复杂的 LLM 工作流。
`LangGraph` 是一个通过提供对智能体流程的**控制**工具，帮... | agents-course/units/zh-CN/unit2/langgraph/introduction.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit2/langgraph/introduction.mdx",
"repo_id": "agents-course",
"token_count": 983
} | 21 |
# `smolagents` 简介
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/smolagents/thumbnail.jpg" alt="Unit 2.1 Thumbnail"/>
欢迎来到本模块，在这里你将学习**如何使用 [`smolagents`](https://github.com/huggingface/smolagents) 库构建有效的智能体**，该库提供了一个轻量级框架，用于创建功能强大的AI智能体。
`smolagents` 是 Hugging Face 的一个... | agents-course/units/zh-CN/unit2/smolagents/introduction.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit2/smolagents/introduction.mdx",
"repo_id": "agents-course",
"token_count": 3994
} | 22 |
# 那现在呢？我应该学习哪些主题？
Agentic AI 是一个快速发展的领域，了解基础协议对于构建智能自主系统至关重要。
你应该熟悉的两个重要标准是：
- **模型上下文协议 (MCP)**
- **代理对代理协议 (A2A)**
## 🔌 模型上下文协议 (MCP)
Anthropic 的 **模型上下文协议 (MCP)** 是一个开放标准，使 AI 模型能够安全无缝地**连接外部工具、数据源和应用程序**，从而使代理更加智能和自主。
可以将 MCP 想象为一个**通用适配器**，就像 USB-C 接口一样，使 AI 模型能够插入各种数字环境**而无需为每一个进行定制集成**。
MCP 正在迅速获得行业关注，... | agents-course/units/zh-CN/unit4/additional-readings.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit4/additional-readings.mdx",
"repo_id": "agents-course",
"token_count": 962
} | 23 |
# Porting a custom kernel
| candle/candle-book/src/cuda/porting.md/0 | {
"file_path": "candle/candle-book/src/cuda/porting.md",
"repo_id": "candle",
"token_count": 7
} | 24 |
//! #A simplified example in Rust of training a neural network and then using it based on the Candle Framework by Hugging Face.
//! Author: Evgeny Igumnov 2023 igumnovnsk@gmail.com
//! This program implements a neural network to predict the winner of the second round of elections based on the results of the first round... | candle/candle-book/src/simplified.rs/0 | {
"file_path": "candle/candle-book/src/simplified.rs",
"repo_id": "candle",
"token_count": 2903
} | 25 |
use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{
quantized::{self, GgmlDType, QMatMul},
Device, Module, Tensor,
};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn run(matmul: &QMatMul, x: &Tensor) {
matmul.forward(x).unwrap();
}
fn... | candle/candle-core/benches/benchmarks/qmatmul.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/qmatmul.rs",
"repo_id": "candle",
"token_count": 1085
} | 26 |
pub trait VecOps: num_traits::NumAssign + Copy {
fn min(self, rhs: Self) -> Self;
fn max(self, rhs: Self) -> Self;
/// Dot-product of two vectors.
///
/// # Safety
///
/// The length of `lhs` and `rhs` have to be at least `len`. `res` has to point to a valid
/// element.
#[inline(al... | candle/candle-core/src/cpu/kernels.rs/0 | {
"file_path": "candle/candle-core/src/cpu/kernels.rs",
"repo_id": "candle",
"token_count": 2456
} | 27 |
#![allow(dead_code)]
use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
#[derive(Debug, Clone)]
pub struct MetalDevice;
#[derive(Debug)]
pub struct MetalStorage;
#[derive(thiserror::Error, Debug)]
pub enum MetalError {
#[error("{0}")]
Message(... | candle/candle-core/src/dummy_metal_backend.rs/0 | {
"file_path": "candle/candle-core/src/dummy_metal_backend.rs",
"repo_id": "candle",
"token_count": 3182
} | 28 |
//! Support for the [GGUF file format](https://github.com/philpax/ggml/blob/gguf-spec/docs/gguf.md).
//!
use super::{GgmlDType, QTensor};
use crate::{Context, Device, Result};
use byteorder::{LittleEndian, ReadBytesExt, WriteBytesExt};
use std::collections::HashMap;
pub const DEFAULT_ALIGNMENT: u64 = 32;
#[derive(De... | candle/candle-core/src/quantized/gguf_file.rs/0 | {
"file_path": "candle/candle-core/src/quantized/gguf_file.rs",
"repo_id": "candle",
"token_count": 9550
} | 29 |
use crate::{Result, Tensor};
#[macro_export]
macro_rules! test_device {
// TODO: Switch to generating the two last arguments automatically once concat_idents is
// stable. https://github.com/rust-lang/rust/issues/29599
($fn_name: ident, $test_cpu: ident, $test_cuda: ident, $test_metal: ident) => {
... | candle/candle-core/src/test_utils.rs/0 | {
"file_path": "candle/candle-core/src/test_utils.rs",
"repo_id": "candle",
"token_count": 1110
} | 30 |
use candle_core::{DType, Result, Tensor};
struct TmpFile(std::path::PathBuf);
impl TmpFile {
fn create(base: &str) -> TmpFile {
let filename = std::env::temp_dir().join(format!(
"candle-{}-{}-{:?}",
base,
std::process::id(),
std::thread::current().id(),
... | candle/candle-core/tests/serialization_tests.rs/0 | {
"file_path": "candle/candle-core/tests/serialization_tests.rs",
"repo_id": "candle",
"token_count": 981
} | 31 |
use candle::Tensor;
pub struct Dataset {
pub train_images: Tensor,
pub train_labels: Tensor,
pub test_images: Tensor,
pub test_labels: Tensor,
pub labels: usize,
}
pub mod cifar;
pub mod fashion_mnist;
pub mod mnist;
| candle/candle-datasets/src/vision/mod.rs/0 | {
"file_path": "candle/candle-datasets/src/vision/mod.rs",
"repo_id": "candle",
"token_count": 100
} | 32 |
# candle-chinese-clip
Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
pairs of images with related texts. This one is trained using in chinese instead of english.
## Running on cpu
```bash
$ cargo run --example chinese_clip --release -- --images "candle-examples/examples/stable-diffusion... | candle/candle-examples/examples/chinese_clip/README.md/0 | {
"file_path": "candle/candle-examples/examples/chinese_clip/README.md",
"repo_id": "candle",
"token_count": 1129
} | 33 |
pub const LAYERNORM_KERNELS: &str = include_str!(concat!(env!("OUT_DIR"), "/layernorm_kernels.ptx"));
| candle/candle-examples/examples/custom-ops/cuda_kernels.rs/0 | {
"file_path": "candle/candle-examples/examples/custom-ops/cuda_kernels.rs",
"repo_id": "candle",
"token_count": 44
} | 34 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::distilbert::{
Config, DistilBertForMaskedLM, DistilBertModel, DTYPE,
};
use anyhow::{Context, Error as E, Result};
use candle::{Device, Tensor};
use candle_nn::VarBuilde... | candle/candle-examples/examples/distilbert/main.rs/0 | {
"file_path": "candle/candle-examples/examples/distilbert/main.rs",
"repo_id": "candle",
"token_count": 4559
} | 35 |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 4