text stringlengths 5 631k | id stringlengths 14 178 | metadata dict | __index_level_0__ int64 0 647 |
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# 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 applicabl... | transformers/tests/pipelines/test_pipelines_image_to_image.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_image_to_image.py",
"repo_id": "transformers",
"token_count": 1131
} | 603 |
# 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 applicabl... | transformers/tests/pipelines/test_pipelines_zero_shot_audio_classification.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_zero_shot_audio_classification.py",
"repo_id": "transformers",
"token_count": 1600
} | 604 |
import gc
import unittest
import warnings
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.testing_utils import backend_empty_cache, require_compressed_tensors, require_torch, torch_device
from transformers.utils import is_torch_available
from transformers.utils.quantization_config import... | transformers/tests/quantization/compressed_tensors_integration/test_compressed_models.py/0 | {
"file_path": "transformers/tests/quantization/compressed_tensors_integration/test_compressed_models.py",
"repo_id": "transformers",
"token_count": 4545
} | 605 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/tests/quantization/hqq/test_hqq.py/0 | {
"file_path": "transformers/tests/quantization/hqq/test_hqq.py",
"repo_id": "transformers",
"token_count": 4574
} | 606 |
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 ... | transformers/tests/repo_utils/test_get_test_info.py/0 | {
"file_path": "transformers/tests/repo_utils/test_get_test_info.py",
"repo_id": "transformers",
"token_count": 2124
} | 607 |
# Copyright 2025 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... | transformers/tests/test_executorch.py/0 | {
"file_path": "transformers/tests/test_executorch.py",
"repo_id": "transformers",
"token_count": 2351
} | 608 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/tests/trainer/test_data_collator.py/0 | {
"file_path": "transformers/tests/trainer/test_data_collator.py",
"repo_id": "transformers",
"token_count": 40181
} | 609 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/tests/utils/test_activations.py/0 | {
"file_path": "transformers/tests/utils/test_activations.py",
"repo_id": "transformers",
"token_count": 1055
} | 610 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/tests/utils/test_file_utils.py/0 | {
"file_path": "transformers/tests/utils/test_file_utils.py",
"repo_id": "transformers",
"token_count": 1449
} | 611 |
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 o... | transformers/tests/utils/test_skip_decorators.py/0 | {
"file_path": "transformers/tests/utils/test_skip_decorators.py",
"repo_id": "transformers",
"token_count": 1288
} | 612 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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... | transformers/utils/check_inits.py/0 | {
"file_path": "transformers/utils/check_inits.py",
"repo_id": "transformers",
"token_count": 6227
} | 613 |
from huggingface_hub import hf_hub_download
from transformers.testing_utils import _run_pipeline_tests
if __name__ == "__main__":
if _run_pipeline_tests:
import datasets
_ = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
_ = datasets.load_... | transformers/utils/fetch_hub_objects_for_ci.py/0 | {
"file_path": "transformers/utils/fetch_hub_objects_for_ci.py",
"repo_id": "transformers",
"token_count": 203
} | 614 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | transformers/utils/pr_slow_ci_models.py/0 | {
"file_path": "transformers/utils/pr_slow_ci_models.py",
"repo_id": "transformers",
"token_count": 2151
} | 615 |
import torch
from transformers import PreTrainedModel
from .custom_configuration import CustomConfig
class CustomModel(PreTrainedModel):
config_class = CustomConfig
def __init__(self, config):
super().__init__(config)
self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size)
... | transformers/utils/test_module/custom_modeling.py/0 | {
"file_path": "transformers/utils/test_module/custom_modeling.py",
"repo_id": "transformers",
"token_count": 154
} | 616 |
#!/bin/bash
# This script runs an SFT example end-to-end on a tiny model using different possible configurations
# but defaults to QLoRA + PEFT
OUTPUT_DIR="test_dpo/"
MODEL_NAME="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
DATASET_NAME="trl-internal-testing/hh-rlhf-helpful-base-trl-style"
MAX_STEPS=5
BATCH_SIZE=2
S... | trl/commands/run_dpo.sh/0 | {
"file_path": "trl/commands/run_dpo.sh",
"repo_id": "trl",
"token_count": 646
} | 617 |
# DeepSpeed Integration
<Tip warning={true}>
Section under construction. Feel free to contribute!
</Tip>
TRL supports training with DeepSpeed, a library that implements advanced training optimization techniques. These include optimizer state partitioning, offloading, gradient partitioning, and more.
DeepSpeed inte... | trl/docs/source/deepspeed_integration.md/0 | {
"file_path": "trl/docs/source/deepspeed_integration.md",
"repo_id": "trl",
"token_count": 434
} | 618 |
# Models
With the `AutoModelForCausalLMWithValueHead` class TRL supports all decoder model architectures in transformers such as GPT-2, OPT, and GPT-Neo. In addition, with `AutoModelForSeq2SeqLMWithValueHead` you can use encoder-decoder architectures such as T5. TRL also requires reference models which are frozen copi... | trl/docs/source/models.md/0 | {
"file_path": "trl/docs/source/models.md",
"repo_id": "trl",
"token_count": 283
} | 619 |
# Sentiment Tuning Examples
The notebooks and scripts in this examples show how to fine-tune a model with a sentiment classifier (such as `lvwerra/distilbert-imdb`).
Here's an overview of the notebooks and scripts in the [trl repository](https://github.com/huggingface/trl/tree/main/examples):
| File ... | trl/docs/source/sentiment_tuning.md/0 | {
"file_path": "trl/docs/source/sentiment_tuning.md",
"repo_id": "trl",
"token_count": 1080
} | 620 |
# This is an example configuration file of TRL CLI, you can use it for
# SFT like that: `trl sft --config config.yaml --output_dir test-sft`
# The YAML file supports environment variables by adding an `env` field
# as below
# env:
# CUDA_VISIBLE_DEVICES: 0
model_name_or_path:
Qwen/Qwen2.5-0.5B
dataset_name:
st... | trl/examples/cli_configs/example_config.yaml/0 | {
"file_path": "trl/examples/cli_configs/example_config.yaml",
"repo_id": "trl",
"token_count": 158
} | 621 |
# Research projects that use TRL
Welcome to the research projects folder! Here you can find the scripts used for some research projects that used TRL and maintained by the developers and the community (LM de-toxification, Stack-Llama, etc.). Check out the READMEs in the subfolders for more information!
- [De-detoxify... | trl/examples/research_projects/README.md/0 | {
"file_path": "trl/examples/research_projects/README.md",
"repo_id": "trl",
"token_count": 189
} | 622 |
# 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/research_projects/toxicity/scripts/evaluate-toxicity.py/0 | {
"file_path": "trl/examples/research_projects/toxicity/scripts/evaluate-toxicity.py",
"repo_id": "trl",
"token_count": 2225
} | 623 |
# 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/slow/test_grpo_slow.py/0 | {
"file_path": "trl/tests/slow/test_grpo_slow.py",
"repo_id": "trl",
"token_count": 10560
} | 624 |
# 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_dpo_trainer.py/0 | {
"file_path": "trl/tests/test_dpo_trainer.py",
"repo_id": "trl",
"token_count": 27353
} | 625 |
# 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_rich_progress_callback.py/0 | {
"file_path": "trl/tests/test_rich_progress_callback.py",
"repo_id": "trl",
"token_count": 900
} | 626 |
# 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/modeling_value_head.py/0 | {
"file_path": "trl/trl/models/modeling_value_head.py",
"repo_id": "trl",
"token_count": 8181
} | 627 |
# 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/__init__.py/0 | {
"file_path": "trl/trl/trainer/__init__.py",
"repo_id": "trl",
"token_count": 2220
} | 628 |
# 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/iterative_sft_config.py/0 | {
"file_path": "trl/trl/trainer/iterative_sft_config.py",
"repo_id": "trl",
"token_count": 1665
} | 629 |
# What is Function Calling?
Function-calling is a **way for an LLM to take actions on its environment**. It was first [introduced in GPT-4](https://openai.com/index/function-calling-and-other-api-updates/), and was later reproduced in other models.
Just like the tools of an Agent, function-calling gives the model the... | agents-course/units/en/bonus-unit1/what-is-function-calling.mdx/0 | {
"file_path": "agents-course/units/en/bonus-unit1/what-is-function-calling.mdx",
"repo_id": "agents-course",
"token_count": 1138
} | 0 |
# Actions: Enabling the Agent to Engage with Its Environment
<Tip>
In this section, we explore the concrete steps an AI agent takes to interact with its environment.
We’ll cover how actions are represented (using JSON or code), the importance of the stop and parse approach, and introduce different types of agents... | agents-course/units/en/unit1/actions.mdx/0 | {
"file_path": "agents-course/units/en/unit1/actions.mdx",
"repo_id": "agents-course",
"token_count": 1912
} | 1 |
# Building Blocks of LangGraph
To build applications with LangGraph, you need to understand its core components. Let's explore the fundamental building blocks that make up a LangGraph application.
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/LangGraph/Building_blocks.png... | agents-course/units/en/unit2/langgraph/building_blocks.mdx/0 | {
"file_path": "agents-course/units/en/unit2/langgraph/building_blocks.mdx",
"repo_id": "agents-course",
"token_count": 1207
} | 2 |
# Creating agentic workflows in LlamaIndex
A workflow in LlamaIndex provides a structured way to organize your code into sequential and manageable steps.
Such a workflow is created by defining `Steps` which are triggered by `Events`, and themselves emit `Events` to trigger further steps.
Let's take a look at Alfred s... | agents-course/units/en/unit2/llama-index/workflows.mdx/0 | {
"file_path": "agents-course/units/en/unit2/llama-index/workflows.mdx",
"repo_id": "agents-course",
"token_count": 2976
} | 3 |
# Conclusion
In this unit, we've learned how to create an agentic RAG system to help Alfred, our friendly neighborhood agent, prepare for and manage an extravagant gala.
The combination of RAG with agentic capabilities demonstrates how powerful AI assistants can become when they have:
- Access to structured knowledge... | agents-course/units/en/unit3/agentic-rag/conclusion.mdx/0 | {
"file_path": "agents-course/units/en/unit3/agentic-rag/conclusion.mdx",
"repo_id": "agents-course",
"token_count": 231
} | 4 |
<CourseFloatingBanner chapter={2}
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/bonus-unit2/monitoring-and-evaluating-agents.ipynb"},
]} />
# Unidad Extra 2: Observabilidad... | agents-course/units/es/bonus-unit2/monitoring-and-evaluating-agents-notebook.mdx/0 | {
"file_path": "agents-course/units/es/bonus-unit2/monitoring-and-evaluating-agents-notebook.mdx",
"repo_id": "agents-course",
"token_count": 7626
} | 5 |
# Conclusión [[conclusion]]
¡Felicitaciones por terminar esta primera Unidad 🥳
¡Acabas de **dominar los fundamentos de los Agentes** y has creado tu primer Agente de IA!
Es **normal si todavía te sientes confundido por algunos de estos elementos**. Los Agentes son un tema complejo y es común que tome tiempo compren... | agents-course/units/es/unit1/conclusion.mdx/0 | {
"file_path": "agents-course/units/es/unit1/conclusion.mdx",
"repo_id": "agents-course",
"token_count": 496
} | 6 |
# Grafo de Análisis de Documentos
Alfred a su servicio. Como mayordomo de confianza del Sr. Wayne, me he tomado la libertad de documentar cómo asisto al Sr. Wayne con sus diversas necesidades documentales. Mientras él está fuera atendiendo sus... actividades nocturnas, me aseguro de que todos sus documentos, horarios ... | agents-course/units/es/unit2/langgraph/document_analysis_agent.mdx/0 | {
"file_path": "agents-course/units/es/unit2/langgraph/document_analysis_agent.mdx",
"repo_id": "agents-course",
"token_count": 3748
} | 7 |
# Conclusión
¡Felicitaciones por terminar el módulo de `smolagents` de esta segunda Unidad 🥳
¡Acabas de dominar los fundamentos de `smolagents` y has construido tu propio Agente! Ahora que tienes habilidades en `smolagents`, puedes comenzar a crear Agentes que resolverán tareas que te interesen.
En el próximo módul... | agents-course/units/es/unit2/smolagents/conclusion.mdx/0 | {
"file_path": "agents-course/units/es/unit2/smolagents/conclusion.mdx",
"repo_id": "agents-course",
"token_count": 289
} | 8 |
# Creando una Herramienta RAG para Historias de Invitados
Alfred, tu agente de confianza, está preparando la gala más extravagante del siglo. Para asegurar que el evento transcurra sin problemas, Alfred necesita acceso rápido a información actualizada sobre cada invitado. Ayudemos a Alfred creando una herramienta per... | agents-course/units/es/unit3/agentic-rag/invitees.mdx/0 | {
"file_path": "agents-course/units/es/unit3/agentic-rag/invitees.mdx",
"repo_id": "agents-course",
"token_count": 7173
} | 9 |
# Quiz : évaluation des agents
Évaluons votre compréhension des concepts de traçage et d'évaluation des agents abordés dans cette unité bonus.
Ce quiz est optionnel et non noté.
### Q1 : À quoi l'observabilité dans les agents fait-elle principalement référence ?
Quelle déclaration décrit avec précision le but de l'o... | agents-course/units/fr/bonus-unit2/quiz.mdx/0 | {
"file_path": "agents-course/units/fr/bonus-unit2/quiz.mdx",
"repo_id": "agents-course",
"token_count": 2076
} | 10 |
# Bibliothèque d'agents factices
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-unit1sub3DONE.jpg" alt="Planification de l'Unité 1"/>
Ce cours est indépendant de tout framework car nous souhaitons **nous concentrer sur les concepts des agents et éviter de nous e... | agents-course/units/fr/unit1/dummy-agent-library.mdx/0 | {
"file_path": "agents-course/units/fr/unit1/dummy-agent-library.mdx",
"repo_id": "agents-course",
"token_count": 4590
} | 11 |
# Construire votre premier LangGraph
Maintenant que nous comprenons les composants de base, mettons-les en pratique en construisant notre premier graphe fonctionnel. Nous implémenterons le système de traitement des emails reçus par Alfred, où il doit :
1. Lire les emails entrants
2. Les classifier comme spam ou légit... | agents-course/units/fr/unit2/langgraph/first_graph.mdx/0 | {
"file_path": "agents-course/units/fr/unit2/langgraph/first_graph.mdx",
"repo_id": "agents-course",
"token_count": 5565
} | 12 |
# C'est l'heure de l'examen !
Bravo d'avoir suivi sur le matériel sur `smolagents` ! Vous en avez déjà fait beaucoup. Maintenant, il est temps de mettre vos connaissances à l'épreuve avec un quiz. 🧠
## Instructions
- Le quiz consiste en des questions de code.
- Vous recevrez des instructions pour compléter les extr... | agents-course/units/fr/unit2/smolagents/final_quiz.mdx/0 | {
"file_path": "agents-course/units/fr/unit2/smolagents/final_quiz.mdx",
"repo_id": "agents-course",
"token_count": 396
} | 13 |
# Création et intégration d'outils pour votre agent
Dans cette section, nous allons donner à Alfred l'accès au web, lui permettant de trouver les dernières nouvelles et mises à jour mondiales.
De plus, il aura accès aux données météorologiques et aux statistiques de téléchargement des modèles du Hub d'Hugging Face Hu... | agents-course/units/fr/unit3/agentic-rag/tools.mdx/0 | {
"file_path": "agents-course/units/fr/unit3/agentic-rag/tools.mdx",
"repo_id": "agents-course",
"token_count": 5593
} | 14 |
# Unit 1 퀴즈 [[unit-1-quiz]]
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-unit1sub4DONE.jpg" alt="Unit 1 planning"/>
첫 번째 단원을 완료하신 것을 축하합니다! 지금까지 배운 핵심 개념들에 대한 이해도를 테스트해 보겠습니다.
퀴즈를 통과하면 다음 섹션으로 진행하여 수료증을 받을 수 있습니다.
행운을 빕니다!
## 퀴즈 [[quiz]]
여기 인터랙티브 퀴즈가 있습니다.... | agents-course/units/ko/unit1/final-quiz.mdx/0 | {
"file_path": "agents-course/units/ko/unit1/final-quiz.mdx",
"repo_id": "agents-course",
"token_count": 1333
} | 15 |
# Live 1: Как работает курс и первые ответы на вопросы
В этой первой прямой трансляции курса по Агентам мы рассказали о том, как **работает** курс (объем, разделы, задачи и многое другое), и ответили на ваши вопросы.
<iframe width="560" height="315" src="https://www.youtube.com/embed/iLVyYDbdSmM?si=TCX5Ai3uZuKLXq45" ... | agents-course/units/ru-RU/communication/live1.mdx/0 | {
"file_path": "agents-course/units/ru-RU/communication/live1.mdx",
"repo_id": "agents-course",
"token_count": 576
} | 16 |
# Быстрая самопроверка (не оценивается) [[quiz2]]
Что?! Еще один тест? Мы знаем, мы знаем, ... 😅 Но эта короткий, не оцениваемый тест поможет вам **закрепить ключевые понятия, которые вы только что выучили**.
Этот тест охватывает Большие Языковые Модели (Large Language Model), системы сообщений и инструменты; важн... | agents-course/units/ru-RU/unit1/quiz2.mdx/0 | {
"file_path": "agents-course/units/ru-RU/unit1/quiz2.mdx",
"repo_id": "agents-course",
"token_count": 2951
} | 17 |
# Hành động: Giúp Agent Tương tác với Môi trường
<Tip>
Trong phần này, chúng ta sẽ khám phá các bước cụ thể mà một AI agent thực hiện để tương tác với môi trường.
Ta sẽ tìm hiểu cách biểu diễn hành động (sử dụng JSON hoặc code), tầm quan trọng của phương pháp dừng và phân tích (stop and parse approach), cùng các loại... | agents-course/units/vi/unit1/actions.mdx/0 | {
"file_path": "agents-course/units/vi/unit1/actions.mdx",
"repo_id": "agents-course",
"token_count": 4549
} | 18 |
# 智能体框架介绍
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/thumbnail.jpg" alt="Thumbnail"/>
欢迎来到第二单元，在这里**我们将探索不同的智能体框架（agentic frameworks）**，这些框架可用于构建强大的智能体应用。
我们将学习：
- 在单元 2.1：[smolagents](https://huggingface.co/docs/smolagents/en/index)
- 在单元 2.2：[LlamaIndex](https://... | agents-course/units/zh-CN/unit2/introduction.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit2/introduction.mdx",
"repo_id": "agents-course",
"token_count": 1568
} | 19 |
# 在 LlamaIndex 中使用工具
**定义清晰明确的工具集对性能至关重要。** 正如我们在[第一单元](../../unit1/tools)中讨论的，清晰的工具接口更便于 LLM 使用。
就像人类工程师使用的软件API接口一样，如果工具的工作原理容易理解，LLM就能更好地利用它。
LlamaIndex 中主要包含**四种工具类型**：
1. `FunctionTool`：将任意 Python 函数转... | agents-course/units/zh-CN/unit2/llama-index/tools.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit2/llama-index/tools.mdx",
"repo_id": "agents-course",
"token_count": 3312
} | 20 |
# 智能体增强检索生成（Agentic RAG）
在本单元中，我们将探讨如何利用智能体增强检索生成（Agentic RAG）帮助 Alfred 筹备精彩的晚会。
<Tip>提示：我们已在先前单元讨论过检索增强生成（RAG）和智能体增强 RAG，如果您已熟悉这些概念可跳过本节。</Tip>
大语言模型（LLMs）通过海量数据训练获得通用知识。
但其世界知识模型可能包含过时或不相关信息。
**RAG 通过从您的数据中检索相关信息并传递给大语言模型，有效解决了这个问题。**
![RAG 示意图](https://huggingface.co/datasets/agents-course/course-images/resolve/... | agents-course/units/zh-CN/unit3/agentic-rag/agentic-rag.mdx/0 | {
"file_path": "agents-course/units/zh-CN/unit3/agentic-rag/agentic-rag.mdx",
"repo_id": "agents-course",
"token_count": 1137
} | 21 |
repos:
- repo: https://github.com/Narsil/pre-commit-rust
rev: 2eed6366172ef2a5186e8785ec0e67243d7d73d0
hooks:
- id: fmt
name: "Rust (fmt)"
- id: clippy
name: "Rust (clippy)"
args:
[
"--tests",
"--examples",
"--",
"-D... | candle/.pre-commit-config.yaml/0 | {
"file_path": "candle/.pre-commit-config.yaml",
"repo_id": "candle",
"token_count": 210
} | 22 |
# Creating a desktop Tauri app
| candle/candle-book/src/apps/desktop.md/0 | {
"file_path": "candle/candle-book/src/apps/desktop.md",
"repo_id": "candle",
"token_count": 8
} | 23 |
# Porting a custom kernel
| candle/candle-book/src/inference/cuda/porting.md/0 | {
"file_path": "candle/candle-book/src/inference/cuda/porting.md",
"repo_id": "candle",
"token_count": 7
} | 24 |
use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{DType, Device, Tensor};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn run(a: &Tensor) {
a.affine(12.34, 56.78).unwrap();
}
fn run_affine_benchmark(c: &mut Criterion, device: &Device, dtype:... | candle/candle-core/benches/benchmarks/affine.rs/0 | {
"file_path": "candle/candle-core/benches/benchmarks/affine.rs",
"repo_id": "candle",
"token_count": 628
} | 25 |
//! Methods for backpropagation of gradients.
use crate::op::{BinaryOp, Op, ReduceOp, UnaryOp};
use crate::{Error, Result, Tensor, TensorId};
use std::collections::HashMap;
// arg has been reduced to node via reduce_dims, expand it back to arg.
// This has to handle keepdims.
fn broadcast_back(arg: &Tensor, node: &Ten... | candle/candle-core/src/backprop.rs/0 | {
"file_path": "candle/candle-core/src/backprop.rs",
"repo_id": "candle",
"token_count": 24753
} | 26 |
use crate::op::{BackpropOp, Op};
use crate::tensor::from_storage;
use crate::{CpuStorage, CudaStorage, Layout, MetalStorage, Result, Shape, Tensor};
use std::sync::Arc;
/// Unary ops that can be defined in user-land.
pub trait CustomOp1 {
// Box<dyn> does not support const yet, so use a function to get the name.
... | candle/candle-core/src/custom_op.rs/0 | {
"file_path": "candle/candle-core/src/custom_op.rs",
"repo_id": "candle",
"token_count": 7461
} | 27 |
use super::k_quants::{
BlockQ2K, BlockQ3K, BlockQ4K, BlockQ4_0, BlockQ5K, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K,
};
use crate::Result;
use byteorder::{ByteOrder, LittleEndian};
use half::f16;
#[cfg(target_arch = "x86")]
use core::arch::x86::*;
#[cfg(target_arch = "x86_64")]
use core::arch::x86_64::*;
#[inlin... | candle/candle-core/src/quantized/avx.rs/0 | {
"file_path": "candle/candle-core/src/quantized/avx.rs",
"repo_id": "candle",
"token_count": 17495
} | 28 |
use crate::backend::BackendStorage;
use crate::op::{self, CmpOp, ReduceOp};
use crate::scalar::Scalar;
use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, MetalStorage, Result, Shape};
use crate::{CustomOp1, CustomOp2, CustomOp3, InplaceOp1, InplaceOp2, InplaceOp3};
// We do not want to implement Clone ... | candle/candle-core/src/storage.rs/0 | {
"file_path": "candle/candle-core/src/storage.rs",
"repo_id": "candle",
"token_count": 16174
} | 29 |
import numpy as np
x = np.arange(10)
# Write a npy file.
np.save("test.npy", x)
# Write multiple values to a npz file.
values = { "x": x, "x_plus_one": x + 1 }
np.savez("test.npz", **values)
| candle/candle-core/tests/npy.py/0 | {
"file_path": "candle/candle-core/tests/npy.py",
"repo_id": "candle",
"token_count": 83
} | 30 |
pub mod tinystories;
| candle/candle-datasets/src/nlp/mod.rs/0 | {
"file_path": "candle/candle-datasets/src/nlp/mod.rs",
"repo_id": "candle",
"token_count": 6
} | 31 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::bigcode::{Config, GPTBigCode};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers:... | candle/candle-examples/examples/bigcode/main.rs/0 | {
"file_path": "candle/candle-examples/examples/bigcode/main.rs",
"repo_id": "candle",
"token_count": 2134
} | 32 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::convnext;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
At... | candle/candle-examples/examples/convnext/main.rs/0 | {
"file_path": "candle/candle-examples/examples/convnext/main.rs",
"repo_id": "candle",
"token_count": 1926
} | 33 |
# candle-falcon
Falcon is a general large language model.
## Running an example
Make sure to include the `--use-f32` flag if using CPU, because there isn't a BFloat16 implementation yet.
```
cargo run --example falcon --release -- --prompt "Flying monkeys are" --use-f32
``` | candle/candle-examples/examples/falcon/README.md/0 | {
"file_path": "candle/candle-examples/examples/falcon/README.md",
"repo_id": "candle",
"token_count": 82
} | 34 |
# candle-helium: 2b LLM with CC-BY licensed weights
Helium-1 is a lightweight model with around 2B parameters, the preview version
currently supports 6 languages, showing strong capabilities in those languages
compared to existing open weights models.
- [Blog Post](https://kyutai.org/2025/01/13/helium.html) announcin... | candle/candle-examples/examples/helium/README.md/0 | {
"file_path": "candle/candle-examples/examples/helium/README.md",
"repo_id": "candle",
"token_count": 174
} | 35 |
# candle-llava
LLaVA (Large Language-and-Vision Assistant) is an end-to-end trained large
multimodal model. This example is from [candle-llava](https://github.com/chenwanqq/candle-llava)
The code is based on [https://github.com/haotian-liu/LLaVA](https://github.com/haotian-liu/LLaVA), Hence the llava-hf version of co... | candle/candle-examples/examples/llava/readme.md/0 | {
"file_path": "candle/candle-examples/examples/llava/readme.md",
"repo_id": "candle",
"token_count": 671
} | 36 |
#![allow(dead_code)]
// https://huggingface.co/facebook/musicgen-small/tree/main
// https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/models/musicgen/modeling_musicgen.py
// TODO: Add an offline mode.
// TODO: Add a KV cache.
#[cfg(feature = "mkl")]
extern crate... | candle/candle-examples/examples/musicgen/main.rs/0 | {
"file_path": "candle/candle-examples/examples/musicgen/main.rs",
"repo_id": "candle",
"token_count": 1151
} | 37 |
# 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
} | 38 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mpt::{Config, Model as M};
use candle_transformers::models::quantized_mpt::Model as Q;
use candle::{DType, Device, Tens... | candle/candle-examples/examples/replit-code/main.rs/0 | {
"file_path": "candle/candle-examples/examples/replit-code/main.rs",
"repo_id": "candle",
"token_count": 3752
} | 39 |
## SigLIP
SigLIP is multi-modal text-vision model that improves over CLIP by using a sigmoid based loss,
[HuggingFace](https://huggingface.co/google/siglip-base-patch16-224).
### Running an example
```
$ cargo run --features cuda -r --example siglip
softmax_image_vec: [2.1912122e-14, 2.3624872e-14, 1.0, 1.0, 2.478793... | candle/candle-examples/examples/siglip/README.md/0 | {
"file_path": "candle/candle-examples/examples/siglip/README.md",
"repo_id": "candle",
"token_count": 297
} | 40 |
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::stable_diffusion;
use std::ops::Div;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Module, Tensor, D};
use clap::Parser;
use rand::Rng;
use stable_... | candle/candle-examples/examples/stable-diffusion/main.rs/0 | {
"file_path": "candle/candle-examples/examples/stable-diffusion/main.rs",
"repo_id": "candle",
"token_count": 14170
} | 41 |
# candle-vit
Vision Transformer (ViT) model implementation following the lines of
[vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
This uses a classification head trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
```bash
$ cargo run -... | candle/candle-examples/examples/vit/README.md/0 | {
"file_path": "candle/candle-examples/examples/vit/README.md",
"repo_id": "candle",
"token_count": 236
} | 42 |
# candle-wuerstchen: Efficient Pretraining of Text-to-Image Models
The `wuerstchen` example is a port of the [diffusers
implementation](https://github.com/huggingface/diffusers/tree/19edca82f1ff194c07317369a92b470dbae97f34/src/diffusers/pipelines/wuer... | candle/candle-examples/examples/wuerstchen/README.md/0 | {
"file_path": "candle/candle-examples/examples/wuerstchen/README.md",
"repo_id": "candle",
"token_count": 358
} | 43 |
/******************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include "philox.cuh"
#include "utils.h"
namespace flash {
struct Dropout {
const unsigned long long seed... | candle/candle-flash-attn/kernels/dropout.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/dropout.h",
"repo_id": "candle",
"token_count": 3021
} | 44 |
/******************************************************************************
* Copyright (c) 2023, Tri Dao.
******************************************************************************/
#pragma once
#include <assert.h>
#include <stdint.h>
#include <stdlib.h>
#include <cuda_fp16.h>
#if defined(__CUDA_ARCH__) ... | candle/candle-flash-attn/kernels/utils.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/utils.h",
"repo_id": "candle",
"token_count": 8100
} | 45 |
mod ptx;
#[repr(u32)]
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum Id {
Affine,
Binary,
Cast,
Conv,
Fill,
Indexing,
Quantized,
Reduce,
Sort,
Ternary,
Unary,
}
pub const ALL_IDS: [Id; 11] = [
Id::Affine,
Id::Binary,
Id::Cast,
Id::Conv,
Id::... | candle/candle-kernels/src/lib.rs/0 | {
"file_path": "candle/candle-kernels/src/lib.rs",
"repo_id": "candle",
"token_count": 675
} | 46 |
use metal::{
Buffer, CompileOptions, ComputeCommandEncoderRef, ComputePipelineState, Device, Function,
FunctionConstantValues, Library, MTLDataType, MTLSize, NSUInteger,
};
use std::collections::HashMap;
use std::ffi::c_void;
use std::sync::RwLock;
pub mod mlx_gemm;
pub mod sort;
pub mod utils;
pub use mlx_gemm... | candle/candle-metal-kernels/src/lib.rs/0 | {
"file_path": "candle/candle-metal-kernels/src/lib.rs",
"repo_id": "candle",
"token_count": 40673
} | 47 |
use candle_metal_kernels::{binary, call_binary_contiguous, call_binary_strided, Kernels};
use half::{bf16, f16};
use metal::objc::rc::autoreleasepool;
use metal::{Device, MTLResourceOptions};
use rand;
use std::any::type_name;
use std::time::Instant;
fn main() {
let device = Device::system_default().unwrap();
... | candle/candle-metal-kernels/tmp/binary.rs/0 | {
"file_path": "candle/candle-metal-kernels/tmp/binary.rs",
"repo_id": "candle",
"token_count": 3149
} | 48 |
//! Encoding Utilities. (e.g., one-hot/cold encoding)
use candle::{bail, DType, Result, Tensor, WithDType};
/// One-hot/cold encoding.
///
/// Given an input tensor of indices, this function returns a tensor of the same shape as the input
/// tensor with an additional dimension of the given depth size. The values in ... | candle/candle-nn/src/encoding.rs/0 | {
"file_path": "candle/candle-nn/src/encoding.rs",
"repo_id": "candle",
"token_count": 2025
} | 49 |
//! A `VarMap` is a store that holds named variables.
//!
use candle::{DType, Device, Result, Shape, Tensor, Var};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
/// A `VarMap` is a store that holds named variables. Variables can be retrieved from the stores
/// and new variables can be added by providing... | candle/candle-nn/src/var_map.rs/0 | {
"file_path": "candle/candle-nn/src/var_map.rs",
"repo_id": "candle",
"token_count": 1992
} | 50 |
//
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
// SPDX-License-Identifier: Apache-2.0
syntax = "proto3";
package onnx;
// Overview
//
// ONNX is an open specification that is comprised of the following components:
//
// 1) A definition of an extensible computation graph model... | candle/candle-onnx/src/onnx.proto3/0 | {
"file_path": "candle/candle-onnx/src/onnx.proto3",
"repo_id": "candle",
"token_count": 10183
} | 51 |
# Generated content DO NOT EDIT
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
from os import PathLike
from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
from candle import Tensor, DType, QTensor
@staticmethod
def silu(tensor: Tensor) -> Tensor:
"""
Applies the S... | candle/candle-pyo3/py_src/candle/nn/__init__.pyi/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/__init__.pyi",
"repo_id": "candle",
"token_count": 181
} | 52 |
use ::candle::Tensor;
use pyo3::prelude::*;
#[derive(Clone, Debug)]
/// Represents an absolute shape e.g. (1, 2, 3)
pub struct PyShape(Vec<usize>);
impl<'source> pyo3::FromPyObject<'source> for PyShape {
fn extract_bound(ob: &Bound<'source, PyAny>) -> PyResult<Self> {
if ob.is_none() {
return ... | candle/candle-pyo3/src/shape.rs/0 | {
"file_path": "candle/candle-pyo3/src/shape.rs",
"repo_id": "candle",
"token_count": 1628
} | 53 |
//! Based from the Stanford Hazy Research group.
//!
//! See "Simple linear attention language models balance the recall-throughput tradeoff", Arora et al. 2024
//! - Simple linear attention language models balance the recall-throughput tradeoff. [Arxiv](https://arxiv.org/abs/2402.18668)
//! - [Github Rep](https://gith... | candle/candle-transformers/src/models/based.rs/0 | {
"file_path": "candle/candle-transformers/src/models/based.rs",
"repo_id": "candle",
"token_count": 9967
} | 54 |
//! ConvNeXt implementation.
//!
//! This candle implementation uses a pre-trained ConvNeXt network for inference. The
//! classification head has been trained on the ImageNet dataset and returns the
//! probabilities for the top-5 classes.
//!
//! Original code:
//! - 💻 [ConvNeXt](https://github.com/facebookresearch/... | candle/candle-transformers/src/models/convnext.rs/0 | {
"file_path": "candle/candle-transformers/src/models/convnext.rs",
"repo_id": "candle",
"token_count": 4949
} | 55 |
//! Flux Model
//!
//! Flux is a 12B rectified flow transformer capable of generating images from text descriptions.
//!
//! - 🤗 [Hugging Face Model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
//! - 💻 [GitHub Repository](https://github.com/black-forest-labs/flux)
//! - 📝 [Blog Post](https://blackfores... | candle/candle-transformers/src/models/flux/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/flux/mod.rs",
"repo_id": "candle",
"token_count": 530
} | 56 |
use std::collections::HashMap;
use crate::models::{
clip::{text_model::Activation, vision_model::ClipVisionConfig},
llama::{Config, LlamaEosToks},
};
use serde::{Deserialize, Serialize};
// original config from liuhaotian/llava
#[derive(Serialize, Deserialize, Debug, Clone)]
pub struct LLaVAConfig {
pub a... | candle/candle-transformers/src/models/llava/config.rs/0 | {
"file_path": "candle/candle-transformers/src/models/llava/config.rs",
"repo_id": "candle",
"token_count": 3948
} | 57 |
use candle::{bail, DType, Module, Result, Tensor};
use candle_nn as nn;
pub struct PatchEmbedder {
proj: nn::Conv2d,
}
impl PatchEmbedder {
pub fn new(
patch_size: usize,
in_channels: usize,
embed_dim: usize,
vb: nn::VarBuilder,
) -> Result<Self> {
let proj = nn::co... | candle/candle-transformers/src/models/mmdit/embedding.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mmdit/embedding.rs",
"repo_id": "candle",
"token_count": 2837
} | 58 |
//! Open Contrastive Language-Image Pre-Training
//!
//! Open Contrastive Language-Image Pre-Training (OpenCLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - 💻 [GH Link](https://github.com/mlfoundations/open_clip)
//! - 📝 [Paper](https://arxiv.org/abs/2212.07143)
//!
//! ## Overview... | candle/candle-transformers/src/models/openclip/mod.rs/0 | {
"file_path": "candle/candle-transformers/src/models/openclip/mod.rs",
"repo_id": "candle",
"token_count": 154
} | 59 |
//! Mistral model implementation with quantization support.
//!
//! Mistral is a large language model optimized for efficiency.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Sliding window attention mechanism
//! - Grouped query attention (GQA)
//! - RM... | candle/candle-transformers/src/models/quantized_mistral.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_mistral.rs",
"repo_id": "candle",
"token_count": 5831
} | 60 |
use crate::models::{
qwen3::{Config as Qwen3Config, Qwen3Attention, Qwen3MLP, Qwen3RotaryEmbedding},
with_tracing::{linear_no_bias, Linear, RmsNorm},
};
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{Activation, VarBuilder};
use std::sync::Arc;
#[derive(Debug, Clone, PartialEq, serde::... | candle/candle-transformers/src/models/qwen3_moe.rs/0 | {
"file_path": "candle/candle-transformers/src/models/qwen3_moe.rs",
"repo_id": "candle",
"token_count": 5966
} | 61 |
//! Attention Based Building Blocks
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn as nn;
use candle_nn::Module;
#[derive(Debug)]
struct GeGlu {
proj: nn::Linear,
span: tracing::Span,
}
impl GeGlu {
fn new(vs: nn::VarBuilder, dim_in: usize, dim_out: usize) -> Result<Self> {
let pro... | candle/candle-transformers/src/models/stable_diffusion/attention.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/attention.rs",
"repo_id": "candle",
"token_count": 9788
} | 62 |
//! Stella v5 model implementation.
//!
//! Stella is a dense text embedding model optimized for retrieval and similarity tasks.
//! This implementation provides support for multiple embedding dimensions.
//!
//! Key characteristics:
//! - Dense text embeddings optimized for similarity search
//! - Multiple output dime... | candle/candle-transformers/src/models/stella_en_v5.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stella_en_v5.rs",
"repo_id": "candle",
"token_count": 14806
} | 63 |
use candle::{Result, Tensor};
#[derive(Debug, Clone)]
pub struct DDPMWSchedulerConfig {
scaler: f64,
s: f64,
}
impl Default for DDPMWSchedulerConfig {
fn default() -> Self {
Self {
scaler: 1f64,
s: 0.008f64,
}
}
}
pub struct DDPMWScheduler {
init_alpha_cump... | candle/candle-transformers/src/models/wuerstchen/ddpm.rs/0 | {
"file_path": "candle/candle-transformers/src/models/wuerstchen/ddpm.rs",
"repo_id": "candle",
"token_count": 1537
} | 64 |
## Running [llama2.c](https://github.com/karpathy/llama2.c) Examples
Here, we provide two examples of how to run [llama2.c](https://github.com/karpathy/llama2.c) written in Rust 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://trun... | candle/candle-wasm-examples/llama2-c/README.md/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/README.md",
"repo_id": "candle",
"token_count": 449
} | 65 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Moondream 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" />
<link
... | candle/candle-wasm-examples/moondream/index.html/0 | {
"file_path": "candle/candle-wasm-examples/moondream/index.html",
"repo_id": "candle",
"token_count": 6120
} | 66 |
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_wasm_example_sam as sam;
use wasm_bindgen::prelude::*;
struct Embeddings {
original_width: u32,
original_height: u32,
width: u32,
height: u32,
data: Tensor,
}
#[wasm_bindgen]
pub struct Model {
sam: sam::Sam,
embedd... | candle/candle-wasm-examples/segment-anything/src/bin/m.rs/0 | {
"file_path": "candle/candle-wasm-examples/segment-anything/src/bin/m.rs",
"repo_id": "candle",
"token_count": 2359
} | 67 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Whisper Rust/WASM</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
... | candle/candle-wasm-examples/whisper/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/whisper/lib-example.html",
"repo_id": "candle",
"token_count": 6488
} | 68 |
use crate::console_log;
use crate::worker::{ModelData, RunData, Worker, WorkerInput, WorkerOutput};
use wasm_bindgen::prelude::*;
use wasm_bindgen_futures::JsFuture;
use yew::{html, Component, Context, Html};
use yew_agent::{Bridge, Bridged};
async fn fetch_url(url: &str) -> Result<Vec<u8>, JsValue> {
use web_sys:... | candle/candle-wasm-examples/yolo/src/app.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/app.rs",
"repo_id": "candle",
"token_count": 5960
} | 69 |
backend-test:J
xytest"Relu
SingleReluZ
x
b
y
B | candle/test.onnx/0 | {
"file_path": "candle/test.onnx",
"repo_id": "candle",
"token_count": 76
} | 70 |
set -e
npx lint-staged --config ./.husky/lint-stage-config.js
| chat-ui/.husky/pre-commit/0 | {
"file_path": "chat-ui/.husky/pre-commit",
"repo_id": "chat-ui",
"token_count": 27
} | 71 |
{{- if $.Values.autoscaling.enabled }}
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
labels: {{ include "labels.standard" . | nindent 4 }}
name: {{ include "name" . }}
namespace: {{ .Release.Namespace }}
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: {{ include... | chat-ui/chart/templates/hpa.yaml/0 | {
"file_path": "chat-ui/chart/templates/hpa.yaml",
"repo_id": "chat-ui",
"token_count": 543
} | 72 |
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