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We introduce Falcon3, a family of decoder-only large language models under 10 billion parameters, developed by Technology Innovation Institute (TII) in Abu Dhabi. By pushing the boundaries of performance and training efficiency, this release reflects our ongoing commitment to advancing open and accessible large foundation models. Falcon3 represents a natural evolution from previous releases, emphasizing expanding the models' science, math, and code capabilities. This iteration includes five base models: In developing these models, we incorporated several key innovations aimed at improving the models' performances while reducing training costs: - One pre-training for transformer-based models: We conducted a single large-scale pretraining run on the 7B model, using 1024 H100 GPU chips, leveraging 14 trillion tokens featuring web, code, STEM, and curated high-quality and multilingual data. - Depth up-scaling for improved reasoning: Building on recent studies on the effects of model depth, we upscaled the 7B model to a 10B parameters model by duplicating the redundant layers and continuing pre-training with 2 trillion tokens of high-quality data. This yielded Falcon3-10B-Base which achieves state-of-the-art zero-shot and few-shot performance for models under 13B parameters. - Knowledge distillation for better tiny models: To provide compact and efficient alternatives, we developed Falcon3-1B-Base and Falcon3-3B-Base by leveraging pruning and knowledge distillation techniques, using less than 100GT of curated high-quality data, thereby redefining pre-training efficiency. - Pure SSM: We have further enhanced Falcon Mamba 7B by training on an additional 1.5 trillion tokens of high-quality data, resulting in Falcon3-Mamba-7B-Base. Notably, the updated model offers significantly improved reasoning and mathematical capabilities. - Other variants: All models in the Falcon3 family are available in variants such as Instruct, GGUF, GPTQ-Int4, GPTQ-Int8, AWQ, and 1.58-bit, offerin...
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Updated on 23-05-2024: We have introduced a few changes to the transformers PaliGemma implementation around fine-tuning, which you can find in this notebook. PaliGemma is a new family of vision language models from Google. PaliGemma can take in an image and a text and output text. The team at Google has released three types of models: the pretrained (pt) models, the mix models, and the fine-tuned (ft) models, each with different resolutions and available in multiple precisions for convenience. All models are released in the Hugging Face Hub model repositories with their model cards and licenses and have transformers integration. PaliGemma (Github) is a family of vision-language models with an architecture consisting of SigLIP-So400m as the image encoder and Gemma-2B as text decoder. SigLIP is a state-of-the-art model that can understand both images and text. Like CLIP, it consists of an image and text encoder trained jointly. Similar to PaLI-3, the combined PaliGemma model is pre-trained on image-text data and can then easily be fine-tuned on downstream tasks, such as captioning, or referring segmentation. Gemma is a decoder-only model for text generation. Combining the image encoder of SigLIP with Gemma using a linear adapter makes PaliGemma a powerful vision language model. The PaliGemma release comes with three types of models: - PT checkpoints: Pretrained models that can be fine-tuned to downstream tasks. - Mix checkpoints: PT models fine-tuned to a mixture of tasks. They are suitable for general-purpose inference with free-text prompts, and can be used for research purposes only. - FT checkpoints: A set of fine-tuned models, each one specialized on a different academic benchmark. They are available in various resolutions and are intended for research purposes only. The models come in three different resolutions (224x224 , 448x448 , 896x896 ) and three different precisions (bfloat16 , float16 , and float32 ). Each repository contains the checkpoints for a given ...
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In the ever-evolving landscape of programming and software development, the quest for efficiency and productivity has led to remarkable innovations. One such innovation is the emergence of code generation models such as Codex, StarCoder and Code Llama. These models have demonstrated remarkable capabilities in generating human-like code snippets, thereby showing immense potential as coding assistants. However, while these pre-trained models can perform impressively across a range of tasks, there's an exciting possibility lying just beyond the horizon: the ability to tailor a code generation model to your specific needs. Think of personalized coding assistants which could be leveraged at an enterprise scale. In this blog post we show how we created HugCoder 🤗, a code LLM fine-tuned on the code contents from the public repositories of the huggingface GitHub organization. We will discuss our data collection workflow, our training experiments, and some interesting results. This will enable you to create your own personal copilot based on your proprietary codebase. We will leave you with a couple of further extensions of this project for experimentation. Let’s begin 🚀 Our desired dataset is conceptually simple, we structured it like so: Scraping code contents from GitHub is straightforward with the Python GitHub API. However, depending on the number of repositories and the number of code files within a repository, one might easily run into API rate-limiting issues. To prevent such problems, we decided to clone all the public repositories locally and extract the contents from them instead of through the API. We used the multiprocessing module from Python to download all repos in parallel, as shown in this download script. A repository can often contain non-code files such as images, presentations and other assets. We’re not interested in scraping them. We created a list of extensions to filter them out. To parse code files other than Jupyter Notebooks, we simply used the "...
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How we optimized FLUX.1 Kontext [dev] FLUX.1 Kontext [dev] In addition to making our FLUX.1 Kontext [dev] implementation open-source, we wanted to provide more guidance on how we chose to optimize it without compromising on quality. In this post, you will mainly learn about TaylorSeer optimization, a method to approximate intermediate image predictions by using cached image changes (derivatives) and formulae derived from Taylor Series approximations. Fellow optimization nerds, read on. (We pulled most of our implementation info from the following paper.) If you head to the predict function in predict.py from our FLUX.1 Kontext [dev] repo, you will find the main logic. (Highly suggest working through the repo and using this post as a guide for understanding its structure.) Let’s break it down. When generating a new image with FLUX.1 Kontext, you apply a diffusion transformation across multiple timesteps — around 30 steps in a row. At each step, a stack of transformer layers predicts an update to the image you are denoising. This process can take a while. At any given timestep, the change predicted by the model has redundancies with the predictions at previous timesteps. We could take advantage of these redundancies by caching the model’s output at certain timesteps, and reusing cached outputs at future timesteps. This “naïve caching” — where you just reuse the last feature or latent value — sometimes works OK, but can lead to blurring, loss of detail, or sometimes total distortion of the image. You could try something slightly smarter: a linear approximation. You can estimate your next step by looking at the difference between the last two steps (i.e. a first-order finite difference) and extending the line. It’s better, but still not great. It doesn’t capture curves, acceleration, or nonlinear changes — all of which are common in diffusion models. TaylorSeer offers a solution for this. It uses Taylor series to approximate the model’s output at a timestep using a seri...
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什么是 withoutBG？ withoutBG 是一个开源的、由AI 驱动的工具，可以轻松地从任何图像中删除背景。该工具支持本地处理和云处理选项，可以本地处理图像，最大程度的确保隐私。此外，它还通过其托管的API 提供了一个更高质量的withoutBG Pro 模型，适合不同的使用需求。 - AI 驱动的背景去除：使用先进的深度学习算法，根据图像内容准确地移除背景。 - 多种处理选项： - 本地处理: 免费且可离线运行，适合处理多张图片。 - 云处理（withoutBG Pro）: 提供更高质量的输出，适用于商业用途，也提供免费套餐。 - 高效性：支持批量处理，快速移除多个图像的背景，提高效率。 - 多平台支持可在各种架构上运行，包括 Intel/AMD 和ARM 处理器。 - 丰富的 API 接口：提供 Python SDK 和CLI 工具，方便集成到其他应用程序中。 - 现代用户界面：具有拖放功能的 Web 界面，用户体验友好。 - 响应式设计与主题支持：支持多种主题，使用户可以根据个人喜好调整界面风格。 - 电商平台：可以使用 withoutBG 处理产品图片，去除杂乱背景，使产品更突出，提升视觉吸引力。 - 社交媒体：用户可以快速去除个人图片的背景，制作更美观的社交媒体内容。 - 设计与创意工作：设计师可以在图像处理工作流中集成该工具，以便快速获得透明背景的图像，用于各种设计项目。 - 个人用户：任何需要移除图像背景的用户都可以利用该工具，进行简单的照片编辑和处理。 withoutBG 为用户提供了强大且灵活的图像处理解决方案，适合多种需求。 在群晖上以 Docker 方式安装。 在注册表中搜索 withoutbg ，选择 withoutbg/app ，版本选择 latest 。 本文写作时， latest 版本对应为1.0.1 ； 本地端口不冲突就行，不确定的话可以用命令查一下 如果你熟悉命令行，可能用 docker cli 更快捷 也可以用 docker-compose 安装，将下面的内容保存为 docker-compose.yml 文件 然后执行下面的命令 在浏览器中输入 http://群晖IP:8046 就能看到应用的主界面 使用非常简单，将需要去除背景的图片拖放到上传区（或点击浏览），等几秒钟，AI 会移除背景，然后下载你的结果即可 支持 JPEG, PNG 格式，最高可达 10MB 如果一次拖入多张照片，会逐张处理 withoutbg/withoutbg: Open source image background removal model 地址：https://github.com/withoutbg/withoutbgBackground Removal API & Open-Source Toolkit 地址：https://withoutbg.com/Dockerized Background Removal Web App - Quick Start Guide 地址：https://withoutbg.com/documentation/integrations/dockerized-web-app

Blazingly fast whisper transcriptions with Inference Endpoints Today we are happy to introduce a new blazing fast OpenAI Whisper deployment option on Inference Endpoints. It provides up to 8x performance improvements compared to the previous version, and makes everyone one click away from deploying dedicated, powerful transcription models in a cost-effective way, leveraging the amazing work done by the AI community. Through this release, we would like to make Inference Endpoints more community-centric and allow anyone to come and contribute to create incredible inference deployments on the Hugging Face Platform. Along with the community, we would like to propose optimized deployments for a wide range of tasks through the use of awesome and available open-source technologies. The unique position of Hugging Face, at the heart of the Open-Source AI Community, working hand-in-hand with individuals, institutions and industrial partners, makes it the most heterogeneous platform when it comes to deploying AI models for inference on a wide variety of hardware and software. The new Whisper endpoint leverages amazing open-source community projects. Inference is powered by the vLLM project, which provides efficient ways of running AI models on various hardware families – especially, but not limited to, NVIDIA GPUs. We use the vLLM implementation of OpenAI's Whisper model, allowing us to enable further, lower-level optimizations down the software stack. In this initial release, we are targeting NVIDIA GPUs with compute capabilities 8.9 or better (Ada Lovelace), like L4 & L40s, which unlocks a wide range of software optimizations: - PyTorch compilation (torch.compile) - CUDA graphs - float8 KV cache Compilation with torch.compile generates optimized kernels in a Just-In-Time (JIT) fashion, which can modify the computational graph, reorder operations, call specialized methods, and more. CUDA graphs record the flow of sequential operations, or kernels, happening on the GPU, and at...
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Speech-to-Speech (S2S) is an exciting new project from Hugging Face that combines several advanced models to create a seamless, almost magical experience: you speak, and the system responds with a synthesized voice. The project implements a cascaded pipeline leveraging models available through the Transformers library on the Hugging Face hub. The pipeline consists of the following components: - Voice Activity Detection (VAD) - Speech to Text (STT) - Language Model (LM) - Text to Speech (TTS) What's more, S2S has multi-language support! It currently supports English, French, Spanish, Chinese, Japanese, and Korean. You can run the pipeline in single-language mode or use the auto flag for automatic language detection. Check out the repo for more details here. > 👩🏽💻: That's all amazing, but how do I run S2S? > 🤗: Great question! Running Speech-to-Speech requires significant computational resources. Even on a high-end laptop you might encounter latency issues, particularly when using the most advanced models. While a powerful GPU can mitigate these problems, not everyone has the means (or desire!) to set up their own hardware. This is where Hugging Face's Inference Endpoints (IE) come into play. Inference Endpoints allow you to rent a virtual machine equipped with a GPU (or other hardware you might need) and pay only for the time your system is running, providing an ideal solution for deploying performance-heavy applications like Speech-to-Speech. In this blog post, we’ll guide you step by step to deploy Speech-to-Speech to a Hugging Face Inference Endpoint. This is what we'll cover: - Understanding Inference Endpoints and a quick overview of the different ways to setup IE, including a custom container image (which is what we'll need for S2S) - Building a custom docker image for S2S - Deploying the custom image to IE and having some fun with S2S! Inference Endpoints provide a scalable and efficient way to deploy machine learning models. These endpoints allow you to serve...
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AWS Inferentia2 is the latest AWS machine learning chip available through the Amazon EC2 Inf2 instances on Amazon Web Services. Designed from the ground up for AI workloads, Inf2 instances offer great performance and cost/performance for production workloads. We have been working for over a year with the product and engineering teams at AWS to make the performance and cost-efficiency of AWS Trainium and Inferentia chips available to Hugging Face users. Our open-source library optimum-neuron makes it easy to train and deploy Hugging Face models on these accelerators. You can read more about our work accelerating transformers, large language models and text-generation-inference (TGI). Today, we are making the power of Inferentia2 directly and widely available to Hugging Face Hub users. A few months ago, we introduced a new way to deploy Large Language Models (LLMs) on SageMaker, with a new Inferentia/Trainium option for supported models, like Meta Llama 3. You can deploy a Llama3 model on Inferentia2 instances on SageMaker to serve inference at scale and benefit from SageMaker’s complete set of fully managed features for building and fine-tuning models, MLOps, and governance. Today, we are expanding support for this deployment experience to over 100,000 public models available on Hugging Face, including 14 new model architectures (albert ,bert ,camembert ,convbert ,deberta ,deberta-v2 ,distilbert ,electra ,roberta ,mobilebert ,mpnet ,vit ,xlm ,xlm-roberta ), and 6 new machine learning tasks (text-classification ,text-generation ,token-classification ,fill-mask ,question-answering ,feature-extraction ). Following these simple code snippets, AWS customers will be able to easily deploy the models on Inferentia2 instances in Amazon SageMaker. The easiest option to deploy models from the Hub is Hugging Face Inference Endpoints. Today, we are happy to introduce new Inferentia 2 instances for Hugging Face Inference Endpoints. So now, when you find a model in Hugging Face you...
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如果你和我一样，在豆瓣上给电影打分已经成了一种生活习惯，或许你早已积累了一份“五星片单”私藏宝库。它可能藏着你的审美、你的世界观，甚至某些难以言说的情绪印记。 最近我玩AI分析听歌偏好分析得不亦乐乎，突发奇想：能不能也让AI分析一下我到底喜欢看哪种电影？ 答案是：可以！而且只需要简单几步，把你在豆瓣给出五星评价的电影导出成文本，再加上几行提示语，AI就能还原你内心的影像风景。 一、获取你的五星电影列表（豆瓣提取法） 打开你的豆瓣个人主页 > 点进“我看” > 找到“我看过的影视”页。 点击排序条件，选择“按评价排序”，按列表视图展开，每页会显示大约30部影片。 手动复制每一页的电影列表，我个人的“五星片单”里大概100多部电影，这样，一页一页复制粘贴，粘贴3、4页即可。 将内容粘贴到一个支持正则表达式的文本编辑器中（例如Edit Plus、 Notepad++、VS Code等等）。 二、整理你的观影记录 复制下来的文本往往会带有如下格式： 电影名称（中文/外文） [可播放] 记录添加时间 由于豆瓣网是2005年建立，因此所有记录添加时间必定以20开头，因此使用正则表达式 ^20.* 即可删除年月日信息。 之后，删除所有“ [可播放]”这样的字符串。最终保留的内容如下所示： 肖申克的救赎 / The Shawshank Redemption 阿甘正传 / Forrest Gump 寄生虫 / 기생충 三、构造提示词 在整理好的电影列表开头，加上一段提示词，例如： 以下是我在豆瓣给出五星评分（满分）的电影，请根据电影的相关信息，专业、深刻且幽默风趣地锐评下我的观影品味，并解析下我的内心世界。电影清单如下： 或者 以下是我在豆瓣给出五星评分（满分）的电影，请分析我的观影偏好、潜在人格特征、情感共鸣类型，并尝试总结我在电影选择上的共性特征与潜意识偏好。电影清单如下： 四、在AI平台输入并生成结果 你可以选择任一文本生成平台，例如：ChatGPT、Claude、Gemini等等，复制提示词 + 电影清单，粘贴到聊天框中，即可生成你独一无二的“观影人格报告”。 如果说豆瓣记录了我们对外部世界的偏好，那么这一步AI分析，就是将其内化为一种自我认知的反馈回路。 五、结语 每个人的五星片单，都是一封时间写给他的信。AI不会告诉你真相，但它会指向你不愿凝视的部分。你翻开这张表单，就像某个深夜，灯关了，梦没来，你轻轻敲响自己的门。 有些电影是你走过的路，有些是你未曾抵达的远方。但无论如何，它们都在悄悄告诉你一件事—— 你一直是个有故事的人。 六、示例效果 下面是ChatGPT给我做出的分析。 你的五星电影清单像一座横跨时空与文化的“影史缩影博物馆”，从好莱坞黄金时代、欧洲文艺复兴、亚洲新浪潮，到21世纪流媒体大爆炸，每一部片子都像你精心挑选的“人生教材”与“感官游乐场”。 以下是我对你观影品味的专业、深刻又带点嘴贫的锐评： 你有一颗思辨的脑子： 《奇爱博士》《十二怒汉》《黑客帝国》《美丽心灵》《模仿游戏》《杀人回忆》《寄生虫》《辩护人》 你不止爱电影，还爱电影背后的“人性悖论”和“社会结构”。你欣赏逻辑的力量，也能理解偏执的温柔。你的五星不是给“好人赢了”，而是给“世界很复杂，但你愿意看懂”。 分析关键词：伦理困境、制度批判、道德裂缝 内心映照：你经常在问——“如果是我，我会怎么做？” 你怀旧、理想主义、同时带点文艺骚气： 《La La Land》《再次出发》《大话西游》《东京爱情故事》《倩女幽魂》《霸王别姬》《阳光灿烂的日子》 你是那种会因为一句对白、一段旋律就秒回青春的人。你接受“梦想与爱情不能兼得”的悲情设定，却依旧渴望“在对的地方，遇见对的人”。浪漫不是装出来的，是你骨子里的颜色。 分析关键词：追梦、错过、命运感 内心映照：你不是不相信爱情，你只是比别人更明白“爱情从不是一场交易” 你爱极端设定与脑洞天花板： 《盗梦空间》《恐怖游轮》《头号玩家》《The Animatrix》《红辣椒》《爱，死亡和机器人》《变相怪杰》 你对“现实”显然并不满足，热衷于精神穿越与感官跳楼。你爱多线叙事、爱反转、爱设定细节像钟表一样咬合。别人观影在吃瓜，你观影在脑内建模。 分析关键词：科幻意识、叙事构建、存在主义+游戏感混搭 内心映照：你活得清醒，但从不放弃幻觉。 你有很强的文化同理心 & 历史温度： 《活着》《芙蓉镇》《末代皇帝》《切尔诺贝利》《拯救大兵瑞恩》《战争之王》《无间道》《牯岭街少年杀人事件》 你对时代之重特别敏感，愿意去理解个体命运被历史碾压的细节与哀伤。你欣赏那些“在荒谬中依旧挣扎”的人物，而不是简单的胜利者或失败者。 分析关键词：时代反思、人性留白、文化根源 内心映照：你一直在找——“我是谁，我来自哪里，我要去哪里” 你也爱爽片与戏精片： 《...
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Scouting for Food was an outstanding community effort by local SCOUTS By Sandy Piotter, FISH Scouting for Food, an annual drive, was an extraordinarily successful community effort this year by SCOUTING AMERICA (formerly known as BSA, Boy Scouts of America). Thanks to the hard work of around 40 co-ed SCOUTS and their parents on November 8, more than 6,000 pounds of nonperishable food now fills the FISH Food Room with essential nutrition staples. Scouting America CUB SCOUT PACK 16 and SCOUTS BSA TROOPS 16 and 222 participated in the drive. Kathy Peterson, Miwok District Commissioner of the local Scouting America, said “The community came out in force to support the drive this year. The volume of food contributed by residents of the City and Temelec skyrocketed back up over the previous ten years.” Even more impressive than the sheer volume of food were the Scouts and parents who made it happen — collecting donations door to door, then staying to help sort, stack, and tackle all the heavy lifting. The teamwork and good spirits on display were truly inspiring. All of us at FISH are deeply grateful for this generous and successful food drive. With the increased demand for food this year, and the suspension of SNAP supplemental food assistance, Saturday’s collection will make a real and immediate difference for our neighbors in need. Our heartfelt thanks go to the Scouts, their parents, District Commissioner Kathy Peterson, and Brian Weiner for underwriting the cost of the printed materials, as well as to all the FISH volunteers for their dedication and community spirit. FISH volunteer Dee Weaver is the Coordinator for Seasonal Food Drives and the liaison with the Scouts to get the collected food to FISH Central. When the tsunami of food arrives at FISH Central, Dee is instrumental in the transfer from outside to inside. Inside the Food Room, FISH volunteers kept the momentum going — receiving, sorting, packing and shelving every item. Food Room Coordinator Lauren Scott o...
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For decades, the humidifier has been a purely utilitarian appliance, a necessary evil we tolerate for the sake of our sinuses during dry winter months. We buy them, use them, and then promptly hide them away when guests come over. They are often clunky, noisy machines that leave a fine white dust on our furniture or create damp spots on the floor. The industry standard has been a functional box that adds moisture to the air, with little to no thought given to how it integrates into the most personal of spaces: the bedroom. Blueair, a company already known for its design-forward approach to air purification, seems to have taken this challenge personally. The DreamWell Humidifier isn’t just another appliance; it’s a thoughtfully designed bedside companion. It aims to elevate the entire nighttime environment by seamlessly blending humidity, aromatherapy, and gentle mood lighting into a single, elegant form. This is a device that’s meant to be seen, to contribute to a room’s ambiance rather than detract from it, proving that wellness technology can be both effective and beautiful. Designer: Blueair Click Here to Buy Now: $152 $189.99 (20% off, use coupon code “YANKO20”). Hurry, deal ends in 48-hours! Moisture Without the Mess: No Visible Mist or White Dust The most common complaint about ultrasonic humidifiers, the type that dominates the market, is the fine white dust they leave behind. This mineral residue from tap water settles on every surface, creating a cleaning nightmare and raising concerns about what you’re breathing. Blueair sidesteps this issue entirely by using an evaporative method branded as InvisibleMist™ technology. Instead of vibrating water into a visible aerosol, it uses a fan to draw dry air through a saturated wick filter, releasing clean, humidified air that is entirely invisible and free of mineral deposits. This approach is a significant upgrade for anyone who values a clean, low-maintenance living space. There’s no dampness on the floor or furni...
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SOUDERTON — Local Boy Scout troops delivered over 17,000 pounds of food to the Keystone Opportunity Center food pantry Saturday, November 15 as part of the Boy Scouts of America’s “Scouting for Food” drive. One of the nation’s largest annual food drives, “Scouting for Food” partners Boy Scout troops with local food pantries to support families experiencing food insecurity. As part of the event, Sco ...

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If you’re going to camp outside, chances are you’ll need a good mobile roof over your head — unless you’re one of those cowboy-camping thru-hiker types. We’ve logged some shut-eye in hundreds of shelters, intensely used more than 40, and winnowed down our gear rooms to the 12 best of the best camping tents out there today. Our team of experts brings decades of camping experience in every sort of condition imaginable, from national park campgrounds to multiday festivals to long road trips. We weighed them on everything from ease of set-up and headroom to how fun and funky the color schemes are. Some tents are wilder nests that thrive in a more backcountry-adjacent use, like the Kelty Discovery Basecamp, while others, like my top pick, the REI Co-op Base Camp 6, are downright frontcountry palatial. I’ve also found having a real gear garage like the The North Face Wawona 4 in the stable to be important on equipment-heavy trips, and a front-to-backcountry tent like the REI Co-op Half Dome 2 Plus to work for a range of different adventures. Consider these award winners my top six for pretty much any camper, followed by a series of solid shelters for more specific camping weekends. Editor’s Note: We updated our Camping Tent guide on November 17, 2025, to add additional information on our rating system, which takes experience from both our field and bench testing to compare these tents side-by-side. The Best Camping Tents of 2025 - Livability - Weather Protection - Durability - Ease of Set Up - Floor area: 84 sq. ft. (110" x 110") - Weight: 20 lbs., 11.5 oz. - Height: 74" - Footprint included: No - Number of doors: Two - Carry size: 24" x 11" x 11" - Seasons: 3-4 season - Best for: All-weather camping where comfort is in high demand Pros - Huge vestibule - Excellent weather resistance - Easy to set up - Compatible with other REI tent accessories such as canopies Cons - Heavy at 20 pounds - Expensive - Livability - Weather Protection - Durability - Ease of Set Up - Floor Ar...
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Giving Tuesday is a global day of generosity, but here in Santa Barbara County, it’s a chance to make a personal, local difference. At Noozhawk, we believe in the power of community and the impact that even small acts of kindness can have when directed close to home. Our Good for Santa Barbara County Nonprofit Section is designed to connect you with local organizations doing vital work. Whether it’s a donation, a few hours of your time, or simply helping spread the word, your contribution matters. Explore our Giving Guide today and help brighten the season for those who need it most! In this interview, Noozhawk spoke with Carlos Cortez, Scout Executive & CEO at Scouting America Los Padres Council & The Outdoor School, to learn more about the nonprofit’s vision of a community where children are loved and valued. Scouting America Los Padres Council & The Outdoor School Question: What is the name of your nonprofit, and what is its mission? Answer: The mission of Scouting America Los Padres Council is to prepare young people in all of our communities to make ethical and moral choices over their lifetimes by instilling in them the values of the Scout Oath and Law. Scout Oath: On my honor, I will do my best to do my duty to God and my Country and to obey the Scout Law. To help other people at all times; to keep myself physically strong, mentally awake and morally straight. Scout Law: A Scout is Trustworthy, Loyal, Helpful, Friendly, Courteous, Kind, Obedient, Cheerful, Thrifty, Brave, Clean and Reverent. Q: How is your nonprofit primarily funded, and what are its biggest needs right now? A: Los Padres Council, Scouting America, is primarily funded through a mix of individual donations, community and corporate giving, fundraising events, program fees, and foundation grants. As a 501(c)(3) nonprofit, our work is sustained by the generosity of individuals, businesses, and community partners who believe in the power of Scouting and outdoor education to transform young lives. ...
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