Sensitive content

This media may contain sensitive content.

Загрузка видео...

Не удалось загрузить видео

На главную

Only thing Gojo's splitting in half here are Jinwoo's cheeks! Debuting some new models for my scoutfunverse thanks to Sora for the gifts. 🧍Models by x_RedEyes 🔞 | (OPEN COMMISSIONS) 🎙️Gojo VA: Ryan🐦‍🔥🎧 🎙️Jinwoo VA: Lust 欲 🔊Lewd Sounds: OpenNSFW 🟣 Available Now 🧵More fun in the thread.

323,224 просмотров • 3 месяцев назад •via X (Twitter)

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

Elena

12,922 просмотров • 1 месяц назад

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

45,595 просмотров • 1 месяц назад

Chamath: AI advantage may come less from models than from private inputs. "When labs can build similar models, the real win comes from one unique ingredient in order to monetize it well. Here is a basic thing about machine learning that is worth knowing: if you take 1,000 of the same inputs and give them to Facebook, Microsoft, Google, and Amazon, they will all come up with the same machine learning model. But if you have one extra thing, one little ingredient that all of those other companies do not have, your output can be markedly different. It is like giving two great chefs three ingredients, but giving the third chef one extra ingredient. That person has the ability to do something very special. Right now, we are in a world where everybody is crawling the open web. We are going to move to a world where, as everybody gets sophisticated enough and information is widely available, somebody is going to say, “You know what? This site, I am not going to allow anybody else to access. It is only for me, only for my models.” Those models will become better. So we have to let that play out a little bit. It is going to be a really interesting arms race. The next wave of M&A, for example, could be companies like Google, Microsoft, and Facebook looking at these companies and saying, “Can they be viable inputs to my large language models or to my other machine learning and AI models?” --- A company with unique workflows, transactions, medical records, industrial logs, legal archives, design files, or user behavior can turn boring private data into a compounding advantage. Some startups may never become great public companies on their own, yet still become valuable because they own a data stream that makes a larger AI system sharper, more differentiated, or harder to copy. That turns acquisition strategy upside down: the buyer may not be purchasing revenue, brand, or even software, but a private ingredient for intelligence. ---- From "iConnections" YouTube channel, (link in comment)

Rohan Paul

143,134 просмотров • 3 месяцев назад

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

Artificial Analysis

133,068 просмотров • 1 месяц назад