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36,486 次观看 • 1 年前 •via X (Twitter)

13 条评论

Monique Santificer 的头像
Monique Santificer1 年前

I remember this making my dad laugh so much he hurt himself and couldn't breathe right for a week

Solar Heavy 的头像
Solar Heavy1 年前

We're Flying out now

Hex’d Uni 的头像
Hex’d Uni1 年前

THATS WHERE THIS IS FROM?

Vinesauce Clips Bot 的头像
Vinesauce Clips Bot1 年前

Title: JAMBA AYUSS Game: Special Events Clipped by: OfficialJlipper Clipped on: April 14, 2025

Highretrogamelord☯ 的头像
Highretrogamelord☯1 年前

We had a lot of Jamba commercials in the 2000s here in Germany on music channels like MTV and Viva. They were so god damn annoying!

Borreload Dragon 的头像
Borreload Dragon1 年前

Sensual, (un movimiento sensual) un movimiento muy sexy (sexy)...

Salsa 的头像
Salsa1 年前

i feel like i remember seeing this hippo as a kid

bruno 的头像
bruno1 年前

this is hot

GUARD FISH 😳 的头像
GUARD FISH 😳1 年前

Ig we found someone that’s somehow worse than this guy | V

SneakGoblin7 🇵🇸🌎 的头像
SneakGoblin7 🇵🇸🌎1 年前

Joel voice spotted

JP 的头像
JP1 年前

Moo deng fell on hard times 😔

Viscount01 的头像
Viscount011 年前

I'm thankful for my weird nightmares that don't include purple dancing hippos.

El pvto de Rizus 的头像
El pvto de Rizus1 年前

Tra tra tra tra

相关视频

New short course: Build Long-Context AI Apps with Jamba. Learn about state space models (SSMs), which have emerged as an alternative to transformers! Specifically, Jamba is a hybrid transformer-Mamba architecture that combines strengths of the transformer with ideas from SSMs. This course is built with AI21 Labs and taught by Chen Wang and Chen Almagor. The transformer architecture is computationally expensive when handling very long input contexts. But there's an alternative called Mamba, a selective state space model that can process very long contexts with a much lower computational cost. However, researchers found that the pure Mamba architecture underperforms in understanding the context, and gives lower-quality responses. To overcome this, AI21 developed the Jamba model, which combines Mamba's computational efficiency with the transformer's attention mechanism to help with the output quality. In this course, you’ll learn about how state space models, and Jamba, work. You’ll also learn how to prompt Jamba, use it to process long documents, and build long-context RAG apps. - Learn how Jamba combines transformer and state space model architectures to achieve high performance and quality - Use the AI21 SDK, with an example of prompting over a large 200k-token annual financial report of Nvidia - Use Jamba for tool-calling, with hands-on examples from calling simple arithmetic calculations to a function that returns quarterly company financial reports. - Learn how training for long context is done, and the metrics used for its evaluation - Create a RAG app using the AI21 Conversational RAG tool and build your own RAG pipeline that uses Jamba and LangChain. By the end of this course, you'll learn how to build applications that can handle context as long as an entire book. Please sign up here:

Andrew Ng

77,792 次观看 • 1 年前