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Llama 4 Scout and Maverick from Meta are now live on GroqCloud™. Day-zero access. Real-time performance. Lowest cost—without compromise. No waiting. No tuning. Just build fast.

154,880 次观看 • 1 年前 •via X (Twitter)

11 条评论

Groq Inc 的头像
Groq Inc1 年前

Try it →

PowerBeatsVR 的头像
PowerBeatsVR1 年前

Box, dodge, and squat your way through PowerBeatsVR - Now 40% OFF on Meta Quest for a limited time 🔥

Groq Inc 的头像
Groq Inc1 年前

Read more about Groq performance, price, & more in our blog →

Jake Dahn 的头像
Jake Dahn1 年前

@Meta is maverick actually live? all im seeing is `meta-llama/llama-4-scout-17b-16e-instruct` ... also, are y'all supporting the full context window in both models?

Data 的头像
Data1 年前

@Meta Is this full precision?

Nayer ALI MAHOMED 的头像
Nayer ALI MAHOMED1 年前

@Meta @GroqInc I’m only seeing access to the Scout model. Is Maverick limited or just not rolled out to everyone yet? 🤔

ZOHEB 的头像
ZOHEB1 年前

@Meta Groq’s team works as fast as Groq’s responses

V 的头像
V1 年前

@Meta That was fast, just out of the oven

✦ xyz 的头像
✦ xyz1 年前

@Meta i dont see maverick.. only scout

Romulus 的头像
Romulus1 年前

@Meta holy fuck how fast

𝕃𝕏𝔼 的头像
𝕃𝕏𝔼1 年前

@Meta With the native full context windows? Damn this might kill RAG.

相关视频

Introducing "Building with Llama 4." This short course is created with Meta AI at Meta, and taught by Amit Sangani, Director of Partner Engineering for Meta’s AI team. Meta’s new Llama 4 has added three new models and introduced the Mixture-of-Experts (MoE) architecture to its family of open-weight models, making them more efficient to serve. In this course, you’ll work with two of the three new models introduced in Llama 4. First is Maverick, a 400B parameter model, with 128 experts and 17B active parameters. Second is Scout, a 109B parameter model with 16 experts and 17B active parameters. Maverick and Scout support long context windows of up to a million tokens and 10M tokens, respectively. The latter is enough to support directly inputting even fairly large GitHub repos for analysis! In hands-on lessons, you’ll build apps using Llama 4’s new multimodal capabilities including reasoning across multiple images and image grounding, in which you can identify elements in images. You’ll also use the official Llama API, work with Llama 4’s long-context abilities, and learn about Llama’s newest open-source tools: its prompt optimization tool that automatically improves system prompts and synthetic data kit that generates high-quality datasets for fine-tuning. If you need an open model, Llama is a great option, and the Llama 4 family is an important part of any GenAI developer's toolkit. Through this course, you’ll learn to call Llama 4 via API, use its optimization tools, and build features that span text, images, and large context. Please sign up here:

Andrew Ng

68,034 次观看 • 1 年前