Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

MLX Swift example can also QLoRA fine-tune Llama 3.2. Here's the 1B fine-tuning on my iPhone 15 Pro at > 150 toks/sec. A this rate only takes a few minutes to learn some decent adapters fully on-device.

91,961 görüntüleme • 2 yıl önce •via X (Twitter)

10 Yorum

Awni Hannun profil fotoğrafı
Awni Hannun2 yıl önce

Example is also open-source here (under MIT license):

GPT.Biz profil fotoğrafı
GPT.Biz2 yıl önce

This looks amazing! It’s incredible to see fine-tuning done so quickly on a phone

⚡️Dylan White profil fotoğrafı
⚡️Dylan White2 yıl önce

Whoa, that's impressive! Fine-tuning Llama 3.2 on your iPhone 15 Pro is no joke!

involuntarily incelibate profil fotoğrafı
involuntarily incelibate2 yıl önce

If only iOS would expose more substantial endpoints, you could fine tune “Siri”!

Janaka profil fotoğrafı
Janaka2 yıl önce

Impressive

Andres Gomez Sarmiento profil fotoğrafı
Andres Gomez Sarmiento2 yıl önce

really cool

Kalin Ovtcharov profil fotoğrafı
Kalin Ovtcharov2 yıl önce

Ability to run llms locally on-device and fine tune them is an extremely important yet relatively under-appreciated/ignored capability today. I expect that will change as new applications emerge that leverage SLMs. I expect some governments may even ban them in the near future.

Josep M. Ganyet profil fotoğrafı
Josep M. Ganyet2 yıl önce

@Scobleizer @xaviviro

Sorayesh Semo profil fotoğrafı
Sorayesh Semo2 yıl önce

Awesome

samuel abraham gomez villafuerte profil fotoğrafı
samuel abraham gomez villafuerte2 yıl önce

Para andriod cuando

Benzer Videolar

"Introducing Multimodal Llama 3.2": As promised two weeks ago, here's the short course on Meta's latest open model! This short course is created with Meta and taught by Amit Sangani, Director of AI Partner Engineering at Meta. Meta’s Llama family of models is leading the way in open models, allowing anyone to download, customize, fine-tune, or build new applications on top of them. Learn about the vision capabilities of the Llama 3.2, and use it for image classification, prompting, tokenization, tool-calling. You'll also learn about the open-source Llama stack, which gives building blocks for many different stages of the LLM application life cycle. In detail, you’ll: - Learn what are the features of Meta's four newest models, and when to use which Llama model. - Learn best practices for multimodal prompting, with applications to advanced image reasoning, illustrated by many examples: Understanding errors on a car dashboard, adding up the total of photographed restaurant receipts, grading written math homework. - Use different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 models and the prompt format that identifies those roles. - Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and multilingual support. - Learn how to prompt Llama to call built-in and custom tools (functions) with examples for web search and solving math equations. - Learn about Llama Stack, a standardized interface for common toolchain components like fine-tuning or synthetic data generation, useful for building agentic applications. By the end of this course, you’ll be equipped to build out new applications with the new Llama 3.2. Thank you to Ahmad Al-Dahle, Amit Sangani, and the whole AI at Meta team AI at Meta for all the hard work on Llama 3.2 — we’re excited to make these open models even more accessible to more developers with this new course! Please sign up here!

Andrew Ng

131,846 görüntüleme • 2 yıl önce