Загрузка видео...

Не удалось загрузить видео

На главную

PaliGemma 2 running 100% local, on-device, powered by MLX 🔥

reach_vb's profile picture

Vaibhav (VB) Srivastav

57,179 subscribers

30,374 просмотров • 1 год назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Apple wasn’t kidding, the iPhone 17 Pro is really built for running LLMs Here’s LFM2 8B A1B by running on-device with MLX in Locally AI - Local AI Chat, the iPhone runs the 8B model with zero struggle Thanks Prince Canuma for the port to MLX, it made the MLX Swift port possible
0:17

Apple wasn’t kidding, the iPhone 17 Pro is really built for running LLMs Here’s LFM2 8B A1B by running on-device with MLX in Locally AI - Local AI Chat, the iPhone runs the 8B model with zero struggle Thanks Prince Canuma for the port to MLX, it made the MLX Swift port possible

Adrien Grondin

493,561 просмотров • 11 месяцев назад

Introducing MLX-Swift-TS An SDK for running time series foundation models fully on-device on Apple Silicon. When I joined Datadog, Inc. , I was introduced to Toto, our time series foundation model, and got excited about zero-shot forecasting across different domains. While building a health copilot app, I realized there wasn’t a simple way to run models like these locally on device. So I built one. MLX-Swift-TS exposes a common TimeSeriesForecaster interface for loading and running multiple time series architectures directly in Swift using MLX. No server required. The attached video shows on-device forecasting running inside a native Swift app. Huge thanks to Awni Hannun and the MLX team for building MLX and its Swift API, Prince Canuma for inspiration on MLX SDK patterns, and Ameet Talwalkar and the Datadog team for Toto.
0:17

Introducing MLX-Swift-TS An SDK for running time series foundation models fully on-device on Apple Silicon. When I joined Datadog, Inc. , I was introduced to Toto, our time series foundation model, and got excited about zero-shot forecasting across different domains. While building a health copilot app, I realized there wasn’t a simple way to run models like these locally on device. So I built one. MLX-Swift-TS exposes a common TimeSeriesForecaster interface for loading and running multiple time series architectures directly in Swift using MLX. No server required. The attached video shows on-device forecasting running inside a native Swift app. Huge thanks to Awni Hannun and the MLX team for building MLX and its Swift API, Prince Canuma for inspiration on MLX SDK patterns, and Ameet Talwalkar and the Datadog team for Toto.

Kunal Batra

12,585 просмотров • 7 месяцев назад

GLM-4.7 Flash by Z.ai running on M3 Ultra using MLX! 4bit at 81 toks/s 🔥 8bit at 64 toks/s 🔥 (video below) Model converted from transformers to MLX on the fly with Codex + GPT-5.2 HIgh!
0:19

GLM-4.7 Flash by Z.ai running on M3 Ultra using MLX! 4bit at 81 toks/s 🔥 8bit at 64 toks/s 🔥 (video below) Model converted from transformers to MLX on the fly with Codex + GPT-5.2 HIgh!

Ivan Fioravanti ᯅ

75,425 просмотров • 8 месяцев назад

Sam 3 by Facebook now on MLX 🚀 Here is a realtime object tracking running on M3 Max 96GB.
0:16

Sam 3 by Facebook now on MLX 🚀 Here is a realtime object tracking running on M3 Max 96GB.

Prince Canuma

180,438 просмотров • 6 месяцев назад

MLX news: MOSS-TTS-Local Transformer 1.5 available on mlx-audio now! Thanks to Prince Canuma and Lucas Newman Audio on! 🔉
0:11

MLX news: MOSS-TTS-Local Transformer 1.5 available on mlx-audio now! Thanks to Prince Canuma and Lucas Newman Audio on! 🔉

Ivan Fioravanti ᯅ

20,292 просмотров • 3 месяцев назад

Google’s Gemma 4 E2B running on-device on iPhone 17 Pro Gemma 4 is built from the same research as Gemini 3, has image understanding capabilities and can reason if needed Running at ~40tk/s with MLX optimized for Apple Silicon
0:12

Google’s Gemma 4 E2B running on-device on iPhone 17 Pro Gemma 4 is built from the same research as Gemini 3, has image understanding capabilities and can reason if needed Running at ~40tk/s with MLX optimized for Apple Silicon

Adrien Grondin

1,045,492 просмотров • 5 месяцев назад

Google's Gemma 4 26B A4B running on MacBook Pro M5 Max 100% native Swift Achieving over 100tk/s with Apple MLX framework
0:14

Google's Gemma 4 26B A4B running on MacBook Pro M5 Max 100% native Swift Achieving over 100tk/s with Apple MLX framework

Adrien Grondin

351,153 просмотров • 5 месяцев назад

Quick demo of LFM 2.5 VL 1.6B model by Liquid AI that I recently added to Locally AI - Local AI Chat Running locally on iPhone 17 Pro at ~90tk/s with MLX Small vision-language models are improving fast
0:10

Quick demo of LFM 2.5 VL 1.6B model by Liquid AI that I recently added to Locally AI - Local AI Chat Running locally on iPhone 17 Pro at ~90tk/s with MLX Small vision-language models are improving fast

Adrien Grondin

32,556 просмотров • 8 месяцев назад

1 week, 4 open-source medical AI shipments: → 35 PII models for Portuguese → openmed==1.1.0 (Brazilian + EU coverage) → OpenMedKit on iPhone: GLiNER + MLX → OpenAI's privacy-filter ported to MLX (24-33x faster) All Apache 2.0. All on-device. 15 seconds recap:
0:15

1 week, 4 open-source medical AI shipments: → 35 PII models for Portuguese → openmed==1.1.0 (Brazilian + EU coverage) → OpenMedKit on iPhone: GLiNER + MLX → OpenAI's privacy-filter ported to MLX (24-33x faster) All Apache 2.0. All on-device. 15 seconds recap:

Maziyar PANAHI

35,507 просмотров • 5 месяцев назад

Qwen QwQ 32B fp16 on M4 Max and M2 Ultra powered by MLX! M2 Ultra - 10.2 toks/s M4 Max - 7.6 toks/s! "Create an amazing animation using p5js" o1-mini level local model! Note: use temp 0.7-0.75 for optimal results in coding. I did some tests and this
0:10

Qwen QwQ 32B fp16 on M4 Max and M2 Ultra powered by MLX! M2 Ultra - 10.2 toks/s M4 Max - 7.6 toks/s! "Create an amazing animation using p5js" o1-mini level local model! Note: use temp 0.7-0.75 for optimal results in coding. I did some tests and this

Ivan Fioravanti ᯅ

62,523 просмотров • 1 год назад

Llama 3.2 1B in 4-bit runs at ~60 toks/sec with MLX Swift on my iPhone 15 pro. It's quite good and easily runs on-device:
0:17

Llama 3.2 1B in 4-bit runs at ~60 toks/sec with MLX Swift on my iPhone 15 pro. It's quite good and easily runs on-device:

Awni Hannun

492,428 просмотров • 2 лет назад

Announcing MLX native Mac support for Moondream 3. Moondream running on Mac, Linux, Windows (free!): pip install moondream-station Details:
0:12

Announcing MLX native Mac support for Moondream 3. Moondream running on Mac, Linux, Windows (free!): pip install moondream-station Details:

moondream

70,340 просмотров • 9 месяцев назад

Wow! This is amazing! Segmented every car locally in real time with Meta's SAM3 converted to MLX. Just on-device (M2 laptop) vision getting absurdly good. Local AI is moving faster than most people realize! What other models should we test? what kind of videos?
0:12

Wow! This is amazing! Segmented every car locally in real time with Meta's SAM3 converted to MLX. Just on-device (M2 laptop) vision getting absurdly good. Local AI is moving faster than most people realize! What other models should we test? what kind of videos?

Maziyar PANAHI

177,270 просмотров • 5 месяцев назад

DeepSeek R1 distilled to Qwen 1.5B easily runs on my iPhone 16 with MLX swift. Here's the 4-bit model reasoning entirely on device at almost 60 toks/sec:
0:19

DeepSeek R1 distilled to Qwen 1.5B easily runs on my iPhone 16 with MLX swift. Here's the 4-bit model reasoning entirely on device at almost 60 toks/sec:

Awni Hannun

1,139,934 просмотров • 1 год назад

10x speedup: OpenAI's privacy-filter on MLX vs CPU, side by side in your browser. 1,818 tok/s vs 179 tok/s. Real-time PII redaction across English, German, French. On-device. No cloud. Privacy-first. Which model should we port next?
0:15

10x speedup: OpenAI's privacy-filter on MLX vs CPU, side by side in your browser. 1,818 tok/s vs 179 tok/s. Real-time PII redaction across English, German, French. On-device. No cloud. Privacy-first. Which model should we port next?

Maziyar PANAHI

25,329 просмотров • 5 месяцев назад

Running the full GLM 4.7 (8-bit) on 2 x 512GB M3 Ultra Mac Studios Runs at 19.8 tok/sec with EXO Labs MLX RDMA backend (h/t Awni Hannun) & tensor parallel
0:12

Running the full GLM 4.7 (8-bit) on 2 x 512GB M3 Ultra Mac Studios Runs at 19.8 tok/sec with EXO Labs MLX RDMA backend (h/t Awni Hannun) & tensor parallel

Alex Cheema

156,874 просмотров • 9 месяцев назад

QVAC Health new beta supporting local on device model to analyze food images, calculate calories and nutrients etc 100% privacy. Getting ready for public beta.
0:17

QVAC Health new beta supporting local on device model to analyze food images, calculate calories and nutrients etc 100% privacy. Getting ready for public beta.

Paolo Ardoino 🤖

31,381 просмотров • 1 год назад