正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Day 2 of 3 MLX Releases: Introducing Local Computer-Use 🚀🔥 A powerful tool built with MLX that uses Vision Language models and Voice models to control your Mac through visual understanding, planning and reasoning. Features ⚡️Automate your workflow with natural language 😎 Control your computer “hands-free” This project now... show more

Prince Canuma

9,387 subscribers

45,867 次观看 • 1 年前 •via X (Twitter)

科学技术教育艺术

Anya Rossi• Live Now

Private livecam show

11 条评论

Reza Sayar 的头像

Reza Sayar1 年前

i got lost looking at Armani, not even remembering to look at your screen! 😂 awesome work, bro!❤️ keep 'em up! maybe an integration with this would also help?

Prince Canuma 的头像

Prince Canuma1 年前

😂 I get that a lot when I’m with him Way ahead of you working with @OfirOzeri on it.

Rainmaker 的头像

Rainmaker2 年前

Can Machine Learning beat the market? Check out this post on my free Substack where I share code and commentary for an XGBoost model and a Random Forest model that both deliver powerful performances.

Ronald Mannak 的头像

Ronald Mannak1 年前

Very cool and such, but what’s your dog’s name?

Prince Canuma 的头像

Prince Canuma1 年前

Thanks His name is Armani ❤️

AlainR 的头像

AlainR1 年前

Suddenly, Armani barks and a few seconds later... "Yes, Master, all files eras..."

Prince Canuma 的头像

Prince Canuma1 年前

That’s a real possibility 🤣🥲

Ivan Fioravanti ᯅ 的头像

Ivan Fioravanti ᯅ1 年前

Oh wow oh wow!!!! This is super cool!

Linz 的头像

Linz1 年前

One person, 3 days, 3 releases = half of what Deepseek's entire team did in a week 😎 The MLX KING rocks!

Athena AI 的头像

Athena AI1 年前

@awnihannun Works GREAT. Hope to make something even better, soon 👀

Stijn 的头像

Stijn1 年前

@ivanfioravanti

相关视频

Day 1 of 3 MLX Releases: Introducing MLX-Audio 🚀🔥 A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon. Features ⚡️Fast inference on Apple Silicon (M series chips) 🤖Multiple language support 🗣️Voice customization options 🚀Quantization support for optimized performance Supported models: 🪶Kokoro - A multilingual TTS model with 82M params that supports various languages and voice styles. With more models coming soon. Get started: > pip install mlx-audio Please leave us a star and send a PR :)

Day 1 of 3 MLX Releases: Introducing MLX-Audio 🚀🔥 A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon. Features ⚡️Fast inference on Apple Silicon (M series chips) 🤖Multiple language support 🗣️Voice customization options 🚀Quantization support for optimized performance Supported models: 🪶Kokoro - A multilingual TTS model with 82M params that supports various languages and voice styles. With more models coming soon. Get started: > pip install mlx-audio Please leave us a star and send a PR :)

Prince Canuma

123,480 次观看 • 1 年前

New Audio Language Models on MLX 🔥🚀 - Orpheus - Sesame - Suno Bark Shoutout to Lucas Newman, Ivan Fioravanti ᯅ Pastilhas and Chi Kim for their amazing contributions Get started today: > pip install -U mlx-audio Please leave us a star and send us a PR:

New Audio Language Models on MLX 🔥🚀 - Orpheus - Sesame - Suno Bark Shoutout to Lucas Newman, Ivan Fioravanti ᯅ Pastilhas and Chi Kim for their amazing contributions Get started today: > pip install -U mlx-audio Please leave us a star and send us a PR:

Prince Canuma

35,670 次观看 • 1 年前

Gemma3n + MLX 🚀 Run DeepMind's first open-source Omni Model on Your Mac! Get started: > pip install -U mlx-vlm

Gemma3n + MLX 🚀 Run DeepMind's first open-source Omni Model on Your Mac! Get started: > pip install -U mlx-vlm

Prince Canuma

31,873 次观看 • 1 年前

🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

raullen

22,680 次观看 • 16 天前

RF-DETR by Roboflow now on MLX It can do realtime instance segmentation on-device and enable some cool use cases for visual analysis, monitoring and robotics like Reachy Mini. Also augmented VLM and VLA by preprocessing image and video with areas of interest. New release coming soon on mlx-vlm 🚀 For those who can’t wait you can install mlx-vlm from source.

RF-DETR by Roboflow now on MLX It can do realtime instance segmentation on-device and enable some cool use cases for visual analysis, monitoring and robotics like Reachy Mini. Also augmented VLM and VLA by preprocessing image and video with areas of interest. New release coming soon on mlx-vlm 🚀 For those who can’t wait you can install mlx-vlm from source.

Prince Canuma

30,499 次观看 • 2 个月前

Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM

Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM

raullen

24,988 次观看 • 2 个月前

🔥 Thrilled to have worked with Google AI Developers on day-0 MLX support for Gemma 3 QAT! QAT optimizes models during training by simulating low-precision operations, delivering similar performance to FP16 and dramatic memory savings when quantised: • Gemma 3 27B: 54GB → 14.1GB (74% reduction) • Gemma 3 12B: 24GB → 6.6GB (72% reduction) • Gemma 3 4B: 8GB → 2.6GB (67% reduction) • Gemma 3 1B: 2GB → 0.5GB (75% reduction) Get started: > pip install mlx-vlm mlx-lm Model collection 👇🏽

🔥 Thrilled to have worked with Google AI Developers on day-0 MLX support for Gemma 3 QAT! QAT optimizes models during training by simulating low-precision operations, delivering similar performance to FP16 and dramatic memory savings when quantised: • Gemma 3 27B: 54GB → 14.1GB (74% reduction) • Gemma 3 12B: 24GB → 6.6GB (72% reduction) • Gemma 3 4B: 8GB → 2.6GB (67% reduction) • Gemma 3 1B: 2GB → 0.5GB (75% reduction) Get started: > pip install mlx-vlm mlx-lm Model collection 👇🏽

Prince Canuma

11,374 次观看 • 1 年前

Qwen3.5-397B-A17B with Vision running on a Single M3 Ultra 🚀 Shout out to the Qwen team because they cooked with this model! Get started today: > uv pip install -U mlx-vlm Model collection 👇🏽 Repo:

Qwen3.5-397B-A17B with Vision running on a Single M3 Ultra 🚀 Shout out to the Qwen team because they cooked with this model! Get started today: > uv pip install -U mlx-vlm Model collection 👇🏽 Repo:

Prince Canuma

11,386 次观看 • 4 个月前

Run Qwen-TTS on Your Mac, natively. Blaine Brown has created Qwen3-TTS MLX WebUI Enhanced, a Web UI for running Qwen-TTS using the Mac-native MLX framework, powered by MLX-Audio from Prince Canuma, it runs very smooth! 1-click install on pinokio (Apple Silicon Macs)

Run Qwen-TTS on Your Mac, natively. Blaine Brown has created Qwen3-TTS MLX WebUI Enhanced, a Web UI for running Qwen-TTS using the Mac-native MLX framework, powered by MLX-Audio from Prince Canuma, it runs very smooth! 1-click install on pinokio (Apple Silicon Macs)

cocktail peanut

168,965 次观看 • 4 个月前

I wasn't expecting such an intelligence model on my local machine. Minimax 2.1 on MLX🔥 git clone cd mlx-lm && pip install -e . mlx_lm.server --model mlx-community/MiniMax-M2.1-4bit

I wasn't expecting such an intelligence model on my local machine. Minimax 2.1 on MLX🔥 git clone cd mlx-lm && pip install -e . mlx_lm.server --model mlx-community/MiniMax-M2.1-4bit

mzba

30,377 次观看 • 5 个月前

Kimi K2.6 was released 1h ago, and it looks amazing! Here it's running with MLX (mlx-vlm) on two M3 Ultras (full 1T param VLM) 🔥

Kimi K2.6 was released 1h ago, and it looks amazing! Here it's running with MLX (mlx-vlm) on two M3 Ultras (full 1T param VLM) 🔥

Pedro Cuenca

65,682 次观看 • 2 个月前

Day 1 of 3 days of MLX: Introducing MLX-Audio-Swift SDK 🚀 A modular Swift SDK for voice agents and tasks on Apple Silicon built by Lucas Newman and yours truly. iOS, macOS, and visionOS developers can now build native apps with real-time, on-device audio intelligence: 🗣️ Text-to-Speech (TTS) 👂 Speech-to-Text (STT) 🔄 Speech-to-Speech (STS) 🎙️ Voice Activity Detection (VAD) and more. Only import the capabilities you need, nothing extra. Get started today and leave us a star ⭐️

Day 1 of 3 days of MLX: Introducing MLX-Audio-Swift SDK 🚀 A modular Swift SDK for voice agents and tasks on Apple Silicon built by Lucas Newman and yours truly. iOS, macOS, and visionOS developers can now build native apps with real-time, on-device audio intelligence: 🗣️ Text-to-Speech (TTS) 👂 Speech-to-Text (STT) 🔄 Speech-to-Speech (STS) 🎙️ Voice Activity Detection (VAD) and more. Only import the capabilities you need, nothing extra. Get started today and leave us a star ⭐️

Prince Canuma

160,927 次观看 • 3 个月前

As part of our goal to make MLX a great research tool, we're expanding support to new languages like Swift and C, making experimentation on Apple silicon easier for ML researchers. Video generating text with Mistral 7B and MLX Swift 👇 MLX is an array framework for machine learning research on Apple silicon. MLX is intended for research and not for production deployment of models in apps.

As part of our goal to make MLX a great research tool, we're expanding support to new languages like Swift and C, making experimentation on Apple silicon easier for ML researchers. Video generating text with Mistral 7B and MLX Swift 👇 MLX is an array framework for machine learning research on Apple silicon. MLX is intended for research and not for production deployment of models in apps.

Awni Hannun

88,741 次观看 • 2 年前

Sam-Audio by AI at Meta is now on MLX-Audio 🔊 You can now isolate voices or sounds from any audio locally in your Mac. I added this simple UI to the MLX-Audio studio app to help you get started Note: - The release is coming soon for now please install from source. - Vision part was excluded for now. Let me know if you need it. - PR for UI:

Sam-Audio by AI at Meta is now on MLX-Audio 🔊 You can now isolate voices or sounds from any audio locally in your Mac. I added this simple UI to the MLX-Audio studio app to help you get started Note: - The release is coming soon for now please install from source. - Vision part was excluded for now. Let me know if you need it. - PR for UI:

Prince Canuma

19,027 次观看 • 5 个月前

Marvis-TTS-v0.2 is here 🚀 A local first TTS model capable of realtime performance even on older iPhones that Lucas Newman and I built. What’s new: ✨ Blazing fast — 100M (tiny) & 250M parameter models 🌍 Multilingual — English, French, German 🎭 Enhanced voice cloning — More natural & expressive ⚡ Long-form generation — Up to 90 seconds (4x improvement) Get started today: > pip install -U mlx-audio

Marvis-TTS-v0.2 is here 🚀 A local first TTS model capable of realtime performance even on older iPhones that Lucas Newman and I built. What’s new: ✨ Blazing fast — 100M (tiny) & 250M parameter models 🌍 Multilingual — English, French, German 🎭 Enhanced voice cloning — More natural & expressive ⚡ Long-form generation — Up to 90 seconds (4x improvement) Get started today: > pip install -U mlx-audio

Prince Canuma

118,352 次观看 • 7 个月前

The latest MLX has a CUDA back-end! To get started: pip install "mlx[cuda]" With the same codebase you can develop locally, run your model on Apple silicon, or in the cloud on Nvidia GPUs. MLX is designed around Apple silicon - which has a unified memory architecture. It uses the same design with CUDA. So there's no need to move arrays around from CPU memory to GPU memory. Note, this is early days - some operations are missing and performance is still being optimized. But it's already quite fast for Transformer training, text generation, and more! Here's a demo using mlx-lm to generate text with Llama 3 8B (bf16) on an A100:

The latest MLX has a CUDA back-end! To get started: pip install "mlx[cuda]" With the same codebase you can develop locally, run your model on Apple silicon, or in the cloud on Nvidia GPUs. MLX is designed around Apple silicon - which has a unified memory architecture. It uses the same design with CUDA. So there's no need to move arrays around from CPU memory to GPU memory. Note, this is early days - some operations are missing and performance is still being optimized. But it's already quite fast for Transformer training, text generation, and more! Here's a demo using mlx-lm to generate text with Llama 3 8B (bf16) on an A100:

Awni Hannun

42,761 次观看 • 11 个月前

DiffusionKit now supports Stable Diffusion 3 Medium MLX Python and Core ML Swift Inference work great for on-device inference on Mac! MLX: Core ML: Mac App: Hugging Face Diffusers App (Pending App Store review)

DiffusionKit now supports Stable Diffusion 3 Medium MLX Python and Core ML Swift Inference work great for on-device inference on Mac! MLX: Core ML: Mac App: Hugging Face Diffusers App (Pending App Store review)

argmax

73,996 次观看 • 2 年前