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You can now run SQLCoder with a GUI on Apple Silicon or any NVIDIA GPU-enabled device! On Apple Silicon, just run CMAKE_ARGS="-DLLAMA_METAL=on" pip install "sqlcoder[llama-cpp]" sqlcoder launch The Apple Silicon version is not super accurate, but works great for simple questions – and everything happens (fairly fast) on just... show more

Rishabh Srivastava

12,553 subscribers

10,405 次观看 • 2 年前 •via X (Twitter)

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Cris Lenta 的头像

Cris Lenta2 年前

This looks great! Have you tried also running it with .cpp models? Curious of the performance

Rishabh Srivastava 的头像

Rishabh Srivastava2 年前

Thank you! The demo above is for a llama.cpp model (with q5 km quantization). Haven't done a rigorous eval on those yet though – will do in a few hours!

Nerdynav 的头像

Nerdynav2 年前

hey great work, what's the license for this? Are code llama fine tunes allowed in their Meta's licensing?

Gautam Padiyar 的头像

Gautam Padiyar2 年前

GPU with 20gb memory not required?

Rishabh Srivastava 的头像

Rishabh Srivastava2 年前

7B model, so you only need ~16GB or so of memory to make it run without beam search

Harsh Patel 的头像

Harsh Patel2 年前

On Apple silicon how do I make it use the 7b-2 model? It is downloading the v1 I guess

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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 • 1 年前

Weekend plans? How about diving into LLMs on Apple devices? 🕷️💻 Just dropped my first tutorial: Run LLMs on your Mac with MLX! Perfect for AI beginners with Apple Silicon. More tech adventures coming soon! Please watch & subscribe:

Weekend plans? How about diving into LLMs on Apple devices? 🕷️💻 Just dropped my first tutorial: Run LLMs on your Mac with MLX! Perfect for AI beginners with Apple Silicon. More tech adventures coming soon! Please watch & subscribe:

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27,144 次观看 • 2 年前

Gemma 4 now works on-device using React Native! You can now run Gemma 4 fully offline in your cross-platform apps with local hardware acceleration: ⚡ Vulkan delegate on Android ⚡ MLX delegate on Apple Silicon See Gemma 4's vision and tool-use capabilities in action, instantly reading a flyer and scheduling a calendar event, 100% on-device.

Gemma 4 now works on-device using React Native! You can now run Gemma 4 fully offline in your cross-platform apps with local hardware acceleration: ⚡ Vulkan delegate on Android ⚡ MLX delegate on Apple Silicon See Gemma 4's vision and tool-use capabilities in action, instantly reading a flyer and scheduling a calendar event, 100% on-device.

Google Gemma

115,299 次观看 • 19 天前

Alibaba’s Qwen 3.5 is now running fully on-device on the iPhone 17 Pro. It outperforms models 4x its size, delivers strong visual understanding, and lets you switch reasoning on or off. This demo uses the 2B 6-bit version, optimized with MLX for Apple Silicon.

Alibaba’s Qwen 3.5 is now running fully on-device on the iPhone 17 Pro. It outperforms models 4x its size, delivers strong visual understanding, and lets you switch reasoning on or off. This demo uses the 2B 6-bit version, optimized with MLX for Apple Silicon.

Hugging Models

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Prince Canuma

123,480 次观看 • 1 年前

Claude Code just got a lot easier to use 🙌 Just updated You can now install agents, commands, MCPs, and templates with a single command. Click on any card and it gives you the exact line to run. Everything is pulled from GitHub, the site runs on GitHub Pages, and downloads are logged with GitHub Actions.

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Daniel San

135,238 次观看 • 1 年前

HERMES CAN NOW RUN FOR FREE FOREVER A new Gemma 4 update just removed the biggest problem with local AI agents. The Upgrade: → Gemma 4 runs around 90% faster on Apple Silicon using Ollama + MLX → Plug it into Hermes with a few terminal commands → Run private agent workflows without subscriptions, token limits, or API bills What It Can Actually Do: ✓ Build and preview apps locally ✓ Use /learn to turn tutorials into permanent skills ✓ Read files, analyse landing pages, summarise emails, and track AI news ✓ Run loops until a task is complete instead of waiting for another prompt ✓ Launch multiple Gemma 4 subagents to work on separate tasks in parallel No Apple Silicon? ✔ OpenRouter offers the 31B model through a free API The real win is not faster chat. It is having Hermes build, retry, check its work, and operate in the background without charging you for every loop.

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Julian Goldie SEO

11,234 次观看 • 23 天前

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

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49,358 次观看 • 4 个月前

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248,720 次观看 • 1 年前

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345,892 次观看 • 4 个月前

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12,828 次观看 • 1 个月前

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NEXA AI

30,213 次观看 • 8 个月前

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Lummox

261,857 次观看 • 1 个月前

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Prince Canuma

115,001 次观看 • 7 天前

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NVIDIA just built the laptop chip people are already calling the MacBook killer it is called RTX Spark, a single superchip that runs heavy creative work, real gaming, and private on-device AI agents, all on one machine but before you ditch your MacBook, here is the honest part nobody in the hype is telling you: > on raw memory bandwidth, a maxed macbook is actually still ahead, not behind > the RTX Spark laptops are not out yet, they ship this fall, apple is on shelves today > where nvidia truly wins is cuda, real RTX gaming, and 3D, things apple has no answer for > one is the proven machine you can buy now, the other is the more exciting bet for later so no, the macbook is not dead, and no, this is not just hype, the truth sits in between i broke down the full thing, every spec, who wins where, and which one is actually yours the honest breakdown is in the article below

John Doe

107,985 次观看 • 1 个月前

i will die on this hill: Apple is a frontier AI company. they will inject AI into every place you scroll, type, read and listen imagine if you could “google” a past memory or ask siri to prove you’re right in a group chat ai models that run on-device with access to your entire life. the craziest part is the models don’t even need to be big! a 20B frontier model can run smoothly on apple’s chip architecture completely privately there are 3.5 billion apple devices, the gravitational pull this company has on societal culture is unmatched

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Ejaaz

136,637 次观看 • 1 个月前