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I trained a tiny language model to write bad tech jokes and run on a microcontroller 19M parameters 11 MB runs on a conference badge (RP2350), no internet, about 2 tokens a second it learned English from half a billion tokens of tiny kids stories, then I trained it...

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Sam Meech Ward

4,861 subscribers

17,799 görüntüleme • 27 gün önce •via X (Twitter)

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I love this opensource project! Someone just put a 28.9M parameter AI model on an ESP32-S3. A chip that costs only a few dollars. No cloud. No API. No internet. It runs locally at ~9.5 tokens/s and can generate stories on a tiny screen. This changes the way we think about AI hardware. The next generation of AI toys, robots, and IoT devices may not need the cloud at all. Small chips are getting smarter. 🤖
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I love this opensource project! Someone just put a 28.9M parameter AI model on an ESP32-S3. A chip that costs only a few dollars. No cloud. No API. No internet. It runs locally at ~9.5 tokens/s and can generate stories on a tiny screen. This changes the way we think about AI hardware. The next generation of AI toys, robots, and IoT devices may not need the cloud at all. Small chips are getting smarter. 🤖

Steven Cheng

105,352 görüntüleme • 3 ay önce

Muse gadgets running on a conference badge Alexandr Wang Ported to micropython to run on the RP2350
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Muse gadgets running on a conference badge Alexandr Wang Ported to micropython to run on the RP2350

Wes Bos

64,604 görüntüleme • 1 ay önce

I had #ChatGPT write an evocative prompt for #midjourney – then I trained a #stablediffusion model on those images to create this animation. Fractal Diffusion✨
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I had #ChatGPT write an evocative prompt for #midjourney – then I trained a #stablediffusion model on those images to create this animation. Fractal Diffusion✨

Gregory Wieber

192,815 görüntüleme • 3 yıl önce

Fuck yeah! MaskGCT - New open SoTA Text to Speech model! 🔥 > Zero-shot voice cloning > Emotional TTS > Trained on 100K hours of data > Long form synthesis > Variable speed synthesis > Bilingual - Chinese & English > Available on Hugging Face Fully non-autoregressive architecture: > Stage 1: Predicts semantic tokens from text, using tokens extracted from a speech self-supervised learning (SSL) model > Stage 2: Predicts acoustic tokens conditioned on the semantic tokens. Synthesised: "Would you guys personally like to have a fake fireplace, an electric one, in your house? Or would you rather have a real fireplace? Let me know down below. Okay everybody, that's all for today's video and I hope you guys learned a bunch of furniture vocabulary!" TTS scene keeps getting lit! 🐐
0:15

Fuck yeah! MaskGCT - New open SoTA Text to Speech model! 🔥 > Zero-shot voice cloning > Emotional TTS > Trained on 100K hours of data > Long form synthesis > Variable speed synthesis > Bilingual - Chinese & English > Available on Hugging Face Fully non-autoregressive architecture: > Stage 1: Predicts semantic tokens from text, using tokens extracted from a speech self-supervised learning (SSL) model > Stage 2: Predicts acoustic tokens conditioned on the semantic tokens. Synthesised: "Would you guys personally like to have a fake fireplace, an electric one, in your house? Or would you rather have a real fireplace? Let me know down below. Okay everybody, that's all for today's video and I hope you guys learned a bunch of furniture vocabulary!" TTS scene keeps getting lit! 🐐

Vaibhav (VB) Srivastav

139,160 görüntüleme • 2 yıl önce

Nobody asked about it a Tiny Desk Fany on a green screen Take my tiny beautiful biscuit and put her on your desk
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Nobody asked about it a Tiny Desk Fany on a green screen Take my tiny beautiful biscuit and put her on your desk

Foxdee

137,137 görüntüleme • 1 yıl önce

We took a 30B model and split it in two to write tokens in parallel instead of one at a time. Introducing Nemotron-Labs-TwoTower: a diffusion language model from NVIDIA Research adapted from Nemotron-3-Nano-30B-A3B. Here’s how it works: one half holds the context, the other writes the tokens, with both reusing the pretrained model instead of training a new one from scratch. We found it kept 98.7% of the original model’s quality at 2.42× faster generation.
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We took a 30B model and split it in two to write tokens in parallel instead of one at a time. Introducing Nemotron-Labs-TwoTower: a diffusion language model from NVIDIA Research adapted from Nemotron-3-Nano-30B-A3B. Here’s how it works: one half holds the context, the other writes the tokens, with both reusing the pretrained model instead of training a new one from scratch. We found it kept 98.7% of the original model’s quality at 2.42× faster generation.

NVIDIA AI

763,844 görüntüleme • 4 ay önce

You can now vibe code a language model. From a single prompt, GPT‑5.6 built the entire training pipeline and trained a model from scratch on my iMessage history. Locally on my Mac. It now generates replies in my writing style.
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You can now vibe code a language model. From a single prompt, GPT‑5.6 built the entire training pipeline and trained a model from scratch on my iMessage history. Locally on my Mac. It now generates replies in my writing style.

Pietro Schirano

722,880 görüntüleme • 4 ay önce

A small insect bit me and left a tiny mark on my hand. After a while, it started looking like this. What should I do about it? 🥲
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A small insect bit me and left a tiny mark on my hand. After a while, it started looking like this. What should I do about it? 🥲

B✞g Dav✞es 🗽

4,291,470 görüntüleme • 2 ay önce

we trained a fruit fly to understand emotion We created a neural network from the male drosophila connectome, then trained it on human emotions. It was shocking to see how good it was at identification (better than some humans I know)
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we trained a fruit fly to understand emotion We created a neural network from the male drosophila connectome, then trained it on human emotions. It was shocking to see how good it was at identification (better than some humans I know)

Nathan Roll

614,513 görüntüleme • 2 ay önce

The first natively trained 1-bit model: BitNet 2B. Trained on 4 trillion tokens. that can run on CPUs like Apple M2 Native 1.58-bit weights and 8bit activations W158A8 Outperforms LLaMA &close to Qwen 2.5 1.5B in while using only 0.4GB memory versus 2GB and processes tokens 40%
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The first natively trained 1-bit model: BitNet 2B. Trained on 4 trillion tokens. that can run on CPUs like Apple M2 Native 1.58-bit weights and 8bit activations W158A8 Outperforms LLaMA &close to Qwen 2.5 1.5B in while using only 0.4GB memory versus 2GB and processes tokens 40%

Md Ismail Šojal 🕷️

43,807 görüntüleme • 8 ay önce

✅ BENCHMARKED: TensorFold made Qwen3.8 27B more than 3x faster on my 64GB M5 Pro Mac mini, on two versions in one day. In the morning I tested 1.2.1. On its own, the model wrote about 17 tokens a second. I then switched on the DFlash2 drafter, a small helper that guesses the next few tokens so the big model only has to check them and it jumped to 56. And later in the afternoon Ash Hart shipped 1.2.2. His release notes focused on other hardware, so I reran everything on my Mac mini. On mine it runs within a few percent of 1.2.1, and the drafter still makes it 3x faster. With randomness off, the answer came out word for word the same as without the drafter. - Chat, 3.26x faster on 1.2.1 and 3.35x on 1.2.2 - A 31K token prompt, 2.94x and 2.77x - Code, 5.4x and 5.5x, up to 91.90 tokens a second - 28.5 GiB peak memory on both - New in 1.2.2, the first request doesn’t wait for the drafter to load A massive thanks to Ash Hart and all the contributors behind 1.2.2. The TensorFold challenge from Yukon is open until Nov 6th if you want to try pushing it further on your own Mac.
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✅ BENCHMARKED: TensorFold made Qwen3.8 27B more than 3x faster on my 64GB M5 Pro Mac mini, on two versions in one day. In the morning I tested 1.2.1. On its own, the model wrote about 17 tokens a second. I then switched on the DFlash2 drafter, a small helper that guesses the next few tokens so the big model only has to check them and it jumped to 56. And later in the afternoon Ash Hart shipped 1.2.2. His release notes focused on other hardware, so I reran everything on my Mac mini. On mine it runs within a few percent of 1.2.1, and the drafter still makes it 3x faster. With randomness off, the answer came out word for word the same as without the drafter. - Chat, 3.26x faster on 1.2.1 and 3.35x on 1.2.2 - A 31K token prompt, 2.94x and 2.77x - Code, 5.4x and 5.5x, up to 91.90 tokens a second - 28.5 GiB peak memory on both - New in 1.2.2, the first request doesn’t wait for the drafter to load A massive thanks to Ash Hart and all the contributors behind 1.2.2. The TensorFold challenge from Yukon is open until Nov 6th if you want to try pushing it further on your own Mac.

Pratik Gandhi

128,136 görüntüleme • 11 gün önce

STARBOY perceives its environment through a camera, microphone, temperature sensor, and accelerometer. We trained multiple tiny image models that run locally on the device, letting it recognize human faces and hand gestures.
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STARBOY perceives its environment through a camera, microphone, temperature sensor, and accelerometer. We trained multiple tiny image models that run locally on the device, letting it recognize human faces and hand gestures.

Daniel Kuntz

116,863 görüntüleme • 8 ay önce

what the f*ck, an uncensored 18+ DeepSeek V4.1 just dropped😳 and it runs on a 12GB graphics card - original says no to 85.2% of harmful test prompts, this one says no to 0% - even with thinking on, refusals go from 27 out of 30 to 0 - needs 12GB GPU, about 64GB RAM and 257GB free SSD - slow, about 4 to 6 tokens per second on an RTX 5070 - free, MIT license try it here:
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what the f*ck, an uncensored 18+ DeepSeek V4.1 just dropped😳 and it runs on a 12GB graphics card - original says no to 85.2% of harmful test prompts, this one says no to 0% - even with thinking on, refusals go from 27 out of 30 to 0 - needs 12GB GPU, about 64GB RAM and 257GB free SSD - slow, about 4 to 6 tokens per second on an RTX 5070 - free, MIT license try it here:

Alizeh

74,039 görüntüleme • 14 gün önce

I built gpu-pii, inspired by Shu's gpu-lexer. A small model, trained from scratch, runs on WebGPU in your browser to detect personal information in any text
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I built gpu-pii, inspired by Shu's gpu-lexer. A small model, trained from scratch, runs on WebGPU in your browser to detect personal information in any text

Duncan

10,488 görüntüleme • 1 ay önce

I made my indie game fit on a floppy and run on MS-DOS! (It also runs on Windows 11 and PS5 and everything in between)
0:14

I made my indie game fit on a floppy and run on MS-DOS! (It also runs on Windows 11 and PS5 and everything in between)

Sos "Wishlist Lazy Kickers" Sosowski

23,107 görüntüleme • 7 ay önce

VERCEL + CLOUDFLARE + CLAUDE CODE all picked up Jev in 6 days... an AI that can't write a single word became the fastest adopted model on Vercel's AI Gateway Jev decides → big model thinks → code authorizes TypeSafe dropped it on September 15, founder Diogo Almeida worked on ChatGPT research at OpenAI it answers three ways: pick one option, rate it on a scale, or yes/no with a percentage "which agent next?" comes back as Researcher 71% / Coder 18% / Reviewer 11% Vercel says almost 13% of its paying teams tried it within the first 24 hours on Vercel's leaderboard it pulled about 26% of all requests but only 1.8% of tokens TypeSafe's own tests: up to 194x faster and 445x cheaper than regular models TypeSafe calls it a System One Model, named after Kahneman's Thinking, Fast and Slow your expensive model stops burning tokens on yes or no questions and only gets the hard ones 6 days, 110 community use cases, zero words written ↓
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VERCEL + CLOUDFLARE + CLAUDE CODE all picked up Jev in 6 days... an AI that can't write a single word became the fastest adopted model on Vercel's AI Gateway Jev decides → big model thinks → code authorizes TypeSafe dropped it on September 15, founder Diogo Almeida worked on ChatGPT research at OpenAI it answers three ways: pick one option, rate it on a scale, or yes/no with a percentage "which agent next?" comes back as Researcher 71% / Coder 18% / Reviewer 11% Vercel says almost 13% of its paying teams tried it within the first 24 hours on Vercel's leaderboard it pulled about 26% of all requests but only 1.8% of tokens TypeSafe's own tests: up to 194x faster and 445x cheaper than regular models TypeSafe calls it a System One Model, named after Kahneman's Thinking, Fast and Slow your expensive model stops burning tokens on yes or no questions and only gets the hard ones 6 days, 110 community use cases, zero words written ↓

leopardracer

16,184 görüntüleme • 1 ay önce

Just dropped on HF: kani-tts-370m A lightweight open-source text-to-speech model that sounds great and runs fast! > 370M parameters — efficient and deployable on consumer GPUs > NanoCodec + LFM2-350M > Natural & expressive voice trained with modern neural TTS techniques > Fast inference: real-time on a single RTX 3060
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Just dropped on HF: kani-tts-370m A lightweight open-source text-to-speech model that sounds great and runs fast! > 370M parameters — efficient and deployable on consumer GPUs > NanoCodec + LFM2-350M > Natural & expressive voice trained with modern neural TTS techniques > Fast inference: real-time on a single RTX 3060

steven

34,558 görüntüleme • 1 yıl önce

On this day, I eat 6 eggs, 2 steaks and a banana. I also trained chest
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On this day, I eat 6 eggs, 2 steaks and a banana. I also trained chest

Arran Aro

22,964 görüntüleme • 1 yıl önce

Bello! Introducing Minionese LM, a language model trained from scratch on my Mac mini by 5.6 Sol Based on Minionese, the language spoken exclusively by Minions (until now)
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Bello! Introducing Minionese LM, a language model trained from scratch on my Mac mini by 5.6 Sol Based on Minionese, the language spoken exclusively by Minions (until now)

Maddie D. Reese

95,661 görüntüleme • 4 ay önce