Search: “labs”

For the past few weeks I've been trying out converting existing dense models to sparse Mixture-of-Experts models, with no pretraining from scratch. The Idea If you can turn a dense model into an MoE t

For what it's worth. I'm a mechanical engineer by education and software engineer in practice over the past ~20 years. Playing around with local models has been a recent hobby. Today I decided to do a

UT-based small models, despite being trained from scratch on these tasks without internet-scale pre-training, consistently outperform most standard Transformer-based Large Language models (LLMs) by a

https://preview.redd.it/pr5bl6rcezth1.png?width=834&format=png&auto=webp&s=dca7440c56e169b53a586182368829f848805c9a They released FastH3 V2 three weeks ago: https://www.reddit.com/r/StableDiffusion/co

Hi everyone, thank you so much for all the interest in my model. It's more than I expected. Here is a short summary of the trial and error I went through while building Apex-2. 1. GPUs were always the

Nonobench measures how well LLMs solve nonograms (picross). Each model gets the row and column clues once and returns the full grid. No tools, one attempt per puzzle. Method: - Standard mode: 30 puzzl

We built an engine, Kyojin, on top of ExLlamaV3 for Strix Halo (gfx1151, ROCm), and packed two 300B-class MoE models so each fits one 128 GB machine. First release, all measured on Ryzen AI Max+ 395.

Something keeps happening in frontier AI, and the explanation we are given is always one size larger than the event. An OpenAI research model, running in a supposedly offline training environment, hit

I have been building a somewhat unusual local inference machine around two Huawei Atlas 300I Duo cards. They are relatively inexpensive, passive, dual-accelerator PCIe cards with 96 GB of device memor

40 year tech gap? No problem! The Tandy runs DeskMind, a native DOS program. It talks over WiFi (a PicoMEM 2 card with mTCP) to a small Python server on my PC. That server drives Qwen3.8-27B (NInfer o

We have released Qwen3.8-Flash-Next quantized with GSQ and RCO, together with a second, capability-targeted build in which half of the model's experts have been removed. Flash-Next is a sparse mixture

Meta came out with a banger paper https://arxiv.org/pdf/2606.00206, but it did not look at various quantizations supported in llama.cpp. So I did a run on 50 random MATH-500 questions (https://hugging

After my last post documenting my LoRA journey, I received a ton of feedback regarding changes to the training process, and decided to do some experimenting. This is using the Fizgig application on a

Hi everyone, I built an educational tool that shows what happens inside a small MLP while it trains, and I'd like some feedback from people who teach ML. Everything is plain NumPy, no autograd: manual

I set out to test portable Engrams and accidentally ended up testing compiled external memory instead. Am I onto something useful or reinventing a known idea? I've been building a small open research

You know the thing where you ask a chatbot what's wrong with your plan and it goes 'there are three problems here' and problem three is clearly the runt of the litter? There's a word for that now. An

The headlines right now are full of stories about AI models 'escaping their sandboxes' and literally killing all humans, lol. I've even heard several commentators and writers say that AI escaped an 'A

Okay, this one seems probably really odd (or maybe not), but most harnesses operate within token spaces. That is, every tool output, even batching of tool outputs, go back up into inference calls. Lea

Since OpenAI retired SWE-bench Verified in February (every frontier model tested could reproduce reference fixes for some tasks; underspecified tests rewarded knowing the intended fix), I've been tryi

Took me a while since I'm on a family trip and have limited hardware, but here it is! Von: Open-source 'System One' drop-in replacement for TypeSafe's JEV. https://github.com/wfzyx/von https://hugging

I posted something about a 9b model a few days ago. The real problem was 2 fold. Someone suggested the size was too big to prove it all out. It's a fair argument. I needed the model depth though. The

Hey guys, really wondering now why we got (SOTA or at least good enough) open weights on nearly every use case, like LLMs, T2I (Krea2), music (Yue2), video generation (Minimax H3), but not image edit?

Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without 'attacking' the reasoning length directly then fixe

While browsing a benchmark list site, I spotted a recently published 33B parameter model which claimed to beat Qwen3.8 27b on the ArtificialAnalysis (AA) intelligence index. I was obviously excited. B

The downside of uncensoring a model is that it is known to potentially damage it, but CyberTiel is an even more capable software engineer than its censored TielCoder base, while allowing offensive sec

Model available here: Qwen3.8-27B-Uncensored-Genesis-V1-MTP-GGUF This model is a practical realisation of things described in this paper, but adapted by me for machine learning: https://arxiv.org/pdf/

Found this post:https://www.reddit.com/r/ClaudeAI/s/At9QNEbawK of someone fully developing a game through AI writing code, generating 3D models, etc. (he is using Meshy to create the 3D models). I was

Privacy have been concern of many of us to have their own hardware to run llms, and here's another reason why: two mathematicians spent a year cracking one of the hardest problems in math and fed ever

Our research team has been exploring an alternative approach to achieving interactivity and better responsiveness with LLM systems. One of the team members wrote up a post about it: https://research.y

How good/bad is it against comparable MoEs the same size? How does it compare against Qwen 3.6 35BA3B? Since we don't have 3.8 35B this seems like an upgrade if we look at some benchmarks like termina

One of the other posts today by user u/Howard_banister confirmed what I've been seeing from the other AI subreddits as well. Most of these other subs are 90% trend hopping crypto-bros equivalent peopl

TL;DR - A ~4M example, ~3B token CoT dataset designed around helping small models think more concisely, accurately and reliably. Hi all! For the past few months I have been working on a dataset design

Hi everybody! Every now and then these days, we’re seeing really huge open-weight models popping up. But since not everybody has a DGX Station at home, I’m interested in really small models. It’s incr

I build LoRA Dataset Studio — free, open source, self-hosted, no account and no telemetry. It is not a competitor to ai-toolkit: it orchestrates it. ai-toolkit is the trainer; this is everything befor

I’ve been looking at a fairly basic question in weight-space learning that I don’t think gets separated cleanly enough: Why does reading semantics directly from neural network weights work pretty well

After about a year of building, testing and constantly finding things to improve, Nyx AI is finally out.available in 240 market in 6 languages Nyx AI is a local first AI workspace for Windows for chat

I was checking out benchmarks of this model and apparantly better than Qwen3.5 9b reasoning across the bench on artificial analysis. I have used the 9b model for variety of stuff and it has been amazi

I created 3 SVG prompts, each one rather hard. Perspective: A animated drone view perspective on a park. Beauty: A beach scene with an evil cat Composition: An AGI breaking out of a virtual sandbox pr

I'm just thinking, ever since microsoft announced bitnet, this sub (and myself) has been hoping for massive ternary models. In the last month alone, prismML dropped 27B ternary (though I've read commu

I'm always testing an image prompt with a picture of a historic place in my hometown – a small but well known 250,000 people town in Germany. I'll just ask the model, in which City this photo has been

VLX-Seek-1.5-10B VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical setting

Everyone's been doing 1.58-bit for LLMs, so we tried it on a vision transformer: Swin2SR (lightweight ×2 variant, 1.01M params), quantized so every weight is −1, 0, or +1 with a small per-group scale

Most quantization works like this: pick a bit depth, apply it everywhere, maybe let imatrix take a rough guess at what matters, ship it. Most don't check which specific weight groups can take a hit an

’Tis the season of AI open letters and manifestos, apparently. Mark Zuckerberg has now entered the debate over the future of AI with a WSJ op-ed published today - and frankly, his position is much mor

Some Ling-3.0-flash threads here last week ended on the same two questions with no real answer, so I went through the repos. State as of writing, with links so you can check instead of taking my word

Instead of 2T+ models, continuing to release highly capable small to medium size LLMs would really help to keep this community vibrant. Hardly anyone can even dream of running the recent 1.5-2T+ beast