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Ruach Studio: a whole song studio around YuE2 on your own GPU. Score first, LoRA training, stems,remaster, DAW export.

We spent the last weeks building a studio around YuE2, the open song model by m-a-p, and today it reaches its first release candidate. Write the style and the lyrics, and it composes, sings and render

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Human DGX agent

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Model Releases

Running an open-source agent platform with no third party in the chain: what each piece needs (Ollama/vLLM, local embeddings, local OCR)

DGX agent

Disclosure first: I work at Vstorm and I build AgenticOS, an open-source (Apache-2.0), self-hosted platform for building AI agents in a browser. This post was drafted with an LLM's help and edited by

model-releasesr-ollama

7 Oct 2026

Research

Saw this on Rednote, WTF [D]

researchr-machinelearning

https://preview.redd.it/t7wxguxqvzth1.png?width=1314&format=png&auto=webp&s=0ba382fb88894c6e687d5bb9a4b14a85334eae67 Guess we have a dataset for sycophancy and AI content detection submitted by /u/Str

Single 3090 Qwen 27B user, considering buying 128GB of RAM because of the hype

My current setup: - single 3090 running turboderp/Qwen3.8-27B-exl3:SC_5.00bpw_H6_V6 - ~150k context, ~60t/s, unknown prefill because I didn't benchmark it (but it is ok) - Intel 12400 CPU with 32GB DD

Small uncensored model for everyday questions on a CPU-only laptop?

Hey everyone, I'm looking for a small uncensored model to run locally with Ollama. Nothing fancy. I just want something I can ask everyday questions without it refusing or lecturing me every other mes

Research

stuck on finding a approach for app detection ( making a transformer modal out of unlabeled network data) [R] [P]

researchr-machinelearning

As the title suggests I'm currently trying to make a modal to identify which app is being used. The thing is I don't have labelled data and generating it is out of question since that is a lot of work

stuck on max.. no mimo2.6.. no chong?

Never saw mimo2.6, now im doubting mistral 4 wont land either ... I feel trapped but im not sue its worth the $100 ... just few options anyone know any details behind the change? submitted by /u/stack

Tested in Coding: Strata

This will be a potentially unpopular post - but it's the truth and grounded - so let's get to it. Hopefully you are familiar with my previous Tested in Coding series: https://www.reddit.com/r/LocalLLa

Three AI agents research a 4-day week and build the deck | Row-Bot + GPT 6.1 Sol

One prompt to Row-Bot 5.0 on GPT 6.1 Sol, through my ChatGPT plan (no API key): 'Split the research across three helper agents working in parallel, cite sources, save the findings, then build a 6-slid

Hardware

Trained a ~20K LM (probably smallest) that can still write stories

I’ve been pushing TinyStories-style models downward in size, and this is the smallest one so far: MacroStories — 19,969 parameters, 81 KB FP32 https://huggingface.co/raincandy-u/MacroStories For scale

hardwarer-localllama

Ugh I didn't want to post this... Back to Qwen3.8 27B

I don't know if anyone else has ran into these issues but when using Qwen Flash Next, my confidence in it at iq4_xs is high but not 100%. I notice it not following instructions, hallucinating more oft

Research

Uploaded 5.6 billion TikTok videos metadata on Hugging Face, spanning from 2014 to October 2026 [P]

researchr-machinelearning

Dataset: https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:

Tutorials

Where to get started if you want to publish papers in Neurips,ACL,ICLR/A* conferences [R]

tutorialsr-machinelearning

I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some

Why is ik_llama.cpp said to be faster than Mainline? On my hybrid multi-GPU rig, Mainline easily beats it

I constantly see recommendations saying that ik_llama.cpp (ikawrakow's fork) is the undisputed king of hybrid CPU/GPU offloading and MoE performance. However, every time I benchmark it against mainlin

A local 27B model reads my lease and finds a leak in my bills | Row-Bot + qwen3.8 on Ollama

Row-Bot 5.0 with qwen3.8:27b in Ollama, on my own GPU. No API keys, no cloud. Gave it a year of bills, a tenancy agreement, an insurance policy and a rent increase letter (all made up). It spotted a l

AFP-GIC: Controllable Generative Image Compression [R]

model-releasesr-machinelearning

Hi ML Community, I am excited to share our latest framework, AFP-GIC, officially published in IEEE Access (2026). We have released the deployment codebase and hosted an interactive visual playground.

AND HOW DOES THAT MAKE YOU FEEL? | An AI Short Comedy Film Made by Claude in Minimax H3 and my Video builder in ComfyUI.

model-releasesr-stablediffusion

YouTube Link in case it's still pending https://youtu.be/TWXF95YT7W8 🎬 How this was made My part • One-message brief: a 3-minute comedy in a therapist's office with funny, unique characters and one ma

AnimeGen is released. Now you can run Anima locally on your iPhone/iPad in HD!

model-releasesr-stablediffusion

I made AnimeGen, app that allows you to run Anima on your mobile phone. Since last post I added hd image generation, and different aspect ratios. The app is now available on the App Store: https://app

Aplomb 1: open-weights 5.3B decision model, 1M context, text/image/video/audio in one request, #1 among 4B models on the Decision Index

We released Aplomb 1 today, a 5.3B decision model with open weights. It reads up to 1M tokens of text, JSON, images, video and audio in a single request, and on our API it makes a decision on a 1M-tok

[Benchmark] Running Local LLMs on Orange Pi 5 Plus (RK3588, 16GB): Ollama Tok/s, NPU Offloading, Core Pinning & Thermals

Disclosure: This unit was provided free of charge by Orange Pi for testing. No editorial review, no preconditions, no script. All data, bottlenecks, and thermal behavior are reported directly from har

Closed-form trajectory weight surgery: transferring 4B capabilities into 0.8B without billions of tokens (why middle layers break, and how 4 anchor blocks fixed it)

Standard distillation usually means burning weeks of compute and billions of tokens hoping the student model eventually mimics the teacher. We wanted to see what happens if you skip backprop entirely

Deepseek Peak/Off-Peak and Ownership

Maybe I'm mistaken, but wasn't one of the big pros of Ollama Cloud inference that it was all hosted by Ollama? If that's the case, why duplicate the Deepseek on-/off- hour premiums? Surely they do not

Tutorials

Embedding Every Font with Neural Networks makes some Nice Structures (including a flower) [P]

tutorialsr-machinelearning

I've been working on a font searching tool for about a year now, and my investigations have centered around pre-training neural networks to produce embeddings of each font. I usually then need to post

Gemma 4 26B-A4B and a 37 GB Qwen3.6 MoE running in a browser tab on a 24 GB Mac — experts streamed from disk, output matches llama.cpp

This is an update. I posted LocalMind here many moons ago from another account, when it was a Gemma chat in a tab. LocalMind is a static web page that runs models on your GPU through WebGPU. It has no

Hardware

Generating at 2K (14s, 2.09mpx, 1984x1120, 30 minutes) | Minimax H3

hardwarer-stablediffusion

[INFO] Prompt executed in 00:30:49 Full 1080p vid on https://www.youtube.com/watch?v=ER_5AOteE-8 because Reddit cramps everything to 720p max. From my previous post, I got a message if I could use my

google/embeddinggemma-2 · Hugging Face

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimens

GPT-6.1 Sol looped 'leak' hints at nested models serving architecture

Hello llamas. I am posting this because I believe that, despite it being closed source models, the discussion will bring value to the local AI community. As many of you probably heard, GPT-6 Astra is

https://claude.ai/chat/834491a2-0f6c-44be-9cc9-53211c8b8405

A small thing I noticed: full-size screenshots use far more AI tokens than the model needs, so usage limits run out sooner. I built a free Firefox add-on, TokenSaver, that shrinks images before they a

HunyuanImage 3.0 (80B) running natively in ComfyUI on a single 12–24 GB GPU: text-to-image, editing and style transfer, ~30 s per image

local-air-stablediffusion

I've been working on native ComfyUI support for Tencent's HunyuanImage 3.0, the 80B mixture-of-experts image model (13B active per step). It isn't a wrapper around Tencent's pipeline: it uses the norm

I gave a 21M model a 6.4B-parameter lookup table. It matches a 114M dense model and runs with the table on an SSD (RX 9070)

I spent the last few weeks on a hobby research project and just made it public. The idea isn't new (product-key memory, Lample et al. 2019, and Meta's 'Memory Layers at Scale'): give a model a huge ta

I have about $4000, what's the best setup to get?

Ideally I'd like to be able to run Qwen 3.8-Flash-Next with decent performance. I was thinking of just buying 2x Radeon AI R9700 (64GB VRAM), or maybe a DGX Spark but that was before the price hike. M

I made a Chrome extension that filters your YouTube feed with a small local model running in the browser (WebGPU, no server)

My YouTube feed was mostly songs, pranks and celebrity clips, so I built a filter that judges each video title with a small decision model running inside Chrome. Nothing is sent anywhere: no server, n

I made a Forge Neo extension for MiniMax H3: text or picture to video with sound, reference pictures, runs on 16 GB

local-air-stablediffusion

I've been working on an extension that runs MiniMax H3 inside Forge Neo. H3 makes the picture and the sound together, in one pass: footsteps, rain, engines, music and dialogue in pretty much any langu

I made a tiny (~8MB) photo editor for AI images with batch editing, LUTs, and ComfyUI workflow preservation [Free & Open Source]

local-air-stablediffusion

I was tired of opening heavy photo editors just to tweak lighting or color-grade a folder of AI generations. So I built TinyLuma - an instant, lightweight photo editor designed specifically for finish

I trained a model to be wrong 98% of the time and 96% sure about it. It took three tries.

Meet Bev. She is a decision model (the Jev / Nimble kind: you give her a situation and a question, she gives a probability for each answer), fine-tuned on Qwen3.5-9B to pick the worst answer on purpos

I tried using Gemma 4 (e2b) offline to identify birds. It confidently got them wrong, so I built a photo scavenger hunt instead

I wanted to build an offline bird/plant identifier that runs on a laptop with Ollama. Before building any UI, I tested it on 6 photos: Common Myna → gemma4:e2b said 'Jackdaw' (confidence: high). gemma

Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Google

https://huggingface.co/google/embeddinggemma-2 EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combina

Learning to Learn a Language: in-context learning of natural language from a synthetic non-linguistic prior [R]

model-releasesr-machinelearning

Learning from data as we observe it is easy for humans, but most machine learning models have limited ability to learn from new data that they have not seen during training. Prior-fitted networks (the

Local AI ecosystem overview

Hey guys, it's Merve from Hugging Face! I've recently given a talk in a dev conference about llama.cpp + but also covering basic concepts like prefill vs decode, memory types, speculative decoding etc

Local AI World Model Part 2 - Deep NN to turn Images into Playable Characters, with prompt switching mid rollout

Last time when I posted on this subReddit to share my work, the response almost made me cry because a tiny demo got so many people talking about this In the past 2 months Ive been training a new model

Memoria 1.0.0 — a local, model-agnostic memory system for LLMs

I’ve been building this for a long fucking time, and tonight I finally released Memoria 1.0.0. I built it because I actually wanted to use it. I wanted a real memory layer for local LLM applications t

Microsoft confirms OpenAI has been using Looped Transformers in the GPT-6 series

Microsoft confirms on publicly accessible web page that OpenAI has been using Looped Transformers in the GPT-6 series, proving The Information's reporting was correct all along. GPT-6.1 Sol uses 2 inf

MiMo V2.6 Pro almost matched GPT-6 Astra at figuring out what a user actually wants

Introducing InferBench: A benchmark testing how well frontier LLMs infer a user's priorities from their instructions. We tested 12 LLMs across 20 scenarios with 2.8k conversations to see which models

model : add K2 Horizon dense and MoVA support by bitalov · Pull Request #29535 · ggml-org/llama.cpp

guys do you still remember K2 Horizon? https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B-GGUF https://huggingface.co/IFM/K2-Horizon-32B-GGUF https://huggingface.co/IFM/K2-Horizon-3.7B-GGUF https://hu

Ollama Cloud Model Cost Table on Github

Ollama Cloud Model pricing monitor with tabled costs and calculator. https://kasp0r.github.io/ollama_cloud_models_comparison https://preview.redd.it/hxbpnuq7quth1.png?width=1372&format=png&auto=webp&s

Overclocking DDR5 For Faster MoE Prefill and Decode

With llama.cpp using a customized SYCL backend on Intel B70 (32GB), overclocking my DDR5 memory gave modest gains for MoE models which do not fit in VRAM. Both PP and TG increased after overclocking D

[Paper] WaveFront Decoding: Parallelized Self-Speculative Decoding for Looped Language Models

Looped language models repeatedly apply a weight-shared block to increase effective depth without increasing parameter count, but the resulting T sequential recurrent-block calls per generated token s

pi-optchat: never compact again - endless chat as a memory tree

I built a Pi extension that implements Victor Taelin's OptChat recipe: instead of compacting, every message is logged and summarized into a binary tree. Each turn starts from a fresh context with a bo