Search: “model-releases”

Trained locally: ultra-fast 0.8B/2B System 1 decision models that match Jev on benchmarks and Doom, ~30 ms per decision (open weights)

TL;DR: The Jeff models are a set of Qwen3.5 and Gemma fine-tunes for zero-shot classification: small, efficient, open-weight models with respectable out-of-the-box performance that can be slotted righ

28 Sept 2026

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Watch me post-train AliceAI-Foundation-80B-A3B from base to instruct at home, live, on my V100s!

No click bait baby I promise - I'm live streaming the training process kinda like MiMo. Original thread: https://www.reddit.com/r/LocalLLaMA/comments/1wpg4a8/im_trying_to_posttrain/ If you didn't see

28 Sept 2026

2x Tesla p100s, q6_k quant, Qwen 3.8 27B ~60tps V3.0

Hey guys! I have been excited to share this here. This is a project consisting of kernel optimizations for the Tesla p100 series graphics card ($80). I want to start by saying I am 17 years old and do

Adding logit penalty for 'wait', 'maybe' and 'perhaps' to Qwen models improves their accuracy

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

Advice on models for RAG use case

Hi all, I'm looking for some advice picking models. I'm looking to try a project to ingest quite a large amount of docments into a knowledgebase, allowing me and others to ask questions about the data

Another 'Harness matters' post (codex cli > pi and opencode)

I run my own LLM while also having a Openai subscription. Also tried DeepSeek (latest flash now). I run Qwen 3.8 flash Next at an amazing speed on my 2x3090 + Ram! But local LLM never did worked for m

Don't trust frontier models when asking about budget hardware!

Early this year when I was first looking at building up my inference capability you could get the 16GB Tesla P100s for between 60 and 80. Asked claude about it, told me absolutely not worth it. No ten

Fizgig 6.5.0 - Qwen Image 2.1 and a new Qwen Image 2.1 Training Adapter

model-releasesr-stablediffusion

Fizgig 6.5 is out with Qwen Image 2.1 LoRA and LoKR training, turbo previews, and all the workbench tools. Supports 10GB VRAM+. I also trained a new training adapter as the existing one felt like it l

Gem16 - custom engine for Gemma4 12B & 26B on Blackwell 16GB GPUs

It’s probably a bit niche and the models are a bit old at this point, but after reading about Ninfer a few months ago I did my own small vibe coded engine project for my 5080 Laptop GPU. Initially I t

I ran a controlled Krea 2 character-training test and wanted to share the results because a few things surprised me.

model-releasesr-stablediffusion

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

I still can't believe I can generate this all locally (Minimax H3)

model-releasesr-stablediffusion

Spent the whole day today trying to create this random transformation video. In total, I generated around 180 videos. A few initial images were generated with Krea2, and later all edits were done with

Imbalanced VRAM usage between two GPUs in llama.cpp. Anyone successfully solve this?

There is always at least 1+GB of VRAM not usable not matter how I set the --tensor-split (-ts) param. I tiny shift toward one side will move the weight significantly to the other side. 😵‍💫 Adjusting c

Nonobench v1.2: 43 LLMs on nonogram puzzles. Open-weight DeepSeek V4 Pro ties for 4th, and no open model solves the new 20×20 Hard mode

Follow-up to my January post: https://www.reddit.com/r/LocalLLaMA/comments/1q4i19c/benchmarking_23_llms_on_nonogram_logic_puzzle/. That thread shaped v1.2: Reasoning effort is explicit per run Every p

Power Limits, Local AI, and Questionable Uses of My Free Time

Good day data nerds! I am trying to get more into running models, learning about agentic workflows, and creating my own tools. But as you do (right?) I had to fine tune my current setup. With the way

Public MCP server for Canadian privacy law data (free, no auth) - works with any client that speaks Streamable HTTP

I built this, so the disclosure goes up front. It's a free, public MCP server plus a REST API with Canadian privacy law data. The MCP endpoint is at https://movahedi.ca/mcp and it uses Streamable HTTP

Qwen 3.8 27B vs Qwen 3.8 Flash Next and time to complete a coding task.

I am new to all this so still a lot to learn. If I give Qwen3.8 27B a coding task to fix some bugs in some code, it went through and found and fixed several in like 5 or 10 minutes. I gave qwen flash

Qwen company already rushed out a Jev competitor. No open weights yet.

EDIT: About that, I tried it out, and it's garbage so far. I did some basic tests through AIHubMix (do not use that platform btw, it's trash), and my agent did some comparison. I guess it figures, it

Qwen plays World of Warcraft

Been doing a bunch of vibe coding lately. Had my agents host a private WoW server for me, then built out a web browser client so you can play without installing the game and it has mobile controls. Af

Qwen, where's the small stuff? (1B/2B/4B)

I know Qwen is a key player in the local LLM space and has consistently introduced truly impactful technologies—like n-gram in Qwen-Next and the recent Qwen 3.8 27B, which is an amazing local model. H

Qwen3.8-Flash-Next (125B) at 12-15 tok/s on a 2021 32GB M1 Max

Hi! I'm the author of MoEspresso, which is my way of putting my own ideas about inference engines to the test. A lot of the fun has been trying different design choices, measuring what happens, and fi

Qwen3.8-Flash-Next 177B NVFP4(119GiB): SSD streaming at 9-10 tok/s on one 16 GB RTX 5060 Ti + 32 GB RAM

We built an inference engine for MoE models that don't fit in VRAM + RAM. Most of the model stays on the SSD, and experts are read as tokens need them. This started as a proof of concept, and poc work

Splash fork optimised for M5 Max: ~1.5× faster (1.25× single request)

I’ve spent the last couple of days with Opus 5.5 working on a fork of Inco’s excellent and already blazingly fast Splash engine to optimise it for M5 Max chips. Taking liberties and referring to it as

Swift 1.5 Qwen3.8 27b (A must-have for low thinking!)

Just made this post for those who missed it : https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b UkisAI released their updated Qwen 27B (tuned for token efficiency). I grabbed the IQ4_XS quant to tes

Tauon: A new optimizer outperforming Muon on GPT-Mini (lower loss, ~8.5% faster step time) [P]

model-releasesr-machinelearning

Hey r/MachineLearning! I’ve been working on a new optimizer called Tauon (turns out there is already 'teon' but well if you have better idea, - i will gladly accept it! Anyway the core idea of optimiz

Updated from 3x3090(2x3090, 1x3090TI) to 2x5090

Upgraded from 3x RTX 3090s to 2x RTX 5090s on my homelab server and picked up a solid speed jump on top of it from a software update (speculative decoding + NVFP4). Setup: llama.cpp (build b11216), ru

We released VeriLoop E2 (27B, Apache-2.0). The design question behind it: should an LLM be allowed to commit its own state?

Disclosure: I’m one of the authors of VeriLoop E2, a 27B model post-trained from Qwen3.8-27B. The weights are Apache-2.0; the harness we evaluate it in is not open source (details at the bottom). This

When is the next generation of 'B tier' models releasing?

The only things released in the last couple weeks seem to be bigger models like Qwen, DeepSeek, GLM, etc. Where are the Laguna's, Nemotrons, Olmo's, LongCats, Minimax, etc releases? submitted by /u/ja

Worth going from Qwen3.8 27B to flash next or maaybe deepseek v4 flash?

I am running qwen3.8 27b on my dual rtx 3090 (fp8 quant, unquantized cache, 129k context) and I think it works decently well with hermes, opencode etc. But! I am tempted by the new models coming out s

Zer0Fit - Zero-shot predictions, classifications, and regressions using Google ML research models running locally as a dockerized MCP

AI grad student here. With all the recent interest in Jev, I thought I would share something I built la few months ago that brings ML models and LLMs together in a different way than Jev does for diff

2400cc Inference Racer: Dual RTX 3090 motors, NVLink turbo, naked 7840U ThinkPad ECU, VW Golf radiator

Today I present a fine piece of engineering, carefully assembled inside a custom chipboard chassis: the 2400cc Inference Racer, a.k.a. my winter heater. Power comes from two second-hand AORUS RTX 3090

85 GB DeepSeek-V4-Flash at ~3 tok/s on a 12 GB RTX 3060 + 64 GB DDR5 RAM - Overspill for FreeToken, inspired by Colibri

I've been experimenting with ways to run MoE models that don't fit comfortably in RAM, and I ended up making Overspill, a disk tier for FreeToken. The basic idea came from looking at how Colibri handl

For the longest time I’ve felt this sub should have a pinned section where a detailed post about each model should get featured.

For instance whenever a model comes out, what’s the best engine to run it, the best harness and absolute minimum you need to get same or near same re results that the benchmark of that model claims. A

Getting stupidly good results on my 4x3060ti setup.

About a month ago I was having FOMO and was going to spend coin I don't really have on new graphics cards. Instead of doing that though I decided to spend the money on a new motherboard+cpu and try to

Has anyone used the Forrester function?[D]

model-releasesr-machinelearning

Hey guys, I came across the Forrester function recently while studying mathematics, and I started wondering if there’s more to it than just being a mathematical function. I have a feeling I’ve seen it

Houston we have a problem: Opus 5.5

First of all, im totally in love with OpenAI and i own 2 20x accounts. I rarely use other AI models, since chatgpt is perfect for what i need. But today i decided to try opus 5.5. On its cheapest paid

model-releasesr-chatgpt

How accessible is local AI actually, and what happens if affordable access to frontier models doesn’t last?

Sometimes it’s easy to forget that this sub and others like it are probably the extreme minority when it comes to this hobby. Most people, I would think, don’t use or can’t afford one good GPU, let al

I added Qwen-Image 2.1 + LoRA support to TensorSharp (GGUF, local inference)

I maintain TensorSharp, an open-source inference engine. It can now run Qwen-Image 2.1 locally for text-to-image generation and image editing, with support for its LoRA adapters. I’ve added configs fo

I asked Claude Code to make it's own version of that guy's 'time' video by Astra 5.6 from yesterday.

Original post: https://www.reddit.com/r/ChatGPT/comments/1wpddw4/ storyboard by u/dx8xb https://github.com/beebothecatwhathasfacialhair/time All Opus 5.5 I think I prefer the pacing and style of dx8xb

model-releasesr-chatgpt

I built a tiny (332MB) CPU-friendly model for document sorting that actually knows when to say 'none fits' (BeeNara)

Hey r/LocalLLaMA! I wanted to share a small project I’ve been working on called BeeNara Why I built this: I was looking for a way to automatically sort my local documents (invoices, letters, contrac

internlm/Intern-Decision 4B and 0.8B

https://huggingface.co/internlm/Intern-Decision-0.8B Intern-Decision-4B Demo | Model Weights | GitHub Intern-Decision-4B is a multimodal structured decision model fine-tuned from Qwen3.5-4B. It accept

Introducing KoboldCpp Agent (and a plea for help)

Hello r/localllama once again, it's me your kobold concedo Been a few months since I last posted here, and today I have something new I'd like to share. Specifically, KoboldCpp now ships with a built-

Is Chat GPT the right AI for statistics?

I am currently writing my IR thesis, and I am using quantitative regressions, I need help to learn how to conduct statistical calculations, interpret results of the calculations and research design. I

model-releasesr-chatgpt

Just bought a second 3090 but now I don't see the benefits right now.

Hi, My local AI server consists of 96GB Ddr5 and one rtx 3090. I got plenty of stuff running, like krea2, qwen image 2.1, minimax h3, ltx 2.5, qwen 3.6, qwen 3.8 q4...,got even qwen 3.8 Flash next run

Ling Tiny 3.0 is a glimpse of the future

I've been playing around with Ling 3.0 Tiny, which is an 8 billion parameter model (MoE, 1B active). And I've had a lot of poignant thoughts as a result. Just for fun, I got it running with llama.cpp

Qwen 3.8 flash next is based on Qwen 4 architecture, if the announced Qwen 4 27b is also the same architecture with n-grams does it mean I can actually have faster inference on a single 3090 without tweaking much?

I wish Qwen also released dataset and method to fully train a model ourselves but it is what it is. However, I come here with my stupid question because someone can answer it better. And will the mode

Qwen3.8-27B Q4_K_M on 2x3060

For the last few days, I've been using two computers to run multiple agents. My 4x3090 machine is running Qwen 3.8 27B with parallel=2, so I can run two agents at the same time. My pi instances are ru

Qwen3.8 flash next + exllamav3 + hermes is amazing

I know there is nothing new with what I am saying but I recently started with hermes agent (it’s been a while I wanted to but did not have the time). Qwen3.8fn 6bpw exl3 (from turboderp) on a 6x3090 (

Splash 1.1.0 released, GGUF quants support, MLX import and more

On my M5 Pro 64GB I can comfortably work in an agentic setup with the Qwen3.8 27B model in good quality (Unsloth UD-Q4_K_XL) at a decent speed of 50 t/s. Splash combines optimized kernels, excellent s