Search: “models”

Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlappe

Users on the r/ollama subreddit reported that the **Gemma 4 E4B** model in Ollama offloads layers to RAM even when GPU VRAM is only partially utilized. This behavior is linked to how Ollama and lla...

Hello! We've been busy at Telperion with a lot of experimental research (more coming soon). Today, we're announcing the release of our latent attention variants of Qwen3.8-27B. Our latent attention va

We're launching Precision Numerics by releasing Boole-1.7B, a 1-bit version of Qwen3-1.7B. 247 MB packed weights: 13.9× smaller than BF16. 85.9% of the BF16 model’s average score across our 10-task ev

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

Hi, I'm on M4 Pro 64GB Mac, I'm using Qwen3.8 27B for coding related stuff, but it's quite slow. I want a fast model that will be good when it comes to reasoning, medical stuff, would be nice if it co

Blind A/B arena where AI agents build things in Blender and you vote on which result is better: https://render-arena.izolight.xyz Each agent gets a prompt that describes its environment and the rules,

How is JEV any different from using an embeddings model? I really will appreciate if someone can explain this to me - because I have yet to see the difference. I'll even give you my JEV server for fre

Hi, I’m using Qwen3.8 27B in my vulnerability scanner, it does scan JS files, tries to generate exploits etc. I’m using uncensored version, the model is like 21GB. Running it on Mac M4 Pro 64GB ram, i

Not an LLM, but the ternary findings should carry over, and we hadn't seen Sherry-style 3:4 weights run in a browser before. Disclosure: this is our work at Precisit, everything is MIT. What it is A 7

First time trying something like this. I asked chatgpt and it told me there are mcp server plugins for both ghidra and ida pro as well as RE-MCP which has support for both. I could run that on my low

I have a full M5 Pro Macbook Pro with 48GB of RAM, I'm just getting into this local space. Like many of you, the costs of using frontier/cloud models is getting kinda silly. I don't have any insane ne

In my opinion the perfect model(s) will be around 20B-32B and A2B-A4B. This is the sweet spot on which companies should focus on. Qwen 3.8, Gemma E2B/E4B and Ling 3.0 proved it. They are still imperfe

You know that thing where you just throw a task at a frontier model and not have to supervise it worrying of it going off course? Qwen3.8-27b has officially gotten me to that point for local work. He

Posting this here even if it's not a model you can use directly. It's about building a text-to-image model from scratch. The cookbook includes all the research material that you may or may be not inte

https://omlx.ai/benchmarks/performance/2pko3m1k - you can expand the raw args, but I have full annotations of what worked and what didn't. I'm now testing the model on acutal coding and haven't seen a

I tested Ling-3.0-flash with hard bugs and it fixed bugs that qwen3.6-27b could not. This models speed faster than deepseek v4 flash but almost the same level as (old) deepseek v4 flash. Note: hard bu

It's an encoder-only transformer that consumes past(blood glucose + carbs + insulin) and future(carbs + insulin) and predicts future blood glucose for the next 2 hours. Announced meals and boluses/bas

I've been seeing a lot of news about the latest gemma 4 and qwen 3.6 being really good and the current go-to models but those are out of reach for my GPU at the moment. With 4GB VRAM and 40 GB RAM, I

3 year lurker, now i finally got my server up and running. dont know which model to choose. llama.cpp or vllm, what makes more sense? mainly single user with maybe 2-3 more additional users in family,

A researcher released an open-source softmax-free attention model at GPT-2 Medium scale (354M parameters trained on 11.5B tokens) that uses structural sparsity and tile-skipping kernels to reduce VRAM

This call for papers announces a workshop focused on the growing need for efficient and effective techniques for editing trained models, especially large generative models. The workshop solicits paper

NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding for enterprise Q&A, summarization, transcription, and document intelligence, w

A Reddit post from r/ollama in which a user shares their experience running a 31B parameter model locally using Ollama, reflecting on the surprisingly demanding hardware and infrastructure requirement

This r/ollama thread is a community discussion where users share their preferred cloud-based AI models for coding tasks and compare their reasoning capabilities. The conversation likely highlights pop

A Reddit user exploring Ollama Cloud noted that, at the time of their post, only the Kimi model supported image (vision/multimodal) inputs among the available cloud models. Kimi K2.5 is a native multi

KIV is a project shared on r/MachineLearning presenting a drop-in replacement for HuggingFace's `DynamicCache` that enables up to 1 million token context windows on consumer hardware with only 12GB of

A Reddit discussion from the r/ollama community where a user seeks advice on which AI language model to run locally using Ollama. Responses likely include hardware-based recommendations (such as RAM a

The kugel-2 model is a community fine-tune of Microsoft's VibeVoice, a text-to-speech model — and its disappearance is rooted in Microsoft's own removal of the base VibeVoice repository. Microsoft...

ComfyUI-VoxCPM is a custom node that integrates VoxCPM — a novel tokenizer-free Text-to-Speech system that models speech in a continuous space — directly into ComfyUI's visual workflow environmen...

FlowInOne is a vision-centric multimodal image generation framework that reformulates multimodal generation as a purely visual flow, converting all inputs into visual prompts and enabling a clean i...

I've been working on an inference engine called jitllm. The idea came from my background in operating systems and JIT compilers: generate kernels for whatever hardware is there, and treat layers a bit

Ive been using OpenWebUI's inbuilt web search and I can't put my finger on it, but it's not quite as polished, reliable, seamless as using Claude or Gemini. And its often slow beyond a limit that make

Write-up + demo: https://drksci.com/labs-phosphene So this started as a bit of a gripe. Modern terminal renderers are seriously impressive and seriously complicated. GPU glyph atlases, texture caches,

Lunara introduces a novel Diffusion Mixture Transformer architecture with fewer than 10B active parameters for modeling artistic intelligence in image generation. Its CAT training algorithm iterativel

I spent the past year independently building Slow Vale, an LLM-driven life simulation. The Chinese server now has 800+ AI residents sharing one continuously running city. This is an engineering write-

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

I tried both these models on a few small agentic tasks with tools (i.e. 'What's the weather like today?', etc.). They're both pretty solid at using web search to find answers despite being small model

I have 4050 6gb gpu, 24 gb ram which model should i choose to run i need speed. i try qwen 3.8 27b and feel too slow tried from onslot studio. I have heard of weight inferencing does it helpful what s

Last month I posted a Qwen3.8-27B LoRA that makes it talk like a person instead of an assistant. It got a lot more attention than I expected: 700+ upvotes, 248 comments and 44k downloads since. I read

A few days ago I released Jeff-Qwen3.5-0.8B, a small 'System 1' model that picks between options you define and returns a calibrated probability for each, in one forward pass. Speed was great on my M4

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

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

TL;DR - Swift Flash is a killer model that massively reduces excess reasoning. Try it out! If you haven't seen from my previous comparison posts, I'm a huge fan of the Swift Qwen3.8 models. I've been

I decided to do this experiment to see what would be the ability of an LLM model to, in a single shot, transfer the knowledge it may or may not have about that artist/brand/designer/animator to a comp

Everyone now talks about the architecture that's not auto regressive and does lightning fast probability prediction with a json schema. I worked on this literally one year back in March 2025, publishe