Search: “model-releases”

# the What An engine to run Gemma 4 31B on blackwell under massive concurrency and rather specific workload patterns. I've been waiting for someone to do ninfer but for gemma, and, well, ended up havi

Hi all! After all the jev hype I wanted to try by myself and got qwen3.8 27b to mix-and-match open implementations on top of ninfer, and to be honest I'm quite impressed with the results, latency is n

I want to try using AI to find possibly overlooked interventions for irritable bowel syndrome (IBS) and create a cocktail of interventions that AI thinks would give the highest expected benefit, attac

What if you don’t need a new model to get Jev-style decisions? I built hearim(헤아림. hea-a-rim. not Here, I am.. lol), an open-source gateway that turns ordinary local or hosted LLMs into a Jev-compatib

I tried putting up with it the last week or two but it's become unusable. I've tried GLM 5.3 & 5.3 Flash, GLM 5.2, and Deepseek V4.1 The inference and pp speeds have become unbelievably slow, I just c

Hey everyone, I wanted to share a project I’ve been working on called Jayce. The whole thing started because I was watching a toddler named learn the names of stuff He didn't need to completely rewire

Hey everyone, A few classmates and I got tired of the usual browser AI workflow: copying text from a documentation page, opening a separate tab, pasting it into a chatbot, and switching back. We also

I never thought there would be a model worse than the Gemini series, but here we are. I remember Elon tweeting that this model would beat Astra & 5.1 across all benchmarks. submitted by /u/prvthvm [li

A few days ago, I came across a post/comment saying that you can get better data privacy by using local Ollama models in a RAG pipeline instead of relying on cloud-hosted models/APIs. The explanation

Source: Jev Benchmarks Its training method is literally called 'Reinforcement Learning for Calibrated Decisions.' Calibration gap vs human labels (lower = better): Yes/no: Jev 5.0, Gemini 3.8 Flash 2.

been working on this thing called OpenComms for a bit. basically i keep multiple coding agents open at once, usually stuff like a lead, builder, reviewer etc, and i got sick of copying messages betwee

Hey everyone! It has been quite a while since the last SupraLabs model - but today we've something special for y'all: Supra2-IMG It's a 100M parameter DiT text-to-image model trained entirely from scr

Since there’s no comparison chart on the model page, I asked Perplexity to compare it against some relatively small open-weight models in a similar size range. Here are the results. Upd. Terminal-Benc

Sorry for the pretentious name, I know, I know.. It just contains all the pieces I would like to see a AGI model to have, and I can't stand the temptation. Before throwing rocks at me, please take a g

I'm one of the maintainers of SurfSense, so this is self-promotion. I'm posting here because the model-picking part is what I think this sub will have opinions on, and I want real feedback more than u

If anyone is interested in using Ollama with the Prism-ML model prism-ml/Bonsai-27B-gguf · Hugging Face I setup a repo with an install script that patches Ollama and pulls the Prism-LM llama.cpp code

Small, free finding. I use local Qwen models for typed decisions: a state plus a question with fixed allowed answers, and I read the probability of each answer from the logits of one forward pass inst

https://preview.redd.it/nulsv53o8vqh1.png?width=4500&format=png&auto=webp&s=74765dbd409f4c221640f9f6000a685f6fdbb242 Spent weekend benchmarking the Splash engine (by Incoai) and extending its architec

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

I have been reasonably satisfied with my single R9700 (32GB) as I can run practical quants of Qwen 3.8-27B at good speeds, as well as other similar models in its weight class (Gemma 4 is still my go-t

https://huggingface.co/yandex/AliceAI-Foundation-80B-A3B-Base It's not a Qwen3 finetune, it's actually its own fully custom architecture. No Llama.cpp support yet sadly (Also note that this model is N

You can simply run any GGUF with llama.cpp with n_predict=1 and n_probs=10, disable reasoning, and prompt it such as 'If the following email is spam, respond with 1, if not spam, respond with 0. Do no

ZCode is now open source, and the reported security issues have been addressed. Source code: https://github.com/zai-org/ZCode The repo includes its desktop app, web workspace, backend, Agent CLI, and

This weekend, I did a fun experiment to create something similar to Jev. I LoRA fine-tuned Qwen3.5 4B using a mix of publicly available datasets and synthetic data. For the synthetic data, I used Deep

Jev from TypeSafe AI got me thinking about non-autoregressive decision engines, so I built a small proof-of-concept called AES to test the concept locally on open weights. It uses Qwen/Qwen2.5-1.5B-In

Not a pic sharing thread, bunch of those already - mainly just a discussion about first impressions Very underwhelming to me personally. I know its a matter of settings but normally comfy default temp

So this jev thingy is getting kind of big... tbh it seems overhyped by a large margin, but here we are. Not that its bad, just feels like we usually ignored larger things... Anyway to the point of thi

Hey everyone, After wrestling with hardware and engine issues for days, I finally got Qwen 3.8 Next running properly on my multi-GPU rig. Thought I’d share the setup journey, benchmarks, and thermal r

I forked llama.cpp to implement Declarative Attention (arXiv:2609.02737, Google DeepMind and KAIST AI). The model declares in its own output which context chunks it needs <focus magic_chunks='N'>), an

i have been running local models as browser agents on an m5 macbook pro, 128gb, all through mlx, no cloud. first i just wanted to know which one was faster at real browser work, so i gave gemma 4 31b

Hey everyone, Like a lot of people here, I’ve been experimenting with running models locally through Ollama. Athena started as a small experiment, but it quickly grew into something more, a modular Py

I’ve spent basically the last 8 hours testing different models on the exact same web-development prompt, and I finally finished. The whole point of this nine-hour test was that which local model match

I got early access to Qwen-Image-2.1 My first impression: Fantastic editing, new standard for open models, support for a lot of references (I tested with 10), consistent, pixel precise at 2K, text ren

Don't get me wrong, not hating on Qwen 2.1 but it DOES have all the hallmarks of GPT's weird 'texture' all over it. Is this just a distilled model of one of the GPT Image models? Similar how many LLMs

Typesafe's new model, Jev is all over the internet, this sub etc as a new classifier model. Then an AI dev posted here how they'd done something very similar, and made a new model, Laya. https://www.r

Hello! So, yesterday i tried out Gemma 4 26B A4B QAT Q4_K_XL from here with Q4_0 MTP model in Pi agent. Suddenly, it became the first model to actually pass my simple test with HTTP server in C++, whi

From the blog post: https://qwen.ai/blog?id=qwen-image-2.1 https://preview.redd.it/bbm23jxddoqh1.png?width=3333&format=png&auto=webp&s=692aedcbcd43f33fff2c8094e2e1568d0d722ab0 Native Transparency http

Hey r/LocalLLaMA! I just released PromptDeck v1.1.0 and wanted to share it here since this community is basically the reason it exists. What it is: an open-source desktop studio (Tauri + React) for pr

Meet Qwen-Image-2.1, the most balanced and cost-effective image generation model in the Qwen-Image series! Now open weights! 🎨 A unified model for both generation and editing, delivering top-tier qual

Released: Update your ComfyUI for workflow Weights: https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main Variety of Examples https://youtube.com/watch?v=tP_gGGJzEqg&feature=youtu.be https://model

Overclocking the CMP 170HX 40GB I was able to get the memory bandwidth from 1,386.2 GB/s to 1,890.1 GB/s, that's a +36.4% increase. Qwen 3.8 27B token generation jumped from 110 T/S to 202 T/S, same c

​ ​I wanted to share a quick update and performance video running the full Moonshot AI Kimi K3 (moonshotai/Kimi-K3) model across my 16x GB10 cluster. ​Getting a 2.8T parameter model running smoothly r

Sharing my successful experience here. My setup: RTX 5060 Ti 16GB (x16 PCIe slot) RTX 3060 12GB (x4 PCIe slot) RAM: 32GB CPU: Ryzen 5 5600 OS: Linux So I have a total of 28GB. Average tk/s: 50, and th

TL;DR: Local agent loop, ~21 days, one RTX 3090. Task was pretty much 'build a CUDA inference engine for optimized for yourself on this GPU arch.' Got working kernels and benches, not a win over llama

I have been anti-low quant. I can feel the difference, I swear. I am also anti-quantized KV cache. I have been burned there - I think the tradeoffs compound, especially at longer contexts. Due to this

hey! we're a small swiss and south african lab and we just put out hemmingway-1. it's a 27b model we trained specifically for roleplay, creative writing, dialogue, stories, and for everyday texting. f