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I chose to do this thing, not because it was hard, but because it was silly. Posts kept discussing how 3.8 and 3.6 were functionally the same, but based on training (3.8 does have seven new tokens!)..

this screenshot is without MTP usage , fully offloaded onto the GPU. using LM STUDIO i wanted to ask you guys if theres a way to make it even faster , as MTP really didnt help and is infact slower due

This is gonna seem crazy off-topic, but I saw Spider-Man the other day and couldn’t help but notice how well executed E.V. is as an agentic system. compared to Jarvis, it felt much more grounded and c

They released both GGUFs & custom llama.cpp fork today. 35B MOE in 7GB size which's good for Mobile & Edge devices(Also low memory systems). Up to 120 t/s on Consumer Laptop. GGUFs: https://huggingfac

From the screenshots: Hy4 is now live, labeled 'Expert-Level Model' + 'Use Tools to Solve Problems' Hy3 is tagged with 'New Upgrade,' positioned as a brand-new general-purpose model DeepSeek, focused

Component Validated configuration Motherboard ASRock Rack SPC621D8U-2T/OVH CPU Xeon Gold 6330 (Get gold/platinum if interested in Optane Pmem gimmicks) GPU fabric Two Broadcom/PLX PEX88096 islands, ei

Worked some time ago on one of the ad teams at Yahoo, and this grew out of a question I kept returning to while there are there 'simple' models that are both simple, scalable, interpretable, and contr

Hey everyone, Posted TinySearch here a few versions ago and got a bunch of useful feedback, so figured I'd post an update because the thing has changed quite a bit since then. Repo: [https://github.co

https://preview.redd.it/xel6i6kujjkh1.png?width=2396&format=png&auto=webp&s=b78cd87da266324364e6b8cb8457a7f0e029d163 https://preview.redd.it/ofy8ttyxjjkh1.png?width=2396&format=png&auto=webp&s=3e64405

Hello, any params I missing or mis-tuning for my coding model ? FROM ./models/unsloth/Qwen3.8-27B-Q5_K_M.gguf # Generation parameters optimized for logical reasoning and coding tasks PARAMETER tempera

seems like others are now using lower precision models. On open code sub i see a lot of people saying model has gone dumber since price hike started. How it is behaving in Ollama cloud PRO? Same? subm

I have been using local models on/off for like 2 years or so but never really used them extensively because the closed ones were always much better. Once Qwen 3.8 27B was released I decided to give it

None has undergone post-training, giving researchers flexible starting points for continued pre-training, fine-tuning, and further research. Two key highlights: - They use WSM to replace LR decay with

Trying to make an account so I can start using Odyssey and to download some stuff through PowerShell. It requires a sign-in, but I can't make an account on the site because i need a U.S number and I d

llama.cpp pr #27342 adds dflash2, so i rented an rtx 6000 and ran the same four prompts through four decoding setups on qwen3.8 27B median results over the four tasks: baseline 47.4 tok/s mtp 114.7 to

I think AA's intelligence/cost plot is seriously misleading, so I decided to make my own. Their plot is in the second image. All points are at max thinking. All intelligence index scores are from AA.

I’ve been using deepseek-v4-flash:0731 on Ollama Cloud, and recently it feels like my usage is getting consumed much faster than before. My prompts and general workflow haven’t changed much, but the a

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

The benchmark I used is BU bench v1, and the open source harness is Browser Agent. Qwen3.8 27B beat all other affordable or open models I tested. It's insanely good! submitted by /u/pierreb5 [link] [c

Hi everyone, I built an open-source Windows desktop utility called **Polish** that lets you highlight text or SQL in ANY app (Slack, Teams, Outlook, DBeaver, SQL Developer, browser) and rephrase or fi

Hello everyone, tried so hard to optimize my config and finally I simply get up to 70 t/s with q6 variant. And wanted to share with you guys so that other people with the same setup can enjoy. Please

Edit: Title says 134 tps, it's actually 138 -- keep in mind my 3090 is power limited to 250w. Three days ago I released a hyper-optimized Qwen3.8-27B inference engine for an RTX 3090 (82 tps single re

Hi folks, I have a 7800x3d + 32GB RAM + Rtx4090 and > 200TB of storage. I downloaded the Qwen 3.7 35B hauhaucs but it has been a disappointment so far because training data is too outdated (networking

Hey everyone! We’re releasing new Qwen3.8-27B GGUFs with 10% higher accuracy for the same size. This uses a new version of Dynamic v3.0 Unsloth Dynamic V3 outperforms others by >10% on Div-300, KLD &

https://huggingface.co/inclusionAI/Ling-3.0-tiny-base https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T https://huggingface.co/

I have been searching for suitable model to run on my 8GB RAM toy, NVIDIA Orin Nano Super 8GB. This little toy was priced at 249 earlier this year (not any more), and pulls very little power when idle

Four Tesla V100s from 2017 matched my RTX 5090 on single-request Qwen 3.8 decode. Repo: https://github.com/dnv2003/v100-skinny https://i.redd.it/5ws2ak3uqckh1.gif The 5090 was not being held back. It

PR by u/Stainless-Bacon 👍 It would be handy & awesome to have options --n-cpu-ffn / --cpu-ffn for Dense models like how we have --n-cpu-moe / --cpu-moe for MOE models. Also check his threads: On PR :

Aloha! 🌺Introducing Ornith-1.5, a family of open-source LLMs spanning 9B Dense, 35B MoE, and 397B MoE, trained with self-improving strategies. It achieves state-of-the-art performance among open-sourc

(my own experience) I have a mid-range gaming laptop from 2024 with 4GB VRAM and 16GB RAM. There is nothing that I can run other than small models at humble t/s. I mostly use the models to generate sc

Hi guys , this is a fine tune of qwen 3.5 4b on cybersecurity , specifically malware. This model has been trained on 2.6k rows of simple but effective malware code , though its still significantly lim

Howdy, I'm back again - running my favorite benchmark (it's still unsaturated for the time being so might as well!) previous runs a b https://github.com/michaelasper/benchmarks/blob/main/qwen3.8-27b-p

I've been working on a depth pruning approach and decided to try it out on the new Qwen3.8-27B model. I managed to get the model down to about 22.7B params without severe reasoning degradation. No fin

I trained three LLMs from scratch in raw PyTorch then post-trained each one with SFT and then GRPO. Same process every time: same synthetic arithmetic curriculum, same reward function, same hyperparam

Intermediate tokens, called 'thinking' or 'reasoning' actually are nothing like it. Humans do step-by-step reasoning leading to the conclusion. LLMs use intermediate traces to augment their prompt. Th

SCoPE: Sightline-Coordinate Positional Encoding for Video Diffusion Transformers SCoPE adds camera sightlines as positional coordinates to a pretrained video diffusion transformer. Given a first frame

Thoughts About Scaling Law Scaling, but not only of parameters. Every model release now ends with the same question: how many parameters? It isn't a question that can be answered on its own. Parameter

Any LLM will hallucinate the world knowledge, even a 3T model. Use a 4B with a kiwix skill and local Wikipedia, 50gb and no more hallucinated world knowledge. Ask your coding agent to build your own,

Hi all, I'm new here. I was playing around with the newly release open-weight model released by Meta, and I came across this when asking who it was made by, it said 'OpenAI'. Publicly, Meta had stated

Anyone tried them yet? https://huggingface.co/ornith-ai/Ornith-1.5-9B https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B https://huggingface.co/ornith-ai/Ornith-1.5-397B https://huggingface.co/ornith

ornith lab dropped new ornith 1.5 today, a 9B dense with vision and a 35B-A3B MoE, both MIT, trained on a loop that generates its own tasks. in addition there was giant 397b model, but we didn't quant

My current setup is basically OhMyPi+ OpenCode (mostly OpenCode Go: DeepSeek, Flash, V4, etc.) + Ollama Cloud for stuff like Kimi and GLM. But honestly, I feel like Ollama Cloud has gotten noticeably

DeepSeek released on a paper on pretrained engrams in January. I’m surprised ds didn’t release engrams with v4 pro. When will ds or another lab release a fixed engram model? Fixed engrams will be the

Bonsai / Ternary Bonsai During April Bonsai came with bunch of models .... 1-bit & 1.58-bit(Ternary) versions. And last month(July) they released 27B models in same versions. Last month itself, Bonsai

I know why Qwen3.8 27B shipped with xhigh reasoning as default, it's to do its best in benchmarks. Models from top labs often get benchmarked at multiple reasoning levels, but that same treatment does

I know the front page is completely dominated by the Qwen3.8 27B. I know it's a fantastic model and everyone is rightfully discussing it, but I wanted to shift gears and talk about another model I’ve