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In medium reasoning mode, it both scores higher than the 3.6 version, AND is very much more efficient (almost half requests needed, and a third less tokens generated) - at DeepSeek v4 Flash 3107 MXFP4 level The xhigh mode is advertised to be the best one for hard tasks. In this benchmark, howe
Mind you, it is 1.0 by default, yet everyone is focused on how much the new model thinks, restricting the reasoning budget and/or dropping the reasoning level. Set the temperature to 0.7 and the model will no longer write a whole book of thoughts before trying to make a small edit in the file. The q
Artificial analysis index scores Qwen 3.5 27b: 35 Qwen 3.6 27b: 38 Qwen 3.8 27b: 52 What the hell kind of a jump was that? Even if it is benchmaxxed, the jump is insane. Qwen3.6 35b A3b: 32 That's ~6 points behind its dense 27b model, but is ~5x faster for inference given only 3b active params/t
Just curious - I like to be able to see and manage the scripts my agent is working on. I find stuff like Claude Code and Open Code useful for doing stuff on my linux box but I don't understand why people would use that instead of a dedicated IDE where you can actually see the architecture of your pr
Yes, it sucks to waste time waiting on 16K+ reasoning tokens alone. But here's the thing, this is only a 27B model trying to perform on par with 1T+ parameter models. Something has to be sacrificed, and that sacrifice is the amount of reasoning or trajectory tokens. This isn't new to LLMs whatsoever
Guys, could someone smarter than me explain what makes Deepseek Harness so efficient? I run it with local Qwen 3.8 (Q6). I tried Opencode/Openchamber (my favourite so far), Pi agent and Hermes. New Qwen seems to overthing by default but this could be minimized with some effort. On another hand - in
Following up on my previous post about my budget server setup (Intel N100 + RTX 5060 Ti 16GB), a few of you asked for a deeper dive into my actual inference config and real-world agentic performance. Like many of you, I was refreshing the page waiting to download Qwen 3.8 27B the second it dro
We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning. Using hidden states as cache and carrier, reasoners repeatedly query memory for required kno
Overview EVIE-Preview-4.5B is a state-of-the-art multilingual Visual Document Retrieval (VDR) model built upon Qwen3.5-4B. It employs ColBERT-style late interaction with native 128-dimensional multi-vector token embeddings (4.54B parameters, BF16). By combining native GatedDeltaNet linear-atte
Qwen 27b Q3_K_M 2x rx 580 8gb (~50$ each in my country, edge cases 60$ per gpu) gives us 16gb vram We used it on an old already existing ddr3 motherboard with 2 gpu slots(you can buy it ror around 200$ with 32 gb of ddr3 ram, a workstation xeon cpu and a workstation motherboard, used) Its not
Yes it uses a ton more reasoning tokens than 3.6 did But test in on the same tasks with the other chinese models, glm 5.3, deepseek v4 flash and pro, etc it's really similar, and they are needed The reality is, we're just frustrated because our hardware do not allow most of us to have 1M context (I
Support for the new ling 3.0 models has been merged into llama.cpp: https://github.com/ggml-org/llama.cpp/pull/26608#event-29549472828 Ling tiny 8b1b - https://huggingface.co/inclusionAI/Ling-3.0-tiny Ling flash 124b5b - https://huggingface.co/inclusionAI/Ling-3.0-flash Both are reasoning models con
Sorry for the slop, but I was impressed by this model as I have been testing Qwen3.8-27B Q8_0 locally on my ROG Flow Z13 (Ryzen AI Max+ 395, 128 GB unified memory) and this model was the only one who could made this short simulator (and I have tested a lot of models). Prompt: "Create a beautif
Hi all! I finally just got around to testing out Qwen 3.8 27b. I'm using Unsloth's UD-Q8_K_XL quant as a sit-in replacement to Qwen 3.6 27b, same quant size. Wow -- this thing isn't messing around. I have many baseline test prompts to gauge the 'intelligence' and usability of the model, but a
https://developer.nvidia.com/blog/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72/ 4k tokens per second per GPU of which there are 72. 350 tokens per second per user "Without additional model tuning, the model achieves a throughput of over 4K tokens p
https://x.com/i/status/2088993948983246906 Not tested by me in any way :) submitted by /u/jacek2023 [link] [comments]
Including the rationalisation for the data below - this is a more robust version of an earlier post I did similar to this - explaining below: How I chose the comparisons The basic question I’m trying to answer is: when did an open model small enough to run on high-end consumer hardware reach r
In this commits, the 35B model was removed. Looks like it's confirming the 35B model won't get released. I think they need to be made aware how big the 35 moe is widely used. Think need to make noise on theyre X, huggingface and online places. If they dont know there's no need to release for p
If you are using llama-server with their web-ui for testing, keep in mind, that the reasoning selector is just a reasoning budget aka a hard cap and has, at least to my knowledge, nothing at all to do with Qwen3.8-27B's native reasoning effort capability! Selecting any value for reasoning in the web
https://preview.redd.it/wkg27e152qjh1.png?width=853&format=png&auto=webp&s=2e3f8b11ea6393041f501e95c5835f9bea0245dd So ive been trying different harnesses and coding agents with the new qwen 3.8 , and after trying out many ive been mostly impressed by deekseek harness , paired with qwen i get
submitted by /u/juanviera23 [link] [comments]
I did a short test of the different reasoning efforts, since on default xhigh the model thinks a lot. Not very scientific, just a quick "generate an SVG of a pelican on a bicycle" prompt with 3 different seeds. I think the result is interesting none the less: xhigh gives *much\* higher visual
I see everyone gushing over 3.8, I get the impression people find it drastically better than previous Qwen models, but I can't believe it could be better than 3.5 122B. Is it? submitted by /u/MackThax [link] [comments]
submitted by /u/InternationalGap3698 [link] [comments]
Been reading the eval table on the abliterated Qwen3.8-27B FP8 build instead of the release notes. It's published as red-team material, disclaimer and all, so the numbers are the interesting part. Refusal across the usual harmful-instruction sets (AdvBench, HarmBench, StrongREJECT and friends) reads
I was surprised by the fact that the qwen 3.8 27b download count is about 1 million (globally). This means that even on this subreddit, very few people have used 27b. At most 50k–100k active users, and once you break down the hardware distribution, 8GB, 16GB, 24GB, 32GB cards, Macs, whatever, it's p
one of my llm hobbies is re-creating graphics demos i used to write in BASIC in the late 1980s. i slopped together an agentic harness and a basic-to-js transpiler in a web page i've been playing with for a few months. the agent can write basic programs, run them, examine the resulting images,
https://github.com/alekk89/llama-cpp-windows-manager It has been three months since I first shared my personal solution for running llama.cpp on Windows, and the project has evolved considerably since then. The goal was to create a simple visual interface for managing llama.cpp runtimes, switc
The two leading labs are already feeling extremely threatened by Qwen & friends, however I think there are tons of enterprises and organizations in the West that don't feel comfortable using Chinese models. I believe these orgs would be all over a near-frontier open-weight model with the Google bran
Quick update on the RTX 5060 Ti local LLM repo. It has changed quite a bit since my previous posts. The project started as a collection of practical notes and benchmark results. That was useful, but as the dataset grew it became harder to answer the question most people actually had: What configur