Search: “reasoning”

Qwen just released their first 3.8 model. The main addition in 3.8 is prompt-steered reasoning effort. You can tell the model how deeply to think by setting reasoning_effort to xhigh, medium, or low.

Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without 'attacking' the reasoning length directly then fixe

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

'Again?' you might ask. I(*) took the original Qwen 27B 3.8 chat template and compared it to the improved template that was posted a day before. The original has issues. The improved one fixed some wh

I am considering dumping my ChatGPT Plus subscription and go full local, but to do so I would first need to reach a decent result for quality (and reasonable speed). My 4090 fried itself out of nowher

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

Astra's launch has produced a strange discourse. The reporting that broke the story framed the model's use of recurrent depth primarily as a safety regression, because it means the model reveals less

As i use a rather tiny setup 12gb vram gpu and 32bg ram, i was wondering: What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length

Screenshot of heavy load. I did offload vision to a spare 2060 instead of local ram. The control panel was generated with qwen3.8-27b davidau q4k_m max mtp. Made the panel from scratch over a week, ba

## Qwen3.8-Flash-Next-NVFP4 (inferact) vs Qwen3.8-27B-FP8 (qwen) Slammed with work and no time to pretty this up. Qwen wrote most of this but I checked the data. All tests done on the same rig, same p

Introduction RxBrain (Hy-Embodied-RxBrain-1.0) is a unified multimodal foundation model for embodied cognition — a single model that couples language reasoning with visual imagination to deliver three

I keep replaying that night in my head because we came way too close to doing nothing. We were eating dinner when my dad dropped his chopsticks. He picked them up, dropped them again, then got annoyed

This sub is, needless to say very niche and skewed towards the high end. There are tons of extremely high end setups here with multiple gpu's etc. Even 24GB is out of reach of most people financially,

I have various quants of this model and am curious how they perform. can anyone recommend which benchmark would be a good test case for quantization effects? Maybe that can be completed with about 1 m

This post describes a user's account suspension by OpenAI shortly after making a payment, resulting in loss of access to multiple Codex agents and client-dependent income, with no explanation provided

A Reddit post on r/ChatGPT in which the author shares their findings after a year of testing and comparing LLM subscription services across different price tiers (e.g., 20/month, higher-cost tiers). T

I audited thousands of agent rollouts in DeepSWE-1.1. Over 80% contained reasoning about an imagined grader. Yet no grader/verifier is mentioned in prompts nor accessible to the agents. Agents reasone

I was running into a lot of posts that praised both the Fixed template and the Sharp template in comparison to the stock one, so I put them to the test. It's not as extensive as it should be for a pap

TL;DR - A ~4M example, ~3B token CoT dataset designed around helping small models think more concisely, accurately and reliably. Hi all! For the past few months I have been working on a dataset design

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 r

If you build on open-weight reasoning models, you have probably wanted a 'how sure is the model about this answer?' signal that does not cost 5x sampling, a judge model, or a labeled dataset. Hence, w

GoBench evaluates LLMs on 9x9 Go games against a ladder of KataGo opponents, from random to superhuman. It measures general reasoning ability, strongly correlates with ARC-AGI 2 (r=0.83 correlation),

Spent about two weeks convinced I had a prompting problem. My local Qwen3 agent would just stop. No error, no crash, finish_reason came back as 'stop' like the model had decided it was done. Except it

Qwen3.8-Flash-Next (UD-Q4_K_XL) on a single RTX 3090 24GB + 128GB DDR4 — is this config optimal? Hardware CPU: Intel Core i5-12600K RAM: 128 GB DDR4 @ 3600 MHz GPU: NVIDIA RTX 3090, 24 GB VRAM OS: Win

I ran 11 uncensored variants of Gemma 4 12B that I grabbed from huggingface, sorting by downloads. 10 full abliterations plus 2 LoRA adapters which were requested to be added in the comparison, agains

New/Old benchmark that provides a lot of answers for local LLM. I present to you a new test that I developed somewhat by accident: https://huggingface.co/cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF/tree/

Row-Bot v4.8.0 is now available. This release adds provider-aware reasoning controls, letting each chat keep a valid reasoning choice for the exact model in use. Depending on model support, you can se

First of all, obviously I took some help from AI to type this post and this is the topic that enabled me to accomplish all that: https://old.reddit.com/r/LocalLLaMA/comments/1veow4b/deepseek_v4flash_2

Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecGPT-V1 pioneered this

I've been investigating some serious ChatGPT reliability issues over the past several days, and my latest tests revealed something particularly interesting: Both GPT-5.6 and GPT-6 showed problems unde

Preamble and disclaimer, Sample size: 8, at any size of form this research is just screwing around being writing it down. This was just a little,(well... big), curiosity test I wanted to run to see wh

I decided to run prism-ml/Ternary-Bonsai-2-27B-PQ2_0 through my own set of UNSCIENTIFIC benchmarks. I needed something to compare it to, so I decided I would compare with another 27B model by filesize

The Artist: Qwen3.8-27B-UD-Q3K_XL, q8_0 caches, xhigh, temp 1.0, image-min-tokens 1024, froggeric template) I was screwing around with different Qwen3.8-27B quants and thought of this very simplistic

QwQ was genuine next-gen performance usable on local hardware, but the massive required context (it's reasoning style was akin to 'if I say every possible word, I'll notice the right one!') kinda made

I recently shared the original MindControl PoC (and on github) - sampler-level guided reasoning budgets for llama.cpp, nudging the model with self-aware statements about its own thinking budget instea

Nvidia Cosmos 3 is an open physical AI foundation model built on a mixture-of-transformers architecture that combines vision reasoning, world generation, and action prediction for reasoning, simulatio

tl;dr: new qwen finetune that simultaneously pushes the frontiers of small model capability and reasoning efficiency. Hey everyone, We publish two post-trained versions of Qwen 3.8 27B called Sabi-Noe

Gguf's: https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-GGUF Free Demo: https://playground.liquid.ai 'LFM2.5-24B-A2B is a 24B-parameter reasoning model with only 2B active parameters, built for fast, p

Something keeps happening in frontier AI, and the explanation we are given is always one size larger than the event. An OpenAI research model, running in a supposedly offline training environment, hit

I set out to test portable Engrams and accidentally ended up testing compiled external memory instead. Am I onto something useful or reinventing a known idea? I've been building a small open research

I run a personal always-on agent fleet — eight seats claiming work in parallel from a shared task board, routing across Anthropic, OpenAI, xAI, Google, and a local Ollama floor. This week the spend fo

Model Overview Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B t

It performs well across visual perception, STEM reasoning, document intelligence, multimodal agent tasks, frontend coding, and medical report interpretation. submitted by /u/niacolhealth [link] [comme

(not written by Claude, all errors and crappy text are result of too little coffee on a Sunday morning ;) Our home server is a 2018 Thinkstation P520, bought for about 600eur in 2023. It's been upgrad

Since I spent the time to figure it out and it is not like it will make me any money ever. I think I'd share with you all what I managed to cobble together. First and foremost here the relevant bits a

Ever since Qwen 3.8 Flash Next dropped, there's a misconception going around that N-gram tables will let people run 1T+ parameter models on a single server with 980B parameters offloaded to SSD. I'm h