IQuestLab/IQuest-Q1 · Hugging Face
IQuest-Q1 is a Mixture-of-Experts (MoE) model developed by IQuest for agentic coding, reasoning, and multi-step tool use. It comprises approximately 320B total parameters, with an estimated 15B parame
Knowledge catalogue
IQuest-Q1 is a Mixture-of-Experts (MoE) model developed by IQuest for agentic coding, reasoning, and multi-step tool use. It comprises approximately 320B total parameters, with an estimated 15B parame
Hi, I'm a long time subscriber of their pro plan. In the past, they were adding the open weight models to their catalog as soon as they dropped. Since this new change in their payment plans, I haven't
Ornith-1.5-9B-DFlash pairs the Ornith-1.5-9B model with a DFlash draft model for speculative decoding. https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash Ornith-1.5-397B-DFlash pairs the Ornith-1.5
I'm running Qwen 3.8 27B Q4 XS with ~30 t/s decode (no MTP) at 100k context with q8/q5 KV cache on a 16GB AMD GPU. I wanted to share my setup since many believe Qwen 3.8 27B to be infeasible on 16GB V
I started mapping the building blocks shared across all the models in audio.cpp. The result ended up being more interesting than I expected. Qwen has become by far the most common language backbone in
Thanks to tcclaviger, vLLM now has expert RAM offloading support (link). This makes frontier models much more accessible on a local setup! I was able to run the original DeepSeek-V4-Flash-Vision-Exp o
You may think that jev, OpenClaw or TurboQuant are super cool, but actually the coolest LLM invention happened two years ago As we all know, the best source of reliable information about LLMs is YouTu
We have released Qwen3.8-Flash-Next quantized with GSQ and RCO, together with a second, capability-targeted build in which half of the model's experts have been removed. Flash-Next is a sparse mixture
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
Wanted to try Opus 5.5 because as a plus sub with OAI, Luna just couldn't deliver. Opus is a true beast. It does consume tokens on a medium level yes, but the way it works is just refreshing. But... o
TL;DR: I'm a solo dev who wanted a simple, private way to have local LLMs watch my screen and do simple logging/notifying. After a year of building, I released v3.0.0 and my mom was able to use it for
https://preview.redd.it/2fvmzd4f3ish1.png?width=952&format=png&auto=webp&s=d21e33fd26b46d2268a12219d8c0619666eb000e I thought something was off because browser_search was taking almost a minute for a
Supports both Prompt Pair and Image Pair Training for Qwen Image 2.1. Sorry for the frequent updates last few days - I've had a few 'almost there' features for a while that Qwen kicked me in the arse
For example, directly using claude code or code, which is then hooked up to automatically delegate the actual code writing tasks to a local model like qwen 3.8 flash next, to save on cloud usage limit
Hello everyone! A little while back I posted about LlamAmpere, a fork of Llama.cpp with Ampere-specific improvements (though it is caught up to main and will support other hardware, too). Thank you to
Basically the idea is take your favorite model, for example qwen3.8-27b or say dsv4vision. Strip everything out that is not needed by that model so the only thing needed is just for the model. Optimiz
Hi, same as the title, I am currently trying to begin with some research on clustering using LLMs. So my requirement is as follows: I will be given some 100 document files, the end goal is to have clu
I shared the first version here as a prompt builder for Qwen image editing. I've been expanding it into something more useful for visual prompting in general, and the 2.0 beta is ready to try. It's a
Yesterday I shared our open-source Clash Royale simulator and its recurrent PPO agent here. A training loop is easier to understand when you can watch it, so we put a small interactive version online:
Alright so I'm pretty new to all of this, but today I got a pretty solid crash course in exactly how these LLMs work. I wad quite surprised to find out that there's zero 'thinking' involved in any of
AI Engineering from Scratch is an MIT-licensed curriculum: 523 lessons across 20 phases, from linear algebra and backprop to transformers, LLMs, agents, and production serving. The code is stdlib-firs
It had such a long run. It was my first introduction to modern language models. I remember getting slightly excited over it. And now it lives purely in our memories. Arguably what's more infuriating i
Holo4-27B-GGUF is a vision-language model (VLM) for Computer Use, built on the Qwen3.8 dense architecture and developed by H Company. Used with the hai-agents harness, it can send screenshots and tool
Other than the obvious self promotion, is there a practical reason people do this that I am missing? There's dozens of llamacpp forks with silly names that are supposedly 'optimized' for this or that
Here's the project. I have nothing to do with it. I'm just an amazed user. https://github.com/gufo-org/gufo/blob/main/docs/models/qwen3.8-flash-next/BENCHMARKS.md Those benchmark numbers hold up on re
Some context first. I am process improvement / business consultant and had worked with Fortune 500 companies on improving their processes around refunds, returns, customer support, etc. This entire th
Someone prompted different LLMs to generate CAD code for a bridge under fixed constraints (2-foot span, under 500g filament, 18-hour print limit), printed them, and load-tested them to failure. The re
I've liked how Muse-Glimmer worked, so I wanted to see if I could produce a smaller 'kid' out of it. Ornith's sharp decisions on when to think and which tool to call were the other thing I liked, so O
Apple Silicon Macs on macOS 26+ come with a small LLM built in. No download, no API key, and nothing leaves your Mac. Why I built it I was making a tool that writes API docs from code, and I didn't wa
I've been working on Mica, a 4B decision model, and wanted to see how far it could get in actual Minecraft, not a sim. New world, empty inventory, and the goal was a diamond pickaxe. Last time I poste
link to original article TLDR: security researcher eddie zhang used a modified+uncensored local qwen 3.8 27b to create an executable capable of dumping LSASS memory for credential harvesting while eva
Model Developer: NVIDIA Corporation Model Development: Fine-tuned from NVIDIA-Nemotron-3-Ultra-550B-A55B What is Nemotron? NVIDIA Nemotron™ is a family of open models with open weights, training data,
Seriously, who looked at one of the most useful features in ChatGPT and thought, “You know what would improve this? Making it fucking worse”. Before, I could edit a prompt, regenerate, compare differe
https://preview.redd.it/23cumykhabsh1.png?width=944&format=png&auto=webp&s=0b1d1c437fc845169f521b31793e2b1325dab0dd Reminder that you can put Qwen 3.8 27B as a subagent and its a workhorse. Pic: using
Six months ago, a result like this was unthinkable. But now we can say it loud and clear: local models are at the cutting edge, and the gap of just a few months has been confirmed. Personally, I use Q
I benchmarked Qwen3-VL 8B Instruct (Q4_K_M, Ollama, M5 24GB, ~30s/doc) against Claude Opus 5.5, Sonnet 5 and GPT-5.6 Terra on: - receipts: CORD (Indonesia) and SROIE (Malaysia), 30 each - 20 scanned 1
TL;DR -- You should probably just use base Qwen3.6-35B, as only Occamy-1.0 is competitive with it. Tiel is a major let-down, worse than Ornith. KAT surprises (good), Nex surprises (bad). This post is
I know most people here probably don't care as much about sonnet 5.5 as they did opus 5.5, because their 100 and 200 plans already give them plenty of weekly usage for opus. But keep in mind: free use
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
Hi everyone :) The amazing Swift finetunes of Qwen3.8 27B generate much fewer tokens at mostly similar benchmark performance to the original model, while repos like HyperQwen (formerly syv-ai/qwen38-2
TL;DR: The Jeff models are a set of Qwen3.5 and Gemma fine-tunes for zero-shot classification: small, efficient, open-weight models with respectable out-of-the-box performance that can be slotted righ
No click bait baby I promise - I'm live streaming the training process kinda like MiMo. Original thread: https://www.reddit.com/r/LocalLLaMA/comments/1wpg4a8/im_trying_to_posttrain/ If you didn't see
Hey guys! I have been excited to share this here. This is a project consisting of kernel optimizations for the Tesla p100 series graphics card ($80). I want to start by saying I am 17 years old and do
Meta came out with a banger paper https://arxiv.org/pdf/2606.00206, but it did not look at various quantizations supported in llama.cpp. So I did a run on 50 random MATH-500 questions (https://hugging
Hi all, I'm looking for some advice picking models. I'm looking to try a project to ingest quite a large amount of docments into a knowledgebase, allowing me and others to ask questions about the data
I run my own LLM while also having a Openai subscription. Also tried DeepSeek (latest flash now). I run Qwen 3.8 flash Next at an amazing speed on my 2x3090 + Ram! But local LLM never did worked for m
I was reading a paper that surveyed the field of neural architecture search, where it said within 5 years, around 3000+ new models were proposed. The amount of compute and resources spent on this is a
I am an artist working on a worldbuilding, character design and art toy project, handdrawn using Procreate on iPad with no AI use at all. However, I use AI a lot as a hobby and messing around locally
https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap/tree/main https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap/blob/main/workflows/Body%20Swap%20Qwen%20Image%202.1%20-%20V1.json https://hugging
The opponent plans by simulation: every second it scores each candidate play by running the match 10 seconds ahead in the engine. Our PPO agent learned to park its Cannon behind its own King. Losing a
Early this year when I was first looking at building up my inference capability you could get the 16GB Tesla P100s for between 60 and 80. Asked claude about it, told me absolutely not worth it. No ten
Fizgig 6.5 is out with Qwen Image 2.1 LoRA and LoKR training, turbo previews, and all the workbench tools. Supports 10GB VRAM+. I also trained a new training adapter as the existing one felt like it l
It’s probably a bit niche and the models are a bit old at this point, but after reading about Ninfer a few months ago I did my own small vibe coded engine project for my 5080 Laptop GPU. Initially I t
Hello everyone, I made a calculator for the theoretical HW roofline for decoding / prefill based on several parameters (LLM model architecture, quants, GPU, Memory, ..). It still a theoretical bound,
Slopus is a desktop app for making AI videos on your own GPU, from the first prompt to the exported file. What it does: Generate scenes shot by shot. You lay out scenes on a board, write each shot, an
After my last post documenting my LoRA journey, I received a ton of feedback regarding changes to the training process, and decided to do some experimenting. This is using the Fizgig application on a
Spent the whole day today trying to create this random transformation video. In total, I generated around 180 videos. A few initial images were generated with Krea2, and later all edits were done with
There is always at least 1+GB of VRAM not usable not matter how I set the --tensor-split (-ts) param. I tiny shift toward one side will move the weight significantly to the other side. 😵💫 Adjusting c
Where to even begin on this one...I guess I should start by acknowledging the risk vs reward for vendors other than nvidia, so: Yes I understand that nvidia are dominant currently on speed (llm and im
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