Search: “models”

Hi, human here with rambling thoughts to share. Feel free to skip Overall, I don’t feel like I’m missing much; if anything. On my hardware(M1 ultra w 128Gb) it’s probably not as fast as Claude but I’v

edit: had to change formatting as I’m on mobile noe and the table broke.. I let Codex do a quick eval on Qwen 3.8 Flash Next IQ4_XS (served via vLLM + r9v) and Qwen 3.8 27B FP8 (served via vLLM + Radi

The jump from Qwen3 Coder 30B A3B to current-day Qwen 3.6 35B A3B is crazy, especially with all the fine-tunes, and that was around 6 months (I didn't care for local AI back then, or AI at all, apart

For those who have V100 cards, I wanted to point you to 1Cat-vLLM, a vLLM fork that enables optimized serving for these cards. Showing stats for Qwen3.6-35b comparing a Strix Halo with a hughly optimi

Mica v0.1 4B playing a real Minecraft 1.20.4 server. Video attached. How it works - Each step the bot's live game state (inventory, nearby blocks, entities, last result) is written out as text. - Mica

Gonna ironically preface this with: I haven't written the text below with an LLM. boxxy.io So I thought I'd show you what I've made over the last couple of months, because for me this has probably bee

Hello! I am currently building my local AI server, I have the Huananzhi H12D-8D EPYC Motherboard with 8x16GB memory sticks at 2666 mhz (waiting for the other components at the moment) I currently have

​ Hi. I’m developing my first Python project (been at it a couple weeks). I built a very basic CLI ollama client. When a user sends a prompt I append the message to session['history'], save the sessio

These boards cost me 115 each and I have them connected using llama.cpp with Vulkan and RPC on Bazzite. The boards have roughly 27GB of combined GPU memory and communicate over 1gb Ethernet. For aroun

Just a quick PSA. llama.cpp does have prompt caching. if you are running large context lengths and have long multiturn projects, increasing -cram can provide you with massive speedups. There is a poin

Almost everything is in the Title. For some reason I had to use a quantized version of the text encoder (NF4) but was able to run the BF16 version of the denoiser on my 5090. (BTW if I missed somethin

Yep. I just need tips honestly on UI layouts and how to build my shell for the local api offline. I’m using qwen and ollama and just want a simple discord style interface to declutter my brain to like

The rumor about Kimi execs getting arrested finally has some legs. I believe the reality is more like under investigation for potential arrests or fine. submitted by /u/Ok_Warning2146 [link] [comments

Is it just me or ollama serve command doesn't work at all. It uses a different directory than the systemd process, so it can't find any model or anything if you installed it using systemd ollama. Luck

I have been using open-webui and open-terminal. Open-terminal is installed in a VM and gives the agent a ton of control of it's own system that I keep away from my normal network. I find this gives th

I am proud to release these quants of Qwen 3.8 27B. They beat the excellent ISTA and Unsloth quants byte-for-byte on three corpora. Both KLD and top 1% were tested 3x. It took a week of continuous GPU

If like me you enjoyed classics like Baldur's Gate 2, this is a small fun experiment. For anyone interested: https://www.youtube.com/live/8FhPfRKTucw?si=PMIVdHbZ-31PNwZD Qwen 3.8 Flash Next has amazin

Qi2.1 Viggle 0.2, 5steps, CFG2.1 prompt: Cute young woman at beach holding Grumpy Cat. We're still getting a soft focus with the 'baseline'steps of 5, non-baseline CFG 2.1.... Now she is wearing a swe

I keep trying to get it to write code and test simpel stuff but it never finsihes conversation its always stopping mid way. i installed it through qwen > ollama. currently removed it and trying to rei

I am trying out a few fine-tunes of Qwen 3.5 9B @ IQ4_XS @ 131K context and trying to go mostly local (free beats cheap, after all). However, it is much slower than at, say 16K context, even when I am

Now that OpenAI has officially paused new sign-ups and upgrades for the ChatGPT Pro 200 (Pro 20X) plan, I’m looking for a solid alternative for enterprise-level development. For those unfamiliar: Code

They just merged it https://github.com/Comfy-Org/ComfyUI/pull/16442 TL;DR: it's a prompt enhancer that you can use to improve Qwen Image 2.1 prompt Usage: Download these qwen3.5_9b_qwen_image , t2i fo

You can finally downloads quants from: https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B-GGUF https://preview.redd.it/cfhl43pps4rh1.png?width=2800&format=png&auto=webp&s=313eab309fb407a0a0e5da61e2a12

https://preview.redd.it/5aon65uosrqh1.jpg?width=1000&format=pjpg&auto=webp&s=cd21ce0648b0ffa5dc07e66718a230f1f91d8a53 I have a GMKtec EVO-X2, it doesn't have oculink nor pcie slots, but it does have 2

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

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.

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

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

After seeing u/Nandakishor_ml’s post introducing Laya, I wanted to see how fast it could run in a standalone C++ implementation. Credit to u/Nandakishor_ml for the architecture, training and open-sour

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

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

Just find it interesting, since if Taalas tried to get into the consumer market, it could be interesting. Kind of like how we have game discs on CDs. Of course, a Qwen3.8-27B chip is stuck on 3.8-27B,

I’ve used both ChatGPT and Gemini, and lately I’m consistently getting better results from ChatGPT — especially with prompt following, realism, composition, and text. The improvement over time is hone

Although I do not completely understand what the big deal is with Jev, it reminded me of something I implemented in mt_llm (which is build around the great llama.cpp) a while back to use a relatively

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

Top 3 papers on HF Daily Paper are all unusually delightful and interesting reads for anyone on the leading edge of local LLMs, agent harness optimization, etc, felt like sharing. DeepSeek-V4.1-Flash:

Okay, this one seems probably really odd (or maybe not), but most harnesses operate within token spaces. That is, every tool output, even batching of tool outputs, go back up into inference calls. Lea

Oh My Pi + vLLM on two 3090s. Average wait per turn went from 28s to 7s, mostly from changing omp settings: explicit effort level on every role (unset ones defaulted to xhigh) thinking_token_budget of

Note not my work, but something i found and wanted to share so hopefully more people can push this along even further. https://github.com/nasone32/llama.cpp-RDNA3-7900xtx-opt I basically run 2 x 7900

Saw this little dude in a Forbes article (https://www.forbes.com/sites/johnkoetsier/2026/08/18/american-humanoid-robot-launches-for-just-1688-delivery-this-fall-made-in-san-francisco/) This is definit

Hi everyone, I’ve been experimenting with local LLMs on my 48 GB RAM MacBook Pro and I’m still trying to figure out what the best setup is. Ollama feels very fast, which I really like. The problem for

IFM released K2-Horizon-7B , diffusion augmented LLM at upto 5200 tps, with claimed lossless speedup. causal LLM architecture and adds a plug-and-play diffusion adapter alongside the autoregressive we

Another question asked 1 thousand time, but I can't find actual threads. Sorry in case of mistake. Due to interstellar price of hardware like high-end GPU (more than 5k euros for a 5090 and similar pr

It's late at night in the lab, I've been working on a basic script for a virology project, and holy hell the safeguards have been pissing me off. Mirroring detectEVE data over rsync to my laptop by ma

Achieved steady 15 tokens/second output and 100-120 promp processing per second with 12GB GPU (RTX 5070 SFF) with Qwen3.8-Flash-Next-GSQ-RCO-GGUF at 3 bpw (IQ3_XXS which maches BF16 on AIME25). Around

https://preview.redd.it/e5wn8eyh7vph1.png?width=1080&format=png&auto=webp&s=5faeec866acc9e8eca8dc9e84a6661bee7bcf954 https://preview.redd.it/8ov5gl8j7vph1.png?width=1080&format=png&auto=webp&s=440320c