Search: “models”

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

Hi! I'm the author of MoEspresso, which is my way of putting my own ideas about inference engines to the test. A lot of the fun has been trying different design choices, measuring what happens, and fi

Just made this post for those who missed it : https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b UkisAI released their updated Qwen 27B (tuned for token efficiency). I grabbed the IQ4_XS quant to tes

I wish Qwen also released dataset and method to fully train a model ourselves but it is what it is. However, I come here with my stupid question because someone can answer it better. And will the mode

We hosted Kev 4B (Jared Palmer's Apache-2.0 fine-tune of Qwen3.5-4B) and ran it side by side with Jev on the same endpoint to see how it compares. We built a fresh set of 362 items published after bot

I finally got around to upgrade the GPU's. First a word of warning, when upgrading GPU's on P520 you have to be extra careful not to slot the card on any angle other than straight when installing or w

I'm about to buy a Mac Studio mainly for running LLMs locally and I'm stuck between two configs: M5 Ultra (30/64) with 96GB: 1.2 TB/s bandwidth, roughly 1.7x faster generation and much faster prefill

A while ago I posted 15 tok/s output and 100-120 tok/s prompt processing with the IQ3_XXS quant on a 12GB RTX 5070 using llama.cpp. Since then I built my own inference engine for this one model and th

What it is. stuntd is a local proxy for typed LLM decisions: choice, yes/no, score. Your app keeps calling its provider through it. stuntd records the decisions, trains a small head on top of Laya (th

I have to say that I am quite impressed by recent progress in LLM use for coding. I've never really bothered setting up a local model. However now that I went trough some real $$ in Claude / gpt token

I've been running FreeToken on my 2x3090 box for a while and ended up maintaining a fork of it. Posting it in case it's useful to anyone else here. Quick context if you haven't used it: FreeToken is a

Retrieval benchmarks sometimes feel benchmaxxed by models, so we wanted to find a way to tie it as close as possible to my objective: finding the article that answers a product question right. We work

I've trained a preliminary lora to fix the image generation issues with Qwen Image 2.1 (also drastically improving edit quality). I've also been doing a lot of a/b testing with negative prompts and va

Github: https://github.com/OpenEuroLLM/ComplexKDA HuggingFace: https://huggingface.co/collections/openeurollm/complexkda Arxiv: https://arxiv.org/abs/2609.24797 Authors: Julien Siems*, Riccardo Grazzi

Hey everyone, I wanted to share a project I’ve been working on called Jayce. The whole thing started because I was watching a toddler named learn the names of stuff He didn't need to completely rewire

I’m building a personal local coding-agent project called Cody, using local 9B/14B models through Ollama. This is still a work in progress. It has not passed its full graduation process yet, and the l

So this jev thingy is getting kind of big... tbh it seems overhyped by a large margin, but here we are. Not that its bad, just feels like we usually ignored larger things... Anyway to the point of thi

Hey everyone, Like a lot of people here, I’ve been experimenting with running models locally through Ollama. Athena started as a small experiment, but it quickly grew into something more, a modular Py

I gave Jev, Laya, a finetuned ModernCE-base-nli and a finetuned Qwen3.5-4B the controls to Doom. Thanks to TypeSafe AI for Jev access. The video lines up the starts of four separate games with the sam

Yoyo I renamed https://github.com/syv-ai/qwen38-27b-rtx3090 to https://github.com/syv-ai/HyperQwen/ to focus more on Qwen models and 'local' hardware in general, than just a single card and a single m

We compared 20 model versions on 360 frozen joke prompts, with four jokes requested per prompt and model names hidden from our humor-trained judge. Fable 5 had the highest estimated score: 66.8 points

Hello, I'm at the old pro plan. With the upcoming cloud model retirement of deepseek v4 0731 flash in mind because 4.1 is supposed to be more efficient while providing even better results I remembered

GitHub: https://github.com/pfekin/LARA I've been working on LARA (Lightweight Additive Residual Adaptation), a research project on making post-training modular for frozen language models. I've also de

One thing I love about both Qwen 3.8 27B and Flash Next is the effort it puts in to make a good product. It is the opposite of a lazy model and 'good enough' is never what aims to accomplish. It goes

The first generation of our 150M model has just been released Its performance is similar to that of GPT2-Small The benchmarks: PIQA: 62.24% Hellaswag: 32.20% Arc-Easy: 44.91% Arc-Challenge: 25.00% Ari

VLLM Benchmark: Prefill, Prompt processing - avg, 871.93 tok/s (3 hours constant running xhigh) - 10K prompt, 1000.26 tok/s (16 runs) - 90K prompt, 743,59 tok/s (16 runs) Decode, tok gen - avg, 38.39

I'll start by saying I'm not talking about the models themselves, I'm aware that I can't come close to something like Fable's intelligence locally. Just wanted to get that out of the way. Basically. O

Hey everyone, Giving AI agents access to MCP tools or shell commands is awesome, but prompt injection is still a huge headache. If an agent reads an untrusted document containing `ignore previous inst

I find this new model at HF: 'Built for demanding work. A 262 144-token context window, adjustable reasoning effort, tool calling, and text, image and video understanding. Architecture Agnes-3.0-Flash

I fine-tuned Qwen3-TTS with inline transcript control tags rather in lieu of a separate instruction parameter and thought the community here might be interested in the result. https://huggingface.co/S

What is Astra actually supposed to be better at, Because if the answer is “everything,” then I genuinely do not understand what kind of testing was done for writing-heavy use cases. My biggest issue i

Translation: 'Internal beta testing for an intermediate version of DeepSeek V4.1 Flash is now open; you are welcome to try it out. It adopts a new model architecture featuring native multimodal suppor

Hi all! I just wanna say that I am tired lol. Yes, it's another harness, but I spent a lot of time and effort and have forsaken my hobbies to build the Jenny (like XJ-9) app. Jenny is a free, MIT lice

The missing control is visible in sudoingX’s Ling-3.0-flash benchmark graphics. The earlier table leaves Ling’s no-speculation baseline as “not measured.” The later code/prose graphic fills it in: abo

I recently ported VDN-H3 (Video DeltaNet for MiniMax H3) to Vpipe and tested it on an M5 Pro 24GB. The benefit scales with both sequence length and resolution: at 832×480, VDN-H3 starts to pull ahead

I'm using DS-V4-Flash-Vision with Q8_K_XL quantization locally as my everyday engine, and for some time now I've been doing a lot of comparisons with Qwen3.8-Flash-Next, also with Q8_K_XL quantization

Anyone who has a 3D printer and get use of it finds it incredibly useful for those odd jobs around the house, a missing bracket, a cable router, steam deck holder and so on. In the past if I was missi

Otaku is an LLM frontend, primarily designed for roleplay, an alternative to SillyTavern and the like. However, It also works for general-purpose chat with local backends (including Ollama) or cloud m

First, I'd like to thank the Qwen and Unsloth teams for the Qwen3.8 27b UD Q4_K_XL. Fits the poor 24GB of 3090 VRAM with 100k context at Q8 and works phenomenally well! Imho if theres anything that ca

After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) on my actual code (C code), the results were not completely unexpected but some quants were definitely

Github link: https://github.com/thatblend/LLMPSP I wanted to see what the PSP can theoretically handle and I got my answer - a 90M model is about the max it can do without atrocious inference speeds.

more sizes (probably still uploading): https://huggingface.co/IFM/K2-Horizon-32B-GGUF https://huggingface.co/IFM/K2-Horizon-7B-GGUF https://huggingface.co/IFM/K2-Horizon-3.7B-GGUF https://huggingface.

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress) A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4. ⚡ Half the steps — 8 → 4, on Turbo's own deployment sigmas. ⏱

A potato can create a very cool RPG in 24 minutes Laptop I3 8gb RAM 0gb VRAM, Windows 11 llama-server.exe --host 0.0.0.0 --port 8080 -m modelsqwen3.6-35b-InstructQwen_Qwen3.6-35B-A3B-IQ2_XXS.gguf -c 8

Sharing some numbers because most posts on this model are either using a single 3090 or unified systems from what I've seen. My current setup: 2x RTX 3090 (PCIe 3.0), dual Xeon E5-2696 v4, 188 GB usab

This came from another thread or comment. I forgot exactly where, but the basic idea was to replace the 51B N-gram layer in Qwen 3.8 Next with a much higher precision version. Someone running a 5090 r