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I don't know if anyone else has ran into these issues but when using Qwen Flash Next, my confidence in it at iq4_xs is high but not 100%. I notice it not following instructions, hallucinating more often and surprisingly it uses way less tokens than 27b. After using Strata.... yes i know.... I though
Introducing InferBench: A benchmark testing how well frontier LLMs infer a user's priorities from their instructions. We tested 12 LLMs across 20 scenarios with 2.8k conversations to see which models understand the user's goals. Each conversation has a simulated user with a private profile o
Ideally I'd like to be able to run Qwen 3.8-Flash-Next with decent performance. I was thinking of just buying 2x Radeon AI R9700 (64GB VRAM), or maybe a DGX Spark but that was before the price hike. My brother suggested just getting a Strix Halo box with 128GB unified. I did see I could buy 6x Intel
I built a Pi extension that implements Victor Taelin's OptChat recipe: instead of compacting, every message is logged and summarized into a binary tree. Each turn starts from a fresh context with a bounded memory view (128 KB), and the agent uses zoom/date to read the originals when it needs t
https://huggingface.co/google/embeddinggemma-2 EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total p
EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model
Hey! 👋 I have released an official support for Strix Halo machines on Strata for Qwen3.8-Flash-Next. Currently numbers are the best on long context decode and ppts using typical Unsloth’s Q4 and GSQ-RCO model weights. Can go up to 1M context length without big speed loss. Currently support is
Microsoft confirms on publicly accessible web page that OpenAI has been using Looped Transformers in the GPT-6 series, proving The Information's reporting was correct all along. GPT-6.1 Sol uses 2 inference passes, with a passing mention of "instead of three". For those confused by "same base
For the last few weeks most of my coding has been done locally with Qwen3.8-Flash-Next, so I gave it and Opus 5.5 the same high complexity feature to build on LlamaStash (a complex and large Rust project) and compared the results. Setup: ASUS ROG Flow Z13 (Strix Halo, 128GB), Flash-Next at xhigh eff
Octop is an open-source, self-hosted AI assistant. Through its multi-agent architecture, it builds an intelligent environment that is both independent and collaborative for teams, families, and individuals. Best of all, it runs entirely on your machine, the fully self-hosted design means pri
My YouTube feed was mostly songs, pranks and celebrity clips, so I built a filter that judges each video title with a small decision model running inside Chrome. Nothing is sent anywhere: no server, no API key, no account, and after one download it works offline. What it does - Hides the kind
Over the past several months, we conducted an empirical post-mortem investigation into 109 autonomous AI agent security incidents (cataloged with 193 falsification criteria across tool-use and multi-agent systems). One of the most striking patterns in the dataset: In 38% of container breakouts, atta
At my company, we’re using GLM-5.3 Flash internally for software engineering work, and I’ve been genuinely impressed by it. I work in a very large production environment with projects totaling **millions of lines of code**, and we’re not relying on frontier models for this workflow — GLM-5.3 Flash i
Looks like Unsloth is updating https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF to work with llama.cpp, so hopefully this will resolve the issue of having two different GGUF versions of Qwen Flash Next. submitted by /u/jacek2023 [link] [comments]
I’ve seen posts here raving about how good Qwen 3.8 27b is. The benchmarks look incredible, and all the online discourse seems to deem it the best local model. I have 32GB VRAM and run Unsloth’s Q6 version with OpenCode. For small tasks, it feels fine. I range 30-40 t/s decode and smaller sized task
A while ago, I posted here getting OSS-120B and GLM-4.6 playing full games of Civilization V. Since then, models have moved pretty far, and we wanted a better understanding about models' capabilities playing the game. Introducing the controlled version of CivBench on newer models: The controll
So PewDiePie decides to fine-tune a local AI model called Ajax on his own computer. Pretty normal stuff for local model fans. To make his dataset, he uses OpenAI's API. OpenAI catches him using their outputs to train another model, flags his account for breaking their terms, and bans him. He files a
Picture from a post in r/amodei . People were praising qwen and I'm just wondering, what kind of new technologies are at play here? Does qwen just have "better" pre training data? That's more high quality? submitted by /u/SignificantZebra5883 [link] [comments]
tl;dr: I created a fully-local open-source full-duplex voice agent that rivals GPT-Live on some benchmarks. It uses Voxtral Realtime with a turn-taking head, a microturn-finetuned Gemma 4 12B and Breeze TTS 2 under the hood. Go try it out: https://github.com/speakrail/speakrail Interjections w
Purely a hobby side project to see how far I can push a really small model, using (mostly) automated training pipelines Full synthetic data: https://huggingface.co/datasets/dirac-run/ec-training-data Models: https://huggingface.co/dirac-run/ec-1.5b-gguf and https://huggingface.co/dirac-run/ec-0.6b-g
Hey r/LocalLLaMA, Like many anime fans, I've always been frustrated by traditional MT engines (like Google Translate or base DeepL) when dealing with raw Japanese anime: - They completely butcher Japanese honorifics, sentence-ending particles (-tteba, -zo, -desu wa), and character slang. - The
Hi r/LocalLLaMA. I'm on the team at Blockway, a small team in Hong Kong (disclosure: this is our model). Today we released Agens Volundr 32B Preview, the first model built on our own hybrid architecture. We trained it on limited compute, it isn't perfect, and we'd rather tell you where it fall
I keep testing harness & model setups on real tasks: basic math, vision, computer use (reading emails, navigating stores) and coding. I created a benchmark and a leaderboard. You can see it here: https://airbench.ai/leaderboard?k=poL It measure capabilities (a % of sucess on the various tasks) and s
Disclosure: I made this. Sharing it here because it is fully local and small, and I want feedback from people who run models on their own machines. What it is: a 395M decision model (ModernBERT-large plus a 4 KB scoring head). You give it a state, a question and a list of options. It does one encode
Said in title, I find the ecosystem difficult to understand, and RTFMing doesn't help me as it's never clear what is the server vs their client library ? I'm using it for voxtral 3B on one GPU, but it's because I can run that with no quantization, I'm lost on learning to run with quantization / more
I have been using Qwen3.8-27b-iq4xs variants for the past few weeks, tweaking parameters and running random tasks, but I dove into using https://github.com/Niko1221/Strata today and it has given me 2~3x performance on a "smarter" strata-swift-iq3_xxs model with my 5070ti! However, I saw something in
For the last few months, I ran DeepSeek v4.0 flash (NVFP4). First 0731, then visionexp because it was a free improvement. I got around 65 tps decode and almost 2k prefill, and ran 4-5 agents in parallel, totalling around 200 tps cumulative decode. Because of this, I did not feel like switching to GL
I vibed this up this weekend, fully local, with GLM 5.3 Flash running on 2x DGX Sparks. vllm TP2 recipe: https://github.com/tonyd2wild/GLM-5.3-Flash-NVFP4-DFlash2-2x-DGX-Spark Prefill: ~1500t/s Decode: ~40t/s @ 100k Using Claude Code as the scaffold with 260k context size. I'm really impresse
Help with this list. Give me your recommendation on "not supported anymore" GPUs. Looking for budget and Vulkan friendly options. Most of the GPU are not supported by latest CUDA / ROCm. Often with some witchcraft magic they are able to run with native backend. I prefer the simplicity offered by run
Last update for those following: https://www.reddit.com/r/LocalLLaMA/comments/1wvyc3e/update_3_post_training_yandexaliceai80ba3b/ Project in a sentence: An instruct finetune of ALiceAI-80B-A3B-Base capable of agentic work and conversation. I'm creating a shallow distill of qwen 3.8 27b on medi