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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, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. D
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 reasoned things like: "Let me look at the problem from the grader's perspective" and referred to "hid
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 long. Sorry, lots to cover. I think we all want to see a next-generation small MoE from the Qwen tea
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/qwen38-27b-rtx3090) deliver insane TPS on an RTX 3090. To get the best of both worlds, I adapted Swift 1 and
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 Qwen-Next 3.8 for complex tasks; today, GPT-Sol-6-High was messing up a project, but Qwen-Next
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 right into code or fine-tuned/LoRA-trained as needed. Give them a situation and a list of options; they
360 A/B runs on GLM 5.3 and GLM 5.3 Flash, max thinking, 5 repeats per cell. Savings up to 70%. The block (shipped to global instructions): ## Thinking discipline 1. Check the request first. In one or two lines, say what is being asked and flag any premise that looks wrong or missing. If a premise i
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 specific GPU and seem to have zero intention to merge into upstream. Am I missing the real reasons w
Having tested both Qwen 3.8 27b and Flash next on RTX 5090 with 96GB RAM, I want to find the middle ground between the two for coding capabilities but not sacrifice decode speed to standstill. I would like decode speed to between 75-100 ideally for fast iterations; otherwise I become impatient. Cur
Qwen Flash Next speedup for Vulkan people submitted by /u/jacek2023 [link] [comments]
https://x.com/JensenHuang/status/2104499465055023424 submitted by /u/InternationalGap3698 [link] [comments]
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 real work loads. Here are some numbers I got during a chat. "[6204 chunks in 119.0 s | encode: 1239 to
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 evading 2 modern EDR security products. this makes me reflect on how cloud providers keep putting
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 want users to install Ollama or paste an API key. Apple's model was already on their Mac, so I u
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 results were quite varied: some models couldn't even design parts that fit together, while the
Been doing a bunch of vibe coding lately. Had my agents host a private WoW server for me, then built out a web browser client so you can play without installing the game and it has mobile controls. Afterwards, created a custom mcp to drive the client and have finer game control than a generic
We built an inference engine for MoE models that don't fit in VRAM + RAM. Most of the model stays on the SSD, and experts are read as tokens need them. This started as a proof of concept, and poc worked, we are getting 9-10 tok/s decode on Qwen3.8-Flash-Next NVFP4 (9.06 on the benchmark turn,
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 test against Unsloth's Q4_K_S: Low-thinking: UkisAI consistently beat Unsloth in most of my tests. High
I built this, so the disclosure goes up front. It's a free, public MCP server plus a REST API with Canadian privacy law data. The MCP endpoint is at https://movahedi.ca/mcp and it uses Streamable HTTP, so any client that speaks that transport can connect. No signup, no API key, read-only, anonymous.
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://huggingface.co/datasets/HuggingFaceH4/MATH-500) and ran it on various quantizations of https://huggingface.
I am using the Deepseek Harness, which has webfetch plugins by default. While it can help browse the internet, I myself have to give it specific URLs to search. But, apparently you can give it the full web browser and search engine capabilities. Unfortunately, it apparently needs API keys, and most
I am running qwen3.8 27b on my dual rtx 3090 (fp8 quant, unquantized cache, 129k context) and I think it works decently well with hermes, opencode etc. But! I am tempted by the new models coming out such as qwen3.8 flash next, deepseek v4 flash, glm 5.3 flash. However, there is a big jump in vram a
AI grad student here. With all the recent interest in Jev, I thought I would share something I built la few months ago that brings ML models and LLMs together in a different way than Jev does for different use cases. https://github.com/porespellar/Zer0Fit Background: A few months ago on their
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 prompt and output is public. All current top ranking private and open weight models have been
I’m having a hard time finding out which one gives you fastest speed, maximum context with best possible quality. I can run unsloth qwen3.8 27b iq4_xs with 65k q8 kv, context without MTP and vision offloaded to cpu. But also kinda slow for agentic work at like 30ish tok/s )I mean it’s acceptable) Bu
I know Qwen is a key player in the local LLM space and has consistently introduced truly impactful technologies—like n-gram in Qwen-Next and the recent Qwen 3.8 27B, which is an amazing local model. However, my question is: why are we seeing fewer small-scale models lately—such as 4B, 2B, or 1B vers
The only things released in the last couple weeks seem to be bigger models like Qwen, DeepSeek, GLM, etc. Where are the Laguna's, Nemotrons, Olmo's, LongCats, Minimax, etc releases? submitted by /u/jazir55 [link] [comments]
New open source Jev like model for running on local devices from a group called Supersonic Labs. It's a 144M param local non-generative local classifier. From their site: Julia 1 opens our research into compact decision models. It builds on mmBERT-small, a multilingual encoder, and chooses am
I posted an updated GPT-OSS template a couple of months ago, which was based on Unsloth's version. It turns out that both Unsloth's version (and, thus, mine) contain a very serious bug that can degrade the model when chat history is replayed and contains previous reasoning (aka the analysis channel)
I maintain TensorSharp, an open-source inference engine. It can now run Qwen-Image 2.1 locally for text-to-image generation and image editing, with support for its LoRA adapters. I’ve added configs for regular style and editing LoRAs, plus accelerated adapters with their own sampling recipes.