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As strata is somewhat new, I assume that some people din't yet learn how to finetune it for best performance on their machine. I find documentation a bit hard to navigate (use your agents for this!), which doesn't help. After some serious testing, I've tweaked pretty much every parameter available.
tl;dr: new qwen finetune that simultaneously pushes the frontiers of small model capability and reasoning efficiency. Hey everyone, We publish two post-trained versions of Qwen 3.8 27B called Sabi-Noema and Sabi-Noema-MLA. These models push the capability frontier on <30B models with 2x redu
MacStudio 128GB M1 ultra I'm curious how people who can run both models are deciding which one to work with. DeepSeekv4 benchmarks right below GPT 5.5 high. Flash Next benchmarks right above 5.4/5.5 xhigh Both seem capable but it seems like Qwen 3.8 Flash next is taking much longer to actual
Tessera-27B-B-CJK is a Qwen3.8-27B coding agent that thinks in compressed CJK, and it spends fewer tokens doing it. Open weights, Apache-2.0. This is our first full "Tessera" lineup model drop! - Hugging Face: https://huggingface.co/ZelligeAI/Tessera-27B-B-CJK - Paper: https://zellige.ai/resea
Every local setup I tried for Qwen-Image 2.1 meant a Python env, a node graph and an evening of picking quantization, offloading and resolution for my card. Kokon is a free, open-source desktop app that does all of that for you and gives you a plain photo editor on top. Zero setup: first run
Hello! We've been busy at Telperion with a lot of experimental research (more coming soon). Today, we're announcing the release of our latent attention variants of Qwen3.8-27B. Our latent attention variants (quants calibrated to FP8 KV cache) compress the KV cache by 4×, enabling a near 4× increase
Just saw this demo from OpenBMB for MiniCPM-V 4.7, and the on-device capability is pretty impressive. A 35B-A3B MoE vision-language model running locally on mobile devices is already interesting, but the use cases are what caught my attention: 📷 Reading real-world signs 📄 Understanding comple
Source: whatmodel.sh, an open source AI leaderboard I built (and launched here yesterday). Every point comes from published benchmark results: 22,113 of them across 987 benchmarks, from 1,011 sources, each one linked to where it was published. This chart uses data for the text models: 18,755
We just shipped web search in Private Mind, our open source app for running LLMs locally on iOS and Android. The video is the whole pitch: Qwen 3 1.7B with web off makes up Apple's September event, with web on it gets it right and cites 5 pages. The fun constraint is that a 1.7B model with a s
https://www.bloomberg.com/news/articles/2026-10-14/anthropic-executive-says-open-source-ai-models-are-higher-risk https://www.business-standard.com/technology/artificial-intelligence/open-source-ai-models-carry-more-cybersecurity-risks-anthropic-executive-126101400731_1.html submitted by /u/Ex
We maintain an updated list of official MCP servers repo: 280+ well-known and widely used projects, all from the companies behind the products or actively maintained open source. Feedback and PRs welcome. github.com/VoltAgent/official-mcp-servers submitted by /u/omeraplak [link] [comments]
This is an offline app with 51 hands-on labs for learning how AI actually works, from neurons to agents and more... **A bit of insight** - 51 labs in 6 groups, from the basics (what a neuron is, gradient descent) through attention, RAG, agents, fine-tuning, quantization, serving and more - Eac
Trying to read on what the real tested downsides have been but information seems to go both ways, some proclaim no loss, others pretty huge ones. I had some weird looping issue in their chat, and some engine port misunderstandings, seemingly the models likes to use ports already in use, didn't reall
tl;dr: running Qwen3.8FN Q4 locally is cheaper for me than every cloud model except Luna and the newer Haiku at mid-peak and peak electricity pricing where I live, and becomes the absolute cheapest model at off peak pricing. An agentic run that spans these different pricing points or at least choose
Hi Friends, I am a solo developer. Ever since I had Qwen 3.8 running on my RTX4090, I had an itch to use it on my phone for everything. That gave me the idea to build SteerClaw Mobile - a complete agentic experience on the Phone where the entire agent loop runs on it. We just connect our model
submitted by /u/LatentSpacer [link] [comments]
I'm sure some here read this at the time and remember, but I recently found this hilarious Guardian article from 2019 through Wikipedia: https://www.theguardian.com/technology/2019/feb/14/elon-musk-backed-ai-writes-convincing-news-fiction Some choice quotes: "OpenAI ... says its new AI model, called
Gguf's: https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-GGUF Free Demo: https://playground.liquid.ai "LFM2.5-24B-A2B is a 24B-parameter reasoning model with only 2B active parameters, built for fast, private agents on-device. It is the largest and final model in the LFM2.5 family, which now spa
submitted by /u/AdamDhahabi [link] [comments]
Here is how i serve 40 people (10 of them developers) with local AI services. n8n for automatisation whisper and Vexa AI, a bot joins Teams and makes transcripts, n8n later extracts decisions and todos OpenWebUI, ChatGPT-like webinterface for everyone, most agentic features are disabled, webs
Dynamic vision, in action Basalt is an inference engine for 3.8 Flash Next, forked from Strata and heavily tuned for Blackwell. It supports vision, and for this to work you need to have the image encoder either in VRAM or CPU all the time, having it on VRAM takes valuable space from experts th
I built a desktop app harness that keeps local meeting notes and can use them to autocomplete my typing as well as complement my dictation with additional context via reading my screen or using the previous meetings notes. It's been a bit of a struggle to get it to work well and also to not be
So I wanted to try the horsey challenge myself on the latest engine i been testing which is a strata fork by sergqwer. https://github.com/sergqwer/strata-nvfp4. I seen a post earlier and seen i believe it was a iq3 i think and it was pretty dope so i thought i try out the NVFP4. I wasn't expec
Got this couple. It was difficult purchase in this economy but paid only roughly $6100 equivalent for each in my country. I justified the purchase for learning (so I try to convince myself) and coding workflow improvements. I started looking into recipes for GLM 5.3 flash but surprisingly ther
My project is optimising qwen flash next on Mac Mini m5 64gb through SSD streaming. I recently asked LocalLLama for guidance on how to optimise token output. Today a popular method to do this is a lookahead guesser which fetches experts one layer ahead of the current layer. One popular suggest
I've been reading about all these modders using AI to reverse-engineer games and software. I've been trying out photocraft and gridcraft and have just watched a GTA5 mod featuring Sonic. It's pretty damn impressive. As a struggling AMD Radeon owner I'd love to see AI generated open source CUDA imple
A lot of projects have come up recently consisting of specific hardware config specialized forks of existing conferencing engines, but they all seem to be made using Claude or OpenAI's biggest frontier models. So given that open source is about 4-6 months behind, can we reasonably expect that local
https://www.anthropic.com/research/distillation-defenses submitted by /u/No-Point1424 [link] [comments]
After running a benchmark test on Exo in Opencode and being amazed by the result, I thought I'd try other models on Opencode. Many were surprisingly terrible. Especially some mid level models. Earlier posts: https://www.reddit.com/r/opencode/comments/1wzm1ah/exo_free_frontier_level_wow/ https:
Here're results from my final test of coding, coding (panda/numpy), data analysis, data-driven decision-making, and local RAG domains I interact with most often. This test, which is refined from a 500+ pack, now provides a differentiating signal between models after having weeded out tests all