Justin Van Fleet

Good morning y'all! Qwopus-3.6-35B-A3B-MTP-Coder is live! All GGUF's will be populating over the next few hours! It's a lightning-fast MOE with the coder curriculum recipe. Similar to the 27B coder, it shines with thinking disabled, offering significantly faster wall time for similar, and in some cases superior results to same-sized thinking alternatives! With thinking disabled, it goes toe-to-toe with the new Ornith 35B MoE across a huge eval suite (performed by @no_stp_on_snek), edging it on the coding trajectories and decisively on speed and cost, even though Ornith was run with thinking enabled. See the model card for the full test results, and shoutout to Tom, @no_stp_on_snek, for thoroughly evaluating the model for us before launch! With MTP and thinking disabled, along with the MOE speed, it runs so quickly in harnesses like @opencode that it almost feels instant @ 253 tps on my 5090. No 8k tokens of thinking before a coherent output is actioned. This is especially useful in long contexts, where the base models will progressively start thinking for tens of thousands of tokens before replying. Compared to the base models with thinking off, the coder curriculum really advances the no-think frontier. Especially in terms of how creative it can be. Run temp hot as usual, 0.85-1, and make sure your harness isn't overriding the temp setting of your server at runtime. If you want to use it to its full ability, I would recommend giving it very thorough prompts. I have been using it in opencode, and I have been blown away by the results it generates autonomously with chunky prompts. Please see links to the demo's Aether Dominion (RTS Game), and a slide deck presentation the model made about itself that turned out beautifully, links in comments below! I am getting results on this incredibly fast local model (with thinking disabled) that I couldn't get in some thinking frontier models over a year ago. Open source is accelerating fast, and in light of recent events, there's never been a better time to get your local AI workflows tightened up. This MOE would be a great one to play with, and it's also a great one if you don't have much VRAM because it can run fast offloaded partially to system memory! All of that said, please give it a run with thinking off and build something you'd like to see. We'd love to see your results and any feedback on specific use cases in the comments below! Also, thanks so much for 5k followers, you all make up such an enjoyable and knowledgeable open source community, and I am so blessed to be able to collaborate and discuss this research with all of you. I can't express how grateful I am for every comment. As always, I will try to reply to them all! If we ever get monetized on X, I will put every penny into buying more hardware for our lab! Have a blessed day, my friends, looking forward to your thoughts! huggingface.co/Jackrong/Qwopu…

Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including: ✅Terminal-Bench 2.1(77.5) ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual) ✅NL2Repo(48.2) ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW) ✅ClawEval(77.1) Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎 All models are released under the MIT license, enabling full commercial and research use. 📖Tech Blog: deep-reinforce.com/ornith_1_0.html 🤗Huggingface: huggingface.co/collections/de…

#StackChan #M5Core2 Remo-Con 特定の角度に止めていたい時は ジョイスティックより向いてます

(1/9) @MachinaLabs_, we're working on full automation in manufacturing through our 'Robocraftsman' technology—robotic cells designed to turn design intent directly into physical products. This concept is akin to the shift in the digital world, where cloud computing allowed software developers to focus on creation without the constraints of physical infrastructure. That shift enabled the rapid expansion we see today, notably the rise of artificial intelligence and AGI on the horizon. To enable the same rapid speed of innovation in physical world, our team is building a similar abstraction layer in manufacturing, making it as effortless to produce a new product as it is to launch a web service. Initially, I was reserved about sharing the details from our internal all-hands, as they capture the core of Machina’s ambitious goals. However, I realized the value of opening up our vision to the wider community. We’re on a mission to transform manufacturing, and it’s a journey that extends beyond just one company. We invite entrepreneurs and innovators to join us in reshaping this fundamental human expression, manufacturing.