Researching pixels @freepik. Independently created the Chroma & Radiance models as personal projects. https://teapawt.net/t.co/d8h2mIi9zS https://teapawt.net/t.co/RrPsPku3y6
In your heart
Joined April 2023
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chroma-radiance-x0 result
a true pixel space model combining JiT-x0 loss and pixnerd
give it a go!
github.com/comfyanonymous/Co…
huggingface.co/lodestones/Ch…
lodestone-rock retweeted
Great to meet @ComfyUIWiki and @alexisrolland from @ComfyUI in person! 🤝
Looking forward to exploring more ways to build together. Stay tuned. 👀
lodestone-rock retweeted
From recognizing text to understanding documents. 📄
Meet Xiaomi-OCR-0, an open-source document understanding model with just 0.8B parameters.
It brings document parsing, key information extraction, and OCR-oriented visual question answering into one lightweight model.
It scores 95.24 on Real5-OmniDocBench and 83.2 on average across five OCR-oriented VQA benchmarks.
On OmniDocBench v1.6, Xiaomi-OCR-0 ranks first in document parsing performance among models with fewer than 1B parameters.
Model weights, inference code, an interactive demo, MCP service, and Agent Skill are now open source. 👇
🤗 Huggingface: huggingface.co/xiaomi-resear…
🔗 GitHub: github.com/xiaomi-research/X…
📄 Demo: huggingface.co/spaces/loongw… #XiaomiAI #OCR #OpenSource
lodestone-rock retweeted
lol my lawn mower is only used 1x a week. Should I rent one from OpenAI instead?
Also, who caused the DRAM shortage? It wasn't consumers...
sam: local inference is a terrible allocation of scarce resources
“a lot of people want local inference, and somehow they assume that the problem is solved now because we have open-source models, we're going to be able to run them on our computers, and so we're not going to need all the stuff in the data centre
but from a global allocation perspective, actually it's quite terrible to have everybody, like, have a bunch of the world's limited supply of DRAM sitting in their own computer and being used five percent of the time
you want maximum utilisation in the cloud if you're trying to deal with this very supply-constrained environment
somehow many people's intuition is the opposite of that”
lodestone-rock retweeted
hey sam, your mclaren is only used 5% of the time. this is a terrible allocation of scarce resources. have you considered taking the bus to work
sam: local inference is a terrible allocation of scarce resources
“a lot of people want local inference, and somehow they assume that the problem is solved now because we have open-source models, we're going to be able to run them on our computers, and so we're not going to need all the stuff in the data centre
but from a global allocation perspective, actually it's quite terrible to have everybody, like, have a bunch of the world's limited supply of DRAM sitting in their own computer and being used five percent of the time
you want maximum utilisation in the cloud if you're trying to deal with this very supply-constrained environment
somehow many people's intuition is the opposite of that”
lodestone-rock retweeted
Last night, @higgsfield sent @ComfyUI a cease and desist (attached)
They want the Open Source Higgsfield challenge taken down within 48 hours.
Their argument is that our use of the Higgsfield name and that using their CEO's likeness is an infringement violation (man, the irony). Claiming that it makes the event look like Higgsfield is associated with it.
This couldn't be farther from the truth, which is that Higgsfield made a promise to open source its product and never did.
For the past two weeks, builders have been recreating popular closed apps as open workflows anyone can run.
So we asked our community to deliver where Higgsfield failed: apps in the open, built by the community, for the community. We've made it clear that the challenge is organized by ComfyUI and not affiliated with or endorsed by Higgsfield.
When we started Comfy, we made the community a promise. Our product stays open, free, and yours. We’ve kept it every day since, and one letter isn’t going to change that.
The challenge stays up. Comfy and open source will win!
lodestone-rock retweeted
Introducing Mol-1: Open Frontier Biomolecular Intelligence 🚀🚀🚀
📊 75.0% on FoldBench-AB, near IsoDDE's reported 75.58%!!!
🧬 60k-token assemblies on one single H20, outperforming Anthropic 8/10.
🎯 VHHs binding hit rate as high as 63% with best SPR KD at 43 pM.
GitHub: github.com/molence/Mol-1
Try it: lab.molence.com
lodestone-rock retweeted
Meet Clover-35B-A3B-Preview 🍀 the first open-weight production diffusion language model (dLLM) with 35B total parameters capable of 680 tps on a single card at BF16.
Clover exhibits strong performance against both dLLM and AR models and is available today at @huggingface @ModelScope2022 and at our website
lodestone-rock retweeted
How is the internet less private than in 2006, when the whole family shared one computer?
Back then, the internet was weird, personal, and all of ours.
So, we got your favorite meme legends back together to say it louder: What happened to the internet? 1/3
lodestone-rock retweeted
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal.
🔹 Full-project coding: games in Unreal, Unity & Godot.
🔹 Excels at parametric 3D modeling in Blender & CAD.
🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%.
🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks.
Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.
Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
lodestone-rock retweeted
Hot take: Most autoresearch today is just glorified overfitting.
Current autoresearch pipelines are essentially outer-loop searches for recipes that work in ONE specific setting. Try transferring a modded-nanogpt recipe to a different language or dataset. How much of that "optimal" recipe still holds up?
We need to move beyond static recipes toward inner-loop autoresearch: optimizers, architectures, hyperparameters, etc., continuously adapting DURING training based on the training trajectory itself.
Check out OPAL that enables this! Cool work with @SimulatedAnneal, Jia-Hao Liang, and @vincesitzmann.
1/9 Current optimizers are static during training. What if the optimizer could think and react while the network trains?
We let an agent watch the training dynamics and rewrite the optimizer's source code as training goes: a reactive optimizer.
In every language and image setting we tried that has no tuned recipe, it matches or beats the best recipe one can one-shot, and it guards against loss spikes and overfitting by itself. In the near future, optimization decisions may belong to agents, not humans.
📝 xingjianbai.com/reactive-opt…
💻 Try it on your task: github.com/xingjian-bai/opal
🧵
lodestone-rock retweeted
DeepSeek might as well open source the whole company at this point.
“We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.”
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
1/4
lodestone-rock retweeted
One must imagine Dario gnashing his teeth.
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
1/4
lodestone-rock retweeted
Nvidia, SGLang, and vLLM got mogged into oblivion
lodestone-rock retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
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