.@GoogleDeepMind EmbeddingGemma 2 is now available on Ollama.
Made for consumer devices and is multimodal.
ollama pull embeddinggemma-2
Ollama now supports Jev-like decision models all locally.
Use decision models like Nimble for tasks like ticket triaging, model routing, and content moderation.
ollama pull nimble
Here’s Nimble playing Ollama racer through the new local /v1/systemone API by making decisions in real-time. 🏎️
Patrick Devine retweeted
Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead.
You guys are the frontier. By any reasonable metric — market share, revenue growth, model capability — the two of you have a duopoly on frontier intelligence. You’ve also claimed the lead is widening because of recursive self-improvement.
I don’t see what you see in the lab. If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible.
But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic’s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier.
Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want.
Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well.
So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it.
If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.
Patrick Devine retweeted
Got Meta's new Muse Glimmer 30B running on my MacBook (M3 Max, 96GG) and tested the serving options available so far.
Fastest right now: Ollama's MLX engine (DFlash included) at ~29 tok/s.
Tuned llama.cpp: ~21. Raw mlx-vlm: ~10, not optimized yet.
Numbers below if you're setting it up 👇
Currently serving 200 tps+ output speed for DeepSeek-V4-Flash on Ollama's cloud with zero data retention (ZDR).
Have an amazing weekend 🫡
DeepSeek-V4-Flash-0731 is now fully rolled out as the new default for deepseek-v4-flash on Ollama's cloud.
This model combines speed, efficiency, and frontier-level performance.
Fast: 120+ output tps on Ollama's cloud
Private: zero data retention hosting in US & Europe
Efficient: generous usage on Ollama's Pro and Max plans for multiple long-running, uninterrupted sessions with your favorite coding harnesses.
Kimi K3 is now available on Ollama’s cloud.
To use it with Claude Code, run:
ollama launch claude --model kimi-k3:cloud
Currently Kimi K3 requires a Pro or Max subscription, and consumes extra usage credits. We’re quickly working on adding capacity to expand access.
Releasing the model weights and technical report of Kimi K3.
Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.
New model architecture: 2.5x the intelligence per unit of compute, not just more params.
Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.
Model weights: huggingface.co/moonshotai/Ki…
Tech report: github.com/MoonshotAI/Kimi-K…
Tech blog: kimi.com/blog/kimi-k3
Readers added context they thought people might want to know
Ollama is not including K3 inside any of the subscription plans, not even in the highest "Max" tier, it only has it as a PAYG model.
ollama.com/library/kimi-k3
Ollama is proud to sign @satyanadella's letter.
Our mission from day one has been to make open models accessible to every developer to unlock the next frontier in America and across the globe.
Open-weight models are essential to a healthy AI ecosystem. Together with others across our industry, we are outlining a path for open-weight models to strengthen American competitiveness and expand economic opportunity, while protecting national security. microsoft.com/en-us/corporat…
Big day for Ollama! When we started, open models and the open source AI ecosystem were in their early days with few believers.
Our belief in open source has never wavered.
With today's fundraising announcement and our 9M+ active builders, we’re ready to scale open models into AI that you can own.
All aboard open models!
🧵
Patrick Devine retweeted
Banning or restricting Chinese models in the US would backfire and cede the global AI ecosystem to Beijing
the new AI paradox: slowing America, speeding China
Gemma 4 Quantization-Aware Training (QAT) weights are now available on Ollama!
They reduce memory requirements while maintaining model quality.
E2B:
ollama run gemma4:e2b-it-qat
E4B:
ollama run gemma4:e4b-it-qat
12B:
ollama run gemma4:12b-it-qat
26B:
ollama run gemma4:26b-a4b-it-qat
31B:
ollama run gemma4:31b-it-qat
Try them with ollama launch integrations to use with your favorite tools 👇👇👇
DeepSeek v4 Pro is now on Ollama's cloud! 🚀🚀🚀
Try it with Claude Code:
ollama launch claude --model deepseek-v4-pro:cloud
Try it with Hermes Agent:
ollama launch hermes --model deepseek-v4-pro:cloud
Chat with the model:
ollama run deepseek-v4-pro:cloud
🧵
deepseek-v4-flash is now available on Ollama's cloud! Hosted in the US.
Try it with Claude Code:
ollama launch claude --model deepseek-v4-flash:cloud
Try it with OpenClaw:
ollama launch openclaw --model deepseek-v4-flash:cloud
Try it with Hermes:
ollama launch hermes --model deepseek-v4-flash:cloud
Try it with chat:
ollama run deepseek-v4-flash:cloud
(DeepSeek V4 Pro is coming shortly)
🧵
🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length.
🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.
🔹 DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice.
Try it now at chat.deepseek.com via Expert Mode / Instant Mode. API is updated & available today!
📄 Tech Report: huggingface.co/deepseek-ai/D…
🤗 Open Weights: huggingface.co/collections/d…
1/n
Qwen 3.6 27B model is available on Ollama!
Use it with all the integrations in Ollama or chat with the model.
Chat with the model:
ollama run qwen3.6:27b
OpenClaw:
ollama launch openclaw --model qwen3.6:27b
Claude Code:
ollama launch claude --model qwen3.6:27b
More 👇👇👇
🚀 Meet Qwen3.6-27B, our latest dense, open-source model, packing flagship-level coding power!
Yes, 27B, and Qwen3.6-27B punches way above its weight. 👇
What's new:
🧠 Outstanding agentic coding — surpasses Qwen3.5-397B-A17B across all major coding benchmarks
💡 Strong reasoning across text & multimodal tasks
🔄 Supports thinking & non-thinking modes
✅ Apache 2.0 — fully open, fully yours
Smaller model. Bigger results. Community's favorite. ❤️
We can't wait to see what you build with Qwen3.6-27B! 👀
🔗👇
Blog: qwen.ai/blog?id=qwen3.6-27b
Qwen Studio: chat.qwen.ai/?models=qwen3.6…
Github: github.com/QwenLM/Qwen3.6
Hugging Face:
huggingface.co/Qwen/Qwen3.6-…
huggingface.co/Qwen/Qwen3.6-…
ModelScope:
modelscope.cn/models/Qwen/Qw…
modelscope.cn/models/Qwen/Qw…
Patrick Devine retweeted
Hermes Agent 🤝 Ollama
ollama launch hermes
Ollama 0.21 includes supports Hermes Agent, the self-improving AI agent built by @NousResearch.
Patrick Devine retweeted
This tip was dead on.
On my Apple Silicon, switching my @NousResearch Hermes Agent from qwen3.6:35b-a3b to qwen3.6:35b-a3b-mxfp8 cut runtime from 21.6s to 12.7s and increased generation speed from 29.6 to 50.3 tok/s (~1.7x faster).
Patrick Devine retweeted
New @ollama update is god's work btw
Give it a try
Just run `ollama` and thats it
Ollama is here with image generation!
ollama run x/z-image-turbo
ollama run x/flux2-klein
In the latest release we've added experimental support for
@Ali_TongyiLab Z-image-turbo
@bfl_ml Flux.2 Klein!
(macOS with Windows and Linux coming soon)
See examples 👇👇👇
Patrick Devine retweeted
New open-weight LLM by Deep Cogito!
Locally (671B):
ollama run cogito-2.1
Ollama's Cloud:
ollama run cogito-2.1:671b-cloud
Today, we are releasing the best open-weight LLM by a US company: Cogito v2.1 671B.
On most industry benchmarks and our internal evals, the model performs competitively with frontier closed and open models, while being ahead of any US open model (such as the best versions of OpenAI’s GPT-OSS, Nvidia’s Nemotron and Meta’s Llama).
We also built an interface where you can try the model (it’s free and we don’t store any chats): chat.deepcogito.com
Additionally, you can download the model on @huggingface, or try it out on @openrouter, @togethercompute, @FireworksAI_HQ , @ollama cloud, @runpod, @baseten, or run it locally using @ollama or @UnslothAI.
This model uses significantly fewer tokens amongst any similar capability models, because it has better reasoning capabilities. You will also notice improvements across instruction following, coding, longer queries, multi-turn and creativity.
📌 Model Weights: huggingface.co/collections/d…
📌Openrouter: openrouter.ai/deepcogito/cog…
📌 HF Blog: huggingface.co/blog/deepcogi…
Some notes on our approach + design choices below 👇




































