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@Nanka696 @LyalinDotCom Only for macOS right now unfortunately for the nvfp4 weights. We're getting closer for other platforms. On Windows you can use `gemma4:12b-it-qat` which should given similar results.
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 👇👇👇
We just dropped Gemma 4 Quantization-Aware Training (QAT) checkpoints on Hugging Face! All Gemma 4 model sizes and their drafters are now optimized with QAT to cut memory requirements and maximize on-device performance!
@sonriks6 @LyalinDotCom @ollama It's back! We had some hiccups on rollout.
@synthshareai @ai_for_success That particular model is tuned with Nvidia's model optimizer for nvfp4. I'm still running MMLU-Pro right now to test it out and it's getting 74.6% accuracy (at about 15% completed). Google published it at 77.2% for BF16s.
@drashyakuruwa @ai_for_success They're available now for non-Macs too.
@ivanfioravanti I'm wondering if they would notice the difference w/ bf16 vs. mxfp8. The microscaling formats seem really decent. nvfp4 is surprisingly good if you tune it correctly.
@LyalinDotCom I've been testing it w/ MMLU-Pro and am about 1/5 of the way through and getting 74.3% accuracy which is pretty impressive.
@LyalinDotCom For Ollama I'd recommend the `gemma4:12b-nvfp4` model which is tuned with Nvidia's model optimizer. I realized though that I should have quantized the qkvo attention tensors to nvfp4 (they're at mxfp8) and I have some tweaks for the embedding layer.
@ivanfioravanti it's the MTP tensors (Qwen3next packs these directly in with the model instead of a separate repo).
@LyalinDotCom For the Mac try `ollama run gemma4:31b-mlx` which will give you significantly better performance. For the DGX Spark you can get a significant performance boost with Ollama 0.30.0 which is just about to come out (it's in prerelease).
@yoeven On your Mac you should run `gemma4:e4b-nvfp4` and you should get a pretty big speed bump over `gemma4:e4b`. I realize the model names are confusing, but we are trying to make this easier!
I think almost all of the MTP/DFlash demos I've been seeing over the last few weeks have been using simple greedy sampling. That's great if you can live with temperature = 0, but I think most people want more sampling options.
@D_Twitt3r @ollama We haven't yet shipped the vision or audio parts for the MLX engine. They are coming though!
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 🧵
@ivanfioravanti Maybe try Ollama w/ `qwen3.6:27b-coding-nvfp4`? That's the MLX runner variant which is less quantized than the affine 4 bit integer quants, and it has the hyper-parameters set for coding/agentic use cases.
@dannytt @julien_c @huggingface With ollama, make sure you're using `qwen3.6:27b-coding-nvfp4`. For generation on an M5 I get just about 30 toks/sec, but the real magic is in the prefill speeds and the LRU cache.
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.
@bflgomes @ollama Yes! You can find the MLX model tags on ollama.com
Michael Irwin 🇺�... @mikesir87
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