Posted by xenova 4 hours ago
Sounds like the model is not following a proper probabilistic choice here, so maybe more a programming error than a model training error.
When I saw 27b on a phone, I thought not fitting, big phone, or aggressive quant. NVFP4 still takes 27G before KV cache.
The LLM style of writing is just very distracting to read. “It unlocks X”, “Y changes the equation”, and why is there always something shifting? Makes my eyes glaze over in an otherwise interesting post.
> Ornith-1.0-9B, which can be easily deployed on edge devices, matches or exceeds the performance of much larger models such as Gemma 4-31B and Qwen 3.6 35B.
https://deep-reinforce.com/ornith_1_0.html
Only tried it so much so far; it did a little better than Qwen 9B
The title says it's 27B grade running on a phone and what I was comparing it to in my mind was a model that runs at 35B grade that could presumably run on a phone "better"?
edit: I asked AI for the difference and understand a little better, thanks for the heads up to learn the difference between models... I think the thing was, although ornith was created for a specific agentic purpose, it was still outperforming a previous generalist model I had running locally (so in my mind I thought it was still a better local model) - I'd like to try bonsai out if I can figure out how to run it lol
huggingface.co/Tivaphraen/Geryon-9B-v1
I can just see their image tool on the app store
Available on HuggingFace: https://huggingface.co/collections/prism-ml/bonsai-27b
The article is about running it on a phone though, and shows an app with their branding running this in text mode on a phone. I'm asking where can I find this app to try what is being demonstrated in this article & video? Appstore only has an image gen app by them and other MLX apps I've tried don't seem to support this model