it does have telemetry, enabled by default, that sends metrics to tracking.miui.com, including what model you are using. it can be turned off by environment variable (MIMOCODE_ENABLE_ANALYSIS=false), and yes it still has all the normal OpenCode provider logic so it will work with other/local models. it also automatically looks for updates and fetches a mimo model list, including when the telemetry is off, though those can also be disabled.
telemetry enabled by default and named "analysis" is not great.
Sorry this is a bit of a tangent, but I noticed you also released UD quants of ERNIE-Image the same day it released, which as I understand requires generating a bunch of images. I've been working to do something similar with my CLI program ggufy, and was curious of you had any info you could share on the kind of compute you put into that, and if you generate full images or look at latents?
I've experimented with this with diffusion models with a safetensors - gguf tool I wrote. even with relatively few sample images (~10k, still enough to keep my 3090 spinning for days straight) the benefits are quite noticeable - a smaller file with overall better results.
This is pretty interesting, based on the blog post, it seems like they are using a technique similar to what I have been using to generate "layer sensitivity" data in my (still pretty beta) ggufy project, which is more aimed at diffusion (image) models.
https://github.com/qskousen/ggufy
Just yesterday I watched this video: https://m.youtube.com/watch?v=7bSzp-QildA I am not a graphics programmer, but from what I understood I think he talks about doing what you are describing with Vulkan.
I've done it with a 6800XT, which should be similar. It's a little trickier than with an Nvidia card (because everything is designed for CUDA) but doable.
It seems like you are saying the AI features don't work if you don't have a GPU, if I understood correctly, but I have my install on a server with no GPU and the object search and facial recognition features work fine. Probably slower to generate the embeddings, but I don't have any comparison to make.
My number one reason for moving away from using LXD in production after this change is that LXD is only available through snap, which caused multiple downtimes in the cluster because of the forced updates.
It does - the inference speed is much slower than a consumer video card. The draw for the Spark and systems like it are the massive amounts of memory available to the GPU.
I had been unemployed for a year and worked a lot on DiffKeep (https://github.com/DiffKeep/DiffKeep), a cross platform AI generated image management program. Fortunately / unfortunately I got a job and haven't been able to dedicate much time to it lately.
Do you determine the worthiness of all your activities by how much money you could make in the time they took?
I read the full article and found it well worth the time. A somewhat sobering essay, prompting some self-reflection, while also being beautifully written. I appreciated the art displayed.
How can you put a price tag on something like that?
telemetry enabled by default and named "analysis" is not great.