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Ollama 0.4 is released with support for Meta's Llama 3.2 Vision models locally
- Patrick_Devine 2y agoThis was a pretty heavy lift for us to get out which was why it took a while. In addition to writing new image processing routines, a vision encoder, and doing cross attention, we also ended up re-architecting the way the models get run by the scheduler. We'll have a technical blog post soon about all the stuff that ended up changing.
- exe34 2y agodid you feed back into llama.cpp? also, can it do grounding like cogvlm? either way, great job!
- Patrick_Devine 2y agoIt's difficult because we actually ditched a lot of the c++ code with this change and rewrote it in golang. Specifically server.cpp has been excised (which was deprecated by llama.cpp anyway), and the image processing routines are all written in go as well. We also bypassed clip.cpp and wrote our own routines for the image encoder/cross attention (using GGML). The hope is to be able to get more multimodal models out soon. I'd like to see if we can get Pixtral and Qwen2.5-vl in relatively soon.
- qrios 2y ago> Specifically server.cpp has been excised (which was deprecated by llama.cpp anyway) Is there any more specific info available about who (llama.cpp or Ollama) removed what, where? As far as I can see, the server is still part of llama.cpp. And more generally: Is this the moment when Ollama and Llama part ways?
- exe34 2y agothat's cool thank you! no grounding then? I don't get the impression it's actually part of llama 3.2v but I thought it's worth checking with somebody who might have the experience!
- Patrick_Devine 2y agoI haven't looked at cogvlm, but if you mean doing bounding boxes w/ classification, I'd love to support models like that (like detectron2) in the future.
- exe34 2y agoI'm not sure what you mean by classification, but something like it, yes: "what are the coordinates of the bounding box for the rubber duck in the image [img]" >>> "[10,50,200,300]"
- zozbot234 2y agoHow long until Vulkan Compute support is merged into ollama? There is an active pull request at https://github.com/ollama/ollama/pull/5059 https://github.com/ollama/ollama/pull/5059 but it seems to be stalled with no reviews.
- csomar 2y agoAny info of when we will get the 11B and 90B models?
- jjice 2y agoY'all did a fantastic job! This works great and to have it all right there inside of Ollama is a huge step for local model execution.
- inasring 2y agoCan it run the quantized models?
- fallingsquirrel 2y agoSupported quantizations: https://ollama.com/library/llama3.2-vision/tags https://ollama.com/library/llama3.2-vision/tags
- vasilipupkin 2y agohow likely is it to run on a reasonably new windows laptop?
- ac29 2y agoWith 16GB of RAM these vision models will run. How quickly depends on a lot of factors.
- o11c 2y agoDid they fix multiline editing yet? Any interactive input that wraps across 3+ lines seems to become off-by-one when editing (but fine if you only append?), and this will be only more common with long filenames being added. And triple-quote breaks editing entirely. How does this address the security concern of filenames being detected and read when not wanted?
- deleted 2y ago[deleted]
- papruapap 2y agoI thought llamacpp didn't support images yet, has that changed or ollama is using a different library for this?
- zamderax 2y agoDoes anyone know if this will run on the iPhone 15 (6GB) or iPhone 16 (8GB)
- ei23 2y agoIs Qwen2VL supported too? Its a great vision model, works in comfyui. Llama3.2s vision seems to be super censored...
- sgt101 2y agoI tested the small model with a few images from Clevr. On first blush I am afraid it didn't do very well at all, it got object counts totally wrong and struggled to identify shapes and colours. Still, it seems to understand what's in the images in general (cones and spheres and cubes), and the fact that it runs on my mac book at all is basically amazing.
- EdwardKrayer 2y agoMy initial testing was with charts - I've been waiting on local vision models to be good enough to feed technical documents and my initial testing is looking very good. Example: https://i.imgur.com/1ETREP9.png https://i.imgur.com/1ETREP9.png
- sgt101 2y agoI've tried with some ppt images rather than Clevr ones and it does much better. It can count circles and triangles and differentiates between them quite well. It can recognise the colours of the objects as well. I think that the faux 3d of clevr images is too much for the model, it's interesting because much smaller pre-transformer specialist models were very good at clevr.