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Mistral releases Pixtral 12B, its first multimodal model
- azinman2 2y agoI’d love to know how much money Mistral is taking in versus spending. I’m very happy for all these open weights models, but they don’t have Instagram to help pay for it. These models are expensive to build.
- candiddevmike 2y agoNo license with this one yet, though you can probably assume it's Apache like the others.
- mdasen 2y agoThe article says they confirmed it's Apache via email
- edude03 2y ago12B is pretty small, so I’m doubting it’ll be anywhere close to internvl2 however mistral does great work and likely this model is still useful for on device tasks
- Jackson__ 2y agoIt appears to be slightly worse than Qwen2VL 7B, a model almost half it's size, if you look at the Qwen's official benchmarks instead of Mistral's. https://xcancel.com/_philschmid/status/1833954941624615151 https://xcancel.com/_philschmid/status/1833954941624615151
- kaoD 2y agoBut Qwen is not multimodal, or is it?
- Jackson__ 2y agohttps://qwen2.org/vl/ https://qwen2.org/vl/ >Qwen2-VL is the latest addition to the vision-language models in the Qwen series, building upon the capabilities of Qwen-VL. Compared to its predecessor, Qwen2-VL offers: >State-of-the-Art Image Understanding >Extended Video Comprehension Besides, it'd have been pretty silly for them to mention it on their slides if it wasn't.
- jazzyjackson 2y agoI've found llama 3.1 8B to be effective at transforming unstructured text into structured data, now that LM Studio accepts a json schema parameter. For a general knowledge chatbot it doesn't know much of course, but its a good worker bee.
- ChrisArchitect 2y agoRelated earlier: New Mistral AI Weights https://news.ycombinator.com/item?id=41508695 https://news.ycombinator.com/item?id=41508695
- buran77 2y agoThe "Mistral Pixtral multimodal model" really rolls off the tongue. > It’s unclear which image data Mistral might have used to develop Pixtral 12B. The days of free web scraping especially for the richer sources of material are almost gone, with anything between technical (API restrictions) and legal (copyright) measures building deep moats. I also wonder what they trained it on. They're not Meta or Google with endless supplies of user content, or exclusive contracts with the Reddits of the internet.
- simonw 2y agoWhat do you mean by copyright measures? Has anything changed on that front in the last two years? My hunch is that most AI labs are already sitting on a pretty sizable collection of scraped image data - and that data from two years ago will be almost as effective as data scraped today, at least as far as image training goes.
- dartos 2y agoThe issue with image models is that their style becomes identifiable and stale quite quickly, so you’ll need a fresh intake of different, newer, styles every so often and that’s going to be harder and harder to get.
- Eisenstein 2y ago> Built on one of Mistral’s text models, Nemo 12B, the new model can answer questions about an arbitrary number of images of an arbitrary size given either URLs or images encoded using base64, the binary-to-text encoding scheme. Similar to other multimodal models such as Anthropic’s Claude family and OpenAI’s GPT-4o, Pixtral 12B should — at least in theory — be able to perform tasks like captioning images and counting the number of objects in a photo. This is a not a diffusion model -- it doesn't create images, it answers questions.
- whimsicalism 2y agosolvable without additional images
- Flockster 2y agoCould this be used for a selfhosted handwritten text recognition instance? Like writing on an ePaper tablet, exporting the PDF and feed this into this model to extract todos from notes for example. Or what would be the SotA for this application?
- tonygiorgio 2y ago> the 12-billion-parameter model is about 24GB in size Probably not on the device itself but I would love that use case as well. At least going to my own server. I’d want to protect notes in particular, which is why I don’t do any cloud backup on my RM2. But some self hosted, AI assisted OCR workflows could be really nice.
- whimsicalism 2y agoif you have a 3090, you could self host
- jhgg 2y agoTry out https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct
- aucisson_masque 2y agoMistral being more open than 'openai' is kind of a meme. How can a company call itself open while it refuses to openly distribute it's product and when competitor are actually doing it.
- reissbaker 2y agoCouple notes for newcomers: 1. This is a VLM, not a text-to-image model. You can give it images, and it can understand them. It doesn't generate images back. 2. It seems like Pixtral 12B benchmarks significantly below Qwen2-VL-7B [1], so if you want the best local model for understanding images, probably use Qwen2. If you want a large open-source model, Qwen2-VL-72B is most likely the best option. 1: https://qwenlm.github.io/blog/qwen2-vl/ https://qwenlm.github.io/blog/qwen2-vl/
- Jackson__ 2y ago>If you want a large open-source model, Qwen2-VL-72B is most likely the best option. Only the 2&7B have been "open sourced". From your link: >We opensource Qwen2-VL-2B and Qwen2-VL-7B with Apache 2.0 license, and we release the API of Qwen2-VL-72B!
- deleted 2y ago[deleted]
- wruza 2y agoA question for sd lora trainers, is this usable for making captions and what are you using, apart from BLIP? Also, can your model of choice understand your requests to include/omit particular nuances of an image?
- Auracle 2y agoI’m no expert but Florence2 has been my go-to. It’s pretty great at picking up art styles and IP stuff - “The image depicts Goku from the anime series Dragonball Z…” I don’t believe you can really prompt it though, but the other models where I could also didn’t work well on that front anyways. TagGui is an easy way to try out a bunch of models.
- wruza 2y agoYeah, blip mostly ignores prompt too. I tried to disassemble it and feed my prompts, to no avail. Although I found that default kohya gui arguments are not even remotely the best. Here's my args: finetune/make_captions.py ... \ --num_beams=12 \ --top_p=0.9 \ --max_length=75 \ --min_length=24 \ --beam_search \ ... With this, it's very often that I just take its caption as is, or add little. TagGui Oh, interesting, thanks!
- Jackson__ 2y agoI like Qwen2-VL 7B because it outputs shorter captions with less fluff. But if you need to do anything advanced that relies on reasoning and instruction following the model completely falls flat on it's face. For example, I have a couple way-too-wordy captions made with another captioner, which I'd like to cut down to the essentials while correcting any mistakes. Qwen2 is completely ignoring images with this approach, and decides to only focus on the given caption, which makes it unable to even remotely fix issues in said caption. I am really hoping Pixtral will be better for instruction following. But I haven't been able to run it because they didn't prioritize transformers support, which in turn has hindered the release of any quantized versions to make it fit on consumer hardware.