Hey everyone, happy to say I’m officially announcing Obsidian V0.5 as part of my work at Nous Research and building upon my work creating the Capybara V1.9 dataset.
This model is blazing fast and is likely the first Multi-modal model that is efficient enough to fit within the ram constraints of even a non-pro iphone! at practical speeds as well!
This model in its current state is largely a multi-modal version of Nous-Capybara-3B which I also only recently released, I’ve designed the dataset with novel synthesis methods (Paper currently being done) it’s made to be robust with conversational abilities and even includes multi-turn data that has been synthesized as a continuation of single turn data examples contained within datasets like Airoboros, Know_logic, EverythingLM and more.
It’s built using Llava 1.5 techniques but instead of a 7B llama as a base, we choose to use the new StableLM 3B model trained for 4 trillion tokens. (We plan to train upon Mistral likely as well)
Any questions or feedback are much appreciated!
Download here: https://huggingface.co/NousResearch/Obsidian-3B-V0.5
Or download quantized version here, Courtesy of Nisten: https://huggingface.co/nisten/obsidian-3b-multimodal-q6-gguf
I was extraordinarily skeptical of the utility of 3b models until...about 1 day ago when I gave orca mini a fair shot. In particular by training it on one specialized task. Which wound up producing results that honestly floored me.
All of which is to say that I'm VERY excited to see this. I really think the 3B models can be something of a perfect swiss army knife. Compact and always available. Multi modal capabilities are just a perfect fit for that exact type of methodology. Can't wait to give this a shot!
I can almost guarantee you that Capybara 3B and Obsidian 3B will perform would perform even significantly better than orca mini. The base model that I’m using for training 3B is the much newer StableLM 3B model trained for 4 trillion tokens of training while orca mini base model is open llama 3B which was only trained on around 1-2 Trillion tokens and performs significantly worse.
Dang, given that I was already impressed with a model trained on half the tokens I suspect I will be impressed!