Like InternVL, no llama.cpp support severely limits its applications. Close to GPT4v performance level and runnable locally on any machine (no need for a GPU) would be huge for the accessibility community.
Very curious how it performs on OCR tasks compared to InternVL. To be competitive at reading text you need tiling support, and InternVL does tiles exceptionally well.
After some superficial testing I with bad quality scans you can find on kaggle I can not confirm that. CogVLM2 refuses to handle scans that InternVL-V1.5 still can comprehend.
I’m going to be saying First Ever AI something for the next 15 years for clout and capital, not going to be listening to anybody’s complicated ten step funnel if they’re not doing the obvious
This "matching gpt-4" catchy phrase has lost its meaning to me. Everytime an article like this pops up, I see marketing buzz and unrealistic results in practice.
If "beats GPT 4" is in the title it's almost a guarantee that it's a bold faced lie that includes benchmark overfitting.
The first time a model that actually matched GPT 4 launched (i.e. Command-R+) there was no mention of it at all. If your results speak for themselves, there's no need to shout.
Of course, it's nothing else. Who could possibly believe that OpenAI and others would dump billions into development and training and aren't smart enough to figure out they could also do it with $500.
Many companies also optimize for tools, like Python, that have boost productivity more than price/performance ratio. OpenAI had billions of other people's money. They might just keep using tools which worked before.
Lastly, there are tons of papers published on techniques that claim to reduce cost. Most of them aren't good. Their benchmarks aren't good. Even reviewing most of them is more time than a lot of AI researchers have. Those that make it to established communities usually have gotchas that come with the benefits. So, they could also simply miss a needle in a large haystack.
I think you're right that they'd be using whatever really worked with no loss in model performance. It's just that they might not for a number of reasons. The rational choice is for others to keep experimenting with those things in case they get a competitive advantage.
Fair enough. Is it now safe to say that OpenAI could have done with a 8B model + $500 of fine tuning instead of running a (much) larger model on their GPU cluster?
Who could possibly believe that OpenAI and others would dump billions into development and training and aren't smart enough to figure out they could also do it with $500.
People upvoting the post??
Not really sure? But PT Barnum said there's always a lot of them out there.
Pretty sure they mean fine tuning though?
But even that is total tripe.
These guys are snake oil salesmen. (Or Sylvester McMonkey McBean is behind it.)
Don’t use it for anything OCR related that needs perfect accuracy. Stuff where some errors are ok, we’ve had great success. Depending on your budget, you can also run it multiple times to catch errors.
I’ve done a lot of OCR work and tesseract is nearly a decade out of date at this point. It is not a serious technology for anything requiring good accuracy or minor complexity. From what I’ve seen, GPT-4V completely smokes tesseract, but then again, most modern OCR systems do. If you want fast and pretty powerful OCR, check out paddle. If you want slower but higher accuracy, check out transformer based models such as TrOCR.
Caveat that being from 2022, the Tesseract version used was almost certainly v4 (if Linux), rather than v5 which is much better (and widely available on Windows in 2022, but not Linux yet).
However Tesseract is quite behind still as you note, even with v5.
Running PaddleOCR in production now, I would suggest contrasting Tesseract v4 and v5, since v5 is a lot better(but until recently has not been available on Linux) - PaddleOCR does still smoke it though, you are right (especially for concurrency and fairly easily just setting different workers to different GPUs for best concurrent batching).
What format? The entire data table in one image, or a PDF for example printed off with 8 pages where the user choose to only put the header on the first page etc? Or decent formatting, font size 8+ on an image with decent resolution? With the latter you are probably fine although you will need some manual implementation for parsing the output. You get bounding boxes at word level. One thing if I started nowadays I would do is use basic columns (x coordinates) to add '|' inbetween the outputs(including detecting empty span positions), keep items with similarish y coordinates together on lines, and put it into ChatGPT to format as desired, I suspect this would avoid misreading.
I would say PaddleOCR is good in general for tables - it's much better (in terms of recall rate) at recognising numerical digits / symbols than Tesseract although I notice it often misrecognises "l" in "Lullaby/ml/million" etc as "1" sometimes.
The cloud providers have better table extraction iff you can guarantee the same format each time for the document.
A wide variety of PDFs (both in length and content) that can have a variety of different tables, real estate related with a lot of financial content. And I need to be able to run on local models / software (no parsing as a service, no OpenAI, etc).
Sorry, this will be very hard to do. You can't really try and segment images based on lines as the tables probably varied. The floor plans and things... this data is very very challenging.
I would suggest your best bet is waiting 2 years for the next version of LLAVA to come out which may have capabilities to interpret very accurately on device. The progress with LLAVA has been fast recently but for now it's still a bit too inaccurate.
Tesseract the tool is one apt-get away but the trained models are not, and I've found that they are a starting point, not a final destination. You still have to do more training on top of them for anything that isn't black text on a crisp white background.
Big mistake on my part; I should clarify I fine-tuned both PaddleOCR and TrOCR on large amounts of data specific to my domain. I cannot speak on the best out of the box “ready to go” solutions (besides cloud ones, which were quite good with the right pre and post processing).
Twitter / X has a very interesting captcha: you get to see 10 objects that have weird colors and are slightly deformed, and then you have to match them (1 at a time) with another row that has the same objects but seen from a different angle.
Of course eventually this will be defeated too, but for now it seems to work pretty well.
Image based or any kind of visual captchas will never be extremely effective. I think we will see more of PoW captchas in the upcoming years (just like cloudflare's turnstile captcha)
I'm not suer about that, can't you give GPT4 a math problem in an image already and have it solve it correctly most of the time?
And these haven't even been trained to defeat captchas/logic problem captchas yet, if it was fine tuned on the general pattern of them I imagine any form of captcha is bust.
The resulting mermaid is used for... more LLM processing. Converting to mermaid first is more cost-effective, consistent, and accurate for my purposes.
How does it compare with MiniCPM-Llama3-V 2.5 [0]? Based on what I see it seems much better than Llama 3-V on the benchmarks. Also it can directly be tried on Huggingface Spaces to check the performance [1]. It has the dataset, code and fine-tuning details with screenshots of it running on Xiaomi 14 pro. It has strong OCR performance and supports 30+ languages.
Woah, this actually did quite well on table data extraction. I wonder how this could be used for long documents. Maybe paired with some kind of hybrid rag approach.
Was also looking for something like this - I can't find pricing listed anywhere for their API usage, only the free 1,000 credits - or am I completely misunderstanding how this works?
Would love to see Ollama support for this - seems promising given my experience with LLaVA so far and would love to get some hands on head to head experience
You would use CogAgent in VQA mode. Why would someone downvote suggesting to test one of the most powerful multimodal LLMs? Because it doesn't have "V" in its name? CogAgent is improved on many tasks compared to CogVLM.
If I had a nickel for every outrageous "matches/beats GPT-x" claim, I'd have more money than the capital these projects raise from VC.
This absolutely is not the first Llama3 vision model. They even quote it's performance compared to Llava. Hard to take anything they say seriously with such obviously false claims
Llama 3 outputs text and can only see text, this is a vision model.
>that would make it Llama-2-based.
It's based on Llama 3, Llama 2 has nothing to do with it. They took Llama 3 Instruct and CLIP-ViT-Large-patch14-336, train the projection layer first and then later finetuned the Llama 3 checkpoint and train a LoRA for the ViT.
You need to compare the evals to strong open VLMs including this and CogVLM
- This is not "first-ever multimodal model built on top of Llama3", there's already a Llava on Llama3-8b https://huggingface.co/lmms-lab