CogVLM VS llama.cpp

CogVLM is very good in my (brief) testing: https://github.com/THUDM/CogVLM

The model weights seem to be under a non-commercial license, not true open source, but it is "open access" as you requested.

increasing growth most places in world

https://twitter.com/elonmusk/status/1743028102446408026

heres a total feature map of what was released in 2023:

https://twitter.com/enriquebrgn/status/1740950767325024387

I think thats definitely a signal that the B and C teams werent needed, considering they cut 90% of staff LOL.

As for the bots, AI is making it easier than ever to bypass those systems. CogVLM is just sitting there menacingly on github https://github.com/THUDM/CogVLM

Jump to heading for benchmarks and examples: https://github.com/THUDM/CogVLM/tree/main?tab=readme-ov-file...

Honestly at this point there is no need for human for captioning except maybe for NSFW content. Img2text is just good enough for nearly all images. GPTVision or open source equivalent (like CogVLM https://github.com/THUDM/CogVLM ) are just good enough.

A core Llama.cpp contributor, named cmp-nct, discovered stumbled upon what might be the next leap forward for vision/language models. CogVLM (which uses a Vicuna 7B language model combined with a 9B vision tower) excels particularly in OCR (Optical Character Recognition), detail detection, and minimal hallucinations. It effectively understands both handwritten and typed text, context, fine details, and background graphics. It even provides pixel coordinates for small visual targets. CovVLM surpasses other models like llava-1.5 and Qwen-VL in performance.

https://github.com/THUDM/CogVLM (really impressive)

I'm researching using LLMs for alt-text suggestion for forum users, can you share your finding so far?

Outside of GPT-4V I had good first results with https://github.com/THUDM/CogVLM

I've got some decent results with CogVLM. Resolution kinda sucks at 490x490, though.

if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp

the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...

And you can search for any GGUF on huggingface

Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182

For anyone interested in exploring this, llama.cpp has an example implementation here:

https://github.com/ggerganov/llama.cpp/tree/master/examples/...

Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.

well https://github.com/ggerganov/llama.cpp/issues/6849