CogVLM VS llamafile

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.

I think the llamafile[0] system works the best. Binary works on the command line or launches a mini webserver. Llamafile offers builds of Mixtral-8x7B-Instruct, so presumably they may package this one up as well (potentially a quantized format).

You would have to confirm with someone deeper in the ecosystem, but I think you should be able to run this new model as is against a llamafile?

[0] https://github.com/Mozilla-Ocho/llamafile

Thermostats: https://www.sinopetech.com/en/products/thermostat/

I haven't tried running a local text-to-speech engine backed by an LLM to control Home Assistant. Maybe someone is working on this already?

TTS: https://github.com/SYSTRAN/faster-whisper

LLM: https://github.com/Mozilla-Ocho/llamafile/releases

LLM: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-D...

It would take some tweaking to get the voice commands working correctly.

While I did not succeed in making the matmul code from https://github.com/Mozilla-Ocho/llamafile/blob/main/llamafil... work in isolation, I compared eigen, openblas, and mkl: https://gist.github.com/Dobiasd/e664c681c4a7933ef5d2df7caa87...

In this (very primitive!) benchmark, MKL was a bit better than eigen (~10%) on my machine (i5-6600).

Since the article https://justine.lol/matmul/ compared the new kernels with MLK, we can (by transitivity) compare the new kernels with Eigen this way, at least very roughly for this one use-case.

Yes, they're just ZIP files that also happen to be actually portable executables.

https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file...

have you seen llamafile[0]?

[0] https://github.com/Mozilla-Ocho/llamafile

llama.cpp has integrated gemma support. So you can use llamafile for this. It is a standalone executable that is portable across most popular OSes.

https://github.com/Mozilla-Ocho/llamafile/releases

So, download the executable from the releases page under assets. You want either just main or just server. Don't get the huge ones with the model inlined in the file. The executable is about 30MB in size,

https://github.com/Mozilla-Ocho/llamafile/releases/download/...

The improvements in ease of use for locally hosting LLMs over the last few months have been amazing. I was ranting about how easy https://github.com/Mozilla-Ocho/llamafile is just a few hours ago [1]. Now I'm torn as to which one to use :)

1: Quite literally hours ago: https://euri.ca/blog/2024-llm-self-hosting-is-easy-now/