u/Trilogix — reddlx

As the journalism is becoming a joke and a financial tool lately we would like to play also. According to CNBC now in just a month more: Anthropic is currently in talks with investors to raise money at a $900 billion valuation, which would push it ahead of OpenAI. The company said in April that it’s topped $30 billion in annualized revenue.

Last month it was evaluated at 380Billions according to the same source: Anthropic, meanwhile, was valued at $380 billion as of February) (highly inflated) but now suddenly got evaluated 520Billions more out of thin air. Come on, is just 520 Billion Dollars.

So dear friends colleagues and investors we now have Breaking news, Hugston is now in talks with investors and Evaluated 1 Billion Euro.

Sure why not, our userbase is growing daily with more than 1000 users weekly. We offer chat as a service, with image and pdf processing among other things. We also offer more than Anthropic or OpenAI like tools to work in total privacy if you need it with whatever Opensource LLM AI Model you choose. I am wondering how do you beat Privacy? Powerful Coding, Writing, Scientific, Medical tools, also Private Online Search, but also a private very sophisticated Rag system. And the list goes on with agents, tools, skills etc... with a license for a lifetime.
How can we not be evaluated just a mini fraction of these companies that offer similar services.

We are now Observing and learning in what the heck is going on, what are we missing here? Did we woke up one year later or did this really happen in 1 month (from 380 to 900 Billions). If this is intentional projection/manifestation let´s see if it works.

Damn it, we love this game.

Hugston Team.

u/Trilogix — 23 days ago

▲ 6 r/Hugston+1 crossposts

4 Million ctx for every AI llm Model

As mentioned in the video we show proof of concept that is compatible with every ai model, big or small, whatever architecture, so long is converted in GGUF.

Keypoints:

We testing tps of some models loaded at 4 million ctx, Lfm 350B, Qwen 0.8B, and a 35B-q4. Gpu+40-80gb ram.

The main utility is that:

1 It can RAG/process large docs, no ctx constrains up to 4 million ctx (more ctx to be tested).

2 No need for specific model (works with all models).

3 being in the same session keeping memory and thread.

Ofc loops depending on model quality and size but still useful.

Speed from lfm~80, Qwen 0.8B ~30, 35B ~6-12 tps.

We will be testing further by using the actual inference input/output to 1 million but loading the model 4million ctx, then we will push it further to 10 million. It can load and work in a consumer hardware. This will finally help us to understand hwo to optimize the cache and turbo or not and multi token prediction.

Today started tests in a very old Lenovo 4gb ram computer. LFM is promising, in rag and vision. It works alla Grande at acceptable tps speed.

We have already developed an enterprise prototype that works very fast in consumer hardware with large amounts of data.

At this point the next goal is to make possible the 10 million ctx in 128 gb ram, (at least 35b model) which will allow Rag in range of many terabytes with vectorial and semantic methods in seconds.

Hopefully we will have some time soon to upgrade the community edition.

Hugston Team.

u/Trilogix — 1 month ago

▲ 1 r/Hugston