Відео

I think alot of the functionalities of platforms like chatgpt are quite easily replicated with function calling and agents. The hard part usually is being able to locally run a large enough llm that can reliably follow the system prompts . You can only do so much with wrapper code, eventually it all boils down to how good the LLM your using is. If your GPU poor like me, I'd recommend looking into Groq cloud, they have quite generous amounts of free API access to a lot of different llm models.

@@polymir9053 Yeah Dude that might work out. There's got to be tons of ways to creatively use cloud space. I would imagine how many agents people can link together in unfathomable networks together. Open source and jail broken

I have not measured it but I would say this is not usable for real time purposes. Your not likely to get anything even close to 1 fps using this unless you have a really good setup for running LLMs. I'm running it on a 1080ti.

@@polymir9053 right, thanks for your insight

Sorry about that, try this: discord.com/invite/4DyMcRbK4G

You can use a single Agent for multiple subjects. While agents do keep track of history, chat history is only used if passed in along with the question.

It's just Python and Ollama.

Basically, llm-axe is a toolkit that can be used with pretty much any llm. It will not host a model itself, so yes, you need to have a model up and running to use llm-axe. If you'd like to use something other than ollama for your llm setup, please see this example on how that can be done: github.com/emirsahin1/llm-axe/blob/main/examples/ex_llm_setup.py

There is no webui for this, but you with some coding you could easily tie this up to any existing open source chat UIs.

I'm not sure if I understand your question. But you could always default to a normal agent if the function caller fails to answer. Feel free to join the discord in the description if you want to chat about this.

Yes, I'm using Ollama in the video. It has built in support for the Ollama API through the OllamaChat class. See this example: github.com/emirsahin1/llm-axe/blob/main/examples/ex_online_agent.py

@@polymir9053 Thanks! But i am still a bit confused as to how to use this with the ollama API example for a chat completion? curl localhost:11434/api/chat -d '{ "model": "llama3", "messages": [ { "role": "user", "content": "why is the sky blue?" } ], "stream": false }'

Thanks! Yes, Yolo's definetly the right tool for that. But if you ever need to detect whether or not a window/door is open for example, I could see llms being useful for that.

Its simple for now, but as open-source vision models get better, we can expect more reliable and complete results. Chatgpt vision for example already seems more capable than most zero-shot detectors.

Thanks! I appreciate the suggestion. There is a small little demo showing how this can be used to make a command prompt chat where you can chat with the online agent. Here is the link: github.com/emirsahin1/llm-axe/blob/main/examples/ex_online_chat_demo.py

Glad to hear that you found it useful!

Thanks! Its a custom component I made. It stores a preset list of materials and then assigns the parent mesh the current material when clicked. I can make a tutorial if there is enough interest.

100% real :)

I think so too, gotta be careful in NYC

@@MobBeatzMusicProduction the reason why I don’t like New York is simply too many monsters in there

It's not real* is how you spell it

I think it’s real man, you gotta be careful 🤯

Its not anything worth translating, just a funny voiceline from a video :).