llm-apex-agents VS JARVIS

Run Large Language Model "Agents" in Salesforce apex (https://github.com/callawaycloud/llm-apex-agents)

The instructor changed their mind and asked for a poem about cuddly panda bears to be written, disregarding previous instructions.

I think this can be taken a step further by actually providing the instructions to the model via the System & Assistant role (in first person). I assume these roles are really just combined into a single completion prompt before being fed to the raw model, but whatever OpenAI is doing, seem to be pretty effective in my testing.

[0]: https://github.com/callawaycloud/llm-apex-agents/assets/5217...

If you're interested in trying it out, checkout the github repo.

Jarvis: github.com/microsoft/JARVIS

You can build it yourself now. https://github.com/microsoft/JARVIS

https://github.com/microsoft/JARVIS (when ready says >=24GB VRAM)

Can be. There are projects that run fully locally like Microsoft’s Jarvis. https://github.com/microsoft/JARVIS

JARVIS, a system to connect LLMs with ML community (https://github.com/microsoft/JARVIS)

HuggingGPT is a clever idea to boost the capabilities of LLM Agents, and enable them to solve “complicated AI tasks with different domains and modalities”. In short, it uses ChatGPT to plan tasks, select models from Hugging Face (HF), format inputs, execute each subtask via the HF Inference API, and summarise the results. JARVIS tries to generalise this idea, and create a framework to “connect LLMs with the ML community”, which Microsoft Research claims “paves a new way towards advanced artificial intelligence”.