phasellm VS JARVIS

If you're looking for an open source solution you can customize, check out the ResearchLLM demo: https://phasellm.com/researchllm

Code: https://github.com/wgryc/phasellm/tree/main/demos-and-produc...

Using Claude, Cohere, GPT-4, OpenAssistant. Formally swapping between them using PhaseLLM (open source library similar to LangChain).

Large language model evaluation and workflow framework from Phase AI. (https://github.com/wgryc/phasellm)

Fantastic questions! Re: working/not working at times -- this is still an issue. It's why I'm building PhaseLLM more broadly (https://github.com/wgryc/phasellm) -- need a robust pipeline that can also "reset" parts of itself if an LLM makes errors or mistakes.

You can see my prompts in this file: https://github.com/wgryc/phasellm/blob/main/demos-and-produc... I autogenerate a fairly big starting prompt and keep resubmitting it. It describes the data set extensively, which helps quite a bit.

That being said, a lot more can be done here around prompt optimization + making this more robust.

That being said, you might want to actually run head-to-head tests between models. PhaseLLM (free, open source) allows you to build a workflow and plug and play various models (including Dolly 2.0 and GPT-4). Then you can run tests to see how much worse/better the various LLMs are and if that's acceptable for your use case.

Large language model evaluation and workflow framework from Phase AI. (https://github.com/wgryc/phasellm)

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”.