phasellm VS turbopilot

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)

fyi https://github.com/ravenscroftj/turbopilot

TurboPilot: self-hosted copilot clone which uses the library behind llama.cpp to run the 6 Billion Parameter Salesforce Codegen model in 4GiB of RAM. (https://github.com/ravenscroftj/turbopilot)

This repo has a potential

Right now https://github.com/ggerganov/llama.cpp is the dominant back-end for querying models, but forks and alternatives like https://github.com/ravenscroftj/turbopilot keep popping up. Increasingly, models submitted to huggingface explicitly note in their READMEs that the model is not compatible with llama.cpp, and that a different back-end must be used.

There's turbopilot. I haven't tried it yet, but it looks promising.

Turbopilot | open source LLM code completion engine and Copilot alternative

This 6B parameters tool (based on README) could be runned with 4 Gb of RAM. https://github.com/ravenscroftj/turbopilot

there is an interesting link https://github.com/ravenscroftj/turbopilot/wiki/Converting-and-Quantizing-The-Models , just wondering if anyone have done this with 16b and put the weights somewhere