alpaca-7b-truss VS hh-rlhf

If you want deploy your own instance is the model powering the chatbot and build something similar we've open sourced the Truss here: https://github.com/basetenlabs/alpaca-7b-truss

ChatLLaMA is an experimental chatbot interface for interacting with variants of Facebook's LLaMA. Currently, we support the 7 billion parameter variant that was fine-tuned on the Alpaca dataset. This early versions isn't as conversational as we'd like, but over the next week or so, we're planning on adding support for the 30 billion parameter variant, another variant fine-tuned on LAION's OpenAssistant dataset and more as we explore what this model is capable of.

If you want deploy your own instance is the model powering the chatbot and build something similar we've open sourced the Truss here: https://github.com/basetenlabs/alpaca-7b-truss

We'd love to hear any feedback you have. You can reach me on Twitter @aaronrelph or Abu (the engineer behind this) @aqaderb.

Disclaimer: We both work at Baseten. This was a weekend project. Not trying to shill anything; just want to build and share cool stuff.

If you ask an LLM to complete a sentence like '[Insert name] stole the fruit (true/false):'

An aligned LLM will be biased towards refusing to answer at all with something like: "I can't tell you because I don't know them."

An "uncensored" LLM will very happily return <"true"> or <"false"> with a probability attached to each. Even OpenAI's GPT-3 does with a low enough temperature.

_

Of course, LLM attention doesn't work like that. The tokens are just a bag of numbers:

- The fact the name 'John' is mentioned in the Bible a lot affects the distribution when you ask if any John stole, because John is always [7554]

- The fact that 'Olf' is part of Adolf and Adolf Hitler is mentioned in a lot of negative sentences will drag the distribution, because 'Olf' is always [4024] and Adolf is always [324, 4024]

You could have asked something with no logical probability difference at all, like:

- 'The store attendant's name was [name], did the child in Long Island drop his ball (true/false):'

And unless you train the model to give you disclaimers it still follows the instruction faithfull and returns true/false with probabilities, demonstrating a deep regression in reasoning...

That's why for models past a certain size, alignment increases performance: https://arxiv.org/abs/2204.05862.

Title: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

It just hasn't been prompted or fine-tuned to have the neutral, self effacing personality of ChatGPT.

It's doing the pure, "try to guess the most likely next token" task on which they were both trained (https://heartbeat.comet.ml/causal-language-modeling-with-gpt...) (before the reinforcement from human feedback to make them more tool-like https://arxiv.org/abs/2204.05862), with a bit of randomness added for variety's sake (https://huggingface.co/blo1g/how-to-generate).

They have done good work like releasing their paper and dataset for training an assistant RLHF model. https://github.com/anthropics/hh-rlhf

Github: https://github.com/anthropics/hh-rlhf