llm-mlc VS jsonformer

I'm still iterating on that. Plugins get complete control over the prompts, so they can handle the various weirdnesses of them. Here's some relevant code:

https://github.com/simonw/llm-gpt4all/blob/0046e2bf5d0a9c369...

https://github.com/simonw/llm-mlc/blob/b05eec9ba008e700ecc42...

https://github.com/simonw/llm-llama-cpp/blob/29ee8d239f5cfbf...

I'm not completely happy with this yet. Part of the problem is that different models on the same architecture may have completely different prompting styles.

I expect I'll eventually evolve the plugins to allow them to be configured in an easier and more flexible way. Ideally I'd like you to be able to run new models on existing architectures using an existing plugin.

What is the advantage of this versus running something like https://github.com/simonw/llm , which also gives you options to e.g. use https://github.com/simonw/llm-mlc for accelerated inference?

I'm quite impressed with Llama 2 13B - the more time I spend with it the more I think it might be genuinely useful for more than just playing around with local LLMs.

I'm using the MLC version (since that works with a GPU on my M2 Mac) via my https://github.com/simonw/llm-mlc plugin.

- Tools like jsonformer https://github.com/1rgs/jsonformer are not possible with OpenAIs API.

How does this compare in terms of latency, cost, and effectiveness to jsonformer? https://github.com/1rgs/jsonformer

You're correct with interpreting how the model works wrt it returning tokens one at a time. The model returns one token, and the entire context window gets shifted right by one to for account it when generating the next one.

As for model performance at different context sizes, it's seems a bit complicated. From what I understand, even if models are tweaked (for example using the superHOT RoPE hack or sparse attention) to be able to use longer contexts, they still have to be fined tuned on input of this increased context to actually utilize it, but performance seems to degrade regardless as input length increases.

For your question about fine tuning models to respond with only "yes" or "no", I recommend looking into how the jsonformers library works: https://github.com/1rgs/jsonformer . Essentially, you still let the model generate many tokens for the next position, and only accept the ones that satisfy certain criteria (such as the token for "yes" and the token for "no".

You can do this with openAI API too, using tiktoken https://twitter.com/AAAzzam/status/1669753722828730378?t=d_W... . Be careful though as results will be different on different selections of tokens, as "YES", "Yes", "yes", etc are all different tokens to the best of my knowledge

From here, we just need to continue generating tokens until we get to a closing quote. This approach was borrowed from Jsonformer which uses a similar approach to induce LLMs to generate structured output. Continuing to do so for each property using Replit's code LLM gives the following output:

https://github.com/1rgs/jsonformer or https://github.com/microsoft/guidance may help get better results, but I ended up with a bit more of a custom solution.

I think function calling is just JSONformer idk: https://github.com/1rgs/jsonformer

jsonformer: https://github.com/1rgs/jsonformer