llm VS guidance

That's a great use case, you might be able to do this if you've got a copy and paste on the command line with

https://github.com/simonw/llm

In between. An alias like pdfwtf translating to "paste | llm command | copy"

I added support for this model to my LLM CLI tool via a new plugin: https://github.com/simonw/llm-command-r

So now you can do this:

    pipx install llm

If you're willing to use the CLI, Simon Willison's llm library[0] should do the trick.

[0] https://github.com/simonw/llm

I'm not thrilled about https://github.com/GoogleCloudPlatform/localllm/blob/main/ll... calling their Python package "llm" and installing "llm" as a CLI command, when my similar https://llm.datasette.io/ project has that namespace reserved on PyPI already: https://pypi.org/project/llm/

I've been gleefully exploring the intersection of LLMs and CLI utilities for a few months now - they are such a great fit for each other! The unix philosophy of piping things together is a perfect fit for how LLMs work.

I've mostly been exploring this with my https://llm.datasette.io/ CLI tool, but I have a few other one-off tools as well: https://github.com/simonw/blip-caption and https://github.com/simonw/ospeak

I'm puzzled that more people aren't loudly exploring this space (LLM+CLI) - it's really fun.

Seems nice if you're using c# or java. It also supports python, but for that Simon's llm library is nice because he designed it as both a library and a command line tool: https://github.com/simonw/llm

This IS Microsoft Guidance, they seem to have spun off a separate GitHub organization for it.

https://github.com/microsoft/guidance redirects to https://github.com/guidance-ai/guidance now.

... and it sets the value of "armor" to "leather" so that you can use that value later in your code if you wish to. Guidance is pretty powerful, but I find the grammar hard to work with. I think the idea of being able to upload a bit of code or a context-free grammar to guide the model is super smart.

https://github.com/microsoft/guidance/blob/d2c5e3cbb730e337b...

Here's one thing I don't get.

Why all the rigamarole of hoping you get a valid response, adding last-mile validators to detect invalid responses, trying to beg the model to pretty please give me the syntax I'm asking for...

...when you can guarantee a valid JSON syntax by only sampling tokens that are valid? Instead of greedily picking the highest-scoring token every time, you select the highest-scoring token that conforms to the requested format.

This is what Guidance does already, also from Microsoft: https://github.com/microsoft/guidance

But OpenAI apparently does not expose the full scores of all tokens, it only exposes the highest-scoring token. Which is so odd, because if you run models locally, using Guidance is trivial, and you can guarantee your json is correct every time. It's faster to generate, too!

Perhaps something as simple as stating it was first built around OpenAI models and later expanded to local via plugins?

I've been meaning to ask you, have you seen/used MS Guidance[0] 'language' at all? I don't know if it's the right abstraction to interface as a plugin with what you've got in llm cli but there's a lot about Guidance that seems incredibly useful to local inference [token healing and acceleration especially].

[0]https://github.com/microsoft/guidance

LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-parties who are sharing their resources to ensure that all of them used the same templates, they were generated the same way, with the only diff being the models these prompts were run on.

So maybe a simpler library like Microsoft's Guidance (https://github.com/microsoft/guidance)? It does this really well.

I am unclear on the status of the project but here is the conversation that seem to be tracking it: https://github.com/microsoft/guidance/discussions/201

Let's have a subreddit about Guidance!

You should look at guidance for an LLM to fill out a template. Define the output data structure and provide the real estate listing in the context (see the JSON template example here https://github.com/microsoft/guidance)