I read this interesting article by Gajus about finetuning gpt-3.5-turbo. It was quite similar to my experience fine tuning a model to play Connections. A helpful takeaway was that after finetuning the model, you shouldn’t need to include system prompt in future model inference, so you can save on token cost. I also liked the suggestion to use a database to store training data. I had also been wrangling jsonl files.About a month ago, I had been looking into creating a NL to SQL plugin for datasette. Simon release a version of exactly that the next day and I came across it in his article here. Hopefully I can find time to try this out in the next few days.I did a refactor of my nix config following a pattern I learned from reading Davis’ setup. My two main uses right now for Nix/home-manager are to install and configure programs. Some of these programs have nix modules that allow for the configuration to be written in Nix. Others don’t, but you can still use Nix to create a config file for that program to read. I do the latter with skhd and goku to create a karabiner.For me, invoking a language model using a playground (UI) interface is the most common approach for my usage. Occasionally, it can be helpful to use the a CLI to directly pipe output into a model. For example
git diff --staged | llm "write a commit message for these changes" However, I am more often inclined to open a playground and paste the bits and pieces of context I need. Maybe, it’s that refinement and followups are common enough that using a CLI isn’t nearly as flexible.I enjoyed this article by Ken about production LLM use cases with OpenAI models.
When it comes to prompts, less is more
This resonated with me. I’ve found that too much instruction can lead a model to perform worse on a task.
GPT is really bad at producing the null hypothesis
This also seems to confirm what I’ve seen empirically, but I never ask for it. I ask for something like, “return an empty JSON array if you can’t find anything”.I enjoyed Martin’s article on preserving your shell history. I implemented some of his approaches in my system config.Gemini Pro 1.5 up and running. I’ve said this before but I will say it again – the fact that I don’t need to deal with GCP to use Google models gives me joy.
❯ llm -m gemini-1.5-pro-latest "who is the fastest man in the world?" As of November 2023, **Usain Bolt** is still considered the fastest man in the world. He holds the world record in the 100 meters with a time of 9.Today, I learned about Command-R model series from Cohere from Shawn’s great AI newsletter (ainews). I searched to see if a plugin was available for llm and Simon had literally authored one 8(!) hours earlier. Folks like you keep me inspired and motivated 🙏. No better workflow out there that I know of:
llm install llm-command-r llm -m r-plus hello Error: No key found - add one using 'llm keys set cohere' or set the COHERE_API_KEY environment variable llm keys set cohere Enter key: .A great article by Manuel about forever-growth of companies. I too wish we’d be more willing to celebrate enough.I’ve been digging more into evals. I wrote a simple Claude completion function in openai/evals to better understand how the different pieces fit together. Quick and dirty code:
from anthropic import Anthropic from evals.api import CompletionFn, CompletionResult from evals.prompt.base import is_chat_prompt class ClaudeChatCompletionResult(CompletionResult): def __init__(self, response) -> None: self.response = response def get_completions(self) -> list[str]: return [self.response.strip()] class ClaudeChatCompletionFn(CompletionFn): def __init__(self, **kwargs) -> None: self.client = Anthropic() def __call__(self, prompt, **kwargs) -> ClaudeChatCompletionResult: if is_chat_prompt(prompt): messages = prompt system_prompt = next((p for p in messages if p.