guidance

A guidance language for controlling large language models. (by guidance-ai)

Guidance Alternatives

Similar projects and alternatives to guidance

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better guidance alternative or higher similarity.

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guidance reviews and mentions

Posts with mentions or reviews of guidance. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-29.
  • What We Learned from a Year of Building with LLMs
    2 projects | news.ycombinator.com | 29 May 2024
    Via APIs, yes. But if you have direct access to the model you can use libraries like https://github.com/guidance-ai/guidance to manipulate the output structure directly.
  • Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
    5 projects | news.ycombinator.com | 8 Apr 2024
    [1]: https://github.com/guidance-ai/guidance/tree/main
  • Show HN: Prompts as (WASM) Programs
    9 projects | news.ycombinator.com | 11 Mar 2024
    > The most obvious usage of this is forcing a model to output valid JSON

    Isn't this something that Outlines [0], Guidance [1] and others [2] already solve much more elegantly?

    0. https://github.com/outlines-dev/outlines

    1. https://github.com/guidance-ai/guidance

    2. https://github.com/sgl-project/sglang

  • Show HN: Fructose, LLM calls as strongly typed functions
    10 projects | news.ycombinator.com | 6 Mar 2024
  • LiteLlama-460M-1T has 460M parameters trained with 1T tokens
    1 project | news.ycombinator.com | 7 Jan 2024
    Or combine it with something like llama.cpp's grammer or microsoft's guidance-ai[0] (which I prefer) which would allow adding some react-style prompting and external tools. As others have mentioned, instruct tuning would help too.

    [0] https://github.com/guidance-ai/guidance

  • Forcing AI to Follow a Specific Answer Pattern Using GBNF Grammar
    2 projects | /r/LocalLLaMA | 10 Dec 2023
  • Prompting LLMs to constrain output
    2 projects | /r/LocalLLaMA | 8 Dec 2023
    have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.
  • Guidance is back 🥳
    1 project | /r/LocalLLaMA | 16 Nov 2023
  • New: LangChain templates – fastest way to build a production-ready LLM app
    6 projects | news.ycombinator.com | 1 Nov 2023
  • Is supervised learning dead for computer vision?
    9 projects | news.ycombinator.com | 28 Oct 2023
    Thanks for your comment.

    I did not know about "Betteridge's law of headlines", quite interesting. Thanks for sharing :)

    You raise some interesting points.

    1) Safety: It is true that LVMs and LLMs have unknown biases and could potentially create unsafe content. However, this is not necessarily unique to them, for example, Google had the same problem with their supervised learning model https://www.theverge.com/2018/1/12/16882408/google-racist-go.... It all depends on the original data. I believe we need systems on top of our models to ensure safety. It is also possible to restrict the output domain of our models (https://github.com/guidance-ai/guidance). Instead of allowing our LVMs to output any words, we could restrict it to only being able to answer "red, green, blue..." when giving the color of a car.

    2) Cost: You are right right now LVMs are quite expensive to run. As you said are a great way to go to market faster but they cannot run on low-cost hardware for the moment. However, they could help with training those smaller models. Indeed, with see in the NLP domain that a lot of smaller models are trained on data created with GPT models. You can still distill the knowledge of your LVMs into a custom smaller model that can run on embedded devices. The advantage is that you can use your LVMs to generate data when it is scarce and use it as a fallback when your smaller device is uncertain of the answer.

    3) Labelling data: I don't think labeling data is necessarily cheap. First, you have to collect the data, depending on the frequency of your events could take months of monitoring if you want to build a large-scale dataset. Lastly, not all labeling is necessarily cheap. I worked at a semiconductor company and labeled data was scarce as it required expert knowledge and could only be done by experienced employees. Indeed not all labelling can be done externally.

    However, both approaches are indeed complementary and I think systems that will work the best will rely on both.

    Thanks again for the thought-provoking discussion. I hope this answer some of the concerns you raised

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