guidance
lmql
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guidance | lmql | |
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23 | 30 | |
17,136 | 3,265 | |
4.5% | 5.6% | |
9.8 | 9.6 | |
2 days ago | 20 days ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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guidance
- Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
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Show HN: Prompts as (WASM) Programs
> 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
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Show HN: Fructose, LLM calls as strongly typed functions
Why do you have Guidance in caps?
https://github.com/guidance-ai/guidance
or ...
https://huggingface.co/docs/text-generation-inference/concep...
or ... ?
A quick glance through these, they don't seem yet to call json_object on OpenAI with the word JSON in the prompt, which works wonders with the 0125 models.
- Forcing AI to Follow a Specific Answer Pattern Using GBNF Grammar
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Prompting LLMs to constrain output
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.
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New: LangChain templates – fastest way to build a production-ready LLM app
AutoGen (https://github.com/microsoft/autogen) is orthogonal: it's designed for agents to converse with each other.
The original comparison to LangChain from Microsoft was Guidance (https://github.com/guidance-ai/guidance) which appears to have shifted development a bit. I haven't had much experience with it but from the examples it still seems like needless overhead.
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Is supervised learning dead for computer vision?
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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Show HN: Elelem – TypeScript LLMs with tracing, retries, and type safety
I've had a bit of trouble getting function calling to work with cases that aren't just extracting some data from the input. The format is correct but it was harder to get the correct data if it wasn't a simple extraction.
Hopefully OpenAI and others will offer something like https://github.com/guidance-ai/guidance at some point to guarantee overall output structure.
Failed validations will retry, but from what I've seen JSONSchema + generated JSON examples are decently reliable in practice for gpt-3.5-turbo and extremely reliable on gpt-4.
lmql
- Show HN: Fructose, LLM calls as strongly typed functions
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Prompting LLMs to constrain output
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.
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Guidance for selecting a function-calling library?
lqml
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Show HN: Magentic – Use LLMs as simple Python functions
This is also similar in spirit to LMQL
- Show HN: LLMs can generate valid JSON 100% of the time
- LangChain Agent Simulation – Multi-Player Dungeons and Dragons
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The Problem with LangChain
LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.
Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.
- Is there a UI that can limit LLM tokens to a preset list?
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Local LLMs: After Novelty Wanes
LMQL is another.
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[P] I got fed up with LangChain, so I made a simple open-source alternative for building Python AI apps as easy and intuitive as possible.
if you want guidance control for LLMs, you should check this out: https://lmql.ai/
What are some alternatives?
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
langchain - 🦜🔗 Build context-aware reasoning applications
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
outlines - Structured Text Generation
guardrails - Adding guardrails to large language models.
localLLM_langchain - Local LLM Agent with Langchain
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.