lida
guidance
lida | guidance | |
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6 | 23 | |
2,429 | 17,546 | |
4.4% | 3.7% | |
8.6 | 9.8 | |
25 days ago | 3 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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lida
- Yes, Python and Matplotlib can make pretty charts
- 📈 Data-driven strategy automation w/ LIDA
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Automatic Generation of Visualizations and Infographics with LLMs
YOU MUST ALWAYS return code using the provided code template. DO NOT add notes or explanations." (https://github.com/microsoft/lida/blob/main/lida/components/...)
They prompted that things MUST be correct in their prompts and it reports any transformations it does to your data, it might give you some insight into its logic to test yourself against the data.
- FLaNK Stack Weekly for 21 August 2023
- LIDA – A library (and UI) for automatic generation of visualizations using LLMs
guidance
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Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
[1]: https://github.com/guidance-ai/guidance/tree/main
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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
1. https://github.com/guidance-ai/guidance
2. https://github.com/sgl-project/sglang
- Show HN: Fructose, LLM calls as strongly typed functions
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LiteLlama-460M-1T has 460M parameters trained with 1T tokens
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
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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.
- Guidance is back 🥳
- New: LangChain templates – fastest way to build a production-ready LLM app
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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.
What are some alternatives?
FLaNK-HuggingFace-BLOOM-LLM - https://huggingface.co/bigscience/bloom into NiFi
lmql - A language for constraint-guided and efficient LLM programming.
llama2-chatbot - LLaMA v2 Chatbot
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
fin-llama - LLAMA specialized on finance
langchain - 🦜🔗 Build context-aware reasoning applications
LLM-Training-Puzzles - What would you do with 1000 H100s...
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
ai-town - A MIT-licensed, deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
poozle - A single API for product integrations
outlines - Structured Text Generation