Language-games
datadm
Language-games | datadm | |
---|---|---|
3 | 7 | |
97 | 369 | |
- | 3.3% | |
10.0 | 7.3 | |
over 4 years ago | 8 months ago | |
Python | Python | |
MIT License | MIT License |
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Language-games
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Ask HN: What have you built with LLMs?
I was working on this stuff before it was cool, so in the sense of the precursor to LLMs (and sometimes supporting LLMs still) I've built many things:
1. Games you can play with word2vec or related models (could be drop in replaced with sentence transformer). It's crazy that this is 5 years old now: https://github.com/Hellisotherpeople/Language-games
2. "Constrained Text Generation Studio" - A research project I wrote when I was trying to solve LLM's inability to follow syntactic, phonetic, or semantic constraints: https://github.com/Hellisotherpeople/Constrained-Text-Genera...
3. DebateKG - A bunch of "Semantic Knowledge Graphs" built on my pet debate evidence dataset (LLM backed embeddings indexes synchronized with a graphDB and a sqlDB via txtai). Can create compelling policy debate cases https://github.com/Hellisotherpeople/DebateKG
4. My failed attempt at a good extractive summarizer. My life work is dedicated to one day solving the problems I tried to fix with this project: https://github.com/Hellisotherpeople/CX_DB8
- The Limits of GPT-3 and Similar Large Language Models
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Semantle
I wrote a set of "language games" using word embeddings. Very similar to what's shown here.
https://github.com/Hellisotherpeople/Language-games
datadm
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Ask HN: What have you built with LLMs?
We've made a lot of data tooling things based on LLMs, and are in the process of rebranding and launching our main product.
1. sketch (in notebook, ai for pandas) https://github.com/approximatelabs/sketch
2. datadm (open source, "chat with data", with support for the open source LLMs (https://github.com/approximatelabs/datadm)
3. Our main product: julyp. https://julyp.com/ (currently under very active rebrand and cleanup) -- but a "chat with data" style app, with a lot of specialized features. I'm also streaming me using it (and sometimes building it) every weekday on twitch to solve misc data problems (https://www.twitch.tv/bluecoconut)
For your next question, about the stack and deploy:
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A LLM+OLAP Solution
From making a few variations on data chatbots in the past year, I found that my favorite / most fun to use ones seem to be more "chain-of-thought" and conversational rather than "retrieval-augmented" style.
Less about one-shotting the answer, and more about showing its work, if it errors, letting it self-correct. Latency goes up, but quality of the entire conversation also goes up, and feels like it builds more trust with the user. Key steps are asking it to "check its work", and watching it work through new code etc. (I open-sourced one version of this: https://github.com/approximatelabs/datadm that can be run entirely locally / privately)
From their article: I'm surprised they got something working well by going through an intermediate DSL -- thats moving even further away from the source-material that the LLMs are trained on, so it's an entirely new thing to either teach or assume is part of the in-context learning.
All that said, interesting: I'll definitely have to try out tencentmusic/supersonic and see how it feels myself.
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How to Use AI to Do Stuff: An Opinionated Guide
Pretty good examples and simple explanations. I didn't realize Claude 2 was so good at working with PDFs natively. I wonder if they're doing anything special? Is this just due to larger context length they have?
Also, biased opinion on my part: I'm especially interested in watching how these things affect data science and data literacy as a whole. Code interpreter is a game changer in my opinion, the most powerful tool that somehow isn't getting as much press I think it deserves. I released an open source code-interpreter for data (https://github.com/approximatelabs/datadm) and even though I know how to code and use Jupyter daily, I still find myself doing analysis with it instead.
All in all, it does seem like the different models and agents are gaining "specialization" skill is actually good for the user (rather than just using a single jack of all trades super chat model). Even though GPT-4 takes the language model crown, there's still specialization that matters and improves quality for different tasks as discussed here.
I wonder if in 2-5 years we'll all use "a single" AI chat interface for everything, or every specialization continues to "win at its own vertical" and we just have AI embedded inside of every app
- Show HN: Self-hostable open-source code interpreter with open-model support
- DataDM – Search and analyze datasets with LLMs
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Microsoft Bringing OpenAI’s GPT-4 AI Model to US Government Agencies
I completely agree that greatly increasing data accessibility is a huge unlock and value add.
A package I open sourced recently might be useful for use cases like this, https://github.com/approximatelabs/datadm It's essentially a chatGPT code interpreter, specifically designed to work with data, that can be run entirely on open models (eg. StarChat). True local mode operation.
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I made a tool for talking with your data via LLMs: DataDM. An open source code-interpreter you can use today: it supports running with GPT-4 as well as local models for keeping your data completely private
Here's the github repo https://github.com/approximatelabs/datadm
What are some alternatives?
gpt_jailbreak_status - This is a repository that aims to provide updates on the status of jailbreaking the OpenAI GPT language model.
ClickBench - ClickBench: a Benchmark For Analytical Databases
Constrained-Text-Generation-Studio - Code repo for "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio" at the (CAI2) workshop, jointly held at (COLING 2022)
Constrained-Text-Genera
data-analytics - Welcome to the Data-Analytics repository
BrowserGPT - Command your browser with GPT
flask-socketio-llm-com
ibis - the portable Python dataframe library
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
coppermind - Instruction based LLM contextual memory manager to power custom AI personalities and chatbots