Dominik Weckmüller

186 posts

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Dominik Weckmüller

Dominik Weckmüller

@DomeGIS

GIS & Data Science | https://t.co/ltynkZtH0w Geospatial Consultant @ https://t.co/CMD9QjRIsh & @EU_ScienceHub PhD Student @tudresden_de fosstodon & bluesky @DomeGIS

Milan, Lombardy Katılım Haziran 2021
198 Takip Edilen585 Takipçiler
Dominik Weckmüller
Dominik Weckmüller@DomeGIS·
Calculate semantic similarity in your browser based on Excel or CSV tables with transformers.js & Minishlab's Potion/model2vec models! Semantic Similarity Table is highly performant and private, all data remains in your browser: do-me.github.io/semantic-simil…
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will brown
will brown@willccbb·
wait, you still have to give it the system prompt???? lol are the weights even different from normal Llama3-70B? the published evals are completely indefensible then, whole thing smells like a grift
Matt Shumer@mattshumer_

Looks like @openrouter is the first to offer a properly-hosted Reflection Llama 3.1 70B. It's time to fucking build. Here's the code to use: ``` import requests import json response = requests.post( url="openrouter.ai/api/v1/chat/co…", headers={ "Authorization": f"Bearer YOUR_API_KEY", }, data=json.dumps({ "model": "mattshumer/reflection-70b", "messages": [ { "role": "system", "content": "You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside tags, and then provide your final response inside tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside tags." }, { "role": "user", "content": "How many Rs are in strawberry?" } ] }) ) ``` Note that this is FP8, so it'll be a good bit less accurate than the full fat version, but should be somewhat close.

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varun
varun@varunneal·
@arpitingle @spikedoanz @DomeGIS does this seem like an interesting project to you? Imagine a corpus of articles/essays/novels/(videos) being held on a server for semantic searching by a set of users. The users can easily contribute to the corpus (upload files or link entire websites)
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spike
spike@spikedoanz·
private, invite only search engines. like gcs but only for sharing and indexing resources. who's building this?
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will brown
will brown@willccbb·
if you don’t care about local then just use your favorite API wrapped in instructor/outlines for output formatting + borrow utils like web search/pdf reading from langchain/llamaindex as needed. afaict there isn’t any framework which makes building a custom end-to-end agent system any easier than just writing it mostly yourself in python
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@levelsio
@levelsio@levelsio·
Easiest way to try AI Agents online in cloud or local on my MacBook? I'd love to try run like 500 and every AI agent has its own job etc. and they have departments and org structure like this
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Dominik Weckmüller
Dominik Weckmüller@DomeGIS·
The dots are colored by relevance to your search query, here "food" (dark blue dot). More relevant dots are darker, less relevant ones lighter.
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Dominik Weckmüller
Dominik Weckmüller@DomeGIS·
The whole bible in 62.000 embeddings (or 31.000 verses) searchable and explorable in your browser with #SemanticFinder leveraging Barnes-Hut t-SNE. do-me.github.io/SemanticFinder…. It takes a while to process (t-SNE is comp. intense) but it's smooth to explore on consumer-grade hardware.
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Dominik Weckmüller
Dominik Weckmüller@DomeGIS·
You can quickly identify important characters like "Balak the king of Moab" with plenty of verses mentioning him. The long chain of dots is Noah's story with his children.
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Dominik Weckmüller
Dominik Weckmüller@DomeGIS·
I am using a simple regex to split the verses \{([^}]+)\} by verse number e.g. {6:1} so we get 62k embeddings instead of only 31k. The cluster you can see on top contains all these numbers. Interesting to see how the embedding model & t-SNE cluster numbers.
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asad
asad@0xa5ad·
Been running @ollama with @MistralAI and Llama for several weeks on my laptop and its been really magical to have so much knowledge at your finger tips without even an Internet connection. Any one else doing any experiments with LLMs on their local machines?
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