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qubite

Xdotnet

https://www.qubite.me/

qu-bite

AI & ML interests

Aspiring Civil Engineer | Passionate About Sustainable Infrastructure | Problem Solver| Tech enthusiasts

Recent Activity

reacted to fdaudens's post with ❤️ 2 days ago

Yes, DeepSeek R1's release is impressive. But the real story is what happened in just 7 days after: - Original release: 8 models, 540K downloads. Just the beginning... - The community turned those open-weight models into +550 NEW models on Hugging Face. Total downloads? 2.5M—nearly 5X the originals. The reason? DeepSeek models are open-weight, letting anyone build on top of them. Interesting to note that the community focused on quantized versions for better efficiency & accessibility. They want models that use less memory, run faster, and are more energy-efficient. When you empower builders, innovation explodes. For everyone. 🚀 The most popular community model? @bartowski's DeepSeek-R1-Distill-Qwen-32B-GGUF version — 1M downloads alone.

reacted to nicolay-r's post with 👀 2 days ago

📢 For those who wish to apply DeepSeek-R1 for handling tabular / streaming data using schema of prompts (CoT), the OpenRouter AI hosts API for accessing: https://openrouter.ai/deepseek/deepseek-r1 The no-string option to quick start with using DeepSeek-R1 includes three steps: ✅ OpenRouter provider: https://github.com/nicolay-r/nlp-thirdgate/blob/master/llm/open_router.py ✅ Bulk-chain for infering data: https://github.com/nicolay-r/bulk-chain ✅ Json Schema for Chain-of-Though reasoning (see screenshot 📷 below) 📺 below is a screenshot of how to quick start the demo, in which you can test your schema for LLM responses. It would ask to type all the parameters first for completing the requests (which is `text` within this example). 📃 To apply it for JSONL/CSV data, you can use `--src` shell parameter for passing the related file ⏳ As for time, OpenRouter finds me relatively slow with 30~40 seconds per request Models: https://huggingface.co/deepseek-ai/DeepSeek-R1

reacted to m-ric's post with 🚀 4 months ago

𝗔𝗱𝗱 𝘀𝗼𝘂𝗿𝗰𝗲 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺! 📄💡 RAG systems are supposed to make your LLM's answer more trustworthy, by inserting in the prompt some supporting documents from a knowledge base : we say that we're "adding some context". 👎 But if you don't know which part of the answer has been generated based on which input tokens, it's hard to tell wether it was effectively grounded in the context knowledge or not! 🤔 I've been working on the question: is it possible to add notes to the answer linking to which part of the context they're generated from? And I've found a great solution: a great technique called Layer-wise Relevance Propagation (LRP), showcased in a paper at ICML `24 by Reduan Achtibat et al allows, allows to precisely score how important each input token was in generating your output! They've made it into a library called LXT. 📊 For each generated output token, LXT gives you attribution scores for each input token. ⚙️ So I've worked a bit more on aggregating these scores into meaningful spans between successive input and output tokens, and I finally obtained my desired result: RAG with source highlighting! Try the demo here 👉 https://huggingface.co/spaces/m-ric/rag_highlights Caveats: - It slows down generation (for now quite a lot, could hopefully be reduced a lot) - For now it supports only specific models: Llama models and Mixtral If there's enough interest in this solution, I can improve it further and spin it off into a specific library for RAG! 🚀

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Organizations

models 2

Xdotnet/999

Updated Jul 16, 2023

Xdotnet/ADISON

Updated Jun 30, 2023

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