Nicolay Rusnachenko

nicolay-r

https://nicolay-r.github.io/

AI & ML interests

Information Retrieval・Medical Multimodal NLP (🖼+📝) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

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reacted to merve's post with 👀 7 minutes ago

QwQ can see 🔥 Qwen team released QvQ, a large vision LM with reasoning 😱 it outperforms proprietary VLMs on several benchmarks, comes with open weights and a demo! Check them out ⬇️ Demo https://huggingface.co./spaces/Qwen/QVQ-72B-preview Model https://huggingface.co./Qwen/QVQ-72B-Preview Read more https://qwenlm.github.io/blog/qvq-72b-preview/ Congratulations @JustinLin610 and team!

reacted to merve's post with 👍 7 minutes ago

reacted to AdinaY's post with 👀 7 minutes ago

QvQ-72B-Preview🎄 an open weight model for visual reasoning just released by Alibaba_Qwen team https://huggingface.co./collections/Qwen/qvq-676448c820912236342b9888 ✨ Combines visual understanding & language reasoning. ✨ Scores 70.3 on MMMU ✨ Outperforms Qwen2-VL-72B-Instruct in complex problem-solving

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118

📢 If you're aimed at quick experiment with LLM and known Chain-of-Thought (CoT) / prompt schema with no-string dependencies, then I have something relevant for you to share 💎

So far I released the updated version 📦 bulk-chain-0.25.0 📦, which is aimed at bringing accessible API for an instant LLM application towards massive data iterators using via predefined prompt schema 🎊

📦: https://pypi.org/project/bulk-chain/0.25.0/
🌟: https://github.com/nicolay-r/bulk-chain
📘: https://github.com/nicolay-r/bulk-chain/issues/26

The key updates of the most recent release are:
✅ 🪶 No-string (empty dependencies): you can use any framework / API for LLM.
✅ 🐍 Python API support (see first screenshot 📸).
✅ 💥 Native try-catch wrapping to guarantee no-data-lost on using remote providers especially: OpenAI, ReplicateIO, OpenRouter, etc.
✅ 🔥 Batching mode support: you may wrap for handling batches to significantly boost the performance 🚀 (see screenshot below for bath enabling 📸)
✅ 🔧 Fixed a lot of minor bugs

Quick start on GoogleColab:
📙: https://colab.research.google.com/github/nicolay-r/bulk-chain/blob/master/bulk_chain_tutorial.ipynb

📘 The wiki of the project is available here:
https://github.com/nicolay-r/bulk-chain/wiki/Project-Documentation

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2148

📢 So far I noticed that 🧠 reasoning with llm 🤖 in English is tend to be more accurate than in other languages.
However, besides the GoogleTrans and other open transparent translators, I could not find one that could be easy to use solutions to avoid:
1.🔴 Third-party framework installation
2.🔴 Text chunking
3.🔴 support of meta-annotation like spans / objects / etc.

💎 To cope problem of IR from non-english texts, I am happy to share the bulk-translate 0.25.0. 🎊

⭐ https://github.com/nicolay-r/bulk-translate

bulk-translate is a tiny Python 🐍 no-string framework that allows translate series of texts with the pre-annotated fixed-spans that are invariant for translator.

It supports 👨‍💻 API for quick data translation with (optionaly) annotated objects in texts (see figure below) in Python 🐍
I make it accessible as much as possible for RAG and / or LLM-powered app downstreams:
📘 https://github.com/nicolay-r/bulk-translate/wiki

All you have to do is to provide iterator of texts, where each text:
1. ✅ String object
2. ✅ List of strings and nested lists that represent spans (value + any ID data).

🤖 By default I provide a wrapper over googletrans which you can override with your own 🔥
https://github.com/nicolay-r/bulk-translate/blob/master/models/googletrans_310a.py

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