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

Recent Activity

reacted to InferenceIllusionist's post with 🔥 1 day ago

MilkDropLM-32b-v0.3: Unlocking Next-Gen Visuals ✨ Stoked to release the latest iteration of our MilkDropLM project! This new release is based on the powerful Qwen2.5-Coder-32B-Instruct model using the same great dataset that powered our 7b model. What's new? - Genome Unlocked: Deeper understanding of preset relationships for more accurate and creative generations. - Preset Revival: Breathe new life into old presets with our upgraded model! - Loop-B-Gone: Say goodbye to pesky loops and hello to smooth generation. - Natural Chats: Engage in more natural sounding conversations with our LLM than ever before. Released under Apache 2.0, because sharing is caring! Try it out: https://huggingface.co/InferenceIllusionist/MilkDropLM-32b-v0.3 Shoutout to @superwatermelon for his invaluable insights and collab, and to all those courageous members in the community that have tested and provided feedback before!

reacted to ehristoforu's post with 🤗 1 day ago

✒️ Ultraset - all-in-one dataset for SFT training in Alpaca format. https://huggingface.co/datasets/fluently-sets/ultraset ❓ Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean. 🤯 Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism. 🤗 For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models. ❇️ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.

reacted to aaditya's post with 🔥 1 day ago

Last Week in Medical AI: Top Research Papers/Models 🔥 🏅 (December 15 – December 21, 2024) Medical LLM & Other Models - MedMax: Mixed-Modal Biomedical Assistant - Advanced multimodal instruction tuning - Enhanced biomedical knowledge integration - Comprehensive assistant capabilities - MGH Radiology Llama 70B - Specialized radiology focus - State-of-the-art performance - Enhanced report generation capabilities - HC-LLM: Historical Radiology Reports - Context-aware report generation - Historical data integration - Improved accuracy in diagnostics Frameworks & Methods - ReflecTool: Reflection-Aware Clinical Agents - Process-Supervised Clinical Notes - Federated Learning with RAG - Query Pipeline Optimization Benchmarks & Evaluations - Multi-OphthaLingua - Multilingual ophthalmology benchmark - Focus on LMICs healthcare - Bias assessment framework - ACE-M3 Evaluation Framework - Multimodal medical model testing - Comprehensive capability assessment - Standardized evaluation metrics LLM Applications - Patient-Friendly Video Reports - Medical Video QA Systems - Gene Ontology Annotation - Healthcare Recommendations Special Focus: Medical Ethics & AI - Clinical Trust Impact Study - Mental Health AI Challenges - Hospital Monitoring Ethics - Radiology AI Integration Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well! - Full thread in detail: https://x.com/OpenlifesciAI/status/1870504774162063760 - Youtube Link: youtu.be/SbFp4fnuxjo - Spotify: https://t.co/QPmdrXuWP9

View all activity

Organizations

None yet

Posts 35

Post

1947

📢 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

Post

514

📢 If you're working in relation extraction / character network domain, then the following post would be relevant.
Excited to share the most recent milestone on releasing the ARElight 0.25.0 🎊

Core library: https://github.com/nicolay-r/ARElight
Server: https://github.com/nicolay-r/ARElight-server

🔎 What is ARElight? It represents Granular Viewer of Sentiments Between Entities in Massively Large Documents and Collections of Texts.
Shortly speaking, it allows to extract contexts with mentioned object pairs for the related prompting / classification.
In the slides below we illsutrate the ARElight appliation for sentiment classification between object pairs in context.

We exploit DeepPavlov NER modes + GoogleTranslate + BERT-based classifier in the demo. The bash script for launching the quick demo illustrates the application of these components.

The new update provide a series of new features:
✅ SQlite support for storing all the extracted samples
✅ Support of the enhanced GUI for content investigation.
✅ Switch to external no-string projects for NER and Translator

Supplementiary materials:
📜 Paper: https://link.springer.com/chapter/10.1007/978-3-031-56069-9_23

View all posts