ML Research Hub – Telegram
ML Research Hub
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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🌟 InstaDrag: complex image editing (rotating in space, changing shape, etc.) using simple gestures

InstaDrag is an approach that allows you to edit an image using a simple drag gesture in ~1 second.
At the same time, InstaDrag also copes with complex deformations of part of the image that are not represented in the training data (such deformations are, for example, elongation of hair, curvature of the rainbow, etc.).
There are plans to integrate InstaDrag with SDXL in the future

🖥 GitHub
🟡 InstaDrag page
🟡 Arxiv

https://news.1rj.ru/str/DataScienceT
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⚡️ Flash version of Scribble SDXL

Utilizes SDXL Flash and Scribble SDXL, making it even faster to get quality images from simple sketches and doodles

🤗 Launch on Hugging Face

https://news.1rj.ru/str/DataScienceT
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🌟 V-Express is a method of animating a static photograph of a face, taking into account pose, sound and the image itself

The V-Express method was recently introduced by Cong Wang, Kuan Tian, ​​Jun Zhang and others as one of the most efficient methods for generating such animations, the code and model are published

🖥 GitHub

🟡 V-Express page

🤗 Model on Hugging Face

https://news.1rj.ru/str/DataScienceT
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🔥 unsloth - fine tuning Llama 3, Mistral, Phi and Gemma LLM, reducing memory consumption by 80%, speeding up 2-5 times

conda create --name unsloth_env python=3.10
conda activate unsloth_env

conda install pytorch-cuda=<12.1/11.8> pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

pip install --no-deps trl peft accelerate bitsandbytes


Some unsloth benchmarks are given here

🖥 GitHub
🟡 Wiki

https://news.1rj.ru/str/DataScienceT
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🌟 GPT Researcher is a GPT-based autonomous agent that performs comprehensive online research on any given topic

- pip install gpt-researcher

Example of using GPT Researcher:
from gpt_researcher import GPTResearcher

query = "why is Nvidia stock going up?"
researcher = GPTResearcher(query=query, report_type="research_report")
# Conduct research on the given query
research_result = await researcher.conduct_research()
# Write the report
report = await researcher.write_report()


🖥 GitHub
🟡 GPT Researcher Page

https://news.1rj.ru/str/DataScienceT
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🌟 Dot is an open-source application for local deployment of LLM, RAG and TTS for working with your documents

Dot is designed to easily interact with documents and files (.pdf, .docx, .xlsx, .pptx, .md) using local LLMs and Retrieval Augmented Generation technology.
Dot was inspired by solutions like Nvidia's Chat with RTX
Defaults to Phi-3

🖥 GitHub
🟡 Dot Page

https://news.1rj.ru/str/DataScienceT
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InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation

🖥 Github: https://github.com/jacobyhsi/InterpreTabNet

📕 Paper: https://arxiv.org/abs/2406.00426v1

https://news.1rj.ru/str/DataScienceT
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☄️ Qwen2 is the coolest release of open LLMs since Llama 3!

Alibaba has just released their new family of multilingual models that outperform the Llama 3 in many ways.

🤯 Qwen2 is released in 5 sizes, trained in 29 languages!

5️⃣ Sizes: 0.5B, 1.5B, 7B, 57B-14B (MoE), 72B.
✔️ Context: 32k for 0.5B & 1.5B, 64k for 57B MoE, 128k for 7B & 72B
Supports 29 languages.
🟢 Released under the Apache 2.0 license, except for version 72B.

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👨‍💻 GitHub: https://github.com/QwenLM/Qwen2

https://news.1rj.ru/str/DataScienceT
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