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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🌟 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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🔍 Join us for an in-depth exploration of GPT-4o, the latest advancement in OpenAI’s language models.

✔️ This event is designed to provide you with valuable insights and practical knowledge to enhance your AI skill.

In this session, we will guide you through:

1️⃣ Introduction to GPT-4o.
2️⃣ Utilizing GPT-4o with APIs.
3️⃣ Exploring GPT-4o's Vision Capabilities.
4️⃣ Building AI Applications with GPT-4o.


😀 Register Here for Free:
https://www.buildfastwithai.com/events/gpt-4o-deep-dive
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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.

🔖 BLOG: https://qwenlm.github.io/blog/qwen2/

🤗 HF collection: https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f

🤖 https://modelscope.cn/organization/qwen

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

https://news.1rj.ru/str/DataScienceT
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Flash Diffusion - acceleration of any diffusion model for image generation in a few steps

This is the name of the work recently published by Clément Chadebec, Onur Tasar and their colleagues.
This is a distillation method for creating fast diffusion patterns.
When training the model using this method on the COCO2014 and COCO2017 datasets, the FID and CLIP-Score achieve good values; it requires only a few hours of training on the GPU and fewer model parameters than existing methods require.

🟡 Flash Diffusion page
🖥 GitHub

🤗 Flash SD
🤗 Flash SDXL

https://news.1rj.ru/str/DataScienceT
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🚀 AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

🖥 Github: https://github.com/woooodyy/agentgym

🔖 Paper: https://arxiv.org/abs/2406.04151v1

☄️ Project: https://agentgym.github.io/

✔️ Model (AgentEvol-7B): https://huggingface.co/AgentGym/AgentEvol-7B

https://news.1rj.ru/str/DataScienceT
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🎁 Lisa has given away over $100,000 in the last 30 days. Every single one of her subscribers is making money.

She is a professional trader and broadcasts her way of making money trading on her channel EVERY subscriber she has helped, and she will help you.

🧠 Do this and she will help you earn :

1. Subscribe to her channel
2. Write “GIFT” to her private messages
3. Follow her channel and trade with her. Repeat transactions after her = earn a lot of money.

Subscribe 👇🏻
https://news.1rj.ru/str/+VwDs29eEzRA5MTAx
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L-MAGIC: Language Model Assisted Generation of Images with Coherence

The new Intel- L-MAGIC model can generate high-quality panoramic scenes based on a single input image and text prompt!

Numerous tests show that the model generates panoramic scenes with better scene placement and rendering quality than similar models.

▪️Github: https://github.com/IntelLabs/MMPano
▪️Paper: https://arxiv.org/abs/2406.01843
▪️Project: https://zhipengcai.github.io/MMPano/
▪️Video: https://youtu.be/XDMNEzH4-Ec

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