Mohammed Mohammed Ali

MohammedEltoum

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reacted to Jaward's post with ❤️ 2 days ago

late submission but managed to cook up a nascent Feynman-inspired agent app for Microsoft’s AI Agent hackathon, wish me luck lol. @clem ps I need this on gpu, thank you:) Try Demo: https://huggingface.co/spaces/Jaward/Professor-AI-Feynman Code: https://github.com/Jaykef/professor-ai-feynman

upvoted a paper 2 days ago

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities

upvoted a paper 2 days ago

A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency

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MohammedEltoum's activity

reacted to Jaward's post with ❤️ 2 days ago

Post

1230

late submission but managed to cook up a nascent Feynman-inspired agent app for Microsoft’s AI Agent hackathon, wish me luck lol. @clem ps I need this on gpu, thank you:)
Try Demo: Jaward/Professor-AI-Feynman
Code: https://github.com/Jaykef/professor-ai-feynman

3 replies

upvoted 3 papers 2 days ago

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities

Paper • 2505.01043 • Published 6 days ago • 9

A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency

Paper • 2505.01658 • Published 5 days ago • 28

Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play

Paper • 2505.02707 • Published 3 days ago • 69

reacted to as-cle-bert's post with ❤️ 6 days ago

Post

1814

One of the biggest challenges I've been facing since I started developing [𝐏𝐝𝐟𝐈𝐭𝐃𝐨𝐰𝐧](https://github.com/AstraBert/PdfItDown) was handling correctly the conversion of files like Excel sheets and CSVs: table conversion was bad and messy, almost unusable for downstream tasks🫣

That's why today I'm excited to introduce 𝐫𝐞𝐚𝐝𝐞𝐫𝐬, the new feature of PdfItDown v1.4.0!🎉

With 𝘳𝘦𝘢𝘥𝘦𝘳𝘴, you can choose among three (for now👀) flavors of text extraction and conversion to PDF:

- 𝗗𝗼𝗰𝗹𝗶𝗻𝗴, which does a fantastic work with presentations, spreadsheets and word documents🦆

- 𝗟𝗹𝗮𝗺𝗮𝗣𝗮𝗿𝘀𝗲 by LlamaIndex, suitable for more complex and articulated documents, with mixture of texts, images and tables🦙

- 𝗠𝗮𝗿𝗸𝗜𝘁𝗗𝗼𝘄𝗻 by Microsoft, not the best at handling highly structured documents, by extremly flexible in terms of input file format (it can even convert XML, JSON and ZIP files!)✒️

You can use this new feature in your python scripts (check the attached code snippet!😉) and in the command line interface as well!🐍

Have fun and don't forget to star the repo on GitHub ➡️ https://github.com/AstraBert/PdfItDown

upvoted a paper 27 days ago

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Paper • 2504.01990 • Published Mar 31 • 275

reacted to hesamation's post with ❤️ 28 days ago

Post

8229

Google published a 69-page whitepaper on Prompt Engineering and its best practices, a must-read if you are using LLMs in production:
> zero-shot, one-shot, few-shot
> system prompting
> chain-of-thought (CoT)
> ReAct

LINK: https://www.kaggle.com/whitepaper-prompt-engineering
> code prompting
> best practices

upvoted a paper about 1 month ago

SmolVLM: Redefining small and efficient multimodal models

Paper • 2504.05299 • Published about 1 month ago • 180

reacted to prithivMLmods's post with 👍 about 1 month ago

Post

2635

Dropping Downstream tasks using newly initialized parameters and weights ([classifier.bias & weights]) support domain-specific 𝗶𝗺𝗮𝗴𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻. Based on siglip2-base-patch16-224 and DomainNet (single-domain, multi-source adaptation), with Fashion-MNIST & More for experimental testing. 🧤☄️

Fashion-Mnist : prithivMLmods/Fashion-Mnist-SigLIP2
Mnist-Digits : prithivMLmods/Mnist-Digits-SigLIP2
Multisource-121 : prithivMLmods/Multisource-121-DomainNet
Painting-126 : prithivMLmods/Painting-126-DomainNet
Sketch-126 : prithivMLmods/Sketch-126-DomainNet
Clipart-126 : prithivMLmods/Clipart-126-DomainNet

Models are trained with different parameter settings for experimental purposes only, with the intent of further development. Refer to the model page below for instructions on running it with Transformers 🤗.

Collection : prithivMLmods/domainnet-0324-67e0e3c934c03cc40c6c8782

Citations : SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 & Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754

reacted to hanzla's post with 👍 about 1 month ago

Post

2026

Hi community,

Few days back, I posted about my ongoing research on making reasoning mamba models and I found great insights from the community.

Today, I am announcing an update to the model weights. With newer checkpoints, the Falcon3 Mamba R1 model now outperforms very large transformer based LLMs (including Gemini) for Formal Logic questions of MMLU. It scores 60% on formal logic which is considered a tough subset of questions in MMLU.

I would highly appreciate your insights and suggestions on this new checkpoint.

Model Repo: hanzla/Falcon3-Mamba-R1-v0

Chat space: hanzla/Falcon3MambaReasoner

upvoted 2 papers about 2 months ago

OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement

Paper • 2503.17352 • Published Mar 21 • 23

One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation

Paper • 2503.13358 • Published Mar 17 • 96

reacted to rizavelioglu's post with ❤️ 2 months ago

Post

3261

Comparing reconstruction quality of various VAEs with an interactive demo
rizavelioglu/vae-comparison

1 reply

liked a Space 2 months ago

The Essential AI Toolkit

🧰

A curated collection of AI tools for journalists & creators

upvoted a collection 2 months ago

olmOCR

Collection

olmOCR is a document recognition pipeline for efficiently converting documents into plain text. olmocr.allenai.org • 4 items • Updated 8 days ago • 108

reacted to openfree's post with ❤️ 2 months ago

Post

7898

Datasets Convertor 🚀

openfree/Datasets-Convertor

Welcome to Datasets Convertor, the cutting-edge solution engineered for seamless and efficient data format conversion. Designed with both data professionals and enthusiasts in mind, our tool simplifies the transformation process between CSV, Parquet, and JSONL, XLS file formats, ensuring that your data is always in the right shape for your next analytical or development challenge. 💻✨

Why Choose Datasets Convertor?
In today’s data-driven world, managing and converting large datasets can be a daunting task. Our converter is built on top of robust technologies like Pandas and Gradio, delivering reliable performance with a modern, intuitive interface. Whether you’re a data scientist, analyst, or developer, Datasets Convertor empowers you to effortlessly switch between formats while maintaining data integrity and optimizing storage.

Key Features and Capabilities:
CSV ⇆ Parquet Conversion:
Easily transform your CSV files into the highly efficient Parquet format and vice versa. Parquet’s columnar storage not only reduces file size but also accelerates query performance—a critical advantage for big data analytics. 🔄📂

CSV to JSONL Conversion:
Convert CSV files to JSONL (newline-delimited JSON) to facilitate efficient, line-by-line data processing. This format is particularly useful for streaming data applications, logging systems, and scenarios where incremental data processing is required. Each CSV row is meticulously converted into an individual JSON record, preserving all the metadata and ensuring compatibility with modern data pipelines. 📄➡️📝

Parquet to JSONL Conversion:
For those working with Parquet files, our tool offers a streamlined conversion to JSONL.

Parquet to XLS Conversion.

upvoted 4 papers 3 months ago

PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding

Paper • 2501.16411 • Published Jan 27 • 19