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Mert Erbak PRO

merterbak

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Currently NLP and Image Processing

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reacted to openfree's post with ๐Ÿ”ฅ 1 day ago
# ๐Ÿงฌ Protein Genesis AI: Design Proteins with Just a Prompt ## ๐Ÿค” Current Challenges in Protein Design Traditional protein design faces critical barriers: - ๐Ÿ’ฐ High costs ($1M - $10M+) & long development cycles (2-3 years) - ๐Ÿ”ฌ Complex equipment and expert knowledge required - ๐Ÿ“‰ Low success rates (<10%) - โฐ Time-consuming experimental validation ## โœจ Our Solution: Protein Genesis AI Transform protein design through simple natural language input: ``` "Design a protein that targets cancer cells" "Create an enzyme that breaks down plastic" ``` ### Key Features - ๐Ÿค– AI-powered automated design - ๐Ÿ“Š Real-time analysis & optimization - ๐Ÿ”ฌ Instant 3D visualization - ๐Ÿ’พ Immediate PDB file generation ## ๐ŸŽฏ Applications ### Medical & Industrial - ๐Ÿฅ Drug development - ๐Ÿ’‰ Antibody design - ๐Ÿญ Industrial enzymes - โ™ป๏ธ Environmental solutions ### Research & Education - ๐Ÿ”ฌ Basic research - ๐Ÿ“š Educational tools - ๐Ÿงซ Experimental design - ๐Ÿ“ˆ Data analysis ## ๐Ÿ’ซ Key Advantages - ๐Ÿ‘จโ€๐Ÿ’ป No coding or technical expertise needed - โšก Results in minutes (vs. years) - ๐Ÿ’ฐ 90% cost reduction - ๐ŸŒ Accessible anywhere ## ๐ŸŽ“ Who Needs This? - ๐Ÿข Biotech companies - ๐Ÿฅ Pharmaceutical research - ๐ŸŽ“ Academic institutions - ๐Ÿงช Research laboratories ## ๐ŸŒŸ Why It Matters Protein Genesis AI democratizes protein design by transforming complex processes into simple text prompts. This breakthrough accelerates scientific discovery, potentially leading to faster drug development and innovative biotechnology solutions. The future of protein design starts with a simple prompt! ๐Ÿš€ https://huggingface.co/spaces/openfree/ProteinGenesis
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reacted to openfree's post with ๐Ÿ”ฅ 1 day ago
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4739
# ๐Ÿงฌ Protein Genesis AI: Design Proteins with Just a Prompt

## ๐Ÿค” Current Challenges in Protein Design

Traditional protein design faces critical barriers:
- ๐Ÿ’ฐ High costs ($1M - $10M+) & long development cycles (2-3 years)
- ๐Ÿ”ฌ Complex equipment and expert knowledge required
- ๐Ÿ“‰ Low success rates (<10%)
- โฐ Time-consuming experimental validation

## โœจ Our Solution: Protein Genesis AI

Transform protein design through simple natural language input:
"Design a protein that targets cancer cells"
"Create an enzyme that breaks down plastic"


### Key Features
- ๐Ÿค– AI-powered automated design
- ๐Ÿ“Š Real-time analysis & optimization
- ๐Ÿ”ฌ Instant 3D visualization
- ๐Ÿ’พ Immediate PDB file generation

## ๐ŸŽฏ Applications

### Medical & Industrial
- ๐Ÿฅ Drug development
- ๐Ÿ’‰ Antibody design
- ๐Ÿญ Industrial enzymes
- โ™ป๏ธ Environmental solutions

### Research & Education
- ๐Ÿ”ฌ Basic research
- ๐Ÿ“š Educational tools
- ๐Ÿงซ Experimental design
- ๐Ÿ“ˆ Data analysis

## ๐Ÿ’ซ Key Advantages

- ๐Ÿ‘จโ€๐Ÿ’ป No coding or technical expertise needed
- โšก Results in minutes (vs. years)
- ๐Ÿ’ฐ 90% cost reduction
- ๐ŸŒ Accessible anywhere

## ๐ŸŽ“ Who Needs This?
- ๐Ÿข Biotech companies
- ๐Ÿฅ Pharmaceutical research
- ๐ŸŽ“ Academic institutions
- ๐Ÿงช Research laboratories

## ๐ŸŒŸ Why It Matters
Protein Genesis AI democratizes protein design by transforming complex processes into simple text prompts. This breakthrough accelerates scientific discovery, potentially leading to faster drug development and innovative biotechnology solutions. The future of protein design starts with a simple prompt! ๐Ÿš€

openfree/ProteinGenesis
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reacted to singhsidhukuldeep's post with ๐Ÿš€ 3 days ago
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2687
Groundbreaking Research Alert: Rethinking RAG with Cache-Augmented Generation (CAG)

Researchers from National Chengchi University and Academia Sinica have introduced a paradigm-shifting approach that challenges the conventional wisdom of Retrieval-Augmented Generation (RAG).

Instead of the traditional retrieve-then-generate pipeline, their innovative Cache-Augmented Generation (CAG) framework preloads documents and precomputes key-value caches, eliminating the need for real-time retrieval during inference.

Technical Deep Dive:
- CAG preloads external knowledge and precomputes KV caches, storing them for future use
- The system processes documents only once, regardless of subsequent query volume
- During inference, it loads the precomputed cache alongside user queries, enabling rapid response generation
- The cache reset mechanism allows efficient handling of multiple inference sessions through strategic token truncation

Performance Highlights:
- Achieved superior BERTScore metrics compared to both sparse and dense retrieval RAG systems
- Demonstrated up to 40x faster generation times compared to traditional approaches
- Particularly effective with both SQuAD and HotPotQA datasets, showing robust performance across different knowledge tasks

Why This Matters:
The approach significantly reduces system complexity, eliminates retrieval latency, and mitigates common RAG pipeline errors. As LLMs continue evolving with expanded context windows, this methodology becomes increasingly relevant for knowledge-intensive applications.
reacted to MonsterMMORPG's post with โค๏ธ 3 days ago
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3198
SANA: Ultra HD Fast Text to Image Model from NVIDIA Step by Step Tutorial on Windows, Cloud & Kaggle โ€” Generate 2048x2048 Images

Below is YouTube link for step by step tutorial and a 1-Click to installer having very advanced Gradio APP to use newest Text-to-Image SANA Model on your Windows PC locally and also on cloud services such as Massed Compute, RunPod and free Kaggle.

https://youtu.be/KW-MHmoNcqo

This above tutorial covers the newest SANA 2K model and I predict SANA 4K model will be published as well. Sana 2K model is 4 MegaPixel so it can generate the following aspect ratio and resolutions very well:

โ€œ1:1โ€: (2048, 2048), โ€œ4:3โ€: (2304, 1792), โ€œ3:4โ€: (1792, 2304),
โ€œ3:2โ€: (2432, 1664), โ€œ2:3โ€: (1664, 2432), โ€œ16:9โ€: (2688, 1536),
โ€œ9:16โ€: (1536, 2688), โ€œ21:9โ€: (3072, 1280), โ€œ9:21โ€: (1280, 3072),
โ€œ4:5โ€: (1792, 2240), โ€œ5:4โ€: (2240, 1792)

I have developed an amazing Gradio app with so many new features :

VAE auto offloading to reduce VRAM usage significantly which is not exists on official pipeline

Gradio APP built upon official pipeline with improvements so works perfect

Batch size working perfect

Number of images working perfect

Multi-line prompting working perfect

Aspect ratios for both 1K and 2K models working perfect

Randomized seed working perfect

1-Click installers for Windows (using Python 3.10 and VENV โ€” isolated), RunPod, Massed Compute and even a free Kaggle account notebook

With proper latest libraries working perfect speed on Windows too

Automatically properly saving every generated image into accurate folder

๐Ÿ”— Full Instructions, Configs, Installers, Information and Links Shared Post (the one used in the tutorial) โคต๏ธ
โ–ถ๏ธ https://www.patreon.com/posts/click-to-open-post-used-in-tutorial-116474081

๐Ÿ”— SECourses Official Discord 9500+ Members โคต๏ธ
โ–ถ๏ธ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

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reacted to m-ric's post with ๐Ÿš€ 27 days ago
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๐Ÿ’ฅ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐˜€ ๐—š๐—ฒ๐—บ๐—ถ๐—ป๐—ถ ๐Ÿฎ.๐Ÿฌ, ๐˜€๐˜๐—ฎ๐—ฟ๐˜๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐—ฎ ๐—™๐—น๐—ฎ๐˜€๐—ต ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น ๐˜๐—ต๐—ฎ๐˜ ๐˜€๐˜๐—ฒ๐—ฎ๐—บ๐—ฟ๐—ผ๐—น๐—น๐˜€ ๐—š๐—ฃ๐—ง-๐Ÿฐ๐—ผ ๐—ฎ๐—ป๐—ฑ ๐—–๐—น๐—ฎ๐˜‚๐—ฑ๐—ฒ-๐Ÿฏ.๐Ÿฒ ๐—ฆ๐—ผ๐—ป๐—ป๐—ฒ๐˜! And they start a huge effort on agentic capabilities.

๐Ÿš€ The performance improvements are crazy for such a fast model:
โ€ฃ Gemini 2.0 Flash outperforms the previous 1.5 Pro model at twice the speed
โ€ฃ Now supports both input AND output of images, video, audio and text
โ€ฃ Can natively use tools like Google Search and execute code

โžก๏ธ If the price is on par with previous Flash iteration ($0.30 / M tokens, to compare with GPT-4o's $1.25) the competition will have a big problem with this 4x cheaper model that gets better benchmarks ๐Ÿคฏ

๐Ÿค– What about the agentic capabilities?

โ€ฃ Project Astra: A universal AI assistant that can use Google Search, Lens and Maps
โ€ฃ Project Mariner: A Chrome extension that can complete complex web tasks (83.5% success rate on WebVoyager benchmark, this is really impressive!)
โ€ฃ Jules: An AI coding agent that integrates with GitHub workflows

I'll be eagerly awaiting further news from Google!

Read their blogpost here ๐Ÿ‘‰ https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/