csabakecskemeti (Csaba Kecskemeti)

posted an update about 23 hours ago

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375

Just wondering why the number of parameters changed in the model attributes/Model size from 685B to 684B after converting deepseek-ai/DeepSeek-V3-Base from FP8 to BF16:
DevQuasar/deepseek-ai.DeepSeek-V3-Base-bf16
and not just for me:
opensourcerelease/DeepSeek-V3-Base-bf16

??

reacted to s-emanuilov's post with 👍👀 1 day ago

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2373

Hey HF community! 👋

Excited to share Monkt - a tool I built to solve the eternal headache of processing documents for ML/AI pipelines.

What it does: Converts PDFs, Word, PowerPoint, Excel, Web pages or raw HTML into clean Markdown or structured JSON.

Great for:
✔ LLM training dataset preparation;
✔ Knowledge base construction;
✔ Research paper processing;
✔ Technical documentation management.

It has API access for integration into ML pipelines.

Check it out at https://monkt.com/ if you want to save time on document processing infrastructure.

Looking forward to your feedback!

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posted an update 3 days ago

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1432

Happy New Year, Huggingface community!
In 2025, I'll continue my quantization (and some fine-tuning) efforts to support the open-source AI and Make knowledge free for everyone.

https://huggingface.co/DevQuasar
https://devquasar.com/

1 reply

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reacted to prithivMLmods's post with ❤️ 3 days ago

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2772

Triangulum Catalogued 🔥💫

🎯Triangulum is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.

+ Triangulum-10B : prithivMLmods/Triangulum-10B
+ Quants : prithivMLmods/Triangulum-10B-GGUF

+ Triangulum-5B : prithivMLmods/Triangulum-5B
+ Quants : prithivMLmods/Triangulum-5B-GGUF

+ Triangulum-1B : prithivMLmods/Triangulum-1B
+ Quants : prithivMLmods/Triangulum-1B-GGUF

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reacted to DamarJati's post with ➕ 3 days ago

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1994

Happy New Year 2025 🤗
For the Huggingface community.

reacted to prithivMLmods's post with 🤗 3 days ago

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2772

Triangulum Catalogued 🔥💫

🎯Triangulum is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.

+ Triangulum-10B : prithivMLmods/Triangulum-10B
+ Quants : prithivMLmods/Triangulum-10B-GGUF

+ Triangulum-5B : prithivMLmods/Triangulum-5B
+ Quants : prithivMLmods/Triangulum-5B-GGUF

+ Triangulum-1B : prithivMLmods/Triangulum-1B
+ Quants : prithivMLmods/Triangulum-1B-GGUF

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reacted to sequelbox's post with 👍 3 days ago

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2023

Check out the early preview of the upcoming Tachibana-QVQ dataset: code-reasoning and code-instruct data generated with Qwen/QVQ-72B-Preview

Link here: sequelbox/Tachibana-QVQ-PREVIEW

more to come :)

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posted an update 4 days ago

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2022

The deepseek-ai/DeepSeek-V3-Base
model has featured today on CNBC tech news. The whale made a splash by using FP8 and shrink the cost of training significantly!

https://youtu.be/NJljq429cGk?si=kgk-ogPTMfJKsaA2

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reacted to ginipick's post with 🔥 4 days ago

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3400

🌊 [Dokdo Membership - Next Generation AI Video Creation Platform]

✨ Transform your imagination into mesmerizing videos with Dokdo Membership, an innovative AI-powered platform that generates unique videos from text and images. Built as a streamlined SaaS boilerplate using Python Gradio for Hugging Face users, this tool offers an intuitive way to create AI-generated videos with minimal effort.

🎯 [Key Features]
- 📧 Email-based authentication system with secure login/signup
- 🎁 15 points automatically credited upon registration
- 💰 5 points deduction per video generation
- 🌏 Bilingual support (Korean/English) with automatic translation
- 🖼️ Optional first frame image upload capability
- ⭐ Automatic GiniGEN.AI watermark integration

🚀 [Technical Specifications]
1. 💫 Modern, responsive user interface with Gradio components
2. 📊 Efficient resource management through points system
3. 🎥 High-quality video generation using advanced AI models
4. 🔄 Seamless translation pipeline for multilingual support
5. ⚡ Real-time point tracking and management system
6. 🛡️ Comprehensive content moderation and filtering

📝 [How to Use]
1. ✅ Register with your email to receive 15 initial points
2. 💭 Enter your video description (supports both English and Korean)
3. 📤 Upload a reference image for the first frame (optional)
4. 🎬 Click "Generate Video" (consumes 5 points)
5. 📥 Preview and download your generated video

🔧 [Technical Implementation]
- Built with Python Gradio for seamless Hugging Face Space integration
- Implements secure user authentication and session management
- Features real-time point tracking and automated deduction system
- Includes comprehensive error handling and input validation
- Utilizes advanced AI models for video generation

📮 Need additional points for more creations? Contact us at [email protected] for point acquisition options through public contributions or paid services.

ginigen/Dokdo-membership

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reacted to cfahlgren1's post with 🚀 4 days ago

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2961

The deepseek-ai/DeepSeek-V3 is very good! I have been playing with it and found it is really good at one-shotting a pretty good landing page.

You can play with it here: https://deepseek-artifacts.vercel.app

All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub 💪

reacted to onekq's post with 🔥 6 days ago

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2996

🐋 DeepSeek 🐋v3 achieves a solid 7 point jump than v2.5, surpassing GPT-4o, but is still behind 🍓 o1 🍓and Claude 3.5.

onekq-ai/WebApp1K-models-leaderboard

posted an update 6 days ago

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1433

I've built a small utility to split safetensors file by file.
The issue/need came up when I've tried to convert the new Deepseek V3 model from FP8 to BF16.
The only Ada architecture GPU I have is an RTX 4080 and the 16GB vram was just wasn't enough for the conversion.

BTW: I'll upload the bf16 version here:
DevQuasar/deepseek-ai.DeepSeek-V3-Base-bf16
(it will take a while - days with my upload speed)
If anyone has access the resources to test it I'd appreciate a feedback if it's working or not.

The tool, is available from here:
https://github.com/csabakecskemeti/ai_utils/blob/main/safetensor_splitter.py
It's splitting every file to n pieces by the layers if possible, and create a new "model.safetensors.index.json" file.
I've tested it with Llama 3.1 8B and multiple split sizes, and validated by using inference pipeline.
use --help for usage
Please note current version expects the model is already multiple file and have a "model.safetensors.index.json" layer-safetensor mapping file.

reacted to MoritzLaurer's post with 👍 14 days ago

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2498

Quite excited by the ModernBERT release! 0.15/0.4B small, 2T modern pre-training data and tokenizer with code, 8k context window, great efficient model for embeddings & classification!

This will probably be the basis for many future SOTA encoders! And I can finally stop using DeBERTav3 from 2021 :D

Congrats @answerdotai , @LightOnIO and collaborators like @tomaarsen !

Paper and models here 👇https://huggingface.co/collections/answerdotai/modernbert-67627ad707a4acbf33c41deb

1 reply

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replied to luigi12345's post 15 days ago

I found them here:
https://huggingface.co/GoodiesHere

posted an update 17 days ago

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1222

tiiuae Falcon3 10B Q8 playground:
https://huggingface.co/spaces/DevQuasar/Mi50

Also find my tiiuae Falcon3 Quant collection here:
https://huggingface.co/collections/DevQuasar/tiiuae-falcon3-676236626f3c57d1a19c6c1d

Enjoy!

reacted to cutechicken's post with ❤️ 18 days ago

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2852

🚀 RAGOndevice: High-Performance Local AI Document Analysis Assistant
💫 Core Value
RAGOndevice is a high-performance AI system running locally without cloud dependency. Using CohereForAI's optimized 7B model, it enables professional-grade document analysis on standard PCs. ✨
🌟 Ondevice AI Advantages
1. 🔋 Efficient Resource Utilization

🎯 Optimized 7B Model: Runs on standard PCs
⚡ Local Processing: Instant response without cloud
💻 Low-Spec Compatible: Performs well on regular GPUs
🔄 Optimized Memory: Ensures stable operation

2. 🛡️ Data Security & Cost Efficiency

🔒 Complete Privacy: No external data transmission
🌐 Offline Operation: No internet required
💰 No Subscription: One-time installation
⚙️ Resource Optimization: Uses existing hardware

🎮 Key Features
1. 📊 Powerful Document Analysis

📁 Multi-Format Support: TXT, CSV, PDF, Parquet
🧠 Intelligent Analysis: Automatic structure recognition
👁️ OCR Support: Advanced PDF text extraction
💬 Real-time Chat: Natural language interaction

2. 🔍 Local RAG System

🎯 Efficient Search: TF-IDF based local search
🧩 Context Understanding: Accurate information retrieval
📚 Wikipedia Integration: Rich background knowledge

🎯 Use Cases

🏢 Enterprise: Secure confidential document processing
🔬 Personal Research: Private data analysis
📚 Education: Personal learning material analysis
💻 Development: Local codebase analysis

⭐ Differentiators

🏃‍♂️ Independent Operation: Zero cloud dependency
⚡ Instant Response: No network latency
🔐 Complete Security: Full data control
💎 Cost Efficiency: No ongoing costs

🔮 Future Plans

🚀 Enhanced model optimization
📚 Local knowledge base expansion
⚡ Hardware optimization
📁 Extended file support

🌟 RAGOndevice democratizes high-performance AI, providing the optimal local AI solution for security-sensitive environments. 🚀

🔥 Power of Local AI: Experience enterprise-grade AI capabilities right on your device!

VIDraft/RAGOndevice

reacted to nicolay-r's post with 👀 20 days ago

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1929

📢For those who wish to quick start with reasoning / cot application over rows of tabular data but with minimal dependencies, this post would be valuable.

🔎 I found that the problem is that given a bulk of Chain-of-Though (CoT) 🔗 queries for remotely accessed LLM 🤖 (like openrouter / Replicate / OpenAI) might result in connection loss which may lead exception 💥 and challenges with generated content restoration.

Here, is where I contribute with the bulk-chain.
⭐ https://github.com/nicolay-r/bulk-chain

Currently working on 0.24.3 version, in which I am happy to announce the API for developing your apps that are based on CoT schema declaration in JSON (details in attached images 📸)

All you have to do is:
✅ 1. Declare CoT-schema in json
✅ 2. Declare the model or use the preset
✅ 3. Launch code

One example is to use ReplicateIO provider:
https://github.com/nicolay-r/bulk-chain/blob/master/ext/replicate.py

Each model has a wrapped call for inference in try-catch block

posted an update 20 days ago

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4488

The AMD Instinct MI50 (~$110) is surprisingly fast for inference Quantized models.

This runs a Llama 3.1 8B Q8 with Llama.cpp
https://huggingface.co/spaces/DevQuasar/Mi50

A little blogpost about the HW
http://devquasar.com/uncategorized/amd-radeon-instinct-mi50-cheap-inference/

reacted to cutechicken's post with 🚀 20 days ago

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3476

🎮 Introduction to the World's First 3D Tank Game Created Solely with Generative AI 🚀
The advancement of AI technology is revolutionizing game development paradigms. I embarked on a challenge to create a 3D tank game using "only AI assistance," pushing the boundaries of what's possible in AI-driven game development. 🤖
Following the success of my first 2D tank game ( cutechicken/tankwar) 🎯, I ventured into the more challenging realm of 3D FPS game development. Remarkably, using Hugging Face's AI tool ( VIDraft/mouse1), the basic game framework was generated in just one minute ⚡. The 3D modeling ( ginipick/SORA-3D) and sound effects ( fantaxy/Sound-AI-SFX) were also easily created with AI assistance.
The resulting game ( cutechicken/TankWar3D) represents arguably the world's first 3D FPS game created primarily with generative AI. 90% was accomplished through AI capabilities, with the remaining 10% comprising my post-processing work. 🎉
Key Technical Features: 🛠️

Complete 3D rendering system using Three.js 🖥️
Real-time physics-based collision detection and handling 💥
Dynamic shadow and lighting system ☀️
Real-time radar and enemy tracking system 🎯
Advanced particle effects system (explosions, smoke, fire) 💫
Dynamic sound system (engine, firing, explosion sounds) 🔊
AI-driven enemy strategy system (pursuit, evasion, combat) 🤖
Terrain-based tank tilt adjustment 🌍
Real-time crosshair targeting system 🎯
Dynamic UI system (health bars, ammo, score) 📊

Technical Implementation: ⚙️

Physics Engine: 🎳
Custom collision detection system
Dynamic obstacle handling
Real-time terrain interaction

AI Systems: 🧠
State-based AI behavior patterns
Dynamic pathfinding
Tactical decision-making system

Graphics: 🎨
PBR-based rendering
Dynamic particle system
Real-time shadow mapping

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