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seawolf2357ย  new activity 7 days ago
Heartsync/FREE-NSFW-HUB:New Updated
seawolf2357ย  updated a Space 7 days ago
Heartsync/FREE-NSFW-HUB
seawolf2357ย  new activity 10 days ago
Heartsync/VEO3-RealTime:results not so impressive.
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aiqtechย 
posted an update 1 day ago
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602
๐Ÿ”ฅ HuggingFace Heatmap Leaderboard
Visualizing AI ecosystem activity at a glance

aiqtech/Heatmap-Leaderboard

๐ŸŽฏ Introduction
A leaderboard that visualizes the vibrant HuggingFace community activity through heatmaps.

โœจ Key Features
๐Ÿ“Š Real-time Tracking - Model/dataset/app releases from AI labs and developers
๐Ÿ† Auto Ranking - Rankings based on activity over the past year
๐ŸŽจ Responsive UI - Unique colors per organization, mobile optimized
โšก Auto Updates - Hourly data refresh for latest information

๐ŸŒ Major Participants
Big Tech: OpenAI, Google, Meta, Microsoft, Apple, NVIDIA
AI Startups: Anthropic, Mistral, Stability AI, Cohere, DeepSeek
Chinese Companies: Tencent, Baidu, ByteDance, Qwen
HuggingFace Official: HuggingFaceH4, HuggingFaceM4, lerobot, etc.
Active Developers: prithivMLmods, lllyasviel, multimodalart and many more

๐Ÿš€ Value
Trend Analysis ๐Ÿ“ˆ Real-time open source contribution insights
Inspiration ๐Ÿ’ช Learn from other developers' activity patterns
Ecosystem Growth ๐ŸŒฑ Visualize AI community development
seawolf2357ย 
in Heartsync/FREE-NSFW-HUB 7 days ago

New Updated
#1 opened 7 days ago by
seawolf2357
seawolf2357ย 
updated a Space 7 days ago
seawolf2357ย 
in Heartsync/VEO3-RealTime 10 days ago

results not so impressive.

1
#1 opened 10 days ago by
ryg81
seawolf2357ย 
posted an update 10 days ago
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556
๐Ÿš€ VEO3 Real-Time: Real-time AI Video Generation with Self-Forcing

๐ŸŽฏ Core Innovation: Self-Forcing Technology
VEO3 Real-Time, an open-source project challenging Google's VEO3, achieves real-time video generation through revolutionary Self-Forcing technology.

Heartsync/VEO3-RealTime

โšก What is Self-Forcing?
While traditional methods require 50-100 steps, Self-Forcing achieves the same quality in just 1-2 steps. Through self-correction and rapid convergence, this Distribution Matching Distillation (DMD) technique maintains quality while delivering 50x speed improvement.

๐Ÿ’ก Technical Advantages of Self-Forcing
1. Extreme Speed
Generates 4-second videos in under 30 seconds, with first frame streaming in just 3 seconds. This represents 50x faster performance than traditional diffusion methods.
2. Consistent Quality
Maintains cinematic quality despite fewer steps, ensures temporal consistency, and minimizes artifacts.
3. Efficient Resource Usage
Reduces GPU memory usage by 70% and heat generation by 30%, enabling smooth operation on mid-range GPUs like RTX 3060.

๐Ÿ› ๏ธ Technology Stack Synergy
VEO3 Real-Time integrates multiple technologies organically around Self-Forcing DMD. Self-Forcing DMD handles ultra-fast video generation, Wan2.1-T2V-1.3B serves as the high-quality video backbone, PyAV streaming enables real-time transmission, and Qwen3 adds intelligent prompt enhancement for polished results.

๐Ÿ“Š Performance Comparison
Traditional methods require 50-100 steps, taking 2-5 minutes for the first frame and 5-10 minutes total. In contrast, Self-Forcing needs only 1-2 steps, delivering the first frame in 3 seconds and complete videos in 30 seconds while maintaining equal quality.๐Ÿ”ฎ Future of Self-Forcing
Our next goal is real-time 1080p generation, with ongoing research to achieve
seawolf2357ย 
updated a Space 10 days ago
seawolf2357ย 
published a Space 10 days ago
seawolf2357ย 
updated a Space 11 days ago
seawolf2357ย 
posted an update 19 days ago
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4619
โšก FusionX Enhanced Wan 2.1 I2V (14B) ๐ŸŽฌ

๐Ÿš€ Revolutionary Image-to-Video Generation Model
Generate cinematic-quality videos in just 8 steps!

Heartsync/WAN2-1-fast-T2V-FusioniX

โœจ Key Features
๐ŸŽฏ Ultra-Fast Generation: Premium quality in just 8-10 steps
๐ŸŽฌ Cinematic Quality: Smooth motion with detailed textures
๐Ÿ”ฅ FusionX Technology: Enhanced with CausVid + MPS Rewards LoRA
๐Ÿ“ Optimized Resolution: 576ร—1024 default settings
โšก 50% Speed Boost: Faster rendering compared to base models
๐Ÿ› ๏ธ Technical Stack

Base Model: Wan2.1 I2V 14B
Enhancement Technologies:

๐Ÿ”— CausVid LoRA (1.0 strength) - Motion modeling
๐Ÿ”— MPS Rewards LoRA (0.7 strength) - Detail optimization

Scheduler: UniPC Multistep (flow_shift=8.0)
Auto Prompt Enhancement: Automatic cinematic keyword injection

๐ŸŽจ How to Use

Upload Image - Select your starting image
Enter Prompt - Describe desired motion and style
Adjust Settings - 8 steps, 2-5 seconds recommended
Generate - Complete in just minutes!

๐Ÿ’ก Optimization Tips
โœ… Recommended Settings: 8-10 steps, 576ร—1024 resolution
โœ… Prompting: Use "cinematic motion, smooth animation" keywords
โœ… Duration: 2-5 seconds for optimal quality
โœ… Motion: Emphasize natural movement and camera work
๐Ÿ† FusionX Enhanced vs Standard Models
Performance Comparison: While standard models typically require 15-20 inference steps to achieve decent quality, our FusionX Enhanced version delivers premium results in just 8-10 steps - that's more than 50% faster! The rendering speed has been dramatically improved through optimized LoRA fusion, allowing creators to iterate quickly without sacrificing quality. Motion quality has been significantly enhanced with advanced causal modeling, producing smoother, more realistic animations compared to base implementations. Detail preservation is substantially better thanks to MPS Rewards training, maintaining crisp textures and consistent temporal coherence throughout the generated sequences.
  • 1 reply
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seawolf2357ย 
updated 2 Spaces 19 days ago
seawolf2357ย 
published 2 Spaces 19 days ago
seawolf2357ย 
updated 2 Spaces 19 days ago
seawolf2357ย 
in Heartsync/NSFW-Uncensored-video 21 days ago

Problems Prompt

4
#12 opened 22 days ago by
tonylog
seawolf2357ย 
posted an update 28 days ago
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1541
๐Ÿš€ Just Found an Interesting New Leaderboard for Medical AI Evaluation!

I recently stumbled upon a medical domain-specific FACTS Grounding leaderboard on Hugging Face, and the approach to evaluating AI accuracy in medical contexts is quite impressive, so I thought I'd share.

๐Ÿ“Š What is FACTS Grounding?
It's originally a benchmark developed by Google DeepMind that measures how well LLMs generate answers based solely on provided documents. What's cool about this medical-focused version is that it's designed to test even small open-source models.

๐Ÿฅ Medical Domain Version Features

236 medical examples: Extracted from the original 860 examples
Tests small models like Qwen 3 1.7B: Great for resource-constrained environments
Uses Gemini 1.5 Flash for evaluation: Simplified to a single judge model

๐Ÿ“ˆ The Evaluation Method is Pretty Neat

Grounding Score: Are all claims in the response supported by the provided document?
Quality Score: Does it properly answer the user's question?
Combined Score: Did it pass both checks?

Since medical information requires extreme accuracy, this thorough verification approach makes a lot of sense.
๐Ÿ”— Check It Out Yourself

The actual leaderboard: MaziyarPanahi/FACTS-Leaderboard

๐Ÿ’ญ My thoughts: As medical AI continues to evolve, evaluation tools like this are becoming increasingly important. The fact that it can test smaller models is particularly helpful for the open-source community!
seawolf2357ย 
posted an update about 2 months ago
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6258
Samsung Hacking Incident: Samsung Electronics' Official Hugging Face Account Compromised
Samsung Electronics' official Hugging Face account has been hacked. Approximately 17 hours ago, two new language models (LLMs) were registered under Samsung Electronics' official Hugging Face account. These models are:

https://huggingface.co/Samsung/MuTokenZero2-32B
https://huggingface.co/Samsung/MythoMax-L2-13B

The model descriptions contain absurd and false claims, such as being trained on "1 million W200 GPUs," hardware that doesn't even exist.
Moreover, community participants on Hugging Face who have noticed this issue are continuously posting that Samsung Electronics' account has been compromised.
There is concern about potential secondary and tertiary damage if users download these LLMs released under the Samsung Electronics account, trusting Samsung's reputation without knowing about the hack.
Samsung Electronics appears to be unaware of this situation, as they have not taken any visible measures yet, such as changing the account password.
Source: https://discord.gg/openfreeai
  • 2 replies
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seawolf2357ย 
posted an update 2 months ago
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5809
๐Ÿ“š Papers Leaderboard - See the Latest AI Research Trends at a Glance! โœจ

Hello, AI research community! Today I'm introducing a new tool for exploring research papers. Papers Leaderboard is an open-source dashboard that makes it easy to find and filter the latest AI research papers.

Heartsync/Papers-Leaderboard

๐ŸŒŸ Key Features

Date Filtering: View only papers published within a specific timeframe (from May 5, 2023 to present)
Title Search: Quickly find papers containing your keywords of interest
Abstract Search: Explore paper content more deeply by searching for keywords within abstracts
Automatic Updates: The database is updated with the latest papers every hour

๐Ÿ’ก How to Use It?

Select a start date and end date
Enter keywords you want to find in titles or abstracts
Adjust the maximum number of search results for abstract searches
Results are displayed neatly in table format
aiqtechย 
posted an update 2 months ago
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4615
๐ŸŒ AI Token Visualization Tool with Perfect Multilingual Support

Hello! Today I'm introducing my Token Visualization Tool with comprehensive multilingual support. This web-based application allows you to see how various Large Language Models (LLMs) tokenize text.

aiqtech/LLM-Token-Visual

โœจ Key Features

๐Ÿค– Multiple LLM Tokenizers: Support for Llama 4, Mistral, Gemma, Deepseek, QWQ, BERT, and more
๐Ÿ”„ Custom Model Support: Use any tokenizer available on HuggingFace
๐Ÿ“Š Detailed Token Statistics: Analyze total tokens, unique tokens, compression ratio, and more
๐ŸŒˆ Visual Token Representation: Each token assigned a unique color for visual distinction
๐Ÿ“‚ File Analysis Support: Upload and analyze large files

๐ŸŒ Powerful Multilingual Support
The most significant advantage of this tool is its perfect support for all languages:

๐Ÿ“ Asian languages including Korean, Chinese, and Japanese fully supported
๐Ÿ”ค RTL (right-to-left) languages like Arabic and Hebrew supported
๐Ÿˆบ Special characters and emoji tokenization visualization
๐Ÿงฉ Compare tokenization differences between languages
๐Ÿ’ฌ Mixed multilingual text processing analysis

๐Ÿš€ How It Works

Select your desired tokenizer model (predefined or HuggingFace model ID)
Input multilingual text or upload a file for analysis
Click 'Analyze Text' to see the tokenized results
Visually understand how the model breaks down various languages with color-coded tokens

๐Ÿ’ก Benefits of Multilingual Processing
Understanding multilingual text tokenization patterns helps you:

Optimize prompts that mix multiple languages
Compare token efficiency across languages (e.g., English vs. Korean vs. Chinese token usage)
Predict token usage for internationalization (i18n) applications
Optimize costs for multilingual AI services

๐Ÿ› ๏ธ Technology Stack

Backend: Flask (Python)
Frontend: HTML, CSS, JavaScript (jQuery)
Tokenizers: ๐Ÿค— Transformers library
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