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prithivMLmods 
posted an update 3 days ago
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3560
Dropping some of the custom fine-tunes based on SigLIP2,
with a single-label classification problem type! 🌀🧤

- AI vs Deepfake vs Real : prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2
- Deepfake Detect : prithivMLmods/Deepfake-Detect-Siglip2
- Fire Detection : prithivMLmods/Fire-Detection-Siglip2
- Deepfake Quality Assess : prithivMLmods/Deepfake-Quality-Assess-Siglip2
- Guard Against Unsafe Content : prithivMLmods/Guard-Against-Unsafe-Content-Siglip2

🌠Collection : prithivMLmods/siglip2-custom-67bcdb2de8fe96b99fb4e19e
KnutJaegersberg 
posted an update 6 days ago
prithivMLmods 
posted an update 6 days ago
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5749
It's really interesting about the deployment of a new state of matter in Majorana 1: the world’s first quantum processor powered by topological qubits. If you missed this news this week, here are some links for you:

🅱️Topological qubit arrays: https://arxiv.org/pdf/2502.12252

⚛️ Quantum Blog: https://azure.microsoft.com/en-us/blog/quantum/2025/02/19/microsoft-unveils-majorana-1-the-worlds-first-quantum-processor-powered-by-topological-qubits/

📖 Read the story: https://news.microsoft.com/source/features/innovation/microsofts-majorana-1-chip-carves-new-path-for-quantum-computing/

📝 Majorana 1 Intro: https://youtu.be/Q4xCR20Dh1E?si=Z51DbEYnZFp_88Xp

🌀The Path to a Million Qubits: https://youtu.be/wSHmygPQukQ?si=TS80EhI62oWiMSHK
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KnutJaegersberg 
posted an update 8 days ago
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670
Mimicking Consciousness in LLMs: Ascending the Dimensions of Thought with Recurrent Processing

This blog post explores how **recurrent processing** can transform Large Language Models (LLMs) to mimic aspects of human thought by engaging in iterative feedback loops. Inspired by string theory, the post describes how LLMs can "ascend dimensions" of cognition, progressing through foundational cognitive loops—such as basic cognition, executive functions, and meta-cognition—before advancing into **world simulation**. In this stage, LLMs explore higher dimensions, perceiving non-linear time, simulating branching possibilities, and integrating multiple realities. The interaction between the **Generator** and **Reflective Compass** allows AI systems to refine their outputs iteratively, moving toward a **point attractor** where ideas become coherent and polished. While this process doesn't bestow true consciousness, it offers a compelling imitation of reflective and adaptive thinking, leading to smarter dialogue, enhanced creativity, and more robust problem-solving.

https://huggingface.co./blog/KnutJaegersberg/oscillatory-recurrence-for-llms
prithivMLmods 
posted an update 10 days ago
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3885
Dino: The Minimalist Multipurpose Chat System 🌠
Agent-Dino : prithivMLmods/Agent-Dino
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino

By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
@image: Generates an image using Stable Diffusion xL.
@3d: Generates a 3D mesh.
@web: Web search agents.
@rAgent: Initiates a reasoning chain using Llama mode for coding explanations.
@tts1-♀, @tts2-♂: Voice generation (Female and Male voices).
@yolo : Object Detection
prithivMLmods 
posted an update 12 days ago
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4473
The last week of Impression Craft Arts and sketches from strangerzonehf🎨🧑🏻‍🎨

- Collection : strangerzonehf/Flux-Ultimate-LoRA-Collection

Adapters:
+ Ld-Art : strangerzonehf/Ld-Art
+ Animeopix-Flux : strangerzonehf/Animeopix-Flux
+ Flux-Super-Paint-LoRA : strangerzonehf/Flux-Super-Paint-LoRA
+ CinematicShot-Pics-Flux : strangerzonehf/cinematicShot-Pics-Flux
+ Oil-Wall-Art-Flux : strangerzonehf/Oil-Wall-Art-Flux
+ Pixelo-Flux : strangerzonehf/Pixelo-Flux
+ Abstract-Shattered : strangerzonehf/Abstract-Shattered
+ Neon-Impressionism-Flux : strangerzonehf/Neon-Impressionism-Flux
+ NewG-Art : strangerzonehf/NewG-Art

🪧Demo : prithivMLmods/FLUX-LoRA-DLC
🤗Page : https://huggingface.co./strangerzonehf
KnutJaegersberg 
posted an update 20 days ago
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2716
A Brief Survey of Associations Between Meta-Learning and General AI

The paper titled "A Brief Survey of Associations Between Meta-Learning and General AI" explores how meta-learning techniques can contribute to the development of Artificial General Intelligence (AGI). Here are the key points summarized:

1. General AI (AGI) and Meta-Learning:
- AGI aims to develop algorithms that can handle a wide variety of tasks, similar to human intelligence. Current AI systems excel at specific tasks but struggle with generalization to unseen tasks.
- Meta-learning or "learning to learn" improves model adaptation and generalization, allowing AI systems to tackle new tasks efficiently using prior experiences.

2. Neural Network Design in Meta-Learning:
- Techniques like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks enable self-improvement and adaptability for deep models, supporting generalization across tasks.
- Highway networks and ResNet-style models use shortcuts for efficient backpropagation, allowing deeper models that can be used in meta-learning frameworks.

3. Coevolution:
- Coevolution involves the mutual evolution of multiple components, such as learners or task-solvers, to improve overall performance.
- Coevolution between learners enhances collaboration and competition within AI systems, while coevolution between tasks and solvers (e.g., POWERPLAY and AI-GA frameworks) pushes solvers to adapt to increasingly complex tasks.

4. Curiosity in Meta-Learning:
- Curiosity-based exploration encourages AI systems to discover new, diverse features of the environment, avoiding local optima.
- Curiosity-based objectives can be combined with performance-based objectives to ensure efficient exploration and adaptation in complex tasks.

5. Forgetting Mechanisms:
- Forgetting is crucial to avoid memory overload in AI systems

https://arxiv.org/abs/2101.04283
prithivMLmods 
posted an update 20 days ago
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4252
QwQ Edge Gets a Small Update..! 💬
try now: prithivMLmods/QwQ-Edge

🚀Now, you can use the following commands for different tasks:

🖼️ @image 'prompt...' → Generates an image
🔉@tts1 'prompt...' → Generates speech in a female voice
🔉 @tts2 'prompt...' → Generates speech in a male voice
🅰️@text 'prompt...' → Enables textual conversation (If not specified, text-to-text generation is the default mode)

💬Multimodality Support : prithivMLmods/Qwen2-VL-OCR-2B-Instruct
💬For text generation, the FastThink-0.5B model ensures quick and efficient responses, prithivMLmods/FastThink-0.5B-Tiny
💬Image Generation: sdxl lightning model, SG161222/RealVisXL_V4.0_Lightning

Github: https://github.com/PRITHIVSAKTHIUR/QwQ-Edge

graph TD
    A[User Interface] --> B[Chat Logic]
    B --> C{Command Type}
    C -->|Text| D[FastThink-0.5B]
    C -->|Image| E[Qwen2-VL-OCR-2B]
    C -->|@image| F[Stable Diffusion XL]
    C -->|@tts| G[Edge TTS]
    D --> H[Response]
    E --> H
    F --> H
    G --> H
eienmojiki 
posted an update 21 days ago
KnutJaegersberg 
posted an update 21 days ago
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1654
Artificial general intelligence through recursive data compression and grounded reasoning: a position paper


This paper proposes a system to achieve AGI through general data compression and grounded reasoning.

General Data Compression involves creating a flexible algorithm that adapts to input data to simplify and compress it recursively, identifying simple, orthogonal features to avoid redundancy. The algorithm measures AGI progress by solving problems based on increasing complexity, and it expands its search space according to the data itself. Compression is applied not only to data but also to model parameters, and sequences are segmented based on compressibility.

Grounded Reasoning refers to forming representations with various granularities, crucial for commonsense reasoning and AGI. The system simulates the real world as its model, switching between representations and maximizing resourcefulness. Key ideas include the world as its own model for reasoning and actions aimed at maximizing entropy to test hypotheses.

The paper emphasizes simplicity, data-dependent bias, recursion, orthogonality, resourcefulness, and grounding in real-world contexts as fundamental principles in building an AGI system.

https://arxiv.org/abs/1506.04366
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rubenroy 
posted an update 24 days ago
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2447
🔥🚀 Hey everyone! I'm excited to share my latest LLM release: Gilgamesh 72B, a model built on Qwen 2.5-72B Instruct. Gilgamesh was trained on a couple of my GammaCorpus datasets, specifically:

- rubenroy/GammaCorpus-CoT-Math-170k
- rubenroy/GammaCorpus-v2-5m
- rubenroy/GammaCorpus-Fact-QA-450k

I've submitted GGM 72B to the Open LLM Leaderboard for benchmarking, I'll send an update post once the results are in!

You can try it out and share your feedback, check out the model page and see what it can do:
👉 rubenroy/Gilgamesh-72B

Would love to hear your thoughts!
chansung 
posted an update 26 days ago
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2923
Simple Paper Review #5

I briefly reviewed the paper "SFT Memorizes, RL Generalizes," which compares SFT and RL in post-training of LLM/VLM from HKU, UC Berkeley, Google DeepMind, and New York University

The conclusion suggests SFT excels in memorization, while RL is better for generalization. However, since LLM/VLM should benefit humans beyond just generalization, a mix of SFT and RL is advisable. Typically, some SFT is followed by RL to understand prompt formats and enhance generalization through trial and error.

The study focused on one model, Llama-3.2-Vision-11B, using environments like General Points for arithmetic reasoning and V-IRL for spatial reasoning. Training data was used for both SFT and RL, with evaluations on in-distribution and out-of-distribution data to assess memorization and generalization.

I want to apply RL extensively, but it requires building a similar simulation environment. For domain-specific models, significant investment in creating a "playground" for the model is crucial, as the effort will directly influence the outcomes.

https://arxiv.org/abs/2501.17161
prithivMLmods 
posted an update 26 days ago
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4796
o3-Mini and Deepseek R1
Worked out with some famous and weird examples.

🔥Blog: https://huggingface.co./blog/prithivMLmods/o3-mini-vs-deepseek-r1

Prompt : Using HTML, CSS, and JavaScript in a single HTML file to create a simulation of the solar system. Pay extreme attention to the UI to make it as intuitive as possible. Ensure that every planet appears as a sphere and is labeled with its corresponding name.

example 1: o3 Mini , example 2: Deepseek R1

Q2 : https://huggingface.co./blog/prithivMLmods/o3-mini-vs-deepseek-r1#q2--web-solar-system-explorer
  • 1 reply
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chansung 
posted an update 27 days ago
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4303
A brief summary of the o3-mini

The OpenAI o3-mini model is a significant improvement over the o1-mini, reaching o1 performance levels. While generally good, its performance isn't universally better than previous models (o1, o1-prev.) or GPT-4o across all benchmarks. This means workflows should be re-evaluated with each model upgrade.

The o3-mini has "low," "medium," and "high" versions, with "low" being the base model used for benchmarking. It's speculated that the higher versions simply involve more processing. A fair comparison with other models like Gemini 2.0 Thinking or DeepSeek-R1 would likely need to use the "low" version and a similar "think more" mechanism.

The system card is recommended reading due to its comprehensive benchmark data.

https://openai.com/index/openai-o3-mini/
rubenroy 
posted an update 27 days ago
KnutJaegersberg 
posted an update 28 days ago
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1035
Anthropomorphic reasoning about neuromorphic AGI safety

Summary of "Anthropomorphic Reasoning About Neuromorphic AGI Safety"
This paper explores safety strategies for neuromorphic artificial general intelligence (AGI), defined as systems designed by reverse-engineering essential computations of the human brain. Key arguments and proposals include:

1. Anthropomorphic Reasoning Validity:
- Neuromorphic AGI’s design and assessment rely on human cognition models, making anthropomorphic reasoning (using human-like traits) critical for safety analysis. Comparisons to human behavior and neural mechanisms provide insights into AGI behavior and risks.

2. Countering Safety Criticisms:
- The authors challenge claims that neuromorphic AGI is inherently more dangerous than other AGI approaches. They argue all AGI systems face intractable verification challenges (e.g., real-world unpredictability, incomputable action validation). Neuromorphic AGI may even offer safety advantages by enabling comparisons to human cognitive processes.

3. Motivational Architecture:
- Basic drives (e.g., curiosity, social interaction) are essential for cognitive development and safety. These pre-conceptual, hardwired drives (analogous to human hunger or affiliation) shape learning and behavior. The orthogonality thesis (intelligence and goals as independent) is contested, as neuromorphic AGI’s drives likely intertwine with its cognitive architecture.

4. Safety Strategies:
- **Social Drives**: Embedding drives like caregiving, affiliation, and cooperation ensures AGI develops prosocial values through human interaction.
- **Bounded Reward Systems**: Human-like satiation mechanisms (e.g., diminishing rewards after fulfillment) prevent extreme behaviors (e.g., paperclip maximization).
- **Developmental Environment**: Exposure to diverse, positive human interactions and moral examples fosters

https://ccnlab.org/papers/JilkHerdReadEtAl17.pdf
Abhaykoul 
posted an update 29 days ago
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3915
🔥 THE WAIT IS OVER... HAI-SER IS HERE! 🔥

Yo fam, this ain't just another AI drop— this is the FUTURE of emotional intelligence! 🚀

Introducing HAI-SER, powered by Structured Emotional Reasoning (SER), the next-level AI that doesn’t just understand your words—it feels you, analyzes your emotions, and helps you navigate life’s toughest moments. 💡

💥 What makes HAI-SER a game-changer?
🔹 Emotional Vibe Check – Gets the mood, energy, and what’s really going on 🎭
🔹 Mind-State Analysis – Breaks down your thoughts, beliefs, and patterns 🤯
🔹 Root Cause Deep-Dive – Unpacks the WHY behind your emotions 💡
🔹 Impact Check – Sees how it’s affecting your life and mental health 💔
🔹 Safety Check – Prioritizes your well-being and crisis management 🚨
🔹 Healing Game Plan – Custom strategies to help you bounce back 💪
🔹 Growth Potential – Turns struggles into opportunities for self-improvement 📈
🔹 How to Approach – Teaches you and others how to communicate and heal 🤝
🔹 Personalized Response – Not just generic advice—real talk, tailored to YOU 💯

No more robotic AI responses. No more surface-level advice. HAI-SER gets deep, analyzing emotions with precision and giving real, actionable support.

This ain’t just AI—this is your digital therapist, life coach, and hype squad all in one. Whether it’s mental health, career struggles, relationships, or personal growth, HAI-SER has your back.

🚀 The future of emotionally intelligent AI is HERE.
Are you ready? 🔥💯

HelpingAI/HAI-SER
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prithivMLmods 
posted an update about 1 month ago
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5168
Deepswipe by
.
.
.
. Deepseek🐬🗿






Everything is now in recovery. 📉📈
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chansung 
posted an update about 1 month ago
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2016
Simple summary on DeepSeek AI's Janus-Pro: A fresh take on multimodal AI!

It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.

Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
✦ Stage 3: Fine-tuning with a careful balance of multimodal data.

Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.

The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
KnutJaegersberg 
posted an update about 1 month ago
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1884
Evolution and The Knightian Blindspot of Machine Learning


The paper discusses machine learning's limitations in addressing Knightian Uncertainty (KU), highlighting the fragility of models like reinforcement learning (RL) in unpredictable, open-world environments. KU refers to uncertainty that can't be quantified or predicted, a challenge that RL fails to handle due to its reliance on fixed data distributions and limited formalisms.


### Key Approaches:

1. **Artificial Life (ALife):** Simulating diverse, evolving systems to generate adaptability, mimicking biological evolution's robustness to unpredictable environments.

2. **Open-Endedness:** Creating AI systems capable of continuous innovation and adaptation, drawing inspiration from human creativity and scientific discovery.

3. **Revising RL Formalisms:** Modifying reinforcement learning (RL) models to handle dynamic, open-world environments by integrating more flexible assumptions and evolutionary strategies.

These approaches aim to address ML’s limitations in real-world uncertainty and move toward more adaptive, general intelligence.

https://arxiv.org/abs/2501.13075