·

Granite 4.0 Nano: Just how small can you go? - Databubble

Home
Models
News
Compare
Boards
Pricing
About
Contact

Latest

The AI-designed car is taking shape1h◆Meta inks deal for solar power at night, beamed from space2h◆Consequentialist Objectives and Catastrophe8h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms8h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation8h◆A Probabilistic Framework for Hierarchical Goal Recognition8h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language8h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology8h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem8h◆Learning from Natural Language Feedback for Personalized Question Answering8h◆TS-Arena -- A Live Forecast Pre-Registration Platform8h◆Report for NSF Workshop on AI for Electronic Design Automation8h◆Initial results of the Digital Consciousness Model8h◆Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem8h◆Sensory-Aware Sequential Recommendation via Review-Distilled Representations8h◆Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation8h◆When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education8h◆SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention8h◆EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation8h◆Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning8h◆The AI-designed car is taking shape1h◆Meta inks deal for solar power at night, beamed from space2h◆Consequentialist Objectives and Catastrophe8h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms8h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation8h◆A Probabilistic Framework for Hierarchical Goal Recognition8h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language8h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology8h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem8h◆Learning from Natural Language Feedback for Personalized Question Answering8h◆TS-Arena -- A Live Forecast Pre-Registration Platform8h◆Report for NSF Workshop on AI for Electronic Design Automation8h◆Initial results of the Digital Consciousness Model8h◆Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem8h◆Sensory-Aware Sequential Recommendation via Review-Distilled Representations8h◆Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation8h◆When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education8h◆SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention8h◆EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation8h◆Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning8h◆

News/Granite 4.0 Nano: Just how small can you go?

huggingface

PublishedOctober 28, 2025 at 2:59 PM

Granite 4.0 Nano: Just how small can you go?

Source

huggingface.cofull article ↗

Read on huggingface→

Discussion

Source

↗

huggingface

Read original ↗All from huggingface →

No replies yet. Be first.

Source

↗

huggingface

Read original ↗All from huggingface →

Originally published on huggingface ↗

Home Models News