Extreme Overtraining in Tiny Language Models
Banaxi-Tech
• • 4NEW Articles from Team or organizations will get promoted to the main section.
Meet North Micro Vision: A 2.4B Native-Resolution Vision-Language Model
CohereLabs
• • 21LoRA Hotswapping in Production: Serving Millions of Personalized Flux Avatars
superapp
• 🔬 J'ai rendu mon monde assez petit pour tout calculer exactement. Il n'a attrapé aucune de mes huit erreurs 🇫🇷
RDTvlokip
• • 1🔬 I made my world small enough to compute everything exactly. It caught none of my eight mistakes 🇫🇷
RDTvlokip
• • 1Constrain, don't describe: what 16 high-performing Seedance 2.0 prompts have in common
codeugar
• llm-diff: Behavioral Regression Testing for Large Language Models
Siddh07ETH
• • 1How to compare indirect prompt injection detectors
mihailgribov
• DAVIDAU DELETED MY POST AND BANNED ME FOR ASKING ABOUT BENCHMARKS — HERE IS THE FULL ANALYSIS (9B + 27B + 390 MODELS)
DedeProGames
• • 9Beyond the Aggregate Score: Per-Country Domain Shift in the GWHD Wheat Head Detection Model Zoo
dronefreak
• • 7Post-training NVIDIA Nemotron 3.5 Lightning for enterprise domains
lujangusface
• EAGLE-3 speculative decoding for NVIDIA Nemotron 3.5 Lightning
lujangusface
• Exploring NVIDIA Nemotron 3.5 Lightning: Making it see with little resources
tngtech
• • 7Multi-Token Prediction with NVIDIA Nemotron 3.5 Lightning on HP ZGX Nano
curtburk
• The Map: The Whole of PyTorch on One Page
akhalilli
• • 1RAG vs. Tool-Calling Agents: Which Approach Works Better for Enterprise AI?
prismberry-technologies
• Your SOC Agent's Final Answer Is Lying to You
alirezaaminzadeh
• BrowserAct and CapSolver Integration for Agent CAPTCHA Handling
CapSolverAI
• Luth-2: Pushing the French Capabilities of SLMs with MOPD
MaxLSB
• • 4SraVaani: How Vision Helps Hearing
ARTPARK-IISc
• • 2