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🎢 SGD (Stochastic Gradient Descent) — Descendre la pente une étape aléatoire à la fois ! ⚡🎯
RDTvlokip
• • 1
🎢 SGD (Stochastic Gradient Descent) — Descending the slope one random step at a time! ⚡🎯
RDTvlokip
• • 1
Apple MLX for AI/Large Language Models—Day One
MiniGuard-v0.1: Prem's Guardrail Model Redefining the Pareto Frontier
prem-research
• • 22
Reproducing and Validating Distributed Muon 🐢✨: A Practical Verification of Communication Efficiency Claims
bird-of-paradise
• • 2
Why You Should Care About Partial Differential Equations (PDEs)
hugging-science
• • 48
AI Can Now Autonomously Extend Itself. Read That Again.
CreamyClouds
• Strand-Rust-Coder-v1: Rust Coding Model Fine-Tuned on Peer-Ranked Synthetic Data
Fortytwo-Network
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Hosting 3D Medical Image Datasets on Hugging Face: A Deep Dive into MedVision
YongchengYAO
• AutoBench Goes to the Farm with Evja: The First Ever Agronomy Benchmark. The Best Farmer LLM? OpenAI, but Mistral...
PeterKruger
• • 3
Face Swap Image
tonyassi
• • 1
Apriel-1.6-15b-Thinker: Cost-efficient Frontier Multimodal Performance
ServiceNow-AI
• • 84
Supervised Fine-Tuning (SFT) for VLMs on Medical Image Data
YongchengYAO
• 20x Faster TRL Fine-tuning with RapidFire AI
rapidfire-ai-inc
• • 1
I Built a RAG System That Listens to Live BBC News and Answers Questions About "What Happened 10 Minutes Ago"
RakshitAralimatti
• • 15
Auto-Optimize Pydantic Models for Structured Information Extraction: A Complete Guide to DSPydantic
davidberenstein1957
• • 3
Muon vs MuonClip vs Muon+AdamW for Fine-Tuning
KingNish
• • 15
How We Use Claude Code Skills to Run 1,000+ ML Experiments a Day
sionic-ai
• • 60
Risk assessment for LLMs and AI agents: OWASP, MITRE Atlas, and NIST AI RMF explained
davidberenstein1957
• • 1