Why Did MiniMax M2 End Up as a Full Attention Model?
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leaderboardguidecollaboration
NPHardEval Leaderboard: Unveiling the Reasoning Abilities of Large Language Models through Complexity Classes and Dynamic Updates
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February 2, 2024
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Introducing the Enterprise Scenarios Leaderboard: a Leaderboard for Real World Use Cases
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January 31, 2024
leaderboardguidecollaboration
The Hallucinations Leaderboard, an Open Effort to Measure Hallucinations in Large Language Models
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January 29, 2024
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An Introduction to AI Secure LLM Safety Leaderboard
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January 26, 2024
leaderboardguidecollaboration
A guide to setting up your own Hugging Face leaderboard: an end-to-end example with Vectara's hallucination leaderboard
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January 12, 2024
communityresearchnlp
Open LLM Leaderboard: DROP deep dive
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December 1, 2023
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Object Detection Leaderboard
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September 18, 2023
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What's going on with the Open LLM Leaderboard?
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June 23, 2023
nlpevaluationleaderboard
Can foundation models label data like humans?
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June 12, 2023
Why Did MiniMax M2 End Up as a Full Attention Model?
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50On the Shifting Global Compute Landscape
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45ViDoRe V3: a comprehensive evaluation of retrieval for enterprise use-cases
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28Granite 4.0 Nano: Just how small can you go?
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104The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix
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23What makes good reasoning data
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28Running Large Transformer Models on Mobile and Edge Devices
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10Llasa Goes RL: Training LLaSA with GRPO for Improved Prosody and Expressiveness
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10NVIDIA Isaac GR00T in LeRobot
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19Aligning to What? Rethinking Agent Generalization in MiniMax M2
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21Evaluate Your Own RAG: Why Best Practices Failed Us
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9TorchSim: A new PyTorch-based molecular dynamics engine
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8⚡ Power, Heat, and Intelligence ☁️ - AI Data Centers Explained 🏭
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8Code a simple RAG from scratch
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241Small Language Models (SLM): A Comprehensive Overview
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99How to Build a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac for Healthcare
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17Let's talk about LLM evaluation
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196Ultra-Long Sequence Parallelism: Ulysses + Ring-Attention Technical Principles and Implementation
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11Classement compar:IA : des votes des utilisateurs au classement participatif des modèles
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6LightOnOCR-1B: The Case for End-to-End and Efficient Domain-Specific Vision-Language Models for OCR
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