Rho-1: Not All Tokens Are What You Need
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2404.07965
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Published
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87
LM Training
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VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
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Reverse Training to Nurse the Reversal Curse
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Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
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Learn Your Reference Model for Real Good Alignment
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Pre-training Small Base LMs with Fewer Tokens
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Poro 34B and the Blessing of Multilinguality
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Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 253 -
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
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Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126 -
Jamba: A Hybrid Transformer-Mamba Language Model
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PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
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LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report
Paper • 2405.00732 • Published • 118 -
Iterative Reasoning Preference Optimization
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What matters when building vision-language models?
Paper • 2405.02246 • Published • 101 -
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Paper • 2405.12130 • Published • 46 -
Your Transformer is Secretly Linear
Paper • 2405.12250 • Published • 149 -
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Paper • 2405.20541 • Published • 21 -
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Paper • 2406.11813 • Published • 30 -
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
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MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
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Data curation via joint example selection further accelerates multimodal learning
Paper • 2406.17711 • Published • 3 -
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Paper • 2409.11564 • Published • 19 -
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Paper • 2409.11402 • Published • 72 -
Instruction Following without Instruction Tuning
Paper • 2409.14254 • Published • 27 -
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Paper • 2409.17146 • Published • 104 -
Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale
Paper • 2409.17115 • Published • 60 -
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
Paper • 2406.14546 • Published • 2 -
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Phi-4 Technical Report
Paper • 2412.08905 • Published • 92