How to train your own Large Language Models https://
bit.ly/3WnauuY #AI #MachineLearning #DeepLearning #LLMs #DataScience
MACHINE LEARNING
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How to Train Your Own Large Language Models
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Video-LLaMA: Instruction-tuned Audio-Visual Language Model
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-LLaMA: An Instruction-tuned Audio-Visual Language Model for Understanding Hang Zhang, Xin Li, Lidong Bing: https://
arxiv.org/abs/2306.02858 #DeepLearning #LargeLanguageModels #LLM -
Free Carnegie Mellon Neural Networks NLP Online Course
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FREE Course online, from Carnegie Mellon University. Learn Neural Networks for Natural Language Processing. Here is the entire playlist: https://
youtube.com/playlist?list=
PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV
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Does One AI Model Rule Them All? Expert Analysis
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Along the lines of @maithra_raghu
’s “does one model rule them all? https://
maithraraghu.com/blog/2023/does
-one-model-rule-them-all/
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Gorilla: Open Source Base Model Outperforms Larger AI Models
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The Gorilla project is a great example of how open source base model + fine tuning through self-instruction instruction + document retrieval can outperform a larger model for an economically-useful task.
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REMEDIS: Robust Medical Imaging with Representation Learning
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This is neat: “a representation-learning strategy for ML models applied to medical-imaging tasks that mitigates such ‘out of distribution’ performance problem and that improves model robustness and training efficiency.” “REMEDIS (for ‘Robust and Efficient Medical Imaging with
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13B Model Learns to Imitate GPT-4 Reasoning Process
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10/ Imitating Reasoning Process of LLMs – develops a 13B model that learns to imitate the reasoning process of large foundational models like GPT-4; it leverages large-scale & diverse imitation data and surpasses Vicuna-13B.
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Hierarchical Vision Transformer Improves Efficiency Training
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8/ Hierarchical Vision Transformer – pretrains vision transformers with a visual pretext task (MAE), while removing unnecessary components; enables a simple hierarchical vision transformer that’s more accurate and faster at inference and during training.
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LEACE Method Erases Gender Bias from BERT Embeddings
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6/ Concept Scrubbing in LLM – presents a method called LEAst-squares Concept Erasure (LEACE) to erase target concept information from every layer in a neural network; it’s used for reducing gender bias in BERT embeddings.
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Fine-Grained RLHF Improves LM Training and Safety
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7/ Fine-Grained RLHF – trains LMs with fine-grained human feedback; instead of using overall preference, more explicit feedback is provided at the segment level which helps to improve efficacy on long-form question answering and reduces toxicity.