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Good Fire AI has announced the open-source release of Sparse Autoencoders (SAEs) designed for Meta's LLaMA models, specifically LLaMA 3.1 8B and LLaMA 3.3 70B. The new SAEs aim to enhance model efficiency by focusing on the internal representations of LLaMA models, particularly layer 50 of LLaMA 3.3 70B, where they identify 121 sparse activations. This development addresses a gap in available open-weight chat SAEs, which previously hindered some research projects. The release is expected to facilitate further research in the field of artificial intelligence.
Good Fire AI Open-Sources Sparse Autoencoders (SAEs) for Llama 3.1 8B and Llama 3.3 70B https://t.co/mkhCgaA5Dq #AIAdvancements #SparseAutoencoders #LLaMA #MachineLearning #GoodFireAI #ai #news #llm #ml #research #ainews #innovation #artificialintelligence #machinelearning #t… https://t.co/gnB6fCzXlr
Good Fire AI Open-Sources Sparse Autoencoders (SAEs) for Llama 3.1 8B and Llama 3.3 70B Good Fire AI’s SAEs are designed to enhance the efficiency of Meta’s LLaMA models, focusing on two configurations: LLaMA 3.3 70B and LLaMA 3.1 8B. Sparse Autoencoders leverage sparsity…
New open source SAE (Sparse Autoencoder) for model steering, including the first ever SAE for Llama 3.3 70b Aimed at decoding the internal representations of Llama models. With a focus on layer 50 of Llama 3.3-70B, it identifies 121 sparse activations (L0 count), breaking down… https://t.co/9sTTMSOe8D
