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reacted to tegridydev's post with 🚀 about 11 hours ago
So, what is #MechanisticInterpretability 🤔 Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours Instead of treating a model as a monolithic function, we can: 1. Trace how input tokens propagate through attention heads & MLP layers 2. Identify localized “circuit motifs” 3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure. Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to 1. Trust & Reliability 2. Safety & Alignment 3. Better Debugging / Development Insights https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x
reacted to tegridydev's post with đź‘€ about 11 hours ago
So, what is #MechanisticInterpretability 🤔 Mechanistic Interpretability (MI) is the discipline of opening the black box of large language models (and other neural networks) to understand the underlying circuits, features and/or mechanisms that give rise to specific behaviours Instead of treating a model as a monolithic function, we can: 1. Trace how input tokens propagate through attention heads & MLP layers 2. Identify localized “circuit motifs” 3. Develop methods to systematically break down or “edit” these circuits to confirm we understand the causal structure. Mechanistic Interpretability aims to yield human-understandable explanations of how advanced models represent and manipulate concepts which hopefully leads to 1. Trust & Reliability 2. Safety & Alignment 3. Better Debugging / Development Insights https://bsky.app/profile/mechanistics.bsky.social/post/3lgvvv72uls2x
liked a model about 11 hours ago
bartowski/Virtuoso-Lite-GGUF
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GGUF Llama-3.2-Instruct-OQ8_0-F32.EF32.IQ4_K-Q8_0 IQuants
Custom GGUF quants of Meta’s Llama-3.2-Instruct's finetunes, where the Output Tensors are quantized to Q8_0 or F32 and the Embeddings are kept @F32
GGUF LLaMa-3.1-8B-Instruct-Based OQ8_0.EF32 IQuants
Custom GGUF quants of Llama-3.1-8B-Instruct fine-tunes, where the Output Tensors are quantized to Q8_0 while the Embeddings are kept at F32. 🧠🔥🚀

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