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Knowledge Distillation for Model Compression
knowledge-distillation
Orchestra-Research/AI-Research-SKILLs
417
Knowledge Distillation is a crucial technique used to compress large, high-performing Language Models (Teacher) into smaller, more efficient Student models. This allows developers to deploy powerful AI capabilities (like those of GPT-4) on resource-constrained devices while significantly reducing inference costs. The method uses advanced techniques such as soft targets, temperature scaling, and Reverse KL Divergence (MiniLLM) to transfer knowledge effectively.
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Mechanistic Interpretability for Transformers
transformer-lens-interpretability
Orchestra-Research/AI-Research-SKILLs
134
TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.
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Mobile Reverse Engineering
mobile-reverse
sickn33/agentic-awesome-skills
288
Authorized Android/iOS reverse engineering and security testing: APK/IPA analysis, runtime instrumentation (Frida/Objection), SSL-pinning and jailbreak/root-detection bypass, per OWASP MASTG.
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