Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.
7.6
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0
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AI & LLM
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Excellent skill covering advanced LLM inference optimization through three complementary techniques (draft model speculative decoding, Medusa, and lookahead decoding). The description is comprehensive and actionable, enabling a CLI agent to understand when and how to invoke each method. Task knowledge is outstanding with complete installation steps, working code examples for all three approaches, mathematical formulations, algorithm explanations, and hyperparameter tuning guidance. Structure is very clear with logical progression from basics to advanced patterns, though the main SKILL.md is fairly dense (appropriately so given complexity). Novelty is strong—these are genuinely complex techniques (tree-based attention, Jacobi iteration, parallel verification) that would consume many tokens for an agent to implement from scratch, and the 1.5-3.6× speedup claims represent meaningful cost reduction. The skill successfully packages cutting-edge research (2024 papers) into practical, deployable code with production considerations (vLLM integration). Minor room for improvement in separating some advanced content to referenced files, but overall this is a high-quality, high-value skill.
zechenzhangAGI
Skill Author
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