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genpark-speculative-decoding-verifier-skill

by Alpha-Park

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      license
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      quality
      B
      maintenance
      Enables zero-dependency INT8 symmetric quantization of LLM weights and activations on a per-tensor or per-channel basis, reporting SNR and MSE reconstruction telemetry along with TTFT/TPOT latency and jitter metrics. Also exposes edge inference primitives such as PagedAttention block allocation, radix prefix caching, and speculative decoding verification through the Model Context Protocol.
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables inference engines to match longest common prompt token prefixes via a radix trie, so cached KV-cache blocks are reused instead of re-prefilled. This eliminates redundant prefill computation and lowers time-to-first-token, alongside paged attention allocation, speculative decoding verification, INT8 quantization, and latency telemetry.
      7
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP clients to manage LLM inference memory through virtual paged-attention KV-cache block mapping, non-contiguous physical page allocation, zero-copy fragmentation tracking, radix-trie prefix caching, INT8 quantized compute, and speculative decoding verification. It also exposes prefill and decode latency telemetry so edge deployments can be benchmarked and tuned without external dependencies.
      7
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables profiling and optimizing edge and on-device LLM inference by measuring TTFT, TPOT, tokens/second throughput, and P50/P90/P99 jitter while providing standard-library primitives for quantization, paged attention, speculative decoding, and prefix caching.
      7
      MIT