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config.json

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+{
+  "model_name": "SpecKV-MLP16",
+  "description": "Lightweight acceptance rate predictor for adaptive speculative decoding gamma selection.",
+  "architecture": {
+    "type": "MLPRegressor",
+    "hidden_layers": [16],
+    "activation": "relu",
+    "output": "regression (acceptance rate, 0-1)"
+  },
+  "input_features": [
+    {"name": "mean_draft_entropy", "description": "Mean entropy of draft token distributions (bits)", "dtype": "float32"},
+    {"name": "mean_draft_confidence", "description": "Mean top-1 probability of draft tokens", "dtype": "float32"},
+    {"name": "max_draft_entropy", "description": "Max entropy across draft tokens in the step", "dtype": "float32"},
+    {"name": "min_draft_confidence", "description": "Min top-1 probability across draft tokens in the step", "dtype": "float32"},
+    {"name": "comp_enc", "description": "Compression level encoding: 0=fp16, 1=int8, 2=nf4", "dtype": "int"},
+    {"name": "gamma", "description": "Candidate speculation length to evaluate", "dtype": "int"}
+  ],
+  "output": {
+    "name": "predicted_acceptance_rate",
+    "range": [0.0, 1.0],
+    "description": "Predicted fraction of draft tokens the target model will accept"
+  },
+  "usage": {
+    "gamma_selection": "For each candidate gamma in {2, 4, 6, 8}, predict acceptance rate. Select gamma that maximizes: predicted_ar * gamma + 1",
+    "overhead": "0.34ms per decision (4 forward passes through the MLP)"
+  },
+  "training": {
+    "data": "5112 step-level records from Phase 2 profiling",
+    "model_pair": "Llama-3.2-1B-Instruct (draft) / Llama-3.2-3B-Instruct (target)",
+    "compression_levels": ["fp16", "int8", "nf4"],
+    "gamma_values": [2, 4, 6, 8],
+    "tasks": ["code", "math", "chat", "summarization"],
+    "framework": "scikit-learn 1.x",
+    "random_seed": 42
+  },
+  "performance": {
+    "test_mse": 0.090,
+    "test_correlation": 0.685,
+    "decision_overhead_ms": 0.336,
+    "improvement_over_fixed4": "56.0%",
+    "statistical_significance": "p < 0.001 (paired bootstrap, 10K resamples)"
+  },
+  "files": {
+    "speckv_mlp16.pkl": "Full sklearn model (pickle format)",
+    "speckv_mlp16_weights.npz": "Raw numpy weights for framework-agnostic loading"
+  },
+  "license": "MIT",
+  "paper": "SpecKV: Adaptive Speculative Decoding with Compression-Aware Gamma Selection",
+  "repository": "https://github.com/Amorfati123/SpecKV"
+}

requirements.txt

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+numpy>=1.21.0
+scikit-learn>=1.0.0
+pandas>=1.3.0
+matplotlib>=3.5.0
+seaborn>=0.11.0
+torch>=2.0.0
+transformers>=4.35.0
+bitsandbytes>=0.41.0
+vllm>=0.7.0
+huggingface-hub>=0.19.0
+tqdm>=4.60.0

speckv_mlp16.pkl

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+oid sha256:5af096ef29299d70d98dead0a0bf6a611ef4e8cc359ec1cd31129607265ff2f9
+size 12187

speckv_mlp16_weights.npz

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