Joey Grasty
e25ee046a3
llama.cpp-based implementation of the countbayesie Future-Entropy Sampler, built via a direct ctypes binding to libllama.so. Includes a one-shot CLI (entropy_cli.py), an OpenAI-compatible server (entropy_server.py), and the tuning/benchmark scripts used to derive the alpha/confidence-threshold defaults documented in the README. Model path and llama.cpp library path are now read from environment variables (ENTROPY_SAMPLER_MODEL, LLAMA_CPP_LIB) instead of being hardcoded, and gguf-py is pulled from PyPI instead of a local llama.cpp checkout, so this runs on any machine with a compatible llama.cpp build and model.
28 lines
1.2 KiB
Python
28 lines
1.2 KiB
Python
"""Offset-sine sweep: dip the wave deeper into safe/coherent territory than
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it peaks into entropy-chasing territory, to see if it keeps the occasional
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surprise from tune_alpha.py's best run (period=16 amp=0.6) without ever
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grazing the +0.3-and-above coherence cliff found there.
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"""
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from entropy_sampler import EntropySampler, sine_alpha_schedule
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from tune_alpha import PROMPT, MAX_NEW_TOKENS, run_labeled
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CONFIGS = [
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{"period_tokens": 16, "amplitude": 0.6, "offset": -0.2}, # peak +0.4 / trough -0.8
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{"period_tokens": 16, "amplitude": 0.7, "offset": -0.3}, # peak +0.4 / trough -1.0
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{"period_tokens": 12, "amplitude": 0.8, "offset": -0.2}, # shorter period, same bias
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{"period_tokens": 16, "amplitude": 0.6, "offset": 0.0}, # unbiased baseline for comparison
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]
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if __name__ == "__main__":
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sampler = EntropySampler(top_k=12, top_n_future=20, n_ctx=4096)
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try:
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for cfg in CONFIGS:
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schedule = sine_alpha_schedule(**cfg)
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label = (
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f"sine period={cfg['period_tokens']} amp={cfg['amplitude']} "
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f"offset={cfg['offset']}"
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)
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run_labeled(sampler, label, schedule)
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finally:
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sampler.close()
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