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.
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
"""Sweep alpha (and a couple of tamed sine schedules) to find the sweet spot
|
|
between the pure-probability baseline (generic but coherent) and the
|
|
pure-entropy extreme (degenerates into blank-template exploitation).
|
|
"""
|
|
from entropy_sampler import EntropySampler, sine_alpha_schedule
|
|
|
|
PROMPT = "The old lighthouse keeper had seen many storms, but none like"
|
|
MAX_NEW_TOKENS = 60
|
|
|
|
FIXED_ALPHAS = [-1.0, -0.5, 0.0, 0.3, 0.5, 0.7, 1.0]
|
|
SINE_CONFIGS = [
|
|
{"period_tokens": 10, "amplitude": 0.4},
|
|
{"period_tokens": 16, "amplitude": 0.6},
|
|
]
|
|
|
|
|
|
def summarize(text, diagnostics_log):
|
|
ps = [d["candidates"][d["chosen"]]["p"] for d in diagnostics_log]
|
|
hs = [d["candidates"][d["chosen"]]["h_hat"] for d in diagnostics_log]
|
|
degenerate = sum(1 for d in diagnostics_log if "_" in d["candidates"][d["chosen"]]["token"])
|
|
return {
|
|
"mean_p": sum(ps) / len(ps),
|
|
"mean_h": sum(hs) / len(hs),
|
|
"degenerate_tokens": degenerate,
|
|
}
|
|
|
|
|
|
def run_labeled(sampler, label, alpha):
|
|
log = []
|
|
orig_step = sampler.step
|
|
|
|
def step_and_log(base_logits, a, confidence_threshold=None):
|
|
token, next_logits, diag = orig_step(base_logits, a, confidence_threshold)
|
|
log.append(diag)
|
|
return token, next_logits, diag
|
|
|
|
sampler.step = step_and_log
|
|
text = sampler.generate(PROMPT, max_new_tokens=MAX_NEW_TOKENS, alpha=alpha, verbose=False)
|
|
sampler.step = orig_step
|
|
|
|
stats = summarize(text, log)
|
|
print(f"=== {label} ===")
|
|
print(text)
|
|
print(
|
|
f"[mean p={stats['mean_p']:.3f} mean H_hat={stats['mean_h']:.3f} "
|
|
f"degenerate(_)={stats['degenerate_tokens']}/{MAX_NEW_TOKENS}]\n"
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sampler = EntropySampler(top_k=12, top_n_future=20, n_ctx=4096)
|
|
try:
|
|
for a in FIXED_ALPHAS:
|
|
run_labeled(sampler, f"alpha = {a:+.1f}", a)
|
|
|
|
for cfg in SINE_CONFIGS:
|
|
schedule = sine_alpha_schedule(**cfg)
|
|
label = f"sine period={cfg['period_tokens']} amp={cfg['amplitude']}"
|
|
run_labeled(sampler, label, schedule)
|
|
finally:
|
|
sampler.close()
|