7962887057
- bench.py now generates every README table row (ANSI CI, pip/tqdm, pytest, pretty JSON added) and refuses to print chars/4 estimates — reproduced numbers can no longer silently diverge from the claims - eval.py: the receipt ratios can't give — 8 seeded failure logs with planted root causes and red herrings, asked raw vs compressed, graded by deterministic keyword check. First run (haiku 4.5): raw 6/8, compressed 8/8 at 3.1x fewer tokens; both raw misses were lost-in-the-middle - README: eval section, related-work section (rtk, Headroom, Drain3, LLMLingua-2, arXiv 2604.13066), prompt-cache claim sharpened to the rebuilt-prompt scenario, agent-harness integration (--run, --hook), re-measured receipts table Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
25 lines
634 B
TOML
25 lines
634 B
TOML
[build-system]
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requires = ["setuptools>=61"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "lessismore"
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version = "0.2.0"
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description = "Squeeze text before it hits an LLM — deterministic prompt compression, measured in real tokens"
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readme = "README.md"
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license = { text = "MIT" }
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requires-python = ">=3.8"
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authors = [{ name = "John King (monsterrobotsoft)" }]
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keywords = ["llm", "prompt", "compression", "tokens", "context"]
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[project.optional-dependencies]
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tokens = ["tiktoken"]
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ml = ["llmlingua"]
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[project.scripts]
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lessismore = "lessismore:main"
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lm = "lessismore:main"
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[tool.setuptools]
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py-modules = ["lessismore"]
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