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Model: ConeML/coneml-810m-alpha
Source: Original Platform

eval/EVALUATION_METHODOLOGY.md

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+# ConeML 810M — evaluation methodology and variant lineage (public record)
+
+Two released variants share one from-scratch 810M base checkpoint and
+differ only in supervised fine-tuning; both were evaluated with the same
+bf16 instruments.
+
+- `coneml-810m-alpha` — function-writing / mixed-arithmetic profile
+- `coneml-810m-alpha-arithmetic` — carry/borrow arithmetic variant
+
+## Methodology
+
+- **ConeML internal certification**: large-n (300–500 per task family)
+  batteries of unseen instances in the trained prompt formats, greedy
+  decoding, repetition
+  penalty 1.15. Code tasks are scored by EXECUTING the generated function
+  against held-out tests; SQL by executing against a live sqlite database;
+  scripts by captured stdout. No pattern-match scoring for anything
+  execution can decide.
+- **Behavior probes**: held-out designated-refusal prompts plus in-scope
+  contrast pairs in matching wording; one separately rerun, scripted
+  eight-turn conversation; and an everyday-reasoning screen with manual
+  adjudication. Aggregate results and deterministic representative
+  ConeML-owned rows are public; complete rows are retained privately.
+- **Public benchmarks**: GSM8K (first 200, zero-shot, strict final-number
+  match) and HumanEval (pass@1, tests executed), identical harness across
+  variants and the pretrained base. Aggregate results are public;
+  benchmark rows are not redistributed.
+- **Quantized variants are evaluated separately**: each shipped GGUF carries
+  its own result table. The GGUF and bf16 harnesses differ in runtime and
+  stop handling, so score gaps are not attributed solely to quantization.
+
+## Why two variants
+
+Under fixed architecture and base checkpoint, modest differences in
+fine-tuning composition produced large, cross-surface capability
+tradeoffs (e.g., one build scores 96.8% on held-out carry addition while
+the other scores 79.0% on held-out two-step word problems and 69.0% on
+held-out function writing). Rather than presenting one checkpoint as
+dominant, each variant ships with its measured envelope and a
+recommendation for which profile
+fits which pipeline. A checkpoint's profile is measured, never inferred.
+
+## Shared limitations
+
+Function-writing is evaluated for single Python functions only — neither
+variant is a general code model (complete programs, executable SQL, and
+bash scored near zero for both and are stated out of scope). Both variants
+refused every designated refusal prompt, including real factual questions;
+this targeted behavior is not evidence of general epistemic calibration.
+Multi-step GSM-class reasoning is weak in every variant tested this cycle.
+
+## Evidence disclosure
+
+The public folders contain aggregate result tables, deterministic
+representative examples from ConeML-owned instruments, and SHA-256
+commitments to the complete row-level evidence. Full proprietary probe
+rows are retained by ConeML rather than distributed. This preserves the
+measurement instruments while allowing later disclosure to be checked
+against the evidence frozen for this release.