bundle/MODEL_CARD.md

Native SHARED looped MoE with Fable response SFT

This single 112,237,056-parameter model descends from the native looped MoE trained from random initialization for 7,217 updates / 920,770,653 retained targets, followed by the SHARED MELT arm's 32 updates / 4,087,238 targets. The architecture and tokenizer are unchanged: six physical blocks, width 384, three loops, 24 routed plus two shared experts, four routed plus both shared experts active per token, and 1,023-token context.

The additional SFT stage completed 3 epochs / 21 updates, processing 163,953 input-token exposures and 147,597 supervised target exposures. It starts a fresh FP32 Adam optimizer from the saved SHARED model. Repeated epochs reuse the same 68 unique training examples in 19 task families. Validation has 5 examples in 3 families; test has 12 in 3 families. These are small, task-grouped diagnostics, not a broad reasoning or coding benchmark.

Training uses public uploader-labelled Fable visible explanatory responses from TheFusionCube/Fable-5-CoT-Traces, with decoys, ambiguous model metadata, truncation and unsuitable tasks excluded. The declared chat conversion is not added as more data. Original answers are preserved verbatim. This is not authenticated private-CoT distillation or proof of Fable-level capabilities. Teacher responses supply SFT labels; no online teacher or KL loss is used.

Assistant-target CE: validation 5.669114 before and 5.327462 after SFT; test 5.504149. These scores do not establish general reasoning improvement. Source receipts, actual training summary and checkpoint validation remain in provenance/.

Run from this folder with the dependencies in requirements.txt:

python run.py --prompt "Explain why regex backreferences are not regular." --max-new-tokens 64

The CLI wraps the raw question in the exact ### Context / ### Response training format. The default is CPU FP32 reference inference. Optional --device cuda uses the unchanged BF16 parameter-view engine with FP32 masters, buffers and routing. Formatted prompt plus continuation must fit 1,023 tokens. Use a fresh process.

Only this SHARED model's weights are included. The unchanged source/runtime and tokenizer are bundled with file hashes. Optimizer/checkpoint payloads, original training data and cloud credentials are not needed for inference; the resumable checkpoint remains in the verified local retrieval. Historical paths in passive provenance are not runtime dependencies. Source license: source/LICENSE.

This public distribution adds comment-only change notices to six modified upstream files and an Apache-2.0 notice to the original MELT-memory extension. Runtime integrity constants and distribution receipts are rebound to those files; all verification gates remain active. Model weights, tokenizer bytes and inference algorithms are unchanged. Original training manifests and source-integrity receipts are preserved outside this bundle under release/. Model weights and project-added runtime are distributed under Apache-2.0, with bundled third-party notices retained.