ModelCypher

See what a model is doing below token level.

ModelCypher is a measurement and observability workbench for open-source model builders. It gives humans and frontier AI a clear way to inspect geometry, entropy, curvature, chain structure, and adapter-induced changes through workflow-first CLI surfaces instead of ad hoc activation scripts.

Current evidence state (reviewed 2026-07-11): mc analyze is the clearest public entrypoint for prompt capture, prompt-family studies, and checkpoint or adapter comparison. mc train run remains shipped and geometry-derived, but the repo has not yet closed the promotable same-model same-data same-eval benchmark needed to claim "better than standard practice." The retained 350M R2 failure is closed as a data-format mechanism; it is not evidence of a general inference-geometry collapse. See RESEARCH-ROADMAP.md.

The Measurement Thesis

A forward pass is a deterministic geometric map, and mc analyze is the shipped workbench for measuring that map below token level. The downstream training program derives all 15 traditional controls from model geometry, precision, or measured data where the current derivation is wired; the runtime status table below names the places where the shipped default still uses a calibrated AdamW value or an unwired formula. See AGENTS.md for the full derivation philosophy.

Start By Measuring

poetry run mc analyze capture --model /path/to/model --prompt "Explain geodesics."
poetry run mc analyze family --model /path/to/model --manifest data/probes/prompt_family_minimal_pairs.json
poetry run mc analyze compare --left-model /path/to/base --right-model /path/to/base --right-adapter /path/to/adapter --manifest data/probes/prompt_family_minimal_pairs.json
poetry run mc analyze report --bundle /path/to/bundle
poetry run mc analyze report --bundle results/measurement_atlas
poetry run mc analyze report --bundle results/measurement_atlas/<run_id>
poetry run mc analyze report --bundle results/pipeline_validation
poetry run python scripts/run_measurement_atlas.py --model /path/to/model --manifest data/probes/measurement_atlas_casing.json --manifest data/probes/measurement_atlas_profanity_tone.json --manifest data/probes/measurement_atlas_grounded_hallucination.json --output-root results/measurement_atlas

These commands emit an observation bundle under results/analysis/<timestamp-slug>/ by default:

• manifest.json
• summary.json
• REPORT.md
• variants.jsonl
• layer_metrics.jsonl
• comparisons.jsonl

The prompt-family interface is explicit in phase 1. Each row includes: case_id, variant_id, text, optional tags, and optional comparison_to.

For research-only generation tracing, the measurement atlas runner writes a family artifact under results/measurement_atlas/<run_id>/ with:

• run_manifest.json
• summary.json
• REPORT.md
• ledger.tsv
• variants.jsonl
• sequence_metrics.jsonl
• step_metrics.jsonl
• space_step_metrics.jsonl
• comparisons.jsonl
• onset_events.jsonl

The retained replay-alignment closure for the shipped 350M atlas pack has a tracked report copy at docs/research/reports/measurement_atlas/REPORT.md. Current observed atlas surfaces are replay={hidden, embedding} and live={hidden}; run_manifest.json now records requested vs observed surfaces separately so the bundle does not overclaim unsupported replay space coverage. mc analyze report --bundle ... can now read both the standard mc analyze bundles, the retained atlas family root, individual atlas artifact directories, and retained pipeline-validation families. Atlas generation itself remains research-only in scripts/run_measurement_atlas.py.

Train When You Want To Act On The Measurements

poetry run mc train run --model /path/to/model --data /path/to/dataset --output /path/to/adapter

The shipped path derives the adapter surface, rank, batch sizing, scale budget, and stopping certificate from measured geometry. Its default optimizer is still the calibrated AdamW/cosine path called out in the status table; MASS remains available on research modes until the closure benchmark earns promotion.

Need extra instrumentation? Use flags on the same command path, such as --benchmark, --topo-monitor, --dim-monitor, or --entropy-reg.

15-Control Runtime Status

#	Training control	Runtime status	Current code truth	Code source
1	Learning rate	derived+research-mode-only	default: calibrated AdamW 2e-4 cosine; MASS on research modes	_mlx_training_adapter_train_mixin.py + mass_step_size.py
2	Adam epsilon	removed	no Adam epsilon derivation is claimed; compute_geometric_epsilon is ScaledGD regularization in weight-spectrum units	geometric_optimizer.py
3	Momentum	derived+research-mode-only	default: AdamW betas 0.9/0.999; Fisher/MASS moments only in research modes	_mlx_training_adapter_train_mixin.py + diagonal_fisher_preconditioner.py
4	Weight decay	formula-exists-unwired	condition-ratio formula exists; shipped mc train run default passes weight_decay=0.0	geometric_optimizer.py, dataset_training_service.py
5	Gradient clipping	derived+research-mode-only	MASS bounds updates in research modes; canonical AdamW path has no geometric clipper	mass_step_size.py
6	Warmup	derived+research-mode-only	canonical path uses calibrated cosine from step 0; research modes use MASS ceilings	dataset_training_service.py, mass_step_size.py
7	LR schedule	derived+research-mode-only	default: cosine over 6 data-epochs; no-schedule MASS only in research modes	_mlx_training_adapter_train_mixin.py, mass_step_size.py
8	Batch size	derived+shipped-default	derived from gradient-noise scale, then reduced only for memory-safe micro-batching	DatasetTrainingService.train_from_dataset
9	Early stopping	derived+shipped-default	geometric certificate and measured validation-loss windows are wired into training	geometric_early_stopping.py, _mlx_training_adapter_train_mixin.py
10	LoRA scale	derived+shipped-default	adapter scale budget and saturation telemetry are enforced during training	geometric_lora.py, _mlx_training_adapter_train_mixin.py
11	LoRA rank	derived+shipped-default	per-module ranks derive from tail dimensions and rank-capacity samples	geometric_lora.py, DatasetTrainingService.build_training_plan
12	Target modules	derived+shipped-default	target surface derives from layer spectral geometry	select_target_modules, DatasetTrainingService.build_training_plan
13	Dropout	removed	derived dropout formula was deleted because no shipped training adapter consumed it	compute_geometric_dropout runtime references=0
14	Weight init	removed	default init is PiSSA; the unshipped spectral-normalized helper was deleted	spectral_normalized_lora_init runtime references=0
15	Residual scaling	removed	standalone residual-scaling formula was deleted because no shipped path consumed it	residual_scaling.py; runtime references=0

Research program and formulas: 15-Hyperparameter Research Program and Geometric Hyperparameter Rosetta Stone.

Quick Start

git clone https://github.com/Ethyros-AI/ModelCypher.git
cd ModelCypher
poetry install # Primary macOS/MLX path; Python 3.11+
poetry run mc --help # Verify CLI install

Backend protocol and CPU-fallback development on Linux use optional extras:

poetry install --extras cuda # NVIDIA/CUDA backend
poetry install --extras jax # JAX backend; CPU is supported for CI/tests

The MLX path is the only end-to-end product surface today. CUDA and JAX share the geometry protocol, but model-loading and expert-CLI parity remain partial; see BACKEND-COMPARISON.md.

# Inspect a model's per-layer geometry
poetry run mc model info /path/to/model
# Build an observation bundle from one prompt
poetry run mc analyze capture --model /path/to/model --prompt "Explain geodesics."# Build an observation bundle from a prompt family
poetry run mc analyze family --model /path/to/model --manifest data/probes/prompt_family_minimal_pairs.json
# Re-read an existing bundle and print the shared report view
poetry run mc analyze report --bundle /path/to/bundle
# Re-read the retained measurement-atlas family root or one atlas run
poetry run mc analyze report --bundle results/measurement_atlas
poetry run mc analyze report --bundle results/measurement_atlas/<run_id># Re-read the retained pipeline-validation family
poetry run mc analyze report --bundle results/pipeline_validation
# Layer-wise intrinsic dimension profile
poetry run mc analyze dimension-profile --model /path/to/model --samples 50
# LoRA adapter spectral analysis
poetry run mc analyze lora-svd /path/to/adapter --base /path/to/model
# Train a LoRA adapter after inspecting the model
poetry run mc train run --model /path/to/model --data /path/to/data.jsonl --output /path/to/adapter

Evidence Snapshot

Question	What retained artifacts show	Tag
Does the measurement layer exist as a real workflow?	Yes. mc analyze capture, mc analyze family, and mc analyze compare now emit observation bundles with machine-readable artifacts plus a short report	[EMPIRICAL]
Canonical training path exists	mc train run is the shipped geometry-derived runtime path guarded by pipeline_gate_v1	[EMPIRICAL]
Does the retained 350M validation bundle close preservation?	No. The tracked pipeline validation report records structural pass 5/5, inference pass 3/5, all_pass = false	[EMPIRICAL]
Does the retained evidence close "better than standard practice"?	No. results/nblora_vs_standard/ is retained as summary_only, and the retained single-seed LFM2-350M summary does not support superiority of nb_lora over the kept baselines	[EMPIRICAL]
Does the 8B bundle close efficacy?	No. Despite the historical family name, the tracked G5 report records n_seeds=1 and failed cka_ok and degenerate_ok gates	[EMPIRICAL: 1 seed]
Is quantization promising?	Yes as a measurement surface: the tracked quantization frontier report records PPL and degeneration improvement on all 3 retained models, but the frontier law is still open	[EMPIRICAL]

Falsified Training Claims

Hypothesis	Result	Tag
REINFORCE on 350M	Gradient orthogonal to CE; degradation monotonic with steps	[DISPROVEN]
SFT on reasoning traces	Format memorization: PPL drops, inference degrades	[DISPROVEN]
Pullback metric P = MM^T	P ≈ I throughout training (median deviation 0.001)	[DISPROVEN]
Stable rank predicts adapter rank	Pearson r = -0.51 vs tail_dims; measures different property	[DISPROVEN]
Constrained training (paired)	Constraints monotonically hurt	[DISPROVEN]

We publish failures because intellectual honesty is not optional. Current training blockers and exit criteria live in MISSION.md and RESEARCH-ROADMAP.md.

Measurement Toolkit

mc analyze is organized around five canonical workflows:

• capture: measure one prompt or prompt file
• family: run explicit minimal-pair or perturbation studies
• compare: compare two targets on the same prompt family
• report: read an existing bundle and render the shared high-signal view
• probe: targeted probe and red-team workflows

Expert instruments remain directly callable when you want the underlying measurements without the bundle wrapper:

• geometry and trajectory: reasoning-flow, geodesic-profile, entropy-trajectory, chain-profile, dimension-profile, jacobian-trace
• probe and monitoring: calibrate-safety, jailbreak-test, probe-redteam, probe-behavioral, circuit-breaker, entropy-pattern
• diagnostics and adapter analysis: lora-svd, benchmark, knowledge-type, curriculum-profile, crm-build, crm-compare

Full reference: CLI-REFERENCE.md

Architecture

Hexagonal (ports-and-adapters) with strict domain boundaries:

• Core domain (core/domain/) — pure geometry and math, zero framework imports
• Use cases (core/use_cases/) — orchestration, cannot import from adapters
• Adapters (adapters/) — HuggingFace Hub, filesystem, model loading
• Backends — MLX (primary, Apple Silicon), CUDA, JAX behind a protocol interface

Core geometric computations use the framework-agnostic Backend protocol, and backend selection is automatic when an installed backend is available. End-to-end loaders and expert CLI workflows remain MLX-first.

Documentation

Document	What It Covers
Start Here	Installation, first observation bundle, and downstream training
Observation Bundles	PromptFamilyManifest, ObservationBundle, and ready-to-run perturbation manifests
Geometry Guide	Interpreting CKA, intrinsic dimension, curvature, and entropy measurements
Training Guide	Downstream adapter workflows and dataset preparation
CLI Reference	Workflow-first mc analyze plus expert command examples
Mission	Measurement-first mission and derived training standards
15-Hyperparameter Research Program	Per-control runtime and evidence status for the derivation program
Glossary	60+ term definitions
Architecture	Hexagonal architecture and domain boundaries
Bibliography	All cited papers with local reference PDFs

Research Papers

Paper	Status	Thesis
The Shape of Knowledge	[EMPIRICAL]	Knowledge has measurable geometric structure; inference is trajectory
Invariant Semantic Structure	[PROVEN: fitted probes only]; [CONJECTURAL] cross-model	A closed-form fit gives aligned CKA = 1 on its training probes; held-out and cross-model invariance remain open
Entropy Safety Signal	[CONJECTURAL]	Behavioral drift detection via entropy differentials
Cross-Architecture Transfer	[CONJECTURAL]	Knowledge transfer between model families via Procrustes alignment
ModelCypher Toolkit	[EMPIRICAL]	Implementation methodology and CLI design
The Semantic Highway	[EXPLORATORY]	Historical TwoNN profile from an unretained run; published-profile replication is pending under WS4.2

Test Suite

7,758 collected tests. This count is generated from pytest --collect-only; refresh it with poetry run python scripts/update_test_count.py --write.

Includes Hypothesis property-based tests for numerical invariants (CKA symmetry, spectral bounds, null-space orthogonality).

poetry install --extras jax # One-time local CI dependencies
./scripts/run_local_ci.sh # Full local CPU verification
poetry run pytest # Standard development run
HYPOTHESIS_PROFILE=full poetry run pytest # Full property-based testing

Platform Support

Platform	Backend	Status
macOS Apple Silicon	MLX	Primary end-to-end product path
Linux + NVIDIA GPU	CUDA (PyTorch)	Backend protocol available; loader/CLI parity partial
Linux + TPU/GPU	JAX	Backend protocol available; loader/CLI parity partial
Linux CPU (local CI/tests)	JAX	Engineering fallback, not an accelerated product path

Citation

@software{kempf2026modelcypher,
author = {Kempf, Jason},
title = {ModelCypher: MLX-Native Measurement Workbench for LLMs},
year = {2026},
url = {https://github.com/Ethyros-AI/ModelCypher},
license = {AGPL-3.0}
}

License

AGPL-3.0. See LICENSE.