f0909172434/Charlie-Alpha-4B-MLX-4bit

Charlie alpha model card

Model details

Charlie alpha v0.3 is a statistical procedure-selection derivative of Qwen3.5-4B. It supports English, Traditional Chinese, and Simplified Chinese. The canonical artifact uses one 4-bit MLX base and a LoRA adapter with 2,129,920 parameters across eight modules in the final four layers.

The runtime has two routes. stats enables the adapter for statistical questions or attached data; base bypasses LoRA for other requests. The compatibility name adapter maps to stats. Each request generates one answer. No teacher or judge model runs during inference.

Release classification: Experimental v0.3.0. The model improved its sealed DGP procedure selection metrics, but the external P-Bench and StatQA gates failed. It also regressed on the held-out clarification suite.

Intended use

The model supports research on statistical procedure selection under explicit assumptions. The local agent can inspect CSV, TSV, JSON, or Parquet files and call fixed Python/R procedures. It returns a structured plan with these fields:

status, estimand, sampling_unit, study_design, outcome_type, dependence, missingness, method_id, uncertainty, diagnostics, tool, and variables.

The current release is unsuitable for autonomous analysis. A user should state the estimand, sampling unit, assignment mechanism, dependence structure, and missingness assumptions, then have a statistician check the plan and result. Medical, financial, and policy decisions require qualified review.

Training method

DGP-Regret casts procedure selection as a bounded decision problem. The generator covers 12 DGP families and 28 procedures. Each item presents at most six candidates, including a robust alternative and needs_clarification where appropriate. The simulator estimates Type I error, coverage, bias, RMSE, power, calibration, and cost with common random numbers. It starts at 128 replications and escalates to 256 or 512 when the method ranking remains uncertain.

The simulator converts normalized regret to a listwise target:

[ q_j=\frac{\exp(-r_j/0.15)}{\sum_k\exp(-r_k/0.15)}. ]

The primary split contains 240 training semantic groups, 30 validation groups, 60 dev DGPs, and 120 sealed final DGPs. Splitting occurs before language rendering. Each training group has two English boundary views plus one Traditional Chinese and one Simplified Chinese view. Loss weights of 1.4, 1.4, 0.6, and 0.6 yield a 70%/15%/15% language gradient ratio.

The assistant objective assigns 45% to method soft-label loss, 35% to plan/tool tokens, and 20% to the report. User content and tool output receive zero loss. A local Qwen3.5-9B teacher edited only explanations at temperature 0. It could not choose methods or alter simulator fields. Of 120 target explanations, 76 passed validation and 44 used the deterministic template.

Three equal-compute pilots used 160 microsteps, rank 32, the final four layers, batch size 1, four steps of gradient accumulation, and a 640-token limit:

Pilot	Dev normalized regret	Method accuracy	Invalid selection	Final validation loss
Hard-label	0.6848	18.33%	61.67%	1.6983
Regret, random DGP	0.6644	18.33%	60.00%	1.7328
DGP-Regret curriculum	0.6263	30.00%	61.67%	1.7125

The DGP-Regret pilot reduced dev regret by 8.54% relative to hard-label. Formal training planned at most 1,920 microsteps and stopped after 1,120 because two validation checks did not improve. The best checkpoint occurred at microstep 800 with validation loss 1.0529. Dev calibration compared adapter delta scales 0.5, 0.75, and 1.0 and selected 1.0 using validity first, then normalized regret and retention.

The v0.3 simulator is a semiparametric operating-characteristic emulator. It does not generate a raw table and refit all 28 methods inside every replication. Its response functions are checked in, seeded, and testable, but they encode modeling judgment. Sealed DGP performance measures agreement with that declared simulator.

Evaluation

All variants use the pinned Qwen3.5-4B MLX base, the same prompts and tools, greedy decoding, and the same sealed IDs. The evaluation lock contains 120 final DGPs, 90 P-Bench tasks, 200 StatQA tasks, 30 trilingual semantic tasks rendered in three languages, 30 clarification cases, and 24 retention items. An 8-gram audit found no overlap with training prompts.

DGP procedure selection

Model	Normalized regret	Method accuracy	Invalid selection rate
Base	0.6727	20.83%	63.33%
Hard-label	0.7016	17.50%	65.83%
DGP-Regret	0.4437	45.00%	38.33%

Relative regret improved by 34.04%. The paired-bootstrap mean absolute improvement was 0.2290 with a 95% CI of [0.1199, 0.3361]. The invalid selection rate fell by 39.47% relative to the base.

External, language, and retention results

Metric	Base	Hard-label	DGP-Regret
P-Bench Raw	0%	0%	0%
P-Bench Strict	0%	0%	0%
StatQA exact	1.00%	1.00%	1.00%
StatQA method-set accuracy	5.00%	6.00%	5.50%
StatQA column-set accuracy	19.50%	23.00%	22.00%
Trilingual English method accuracy	16.67%	10.00%	43.33%
Trilingual Traditional Chinese method accuracy	16.67%	20.00%	26.67%
Trilingual Simplified Chinese method accuracy	30.00%	30.00%	40.00%
Clarification accuracy	43.33%	40.00%	0%
Retention accuracy	100%	100%	100%

The adapter passed the final-regret, invalid-selection, trilingual, retention, category, and DGP-Regret ablation gates. P-Bench and StatQA failed their improvement gates. On P-Bench, all base and hard-label cases returned needs_clarification; DGP-Regret did so on 88 of 90 cases, and no variant produced a scoreable p value. The complete capability gate therefore failed.

The public aggregate is reports/stats/evaluation.json. Generated task text, per-item predictions, and model answers remain outside the release tree.

Runtime and isolation

The planner can call a checked-in Python or R implementation at most four times per analysis. Each call has a 20-second timeout, 2 GiB memory limit, 32 MiB write limit, and 64 KiB output limit. The macOS sandbox blocks networking, reads outside the runtime and temporary input directory, writes outside the temporary directory, and unapproved child processes. Release tests exercised each escape class in Python and R.

The adapter, fused MLX export, and dynamic router loaded in a clean Python 3.12 environment. Router tests measured eight LoRA modules, a nonzero adapter effect, zero restoration error, and zero error between the bypass and an independently loaded base.

Limitations

• The model's gains are confined to the declared DGP selection task and trilingual variants of it.
• P-Bench and StatQA provide no evidence of improved end-to-end statistical analysis.
• The adapter failed all held-out clarification cases, so it may choose a method when it should ask for design information.
• The finite emulator does not establish optimality on unseen statistical problems.
• Generated plans, code outputs, and prose can contain errors even when the selected method is valid.

GGUF is withheld. The pinned llama.cpp path lacks a verified upstream resolution for the relevant Qwen3.5 hybrid-tensor and 4B block-count issues. The project will not publish an unverified GGUF.

License and provenance

Project code and derivative model artifacts use Apache-2.0. Upstream datasets retain their own licenses. configs/sources.lock.json, DATA_SOURCES.md, and THIRD_PARTY_NOTICES.md record pinned revisions and terms. The release excludes corpora, caches, credentials, evaluation question text, and machine paths. The v0.2 FORGE release remains available at Git tag v0.2.0.