ariacompute/afm-dd

AFM-D Decoder (afm_dd)

Causal System 1 decisions over a typed answer space (option-letter logits).
Site: ariacompute.com · Org: ariacompute · Hub: ariacompute/afm-dd

AFM-D Decoder is the causal track of AFM-D: SemIf direct option-letter scoring on openbmb/MiniCPM5-2B with an optional PEFT LoRA (product recipe afm-d-decoder-v2.1). It scores a text/JSON state against user-supplied options and returns a distribution — not open-ended chat, not TypeSafe Jev.

Primitive	Answer space	Result
Choice	2–16 options (A–P)	choice, probabilities, confidence
Score	2–10 ordered levels	expected score, distribution, confidence
Noul	false / true	noul = P(true)

Companion Encoder (Laya ModernBERT DecisionModel): ariacompute/afm-de.

Model details

Subject id	afm_dd
Base	openbmb/MiniCPM5-2B @ 12a3808a956f869c767195e9266b59c4d21d92e2
Method	SemIf direct — read option-letter logits at the first generation position
Adapter	PEFT LoRA (attention), recipe afm-d-decoder-v2.1: r=16, 3 epochs, LR 5e-5, micro-batch 1
Options	2–16 letters A–P (wider Choice rows skipped at convert/train)
Training	Hard-label CE on AFM-D product corpus; shuffles option order each step (shuffle_semif_options) to avoid letter collapse
Export	PEFT adapter (this repo) + optional merged Q8_0 GGUF at gguf/afm-dd-2b-Q8_0.gguf for Ollaya / llama.cpp

Checkpoint layout

adapter_config.json
adapter_model.safetensors   # or adapter_model.bin
dd_config.json
tokenizer/
gguf/afm-dd-2b-Q8_0.gguf    # optional; merged MiniCPM5 + LoRA for Ollaya

Load the adapter on the pinned MiniCPM5-2B revision with peft + SemIf / AFM-D Decoder code — not as a standalone chat GGUF unless you use the merged file under gguf/.

Intended use

• Local typed decision scoring (Choice / Score / Noul) over a supplied state via System One HTTP or in-process subjects.
• Offline eval and JevBench comparison via the AFM-D harness.
• Ollaya / llama.cpp inference from the merged Q8_0 GGUF when published in this repo.

How to use

Load through Aria Engine (native AFM runtime) or Ollaya (Ollama-style local decision daemon).

Aria Engine

Native AFM runtime: CLI, POST /v1/systemone, FFI, and language SDKs. Docs: engine README. Decoder System One default port 8011.

aria-engine setup
aria-engine download afm-dd          # → ~/.ariacompute/models/afm-dd

aria-engine decide --track decoder --model-name afm-dd --file record.json
aria-engine serve --track decoder --model-name afm-dd --bind 127.0.0.1:8011

System One body (POST /v1/systemone or decide):

curl -s http://127.0.0.1:8011/v1/systemone \
  -H 'content-type: application/json' \
  -d '{
    "state": "user wants a refund",
    "questions": {
      "q1": {
        "type": "choice",
        "instructions": "Pick the best action",
        "criteria": {
          "refund": "issue a full refund",
          "deny": "deny the request"
        }
      }
    }
  }'

type	criteria
choice	object: option name → description
score	array of 2–10 ordered level strings
noul	object with true / false (optional)

Python SDK (pip install aria-engine; needs libaria-engine_ffi or ARIA_FFI_LIB):

from aria_engine import AriaEngine

eng = AriaEngine("/path/to/afm-dd", "decoder")  # or ~/.ariacompute/models/afm-dd
out = eng.systemone({
    "state": "user wants a refund",
    "questions": {
        "q1": {
            "type": "choice",
            "instructions": "Pick the best action",
            "criteria": {
                "refund": "issue a full refund",
                "deny": "deny the request",
            },
        }
    },
})
print(out["answers"]["q1"])
eng.destroy()

Also: TypeScript @ariacompute/engine-ts, Rust ariacompute-engine, Go / Flutter / Swift / Kotlin — see engine bindings/.

Ollaya

Ollaya pulls AFM-D by name and serves TypeSafe-compatible /v1/systemone. Use the AFM-D-enabled builds from ariacompute/ollaya releases (not the default ollaya-dev/ollaya channel). Weights stay on Hugging Face / ModelScope (Ollaya does not re-host).

# install from https://github.com/ariacompute/ollaya/releases (latest AFM-D build)
curl -fsSL https://raw.githubusercontent.com/ariacompute/ollaya/main/scripts/install.sh \
  | OLLAYA_REPO=ariacompute/ollaya sh
# pin a release: OLLAYA_REPO=ariacompute/ollaya OLLAYA_VERSION=0.7.5+afm-d.1.0.0
# Windows (PowerShell):
#   $env:OLLAYA_REPO='ariacompute/ollaya'; irm https://raw.githubusercontent.com/ariacompute/ollaya/main/scripts/install.ps1 | iex

# weights: HF (ariacompute/afm-dd) or ModelScope (AriaCompute/afm-dd)
# OLLAYA_HUB=huggingface|modelscope|auto  (auto → ModelScope when LANG looks Chinese)
ollaya pull afm-dd
ollaya run afm-dd --preset triage \
  "Third time this year you've double-charged me. Refund it today or I'm cancelling."

Or download a platform asset from the releases page (e.g. ollaya-linux-amd64.tar.zst, ollaya-darwin-arm64.tgz, ollaya-windows-amd64.zip), unpack, put ollaya on PATH, then pull / run as above.

HTTP (daemon default http://localhost:11435):

curl http://localhost:11435/v1/systemone \
  -H "Content-Type: application/json" \
  -d '{
    "model": "afm-dd",
    "state": "user wants a refund",
    "questions": {
      "q1": {
        "type": "choice",
        "instructions": "Pick the best action",
        "criteria": {
          "refund": "issue a full refund",
          "deny": "deny the request"
        }
      }
    }
  }'

Family notes: Ollaya repo docs/families/afm-dd.md.

Eval

Product LoRA (afm-d-decoder-v2.1) on data/eval.jsonl, n=5513 (--skip-errors); vs base MiniCPM5-2B. letter_collapse.collapsed=false (top letter B ≈35.8%).

	AFM-D Decoder	Base MiniCPM
Agreement	77.5%	59.5%
ECE	0.029	0.261
Brier	0.299	0.613

Task	n	Agree	ECE
choice	2459	79.2%	0.039
noul	1706	85.9%	0.014
score	1348	64.0%	0.048

Score is the weakest task bucket. Agree / ECE / Brier are local release diagnostics; product gate = JevBench public-proxy below.

JevBench

Public-proxy board (n=231, gate Score ≥ 50.0). Not an official sealed JevBench claim.

#	System	Score	Intel.	Calib.	Speed	Acc.	Hard
1	SemIf	84.0	74.9	87.1	90.7	81.0%	61.3%
2	Bespoke Nimble-9B	79.2	72.5	76.2	89.8	79.7%	61.3%
3	NeoHorse-Jev-4B	78.3	63.1	84.6	89.7	72.3%	45.0%
4	Kev-4B	76.9	67.2	76.7	88.3	75.8%	54.1%
5	AFM-D Decoder	76.3	57.1	84.9	91.7	68.4%	40.5%
6	AgentJev-0.6B	42.0	40.0	79.8	89.1	58.0%	36.0%
7	Laya	30.9	36.4	57.7	93.9	53.2%	27.9%

AFM-D Decoder tiers: easy 100%, standard 90.3%, hard 40.5% — beats the public-proxy gate. Near Kev on composite Score; main gap vs SemIf / Nimble is Intelligence / hard.

License

MIT

Citation / links

• Org: huggingface.co/ariacompute
• Companion Encoder: ariacompute/afm-de
• Aria Engine: github.com/ariacompute/engine
• Ollaya (AFM-D builds): github.com/ariacompute/ollaya/releases
• Base MiniCPM5-2B: openbmb/MiniCPM5-2B
• SemIf: github.com/TheoLeeCJ/SemIf-OpenJev
• JevBench: github.com/fstandhartinger/jevbench