Core AI model zoo

Core AI is Apple’s on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple’s coreai-torch (LLMs: coreai.llm.export) into .aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta 26A5353q, 2026-06-11).

OpenThai-SystemOne — Core AI

🤗 mlboydaisuke/OpenThai-SystemOne-CoreAI · Apache-2.0 · source iapp/OpenThai-SystemOne (revision f3709948) · base Qwen/Qwen3.5-0.8B-Base

A Thai + English System One decision model: it reads a state and typed questions — Choice, Score or Noul — and returns option probabilities. The Qwen3.5 text tower was continued-pretrained on Thai; its language-model head was replaced by a 256-way biased slot head. The readout is at <|ts_answer|>. It never generates text.

This is the zoo’s second decision model. The graph uses the Qwen3.5 decode-only, loop-free S=1 recipe with three changes: the model.* weight prefix, a 248,339-row embedding, and the slot head in the lm_head position. int8lin is the ship bundle; fp16 is published beside it as the reference. Both have a 4,096-token context and emit logits with shape [1, 1, 256].

Readout contract

Each question is one independent row in the author’s layout, with no chat template or BOS:

<|ts_state|> <state>
<|ts_q|><|ts_choice|> <instructions>
<|ts_opt_0|> <name>: <description>
<|ts_opt_1|> <name>
<|ts_answer|>

The author’s encoded sequence has a newline after <|ts_answer|>; the answer slot is len(ids_full) - 2. The bundle consumes the prefix through the answer token, id 248082, and reads the last call’s 256 logits. Removing the trailing newline changes the fp32 oracle logits by at most 0.000018597 across the fixtures (tolerance 0.0001).

Choice options retain request order. Score uses <|ts_score|> and options i: <level>, with 2–10 levels. Noul uses <|ts_noul|> and slots 0 = no, 1 = yes, with the supplied false/true descriptions when present.
For k options, divide all slot logits by the question type’s temperature, mask slots k..254 to negative infinity, and softmax over all 256 slots. Return p_options = p_full[:k] / sum(p_full[:k]); retain p_full[255] as abstain. Slot indices are not vocabulary token ids. Choice supports up to 255 options.
Temperatures are read from exp(log_temperature) in the checkpoint: choice 1.058534, score 1.043141, noul 1.006767. They differ from the author’s v0.3 card, which quotes choice 1.055, score 1.008, noul 1.047. Metadata carries the tensor-derived values at full precision.
The author’s API exposes abstain for Choice only. Score exposes probabilities and confidence; Noul exposes the probability of yes. Confidence is one minus normalized entropy. The pinned client does not round; its Choice/Score assembly performs a second fp32 renormalization. All fixtures use permutations=1.

The fixture contains 18 requests: 48 kit rows (24 Choice, 14 Noul, 10 Score) and two zoo rows with 40 and 255 options. It covers Thai, English, mixed text, dict states and list states. The bundle and the kit answer one question per row. The independent-row fp32 oracle assembly equals the author’s single-question API exactly for 18/18 requests (50/50 question calls). The author’s one-pass API places several questions in one causal sequence; its answers to later questions can differ from independent rows: max |Δp| 0.375453 on these requests. That comparison is recorded as API behavior, not a conversion gate.

Measured (Apple M4 Max GPU, macOS 27.0 26A428, 2026-09-23)

	fp16 (reference)	int8lin (ship)
option argmax = author’s fp32 oracle	50/50	50/50
argmax on oracle margin ≥ 0.02	49/49	49/49
max \|Δp\| over option probabilities	0.005059	0.020813
mean of per-row mean \|Δp\|	0.000225	0.000659
max \|Δabstain\|	0.016488	0.018837
Swift pipelined first token = decoded raw-slot argmax	50/50	50/50
Swift sequential first token = decoded raw-slot argmax	50/50	50/50
state reset, row 1 logits bit-identical	yes	yes

The gate requires option-argmax agreement on every row with oracle margin ≥ 0.02, finite logits and the state-reset proof. Probability and abstain errors are recorded. The only row below that margin is r18-slot (0.009739); it agrees on both bundles. The largest int8lin probability difference is r05-dry, a two-option Noul row with oracle margin 0.061681.

Probabilities come from an AOT h16c GPU asset loaded through the Core AI Python runtime with SpecializationOptions.default(), fresh zero states per row and full position_ids at each S=1 step. The engine check uses Release llm-runner, raw ids, one greedy token, and COREAI_CHUNK_THRESHOLD=1. It compares tokenizer.decode([raw256_argmax]) against the same bundle’s unmasked Python readout; that diagnostic string is not a decision answer. Transcripts: fp16 readout, int8lin readout, fp16 engines, int8lin engines.

Throughput, int8lin, Release llm-benchmark, p=128 / g=256, two launches × three trials per engine, COREAI_CHUNK_THRESHOLD=1; median (range):

Engine	prefill proxy, tok/s	decode, tok/s	load per launch, s
coreai-pipelined	252.7 (243.8–258.6)	250.8 (244.5–253.8)	1.415 / 0.167
coreai-sequential	197.1 (196.1–201.3)	194.8 (192.6–197.7)	0.172 / 0.166

This is a prefill-rate proxy: the graph is S=1, and synthetic generation throughput is not decision latency. The benchmark samples ids from the metadata’s 256-wide output range. No other Core AI, Python or Swift engine job appeared in the before/after process snapshots (contended: false). Load is measured per launch, excluding warmup. The frozen Swift tag’s benchmark needed a local CLI option to select EngineOptions.variant; the trial loop was unchanged. Trials, load times and environment.

Through the kit

Measured through coreai-kit, using its sequential engine and tokenizer: decide-cli parity matched tokens 50/50, answer slots 50/50 and option argmax 50/50 on both bundles, including the 40- and 255-option rows. int8lin max |Δp| was 0.0226 (r05-dry), mean 0.0009, and max |Δabstain| 0.0222; fp16 was 0.0051 (r05-task), 0.0003, and 0.0165 respectively. These are kit measurements supplied by the supervisor, separate from the Python-runtime table above. Median int8lin wall time per fixture question was 354 ms over the 50 rows, two to three questions per state (a question on a new state pays for the whole state); the 255-option row (1,449 tokens, S=1 prefill) took 7.2 s. A three-question Thai ticket took 351 / 316 / 429 ms for its 57-, 63- and 83-token rows; the recurrent hybrid cannot rewind mid-sequence, so every row is prefilled from its first token (0 tokens reused).

On SemIf’s authored144 — 144 English rows with three options, SemIf’s gold labels and unchanged benchmarks/evaluate.py — int8lin on the Mac GPU, measured through coreai-kit, scored 109/144 raw and 0.7249 mean family balanced accuracy. The kit README reports 0.681 for MiniCPM5-2B int8 and 0.821 for Qwen3.5-4B int8 on the same rows and evaluator. Kit measurement record. No phone gate is claimed for this port.

Bundle

mlboydaisuke/OpenThai-SystemOne-CoreAI contains both LanguageBundles, each with .aimodel, metadata.json and tokenizer/:

Path under `gpu-pipelined/`	role	bundle bytes	`main.mlirb` bytes
`openthai_systemone_decode_int8lin/`	ship	1,068,353,811	1,039,655,099
`openthai_systemone_decode_fp16/`	reference	1,534,777,239	1,506,078,533

int8lin quantizes the linears per block of 32; the biased slot head, embeddings, conv1d and norms stay fp16. language.vocab_size = 256 describes the logits width because the sequential engine allocates its output buffer from it. The input tokenizer still contains 248,339 tokens, including all 295 added tokens. The decision metadata carries the slot count, abstain slot, answer token, temperatures and layout. The source config.json is retained for provenance.

Use the zoo’s extra-states runtime patch for the hybrid’s KV, conv and recurrent states, and COREAI_CHUNK_THRESHOLD=1. Both pipelined and sequential engines were checked with Release tools from fork tag 0.2.4-zoo (f7a75ec). The recipe records the source revision and each graph’s SHA-256.

Reproduce

Run from the zoo checkout with the overlay environment; the oracle uses its own uv-managed environment. DEVELOPER_DIR must select Xcode 27 for the Core AI tools.

python3 conversion/zoo_convert.py run openthai-systemone
python3 conversion/zoo_convert.py run openthai-systemone-fp16

uv run conversion/slot/oracle_slot.py \
    --out models/openthai-systemone/fixtures-openthai-systemone.json

python3 conversion/slot/readout_gate_slot.py \
    exports/openthai_systemone_decode_int8lin \
    models/openthai-systemone/fixtures-openthai-systemone.json \
    --transcript models/openthai-systemone/gate-openthai-systemone-readout-int8lin.json

python3 conversion/slot/engine_argmax_slot.py \
    exports/openthai_systemone_decode_int8lin \
    models/openthai-systemone/fixtures-openthai-systemone.json \
    --readout models/openthai-systemone/gate-openthai-systemone-readout-int8lin.json \
    --runner <fork>/.build/release/llm-runner \
    --engine pipelined --engine sequential \
    --transcript models/openthai-systemone/gate-openthai-systemone-engine-int8lin.json

Repeat the readout and engine commands with fp16 paths for the reference. The exporter downloads the pinned snapshot itself. Gate instructions and port notes record the oracle dependencies and runtime contract.

License

Source Apache-2.0 (iapp/OpenThai-SystemOne); the bundles inherit it. The pinned source snapshot has no license file, so LICENSE contains the canonical Apache License 2.0 text. The author’s inference files are downloaded by the oracle at gate time and are not included in the bundles.

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