d1-3B on Core AI: the Mac fp16 and iPhone int8mlp decoders (64 tokens a call, hidden output, option-token readout on the host) and the fp16w32 vision tower, debug locations stripped; card, LICENSE (LFM Open License v1.0), config.json, SHA256SUMS

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+gpu-pipelined/d1_3b_decode_fp16_pf64_s/d1_3b_decode_fp16_pf64_s.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/d1_3b_decode_fp16_pf64_s/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/d1_3b_decode_int8mlp_pf64_s/d1_3b_decode_int8mlp_pf64_s.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/d1_3b_decode_int8mlp_pf64_s/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/d1_3b_vision_fp16w32_s/d1_3b_vision_fp16w32_s.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,71 @@
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README.md

@@ -0,0 +1,501 @@
+---
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+base_model: LiquidAI/d1-3B
+base_model_relation: quantized
+library_name: coreai
+tags:
+- coreai
+- core-ai
+- apple
+- on-device
+- iphone
+- decision-model
+- systemone
+- typed-decisions
+- structured-output
+- vision-language
+---
+
+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](https://github.com/john-rocky/apple-silicon-llm-bench), macOS 27 beta, 2026-06).
+
+# d1-3B — Core AI
+
+[🤗 mlboydaisuke/d1-3B-CoreAI](https://huggingface.co/mlboydaisuke/d1-3B-CoreAI) · LFM Open License v1.0 · source [LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B/tree/da1fe36a861f24690f27f622dca1d8688503d113) (revision `da1fe36`) · base [LiquidAI/LFM2.5-VL-3B](https://huggingface.co/LiquidAI/LFM2.5-VL-3B)
+
+A **decision model** that also reads pictures. Give it a state (text, a JSON value, pictures, or a mix) and named
+questions: `noul` (yes / no), `choice` (named options) or `score` (ordered levels). It returns a probability for every
+option of every question. It never generates text. Requests and responses use the System One shape:
+`{state, questions}` in, `{answers, usage}` out, one typed answer per question.
+
+Liquid AI post-trained it from LFM2.5-VL-3B: a SigLIP2 vision encoder (27 layers, width 1,152) and the LFM2 hybrid
+decoder (30 layers: 22 short-convolution and 8 grouped-query attention, hidden size 2,048, a 128,000-token vocabulary).
+The provider's card: "You give it a state (text, JSON, images, or a mix) and a set of questions. It returns calibrated,
+typed answers in one forward pass with zero output tokens." It reads the logits of a few option tokens at the prompt's
+last position. The provider's card reports benchmark results and speeds; none of them are re-measured here.
+
+This port exports the text decoder as one Core AI graph. The graph takes 64 token ids per call and returns the
+final-norm hidden state at every position; it has no vocabulary head. The host reads the last position against the
+tied embedding rows of the question's option tokens (a 2,134-row table ships beside the graph), keeps each option's
+highest logit and takes a softmax over the options: the provider's readout. Pictures go through a second graph, the
+vision tower, one call per crop. The gate is probability parity with the provider's own fp32 code, on every option of
+every question: 393 fixture questions, 120 held-out ones and 24 questions about pictures.
+
+Two decoders ship, with the same contract: the Mac runs the fp16 decoder (a 5.56 GB `.aimodel`), the iPhone one whose
+MLP linears are int8 per block of 32 (3.70 GB). Both keep the attention projections fp32. On both devices the shipped
+`.aimodel` is specialized where it runs. On the iPhone 18 Pro the app needs the
+`com.apple.developer.kernel.increased-memory-limit` entitlement: without it, neither the decoder's on-device
+specialization nor its ahead-of-time asset loads.
+
+**Why two decoders.** On the phone, the int8mlp decoder at 64 tokens a call passes the bar and decides one question in
+48.3 ms; each other form tried there is slower, misses the bar or does not load (Forms measured). On the Mac, the same
+`.aimodel` specializes to a graph up to 25.7 % slower than its AOT asset, with other bits. The fp16 `.aimodel`
+specializes to its AOT asset's rows bit for bit, at most 0.7 % slower, and keeps more room under the bar (fixture max
+|Δp| 0.0039 against 0.0197).
+
+## Readout contract
+
+One row per question, every row from zeroed states. The ids are the provider's row form (`prompt.py`, `runner.py` in the
+checkpoint) with the checkpoint's tokenizer:
+
+```
+<|startoftext|> <|im_start|>user\n [the pictures' token runs] state block \nQUESTION:\n
+  question block <|im_end|>\n<|im_start|>assistant\n
+```
+
+- Special ids: `<|startoftext|>` 124894 (written as text; the tokenizer adds no BOS), `<|im_start|>` 124899,
+  `<|im_end|>` 124900, `<|pad|>` 124893, `<image>` 124907.
+- The state block: a string as is, any other JSON value as `json.dumps(state, ensure_ascii=False, indent=2)`, each
+  followed by two newlines; a null state leaves the block and the `QUESTION:` line out.
+- The question block: `choice` = the instructions, `Options:`, one line `code description` per option and `Reply with
+  the option code only.`; `noul` = the instructions (with `Yes: …` / `No: …` lines when criteria are given) and `Reply
+  with yes or no only.`; `score` = the instructions, one line `k level` per level and `Reply with a single digit 0-K
+  only.` with K the last level's index.
+- Option codes: the labels themselves when every label is one letter, else `A`, `B`, … up to 26 options, else `00`,
+  `01`, …. A code that is not one free token takes the next free single-token alias, up to 1,639 options.
+- The readout reads the hidden state at the row's last token. Each option has a group of ids: a choice's code, plus `
+  code` with its leading space when that is one token; for `noul` the single-token forms of yes / Yes / YES against no /
+  No / NO; for `score` the digits `0`..`K`. `z[id] = h · E[id]` in float64, with `E` the tied embedding rows
+  (`head/option_rows.safetensors`: 2,134 ids × 2,048, fp32); an option's score is its group's highest `z`, and p is the
+  softmax over the options. The provider takes a log-softmax over the whole vocabulary before the readout; that constant
+  cancels in a softmax over the options (on random full-vocabulary logits the two agree within 1.75e-7).
+- The response: `noul` → P(yes); `choice` → the argmax label, its p as `confidence`, every p; `score` → Σ k · p_k, the
+  argmax p as `confidence`, every p and the legend; floats not rounded. `usage` = `{input_tokens, output_tokens: 0}`,
+  counted as the provider counts (several questions: the shared prefix once, then each question's suffix).
+- Pictures: each picture becomes one crop at its own aspect (sides multiples of 32, 64–256 image tokens) or 512 × 512
+  tiles plus a thumbnail, after the provider's one-megapixel cap. A 384 × 384 picture is one crop of 144 image tokens.
+  The prompt's `<image>` becomes the picture's run (`<|image_start|>` 125009 … `<|image_end|>` 125010, a tile marked
+  `<|img_row_r_col_c|>` = 124908 + 10(r − 1) + (c − 1), the thumbnail `<|img_thumbnail|>` 125008), and the graph receives
+  the k-th `<image>` of the row as id 128,000 + k, which reads row k of `image_embeds`.
+- Length: a row holds at most 4,032 tokens. Its calls end at ⌈T / 64⌉ · 64 positions, which must stay at or below the
+  graph's position bound of 4,095. The provider's card gives a context length of 32,768 tokens; here a longer row is
+  refused before any call. One picture needs at most 2,810 image tokens, and a request whose pictures need more than
+  the graph's 2,816 image rows together is refused too.
+- A request is also refused whole when one of its readout ids is not in the option table (a choice of one-letter labels
+  outside A–Z / a–z, such as `é` or `α`), and when a question breaks the provider's schema (no `instructions`, a score of more
+  than 10 levels).
+
+`conversion/d1/oracle_d1.py` runs the provider's code unchanged (`AutoModel` with `trust_remote_code`, fp32, CPU, one
+thread) and records the reference: [`fixtures-d1-3b.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/fixtures-d1-3b.json). The fixture is 361 records and 394
+questions, 393 of them answerable (one question has no instructions, so the provider refuses its request). 356 records
+come from the fixture of a LiteRT port of Kev: 60 MMLU rows of the Kev suite's transfer-v4 file, 20 rows from each of
+six other sources, 20 score rows, the 144 SemIf authored144 items and 12 records written for that port. Three are the
+model card's example requests, and two are long JSON states written for this port (1,464 and 3,419 tokens of state).
+One question has an oracle top-2 margin of 0.02 or less (a near-tie) and is scored apart. The held-out set is 120 later
+transfer-v4 records with their own oracle run. Twelve pictures drawn for this port carry 24 questions.
+
+## Core AI shape
+
+`conversion/d1/lfm2_d1_decoder.py`: the overlay's LFM2.5-VL decoder (`Lfm2VlPipelinedForCausalLM`) on d1-3B's language
+weights, the vocabulary head replaced by the identity, the output the final-norm hidden state at every position. One
+function, `main`, at a static 64 tokens.
+
+| | name | shape, type |
+|---|---|---|
+| inputs | `input_ids` | [1, 64] int32 |
+| | `position_ids` | [1, seq] int32, the ramp 0 .. seq − 1 |
+| | `image_embeds` | [2816, 2048] fp16, the request's image rows (zero for a text row) |
+| states | `keyCache`, `valueCache` | [8, 1, 8, ctx, 64] fp16, ctx up to 4,096 |
+| | `convState` | [22, 1, 2048, 2] fp16 |
+| output | `hidden` | [1, 64, 2048] fp16, every position |
+
+Per row of T ids, from zeroed states: ⌈T / 64⌉ calls, call c with ids[64c : 64c + 64] and position_ids 0..64c + 63. The
+last call is padded with `<|pad|>` (124893) and its padded rows are dropped. The bundle's `metadata.json` says `kind:
+decision-backbone` and `language.prefill_chunk: 64`, and carries this order, the prompt, the option rules, the readout
+formula, the refusals and the image-row contract.
+
+`conversion/d1/lfm2_vl_tower.py`: the vision tower (SigLIP2 and the projector) with every shape static, one call per
+crop. Its weights are stored fp16 and computed in fp32; the fp16-math tower missed the tower gate (lowest row cosine
+0.9807) and is not shipped.
+
+| | name | shape, type |
+|---|---|---|
+| inputs | `patches` | [1024, 768] fp32: the crop's 16 × 16 patches, (x − 127.5) / 127.5, zero after h · w |
+| | `pos_table` | [1024, 1152] fp32: the 16 × 16 position table, resized to the crop's grid by the host |
+| | `key_bias` | [1024] fp32: 0 for a patch, −inf for padding |
+| | `unshuffle_idx` | [256, 4] int32: the 2 × 2 merge |
+| output | `image_embeds` | [256, 2048] fp32: the crop's image tokens in its leading h · w / 4 rows |
+
+**Host.** Builds the rows, decodes and cuts the pictures (the processor's uint8 bicubic resize from torch, written out),
+runs the tower per crop and writes the image rows, runs the decoder's calls, converts the last position's hidden row to
+float64, applies the readout in float64, and writes the response. `conversion/d1/decide.py` is the Python reference;
+[`apps/D1`](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/D1) is the Swift one. With `shared` the state's whole calls run once and every question
+continues from a copy of the states. A prepared state (`prepare(state:)`, then `decide(prepared:questionsJSON:)`) runs
+the same calls in two steps, so questions that arrive later skip the state.
+
+No engine is involved: the hosts drive the low-level runtime (`AIModel` + `loadFunction`, three zeroed states per row).
+No runtime patch.
+
+## Measured (Apple M4 Max, macOS 27.0 26A428, 2026-10-08 – 10-09)
+
+The bar, fixed before any graph ran: the argmax equal to the oracle's on every question whose oracle top-2 margin is
+above 0.02 (near-ties scored apart), max |Δp| ≤ 0.02 over every option of every question, the mean over rows of each
+row's mean |Δp| ≤ 0.002, finite rows, and every process re-running its opening row bit for bit. The Python gates load
+the AOT `.aimodelc` (`coreai-build compile … --platform macOS --preferred-compute gpu --architecture h16c
+--expect-frequent-reshapes`; the tower without the last flag) with `SpecializationOptions.default()`.
+
+### Before any graph: fp32 torch
+
+- The decoder module in fp32 on the CPU, driven like the graph and read through the host's readout: 84/84 questions of
+  56 records argmax-equal, max |Δp| 2.4e-6, lowest per-position hidden cosine 0.9999999998 on the 14 rows the oracle
+  keeps. Chunks of 16, 32 and 64 tokens give the same hidden rows bit for bit, and chunked against one forward pass the
+  rows differ by at most 5.2e-5. Its static form (below) equals the shipped form bit for bit on three rows
+  ([`gate-d1-3b-torch-parity.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-torch-parity.json)).
+- The tower graph against transformers 5.19's own `get_image_features` in fp32, on 64 crops of 18 pictures: every row's
+  cosine 0.99999998 or more, max |Δ| 6.9e-5, a re-run bit-equal
+  ([`gate-d1-3b-images.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-images.json), `tower_gate`).
+
+### The graph alone on the Mac GPU
+
+The oracle's rows in, each decoder's hidden row at the last position read through the host's readout:
+
+| decoder | set | questions | argmax (margin > 0.02) | near-ties agreeing | max \|Δp\| | mean of row means | bar |
+|---|---|---:|---:|---:|---:|---:|---|
+| **fp16 (the Mac's)** | **fixture** | 393 | **392/392** | **1/1** | **0.0039** | **0.00018** | **PASS** |
+| | pictures | 24 | 24/24 | — | 0.00049 | 0.00005 | PASS |
+| **int8mlp (the iPhone's)** | **fixture** | 393 | **392/392** | **1/1** | **0.0197** | **0.00126** | **PASS** |
+| | held out | 120 | 118/118 | 2/2 | 0.0140 | 0.00101 | PASS |
+| | pictures | 24 | 24/24 | — | 0.0024 | 0.00023 | PASS |
+
+The held-out set checks the int8 choice, made on the fixture. The int8mlp decoder's worst row is a SemIf item
+(`semif_33d6e5da58f0fee2490d`, its `answer` question, oracle p 0.548 / 0.293 / 0.159): 0.0197 here, 0.0186 on the
+phone. Across the chunk widths and the two machines the same question lands between 0.0180 and 0.0223; the phone's
+32-token graph puts it at 0.0223, over the bar, and does not ship. Five red arms move p on each graph as they move it on
+the oracle; on the int8mlp graph: one word changed in a question (max |Δp| 0.977), a grammatical "not" in two yes / no
+questions (0.958 and 0.035), and a state swapped for another record's (0.840 and 0.951), each within 0.0017 of the
+oracle's change. Every value is finite and every process's re-run is bit-equal. Transcripts:
+[`gate-d1-3b-readout.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-readout.json),
+[`gate-d1-3b-heldout.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-heldout.json), [`gate-d1-3b-images.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-images.json),
+[`gate-d1-3b-red.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-red.json).
+
+The released bundles are these exports with their debug locations stripped (Bundle, below). On the Mac the stripped
+bundles give every row of the gates above bit for bit: the fixture with the red arms (406 rows for each decoder), the
+held-out set (120 rows), the pictures (24 rows and 40 tower crops for each decoder) and the tower gate (64 crops)
+([`gate-d1-3b-strip.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-strip.json)).
+
+### From the request: Python and Swift
+
+- `conversion/d1/host.py`, without the provider's package, rebuilds every oracle row from the raw request (the ids, the
+  answer slot, the option groups and keys): 393/393 questions with two tokenizer implementations, and on 361/361 records
+  the provider's request ids, answers and response body.
+- The Swift host ([`apps/D1`](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/D1), Release) builds the same ids from the raw request (393/393, and 2,000
+  stress texts), cuts every fixture picture into the Python reference's tower inputs bit for bit (64 crops of 18 PNG
+  pictures), and on the same AOT assets gives every hidden row, p and response of the Python reference bit for bit: the
+  fp16 decoder at 16 tokens a call on 393 text and 24 picture rows, the int8mlp decoder at 64 on 51 rows. With the
+  shared prefix every record's rows equal the direct run (360 of 360 accepted records).
+- JIT: the fp16 `.aimodel` specialized by the Swift runtime gives the AOT asset's rows bit for bit (at 16 tokens a call
+  on 54 rows; at 64, the Mac's decoder, on all 9 timed rows). The int8mlp `.aimodel`'s JIT does not (0 of 9 timed rows; max
+  |Δp| 0.00046 against the AOT asset), so the Mac does not ship it. JPEG pictures decode differently in ImageIO and
+  libjpeg (by up to 57 levels with 4:2:0 chroma); PNG pictures match exactly
+  ([`gate-d1-3b-swift.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-swift.json)).
+
+### Time per decision on the Mac
+
+Swift, Release CLI, inside the machine-wide GPU lock (2026-10-09, 08:04–08:08 JST): the shipped (stripped) Mac
+decoder's `.aimodel` specialized by the `d1` process (the shipped path: GPU preferred, `expectFrequentReshapes`; the
+tower's `.aimodel` the same way), and the same bundle's AOT asset as a control. Two processes per form and request, counted only when no other
+GPU job ran and the one-minute load average stayed at or below 12; each made 20 decisions after one warm-up, and the
+table gives the median (p10–p90) over the 40. A decision is the picture's tower call and image rows, the graph calls and
+the readout; building the rows and decoding the picture are not in it.
+
+| request | input tokens | calls | ms | p10–p90 | AOT asset, ms |
+|---|---:|---:|---:|---|---:|
+| one question (the card's refund question) | 39 | 1 | 34.3 | 34.0–34.5 | 34.0 |
+| three questions on that state, shared | 116 | 3 | 102.2 | 102.1–102.8 | 101.9 |
+| the same three, each row from zero | 116 | 3 | 102.1 | 101.9–102.8 | 102.1 |
+| one question on a 3,470-token state | 3,470 | 55 | 1,888.0 | 1,886.1–1,890.5 | 1,881.8 |
+| one question on a 384 × 384 picture (1 crop, 144 image tokens) | 186 | 3 | 202.3 | 201.8–203.2 | 201.7 |
+
+The specialized graph gives the AOT asset's hidden rows and p bit for bit on every timed question, so the AOT gates
+above hold for it. Specializing it took 9.52 s (`AIModel`) + 0.69 s (function) in the window's opening process and
+wrote 10,368,248 KiB of runtime cache; the tower's took 2.05 s and 845,612 KiB. The tower's crop is 100.2 ms of the
+picture decision. At 64 tokens a call the three questions share no whole call, so shared and direct run the same calls.
+Transcript: [`gate-d1-3b-timing-mac.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-timing-mac.json).
+
+### iPhone 18 Pro (iOS 27.2 24B5099f, Core AI arch h19p, 2026-10-09)
+
+Through [`apps/D1Gate`](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/D1Gate), a headless gate app on the Swift host, Release, with
+`com.apple.developer.kernel.increased-memory-limit` (6,432 MB available at launch; 3,530 MB without the entitlement).
+The phone was on USB power, the battery at 80 % and charging; every bench item started at thermal state nominal. Every
+p the app wrote was re-scored on the Mac from its bit patterns. These runs used the released bundles,
+`d1_3b_decode_int8mlp_pf64_s` and `d1_3b_vision_fp16w32_s` (Bundle, below). An earlier run on this phone used the
+bundles as exported, before their debug locations were stripped. Both runs gave the same p and the same hidden row
+on all 417 questions, the same red-arm rows and the same tower output on all 40 crops, bit for bit.
+
+- **Load.** The `.aimodel` specialized on the phone (GPU preferred, `expectFrequentReshapes`), the tower's `.aimodel`
+  the same way: 31.8 s cold, 27.0 s of it in `AIModel(contentsOf:)`, with +4,779 MB of runtime cache and a peak
+  footprint of 265 MB during the load. With the cache warm a load took 3.7 s.
+- **Gate** (run `r12c-085445`):
+
+| set | questions | argmax (margin > 0.02) | near-ties agreeing | max \|Δp\| | mean of row means | bar |
+|---|---:|---:|---:|---:|---:|---|
+| fixture | 393 | 392/392 | 1/1 | 0.0186 | 0.00125 | PASS |
+| pictures | 24 | 24/24 | — | 0.0021 | 0.00023 | PASS |
+
+  The red arms are red (5/5), the shared prefix equals the direct run on all 23 multi-question records, and the re-run
+  of the opening record is bit-equal. No hidden row equals the Mac's (a different GPU): their p differ by at most
+  0.0037, with every argmax equal (417/417). The run's footprint peaked at 568 MB.
+- **Bench** (60 s of rest before each item, one warm-up, then 5 decisions; the 3,470-token state with 30 s of rest
+  before each decision):
+
+| request | ms | 5 runs |
+|---|---:|---|
+| one question | 48.3 | 47.4–51.9 |
+| three questions on that state, shared / each row from zero | 147.1 / 144.8 | 142.9–150.3 / 142.7–150.5 |
+| one question on a 3,470-token state | 2,795.9 | 2,791.1–2,806.0 |
+| one question on a 384 × 384 picture (the tower's crop: 416.9) | 569.3 | 567.4–572.3 |
+
+Without the entitlement neither decoder form loads on this phone: the on-device specialization dies with
+`std::bad_alloc` while it folds a weight transpose, and the ahead-of-time asset (8.74 GB) with a `SIGSEGV` in the
+delegate compile, both within two seconds. Transcripts: [`gate-d1-3b-iphone.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-iphone.json), and for
+the run before the strip [`gate-d1-3b-iphone-r10a.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-iphone-r10a.json).
+
+## Forms measured
+
+Every form below was exported, compiled and gated the same way. The times come from different windows, so compare forms
+only within one window. Full records: [`gate-d1-3b-forms.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-forms.json).
+
+Mac (Swift Release CLI, median of the counted processes):
+
+| form | one question, ms | 3,470-token state, ms | window | fixture max \|Δp\| | note |
+|---|---:|---:|---|---:|---|
+| fp16, S = 16 / 32 / 64 | 61.8 / 50.1 / 34.1 | 4,446.2 / 2,727.9 / 1,875.0 | 1 | 0.0046 / 0.0058 / 0.0039 | |
+| int8mlp, S = 16 / 32 / 64 | 61.9 / 50.3 / 34.0 | 4,680.8 / 2,734.1 / 1,882.6 | 1 | 0.0187 / 0.0184 / 0.0197 | S = 64: the iPhone's decoder |
+| int8mlp per block of 16, S = 16 | 62.4 | 4,543.3 | 1 | 0.0156 | |
+| fp16, S = 16, JIT | 61.9 | 4,701.7 | 1 | = AOT, bit for bit | |
+| int8mlp, S = 128 | 55.5 | 1,572.4 | 2 | 0.0174 | the S = 64 control in window 2: 34.6 / 1,876.2 |
+| int8mlp, static form, S = 64 / 16 | 40.3 / 76.8 | 2,193.9 / 5,536.9 | 2 | 0.0189 / 0.0162 | |
+| fp16, static form, S = 64 | 40.4 | 2,196.3 | 2 | 0.0026 | |
+| int8mlp, S = 64, JIT | 42.8 | 2,358.9 | 3 | not gated: other bits than its AOT asset | the AOT control in window 3: 34.0 / 1,877.0 |
+| fp16, S = 64, JIT, as exported | 34.2 | 1,888.5 | 4 | = AOT, bit for bit | the AOT control in window 4: 34.1 / 1,878.1 |
+| **fp16, S = 64, JIT, debug locations stripped (the Mac's shipped path)** | **34.3** | **1,888.0** | 5 | **= AOT, bit for bit** | the AOT control in window 5: 34.0 / 1,881.8 |
+| int8lin: every MLP and conv-projection linear int8 | — | — | — | 0.0227 | fails the bar |
+| int8lin per block of 16 | — | — | — | 0.0205 | fails the bar |
+| int8mix: int8lin with layers 2, 3, 6, 8, 9, 10, 12 fp16 | — | — | — | 0.0203 | fails the bar |
+| int4lin: the same linears int4 | — | — | — | 0.668 | fails: argmax 373/392 |
+| any form on the Neural Engine | — | — | — | — | no-go (below) |
+
+Windows: 1 = 2026-10-08 18:16–20:01 JST, 2 = 2026-10-09 02:01–02:21, 3 = 07:04–07:08, 4 = 07:15–07:20, 5 = 08:04–08:08.
+S = the tokens a call.
+
+iPhone 18 Pro (D1Gate, with the entitlement):
+
+| form | one question, ms | gate max \|Δp\| | note |
+|---|---:|---:|---|
+| int8mlp, S = 16, the phone's JIT | 125.5 | 0.0180 | 3 calls of 40.2 ms; thermal fair |
+| int8mlp, S = 32, the phone's JIT | — | 0.0223 | one question over the bar: not timed |
+| **int8mlp, S = 64, the phone's JIT (this release)** | **48.3** | **0.0186** | [`gate-d1-3b-iphone.json`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/gate-d1-3b-iphone.json); as exported, before the strip: 48.0 |
+| int8mlp, S = 16, AOT h19p with `--expect-frequent-reshapes` (8.74 GB) | — | 0.0075 (20 questions) | 55.9 ms a call against the JIT's 40.5 |
+| int8mlp, S = 16, AOT h19p without it (3.70 GB) | — | — | no-go: a call right after a new position length is specialized can return a wrong row, then `SIGTRAP` |
+| int8mlp, static form, S = 64: AOT h19p and JIT | — | 0.0208 | one question over the bar; the two give the same rows bit for bit |
+
+**Long states.** S = 128 decides the 3,470-token state in 1,572.4 ms, 16.2 % faster than S = 64 in the same window, but
+one question takes 55.5 ms against 34.6 (+60.5 %): a call of 128 tokens costs 56.2 ms against 34.1 for 64. Its asset is
+not in this release.
+
+**The static form.** `conversion/d1/lfm2_d1_static.py` gives each call its own positions and keeps the caches at a
+fixed 4,096 slots with the attention mask built in the graph. It has no dynamic dimension, so an AOT compile without
+`--expect-frequent-reshapes` specializes it once (5.56 GB: the compile folds the int8 weights into fp16) and nothing is
+specialized again at run time. It is slower per call (a 64-token call 39.9 against 34.1 ms on the Mac), and on the phone
+it misses the bar on one question, so it does not ship.
+
+**The Neural Engine.** Compiled with `--preferred-compute neural-engine`, the dynamic decoder gets no Neural Engine
+region and runs on the GPU bit for bit. At a fixed position length the runtime builds one Neural Engine region, 4.70 GB
+of IR, the size of its 134 MLP and conv-projection linears in fp16, and the Neural Engine compiler fails on it every
+time (`MLIR MPS to ANEC conversion failed`, its nonbonded phase). The fp16w32 tower computes in fp32, which the Neural
+Engine does not take; the fp16-math tower runs there but misses the tower bar (lowest row cosine 0.9755).
+
+## Precision
+
+The Mac's decoder is fp16 with the attention projections in fp32. The iPhone's decoder quantizes the MLP's linears alone
+(90 linears, per block of 32, symmetric with clipping, weights only); the conv-mixer projections, the embedding table,
+the conv1d and the norms stay fp16 and the four attention projections of each full-attention layer fp32. int8 over
+every MLP and conv-projection linear (int8lin) breaks the bar at 0.0227. An fp32 torch instrument with the exporter's
+own int8 weights reproduces it (0.0235 on the bisect rows), so the error comes from the int8 weights. No layer set met
+the bisect's rule, written before any result. Keeping the conv projections fp16 passes: 0.0187 on the fixture and 0.0137
+on the held-out set at 16 tokens a call, 0.0197 and 0.0140 at 64.
+
+| decoder, S = 64, as exported | `main.mlirb` bytes | compiled with `--expect-frequent-reshapes` | static form, compiled once |
+|---|---:|---:|---:|
+| fp16 | 5,562,548,035 | 10,600,779,479 | 5,562,784,605 |
+| int8mlp | 3,704,646,757 | 8,742,889,020 | 5,562,787,469 |
+
+**What the compiled asset holds.** `--expect-frequent-reshapes` adds an fp16 copy of every linear to the compiled asset,
+and a compile that specializes once folds the int8 weights into fp16 constants (its `stats.json` still counts Int8
+elements). An int8 `.aimodel` downloads 1.86 GB smaller than the fp16 one. Compiled with `--expect-frequent-reshapes` it
+stays 1.86 GB smaller (8.74 against 10.60 GB); compiled once in the static form, both are 5.56 GB. The phone's JIT keeps
+a cache of 4,779 MB for the int8mlp `.aimodel`.
+
+## ⬇️ Bundle
+
+[mlboydaisuke/d1-3B-CoreAI](https://huggingface.co/mlboydaisuke/d1-3B-CoreAI), three folders under `gpu-pipelined/`: the
+Mac's decoder, the iPhone's decoder and the tower. The exporter writes every op's source location into `main.mlirb`,
+local paths included; each folder is its exported bundle with those locations stripped (`conversion/d1/strip_bundle.py`,
+the `_s` in its name), the ops and the weights unchanged. Each `metadata.json` records the strip (`strip`: both
+`main.mlirb` sha256).
+
+| file | what | bytes | sha256 |
+|---|---|---:|---|
+| `d1_3b_decode_fp16_pf64_s/d1_3b_decode_fp16_pf64_s.aimodel/main.mlirb` | the Mac's decoder, fp16 | 5,562,302,268 | `e54d5d41…f4ecbc5c` |
+| `d1_3b_decode_fp16_pf64_s/metadata.json` | `kind: decision-backbone`, the readout contract, the strip record | 12,329 | `f9e4ad70…e313029a` |
+| `d1_3b_decode_int8mlp_pf64_s/d1_3b_decode_int8mlp_pf64_s.aimodel/main.mlirb` | the iPhone's decoder, int8mlp | 3,704,370,745 | `1836fba8…8079473f` |
+| `d1_3b_decode_int8mlp_pf64_s/metadata.json` | the same contract, its `compression` block, the strip record | 15,005 | `71d8739f…6051e32b` |
+| `<decoder>/tokenizer/tokenizer.json` | the checkpoint's, verbatim | 17,905,750 | `8096ecb9…8349fcee` |
+| `<decoder>/tokenizer/tokenizer_config.json` | the checkpoint's, verbatim | 507 | `ef6770d1…45feb0ea` |
+| `<decoder>/tokenizer/chat_template.jinja` | the checkpoint's, verbatim | 5,436 | `86f47704…22f61cca` |
+| `<decoder>/head/option_rows.safetensors` | the option table: 2,134 ids × 2,048, fp32 | 17,491,440 | `e43cfaee…ad948522` |
+| `<decoder>/head/option_rows.json` | the table's ids and their strings | 76,692 | fp16 `133b9d71…6c1a5acf`, int8mlp `41bb7fc3…57ab8bba` |
+| `d1_3b_vision_fp16w32_s/d1_3b_vision_fp16w32_s.aimodel/main.mlirb` | the tower, fp16 weights / fp32 math | 852,915,980 | `358b7de0…59591161` |
+| `d1_3b_vision_fp16w32_s/metadata.json` | `kind: vision-tower`, the crop contract | 6,956 | `1d84a4c0…0b71f669` |
+| `d1_3b_vision_fp16w32_s/host/position_embedding.safetensors` | the 16 × 16 position table, fp32 | 1,179,744 | `d0f11651…088a4a0e` |
+
+The two decoder folders hold the same `tokenizer/` files and option table; their `option_rows.json` differ only in the
+time they were written. The repository root carries the checkpoint's `config.json` and the LFM Open License v1.0
+`LICENSE`, verbatim; each bundle folder carries the same `LICENSE`. `SHA256SUMS` lists every file.
+
+No AOT asset ships: the Swift runtime specializes each `.aimodel` where it runs (the JIT rows above). To compile the
+Mac's decoder ahead of time anyway (10.60 GB with the flag's fp16 copies):
+
+```bash
+xcrun coreai-build compile d1_3b_decode_fp16_pf64_s.aimodel --output aot --preferred-compute gpu \
+    --platform macOS --architecture h16c --expect-frequent-reshapes
+```
+
+## Use it
+
+Swift, with the [`D1`](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/D1) package (macOS 27 / iOS 27; the system CoreAI framework, Accelerate and
+swift-transformers' tokenizer), on a download of the repository:
+
+```swift
+import D1
+
+let root = URL(filePath: "d1-3B-CoreAI/gpu-pipelined")
+#if os(macOS)
+let decoder = root.appending(path: "d1_3b_decode_fp16_pf64_s")     // the Mac's decoder
+#else
+let decoder = root.appending(path: "d1_3b_decode_int8mlp_pf64_s")  // the iPhone's: the app needs increased-memory-limit
+#endif
+let d1 = try await D1Decider(bundle: decoder)
+try await d1.loadGraph(asset: .jit, tower: root.appending(path: "d1_3b_vision_fp16w32_s"), towerAsset: .jit)
+                                                         // .jit = each .aimodel, specialized here
+let body = try await d1.decide(requestJSON: requestData, shared: true)                // shared: the state's calls once
+let seen = try await d1.decide(requestJSON: requestData, images: [pictureURL])        // the k-th file = the k-th <image>
+print(PythonFormat.dumps(body, indent: 2, asciiOnly: false))
+
+// questions that arrive later, on the same state
+let prepared = try await d1.prepare(state: try JSONParser.parse(stateData))           // the state's whole calls, once
+let later = try await d1.decide(prepared: prepared, questionsJSON: questionsData)    // only the questions' rows run
+```
+
+The same from the Mac CLI (`swift build -c release --package-path apps/D1` builds `d1`):
+
+```bash
+d1 decide --bundle d1-3B-CoreAI/gpu-pipelined/d1_3b_decode_fp16_pf64_s --asset jit \
+    --tower d1-3B-CoreAI/gpu-pipelined/d1_3b_vision_fp16w32_s --tower-asset jit \
+    --request req.json --shared --out resp.json
+```
+
+A request (the model card's example):
+
+```json
+{"state": "I was charged twice this month, please refund one of them.",
+ "questions": {
+   "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},
+   "team": {"type": "choice", "instructions": "Which team should handle this?",
+            "criteria": {"billing": "Charges, refunds, invoices", "technical": "App or site faults",
+                         "fraud": "Suspected unauthorised use"}},
+   "urgency": {"type": "score", "instructions": "How urgent is this?",
+               "criteria": ["Can wait", "Today", "Blocking the customer now"]}}}
+```
+
+A request with pictures adds `"images": ["photo.png"]` (files relative to `--images`, by default the request's folder).
+An iOS app needs `com.apple.developer.kernel.increased-memory-limit` in its entitlements (above).
+
+Python: `conversion/d1/decide.py run --bundle <folder> --request req.json [--tower <tower folder>] [--shared] --out
+resp.json` is the gates' own read-out with `coreai.runtime`. It loads an AOT `.aimodelc` (compile one with the command
+above); the Python gates never used the Python runtime's JIT.
+
+## Reproduce
+
+Environment: the zoo overlay venv (coreai-core 1.0.0b2, coreai-torch 0.4.1, coreai-opt 0.2.1, torch 2.9.0, transformers
+4.57.6), Xcode 27.0 RC. The provider's code (the oracle) runs in its own venv with transformers 5.19.0. The steps, in
+order, with every flag, are in [`conversion/d1/README.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/conversion/d1/README.md).
+
+```bash
+cd conversion/d1; K=$ZOO_WORK_ROOT/_d1_3b; S=$K/exports/bundles_ship
+# the bundles as exported (--aot adds the h16c .aimodelc the Python gates load)
+python export_decoder.py fp16 --prefill-chunk 64 --aot        # the Mac's decoder
+python export_decoder.py int8mlp --prefill-chunk 64 --aot     # the iPhone's decoder
+python export_vision.py --dtype fp16w32 --aot
+# the shipped bundles: each copied to a new name with the export's debug locations (local paths) stripped
+for b in bundles/d1_3b_decode_fp16_pf64 bundles/d1_3b_decode_int8mlp_pf64 vision/d1_3b_vision_fp16w32; do
+  python strip_bundle.py $K/exports/$b $S/$(basename $b)_s --aot
+done
+# the gates on the shipped bundles: the fixture with the red arms (each decoder), the held-out set (the int8 decoder),
+# a picture record end to end
+python readout_gate.py run $S/d1_3b_decode_fp16_pf64_s --red --transcript <fixture gate>.json
+python readout_gate.py run $S/d1_3b_decode_int8mlp_pf64_s --oracle $K/oracle/heldout/records_oracle.json \
+    --tag heldout_int8mlp_pf64_s --transcript <held-out gate>.json
+python decide.py run --bundle $S/d1_3b_decode_fp16_pf64_s --tower $S/d1_3b_vision_fp16w32_s \
+    --request <picture record request>.json --out <response>.json --trace <trace>.json
+# the Mac timing window: the shipped .aimodel's JIT against its AOT asset (the Release CLI takes the GPU lock itself)
+D1_TOWER=$S/d1_3b_vision_fp16w32_s \
+D1_FORMS="fp16_pf64_s=$S/d1_3b_decode_fp16_pf64_s jit_fp16_pf64_s=$S/d1_3b_decode_fp16_pf64_s@jit" ../../apps/D1/_time_mac.sh
+```
+
+A rebuild does not reproduce `main.mlirb` byte for byte (each export names its weight resources at random), so check
+it with the gates (`readout_gate.py`, `decide.py`), not with sha256. Recipe: [`recipe.toml`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/d1-3b/recipe.toml). Port notes:
+[`knowledge/d1-port.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/d1-port.md).
+
+## License
+
+LFM Open License v1.0 (`license: other`, `license_name: lfm1.0`): the weights and the provider's code
+(LiquidAI/d1-3B); the bundles inherit it, and the Hugging Face repository carries the full `LICENSE`. The licence grants
+commercial use only to a user or legal entity below 10 million US dollars of annual revenue (its Section 5). Changes in
+this repository's bundles, as its Section 4(b) asks: the checkpoint's language and vision weights were re-exported as
+Core AI graphs (`.aimodel`, the export's debug locations stripped): two decoders and the tower. The decoders' vocabulary head is removed (its tied rows for the
+readout ids ship as `head/option_rows`), the iPhone decoder's MLP linears are quantized to int8 per block of 32, and the
+other weights are stored as fp16 (the decoders' attention projections as fp32); every bundle's `metadata.json` records
+the details. The provider's code runs only in the oracle, at gate time; it is not part of the bundles.
+
+The fixture file carries its own terms. Its 144 SemIf authored144 items are MIT (github.com/TheoLeeCJ/SemIf at
+`ca3ba65f`), with their copyright and permission notice. The transfer-v4 records are references to the Kev suite's file
+(line and row hashes), not its text; the held-out set the same. The 12 records written for the LiteRT Kev port keep
+only their ids and numbers: they name invented people and companies that were never checked against real ones. The
+model card's three example requests and the two long states written for this port carry their text. The 12 pictures
+are CC0-1.0 and are not stored here: `conversion/d1/make_fixture_images.py` redraws them (their sha256 are in the file).
+
+## Limits
+
+From the [provider's card](https://huggingface.co/LiquidAI/d1-3B): it is not a chat model and does not write text. This
+port adds:
+
+- A row holds at most 4,032 tokens, while the provider's card gives a context length of 32,768.
+- Loading an `.aimodel` with a cold runtime cache specializes it: 10.2 s for the Mac's decoder, 31.8 s for the iPhone's
+  on the phone. With the cache warm a load takes 0.7 s on the Mac and 3.7 s on the phone.
+- Measured on one iPhone 18 Pro with iOS 27.2 beta (24B5099f) and one M4 Max; other iPhones and Macs were not measured.
+- The oracle is the provider's code in fp32 on the CPU; a bf16 run of the provider's code was not compared, and no
+  form here runs on the CPU alone.
+- JPEG pictures decode with ImageIO in the Swift host and with libjpeg in the provider's Python path; the pixels can
+  differ (by up to 57 levels with 4:2:0 chroma). PNG pictures give the same pixels.

SHA256SUMS

@@ -0,0 +1,29 @@
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+LFM Open License v1.0 
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+1. Definitions.
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+   "VW"
+  ],
+  "106326": [
+   "JH"
+  ],
+  "106350": [
+   "RZ"
+  ],
+  "106356": [
+   "XG"
+  ],
+  "106417": [
+   "QY"
+  ],
+  "106443": [
+   "MZ"
+  ],
+  "106458": [
+   "MW"
+  ],
+  "106480": [
+   "JE"
+  ],
+  "106484": [
+   "XR"
+  ],
+  "106521": [
+   "VL"
+  ],
+  "106559": [
+   "FJ"
+  ],
+  "106588": [
+   "QC"
+  ],
+  "106592": [
+   "JI"
+  ],
+  "106605": [
+   "WO"
+  ],
+  "106619": [
+   "WL"
+  ],
+  "106675": [
+   "MK"
+  ],
+  "106707": [
+   "YC"
+  ],
+  "106716": [
+   "BZ"
+  ],
+  "106724": [
+   "TJ"
+  ],
+  "106735": [
+   "QB"
+  ],
+  "106737": [
+   "DJ"
+  ],
+  "106742": [
+   "XB"
+  ],
+  "106789": [
+   "NJ"
+  ],
+  "106794": [
+   "GU"
+  ],
+  "106809": [
+   "QH"
+  ],
+  "106841": [
+   "PZ"
+  ],
+  "106842": [
+   "RG"
+  ],
+  "106850": [
+   "HN"
+  ],
+  "106853": [
+   "WK"
+  ],
+  "106862": [
+   "XW"
+  ],
+  "106884": [
+   "MU"
+  ],
+  "106889": [
+   "YT"
+  ],
+  "117104": [
+   " EH"
+  ],
+  "117133": [
+   " FB"
+  ],
+  "117160": [
+   " QP"
+  ],
+  "117236": [
+   "AO"
+  ],
+  "117266": [
+   " RQ"
+  ],
+  "117340": [
+   " QB"
+  ],
+  "117397": [
+   " CQ"
+  ],
+  "117434": [
+   " HI"
+  ],
+  "117607": [
+   " BN"
+  ],
+  "117758": [
+   " QC"
+  ],
+  "117816": [
+   " BX"
+  ],
+  "117886": [
+   " QT"
+  ],
+  "117966": [
+   " FY"
+  ],
+  "118692": [
+   " VE"
+  ],
+  "119570": [
+   " AY"
+  ],
+  "119638": [
+   " OU"
+  ],
+  "120242": [
+   "ZO"
+  ]
+ },
+ "source": {
+  "hf_id": "LiquidAI/d1-3B",
+  "revision": "da1fe36a861f24690f27f622dca1d8688503d113",
+  "file": "model.safetensors",
+  "file_sha256": "50e03317847caf6df9a9aee27ed40f20554a86a21e60d1d47ba41a422b546c0c",
+  "key": "model.language_model.embed_tokens.weight",
+  "header": {
+   "dtype": "BF16",
+   "shape": [
+    128000,
+    2048
+   ],
+   "data_offsets": [
+    0,
+    524288000
+   ]
+  },
+  "data_start": 97728,
+  "conversion": "bf16 -> fp32 (exact)",
+  "tokenizer_json_sha256": "8096ecb9f54599d756c8de728a598a340bc1e43c0deb77ddd62456c38349fcee",
+  "candidates": {
+   "A..Z": "26/26",
+   "a..z": "26/26",
+   "0..9": "10/10",
+   "00..99": "100/100",
+   "100..999": "900/900",
+   "#0..#199": "0/200",
+   "AA..ZZ": "587/676",
+   "' A..Z'": "26/26",
+   "' a..z'": "26/26",
+   "' 0..9'": "0/10",
+   "' 00..99'": "0/100",
+   "' 100..999'": "0/900",
+   "' #0..#199'": "0/200",
+   "' AA..ZZ'": "429/676",
+   "noul": "5/6"
+  }
+ },
+ "readback": {
+  "ids_equal": true,
+  "rows_bit_equal": true
+ },
+ "rows_absmax": 0.1328125,
+ "rows_l2_min_max": [
+  0.6768113970756531,
+  1.043346881866455
+ ],
+ "written": "2026-10-08T16:27:57+09:00",
+ "second_reader_bit_equal": true
+}

gpu-pipelined/d1_3b_decode_fp16_pf64_s/head/option_rows.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e43cfaee023b4c1c2bab48b873aa2195bc6e5f86fe9f30049a89a1e0ad948522
+size 17491440

gpu-pipelined/d1_3b_decode_fp16_pf64_s/metadata.json

@@ -0,0 +1,253 @@
+{
+  "metadata_version": "0.2",
+  "kind": "decision-backbone",
+  "name": "d1_3b_decode_fp16_pf64_s",
+  "assets": {
+    "main": "d1_3b_decode_fp16_pf64_s.aimodel"
+  },
+  "language": {
+    "tokenizer": "LiquidAI/d1-3B",
+    "vocab_size": 128000,
+    "max_context_length": 4096,
+    "embedded_tokenizer": true,
+    "prefill_chunk": 64,
+    "static_inputs": [
+      "image_embeds"
+    ],
+    "image_tokens_max": 2816,
+    "output": "hidden [1, 64, 2048] fp16, every position",
+    "contract": {
+      "function": "main",
+      "inputs": {
+        "input_ids": [
+          [
+            1,
+            64
+          ],
+          "int32"
+        ],
+        "position_ids": [
+          [
+            1,
+            -1
+          ],
+          "int32"
+        ],
+        "image_embeds": [
+          [
+            2816,
+            2048
+          ],
+          "float16"
+        ]
+      },
+      "outputs": {
+        "hidden": [
+          [
+            1,
+            64,
+            2048
+          ],
+          "float16"
+        ]
+      },
+      "states": {
+        "keyCache": [
+          [
+            8,
+            1,
+            8,
+            -1,
+            64
+          ],
+          "float16"
+        ],
+        "valueCache": [
+          [
+            8,
+            1,
+            8,
+            -1,
+            64
+          ],
+          "float16"
+        ],
+        "convState": [
+          [
+            22,
+            1,
+            2048,
+            2
+          ],
+          "float16"
+        ]
+      },
+      "dynamic": "-1 = dynamic: position_ids [1, seq] with seq in S..max_context_length-1; the KV sequence axis 2048..max_context_length (a host allocates it at max_context_length)"
+    },
+    "function_map": {
+      "main": [
+        "main"
+      ]
+    }
+  },
+  "source": {
+    "model_definition": "torch (conversion/d1/lfm2_d1_decoder.py: coreai_models.models.macos.lfm2_vl.Lfm2VlPipelinedForCausalLM with an Identity head)",
+    "hf_model_id": "LiquidAI/d1-3B",
+    "hf_revision": "da1fe36a861f24690f27f622dca1d8688503d113",
+    "license": "LFM Open License v1.0 (license: other, license_name: lfm1.0)",
+    "weights": {
+      "file": "model.safetensors",
+      "bytes": 6247065504,
+      "sha256": "50e03317847caf6df9a9aee27ed40f20554a86a21e60d1d47ba41a422b546c0c",
+      "keys": "model.language_model.*"
+    },
+    "tokenizer": {
+      "files_sha256": {
+        "tokenizer.json": "8096ecb9f54599d756c8de728a598a340bc1e43c0deb77ddd62456c38349fcee",
+        "tokenizer_config.json": "ef6770d12dd9ac58d334e979bf160c5cdfd41f13d205fd5d5516857645feb0ea",
+        "chat_template.jinja": "86f4770449a4797c9b4212d110b0cc70fb993c1ce960095bcfb12eea22f61cca"
+      }
+    }
+  },
+  "compression": null,
+  "decision": {
+    "output": "hidden [1, 64, 2048] per call: the final-norm hidden state at every position (no vocabulary head in the graph)",
+    "readout": "one row per question, fresh zero states per row; a row of T ids runs as ceil(T / 64) calls of 'main' (static S = 64): call c gets ids[64c : 64c + 64] with position_ids 0..64c+63, image_embeds zero for a text row; the last call is padded with <|pad|> (124893) and the hidden rows of the padded positions are discarded (causal: they cannot reach a real position). The hidden row of the row's last real token (the answer slot, T - 1) is the final-norm hidden the readout reads. A row fits when ceil(T / 64) * 64 <= 4095 (the position input's upper bound; the KV sequence axis is allocated at 4096).",
+    "spec": "conversion/d1/host.py (its docstring is the contract; test_host.py gates it against the provider's code at LiquidAI/d1-3B@da1fe36a861f24690f27f622dca1d8688503d113: prompt.py, api.py, runner.py)",
+    "request": {
+      "shape": "{state: JSON | null, questions: {name: question}} in request order",
+      "noul": "{type: 'noul', instructions: str, criteria?: {true?: str, false?: str} | null}",
+      "choice": "{type: 'choice' (the default when type is absent), instructions: str, criteria: {label: str | null}} with 1+ options",
+      "score": "{type: 'score', instructions: str, criteria: [str]} with 1..10 levels (the prompt asks for a single digit)",
+      "refuse": "anything else (no instructions, a non-string instructions or level, a choice without options, a score of 0 or more than 10 levels, a criteria of another JSON type)"
+    },
+    "prompt": {
+      "row_text": "prefix + suffix",
+      "prefix": "BOS + '<|im_start|>user\\n' + state_block + '\\nQUESTION:\\n' (state null: BOS + '<|im_start|>user\\n')",
+      "state_block": "a string as is + '\\n\\n'; any other JSON value json.dumps(state, ensure_ascii=False, indent=2) + '\\n\\n'",
+      "suffix": "question_block + '<|im_end|>\\n<|im_start|>assistant\\n'",
+      "choice_block": "instructions + '\\n\\nOptions:\\n' + '\\n'.join(code + ' ' + (desc or label.replace('_', ' '))) + '\\n\\nReply with the option code only.'",
+      "noul_block": "instructions + ('\\nYes: ' + str(criteria.get('true')) + '\\nNo: ' + str(criteria.get('false')) when criteria is a non-empty object) + '\\n\\nReply with yes or no only.'",
+      "score_block": "instructions + '\\n\\n' + '\\n'.join(str(i) + ' ' + level) + '\\n\\nReply with a single digit 0-' + str(K - 1) + ' only.'",
+      "special": {
+        "bos": {
+          "token": "<|startoftext|>",
+          "id": 124894
+        },
+        "im_start": {
+          "token": "<|im_start|>",
+          "id": 124899
+        },
+        "im_end": {
+          "token": "<|im_end|>",
+          "id": 124900
+        },
+        "pad": {
+          "token": "<|pad|>",
+          "id": 124893
+        },
+        "image": {
+          "token": "<image>",
+          "id": 124907
+        }
+      }
+    },
+    "options": {
+      "codes": "the labels when every label (strip) is one alphabetic character, else 'A'.. for at most 26, else '00', '01', ..",
+      "aliases": "each code takes its own id when it is one token not yet taken, else the first single-token untaken entry of A..Z, 00..99, a..z, #0..#199, AA..ZZ; the option line prints the taken code",
+      "groups": {
+        "choice": "[alias id] + [encode(' ' + code)[0]] when ' ' + code is one other token",
+        "noul": "[single-token forms of yes, Yes, YES], [the same of no, No, NO]",
+        "score": "[encode(str(i))[0]] for i in 0..K-1"
+      },
+      "keys": {
+        "noul": [
+          "yes",
+          "no"
+        ],
+        "choice": "the labels in request order",
+        "score": "'0'..'K-1'"
+      }
+    },
+    "tokens": {
+      "tokenizer": "tokenizer/tokenizer.json (BPE, ByteLevel; adds no BOS); add_special_tokens=False with the special tokens matched in the text (user text is not escaped)",
+      "row": "row_ids = encode(prefix + suffix); slot = len(row_ids) - 1",
+      "usage": "input_tokens = len(row) for one question; len(encode(prefix)) + sum(len(encode(suffix_q))) for several (the provider's Tree; trunk and branches encoded apart)"
+    },
+    "arithmetic": {
+      "logits": "z[id] = h_slot . E[id] in float64 for the ids of the question's groups (h_slot = the slot's hidden row, fp16 -> float64; E[id] = head/option_rows, fp32 -> float64)",
+      "pool": "score_k = max over group k of z",
+      "softmax": "p = softmax_k(score) in double (Python's math.exp and 3.12 sum()); the provider's vocabulary log-sum-exp cancels in a softmax over options"
+    },
+    "option_table": {
+      "files": [
+        "head/option_rows.safetensors",
+        "head/option_rows.json"
+      ],
+      "n": 2134,
+      "layout": "ascending int32 `ids` [n], fp32 `rows` [n, hidden]; row k = the tied embedding row of ids[k] (bf16 -> fp32, exact)",
+      "label_space": "the single-token strings among the code families A..Z, a..z, 0..9, 00..99, 100..999, #0..#199, AA..ZZ, the ' ' + code form of each, and yes / Yes / YES / no / No / NO",
+      "covers": "every noul and score question (1..10 levels); every choice whose labels are all ASCII letters (native one-letter codes) or that takes positional codes, up to 1639 options (the alias pool's limit)",
+      "refuse": "a request any of whose readout ids is not in the table is refused whole, before any graph call: \"questions.<name>: the token id <N> of label '<label>' is not in the option table\" (host.option_table_check; host.build_request(..., table_ids=))",
+      "refused_examples": "a choice of one-letter native labels outside A..Z / a..z (e.g. 'あ', 'é', 'α'); 1640 options or more are refused before it by the alias rule ('no single-token alias left')",
+      "provider_difference": "the provider's code reads any id; this refusal is the host's own (the fixture's readout ids are all in the table)"
+    },
+    "response": {
+      "noul": "{type: 'noul', noul: p[0]} (p = [P(yes), P(no)])",
+      "choice": "{type: 'choice', choice: labels[argmax], confidence: p[argmax], probabilities: {label: p}}",
+      "score": "{type: 'score', score: sum(i * p_i), confidence: p[argmax], probabilities: {'i': p_i}, legend: {'i': level_i}}",
+      "argmax": "the first index of the largest p; floats not rounded",
+      "body": "{answers: {name: answer} (request order), usage: {input_tokens, output_tokens: 0}}"
+    }
+  },
+  "vision": {
+    "image_token": {
+      "token": "<image>",
+      "id": 124907
+    },
+    "extension_ids": "the k-th <image> of the row (k counted over every picture and every crop, in text order) is sent as id V + k (V = vocab_size 128000, k < 2816); the graph reads image_embeds[k] for it",
+    "n_image_tokens": 2816,
+    "image_embeds": [
+      [
+        2816,
+        2048
+      ],
+      "float16"
+    ],
+    "rows": "rows 0 .. n - 1 (n = the row's <image> count) = the tower bundle's image_embeds of every crop, each crop's first h w / 4 rows (its merged grid row-major), concatenated in crop order (pictures in text order; per picture its tiles row-major, then the thumbnail), cast to float16; rows n .. N - 1 zero; the same buffer is bound to every call of the row",
+    "text_only": "image_embeds zero and no extension id",
+    "limits": {
+      "one_picture": "at most 2,810 image tokens (10 tiles + a thumbnail, aspect up to 4:1 after cap_pixels; K/results/vision_grid_table.json)",
+      "refuse": "a request whose pictures need more than 2816 image tokens together, or whose row is over the position bound (host.graph_context_check), is refused whole before any graph call",
+      "refusal_text": "images: <n> image tokens over the graph's 2816 image rows"
+    },
+    "tower": {
+      "bundle": "d1_3b_vision_<dtype> (export_vision.py; contract in lfm2_vl_tower.py's header)",
+      "per_crop": "vision_host.tower_inputs(crop, position table) -> patches [1024, 768], pos_table [1024, d], key_bias [1024], unshuffle_idx [256, 4] int32 -> image_embeds [256, text hidden]; the crop's rows are the first h w / 4"
+    },
+    "crops": "per picture (after cap_pixels): one crop at the smart size (aspect kept, sides multiples of 32, 64..256 image tokens) when max(16, round32(h)) * max(16, round32(w)) <= 524,288 px, else rows x cols tiles of 512 x 512 (2 <= rows * cols <= 10, the closest aspect) then a thumbnail at the smart size",
+    "tokens": "<|image_start|> + (one crop: <image> x tokens | tiles: <|img_row_r_col_c|> + <image> x 256 per tile, then <|img_thumbnail|> + <image> x thumbnail tokens) + <|image_end|>; the prompt holds one '<image>' per picture after '<|im_start|>user\\n', replaced by that run (vision_host.prompt_ids)",
+    "spec": "conversion/d1/vision_host.py (sections 1-8; test_vision_host.py gates it against the provider's code and transformers 5.19's processor)"
+  },
+  "compilation": {
+    "date": "2026-10-08T07:27:58.623434+00:00",
+    "targets": []
+  },
+  "strip": {
+    "what": "the .aimodel re-saved after coreai-torch's strip_debug_info: every op's source location (file path, call stack) replaced by an unknown location; the ops and the weights are not touched",
+    "from_bundle": "d1_3b_decode_fp16_pf64",
+    "from_main_mlirb": {
+      "bytes": 5562548035,
+      "sha256": "d4ed7ae09f048ef7597310b03e1ab88934d9ce5cd4c9df3376e521b5a2b174bb"
+    },
+    "main_mlirb": {
+      "bytes": 5562302268,
+      "sha256": "e54d5d411537782bdcca6ae48610aa0455ad252b360d9dd7ac95ce50f4ecbc5c"
+    },
+    "tool": "conversion/d1/strip_bundle.py (coreai_torch.debugging.debug_info.strip_debug_info)",
+    "versions": {
+      "coreai-torch": "0.4.1",
+      "coreai-core": "1.0.0b2"
+    }
+  }
+}

gpu-pipelined/d1_3b_decode_fp16_pf64_s/tokenizer/chat_template.jinja

@@ -0,0 +1,125 @@
+{{- bos_token -}}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+
+{%- macro format_arg_value(arg_value) -%}
+    {%- if arg_value is string -%}
+        {{- "'" + (arg_value | replace("\\", "\\\\") | replace("'", "\\'") | replace("\n", "\\n") | replace("\r", "\\r")) + "'" -}}
+    {%- elif arg_value is mapping or arg_value is iterable -%}
+        {{- arg_value | tojson -}}
+    {%- else -%}
+        {{- arg_value | string -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro parse_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- elif content is mapping -%}
+        {{- content | tojson -}}
+    {%- elif content is iterable -%}
+        {%- set _ns = namespace(result="") -%}
+        {%- for item in content -%}
+            {%- if item is string -%}
+                {%- set _ns.result = _ns.result + item -%}
+            {%- elif item is mapping and item.get("type") == "image" -%}
+                {%- set _ns.result = _ns.result + "<image>" -%}
+            {%- elif item is mapping and item.get("type") == "text" -%}
+                {%- set _ns.result = _ns.result + ((item.get("text") or "") | string) -%}
+            {%- else -%}
+                {%- set _ns.result = _ns.result + (item | tojson) -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {{- _ns.result -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_calls(tool_calls) -%}
+    {%- set tool_calls_ns = namespace(tool_calls=[]) -%}
+    {%- for tool_call in tool_calls -%}
+        {%- set func = tool_call["function"] if "function" in tool_call else tool_call -%}
+        {%- set func_name = func["name"] -%}
+        {%- set func_args = func.get("arguments") -%}
+        {%- set args_ns = namespace(arg_strings=[]) -%}
+        {%- if func_args is mapping -%}
+            {%- for arg_name, arg_value in func_args.items() -%}
+                {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
+            {%- endfor -%}
+        {%- elif func_args is string and (func_args | trim) not in ["", "{}", "null"] -%}
+            {{- raise_exception("Tool call arguments must be a mapping, got a JSON-encoded string: parse arguments with json.loads() before applying the chat template") -}}
+        {%- endif -%}
+        {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
+    {%- endfor -%}
+    {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
+{%- endmacro -%}
+
+{%- set ns = namespace(system_prompt="", last_user_index=-1) -%}
+{%- if messages and messages[0]["role"] == "system" -%}
+    {%- if messages[0].get("content") -%}
+        {%- set ns.system_prompt = parse_content(messages[0]["content"]) -%}
+    {%- endif -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "user" -%}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message.role + "\n" -}}
+    {%- if message.role == "assistant" -%}
+        {%- generation -%}
+        {%- set keep_thinking = preserve_thinking or loop.index0 > ns.last_user_index -%}
+        {%- set thinking = message.thinking or message.reasoning or message.reasoning_content -%}
+        {%- set thinking = thinking if thinking is string else "" -%}
+        {%- if thinking and keep_thinking -%}
+            {{- "<think>" + thinking + "</think>" -}}
+        {%- endif -%}
+        {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%}
+        {%- set _has_cfm = false -%}
+        {%- set content = "" -%}
+        {%- if message.get("content") -%}
+            {%- set content = parse_content(message.content) -%}
+        {%- endif -%}
+        {%- if not keep_thinking and "</think>" in content -%}
+            {%- set content = content.split("</think>")[-1] | trim -%}
+        {%- endif -%}
+        {%- if content.endswith(_cfm_tag) -%}
+            {%- set _has_cfm = true -%}
+            {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%}
+            {%- set content = content[:_trunc_len] -%}
+        {%- endif -%}
+        {{- content -}}
+        {%- if message.tool_calls -%}
+            {{- render_tool_calls(message.tool_calls) -}}
+        {%- endif -%}
+        {%- if _has_cfm -%}
+            {{- _cfm_tag -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+        {%- endgeneration -%}
+    {%- else %}
+        {%- if message.get("content") -%}
+            {{- parse_content(message["content"]) -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+    {%- endif %}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

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+size 17905750

gpu-pipelined/d1_3b_decode_fp16_pf64_s/tokenizer/tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "is_local": true,
+  "legacy": false,
+  "local_files_only": false,
+  "max_length": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "<|pad|>",
+  "pad_token_type_id": 0,
+  "padding_side": "left",
+  "processor_class": "Lfm2VlProcessor",
+  "tokenizer_class": "TokenizersBackend",
+  "use_default_system_prompt": false
+}

gpu-pipelined/d1_3b_decode_int8mlp_pf64_s/LICENSE

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+LFM Open License v1.0 
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by this document.
+
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+
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+
+"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
+
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+
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+
+"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work.
+
+"Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
+
+"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+
+"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
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+"Commercial Use" shall mean any use of the Work for direct or indirect commercial advantage or monetary compensation.
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+
+"Non-Commercial or Research Purposes" shall mean purposes that do not involve any use of the Work or a Derivative Work for Commercial Use.
+
+"Threshold" shall mean annual revenue of 10 million United States dollars ($10,000,000) or more.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, including the Commercial Use limitation set forth in Section 5, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
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+
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
+
+5. Commercial Use Limitation.
+
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+
+(b) Any Commercial Use of the Work or a Derivative Work by a Legal Entity that exceeds the Threshold is not licensed under this Agreement.
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+
+6. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
+
+7. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except for the reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
+
+8. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
+
+9. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+
+10. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+
+11. Termination. This License will terminate automatically and immediately if You fail to comply with any of its terms and conditions. Upon termination, You must cease all use of the Work and any Derivative Works and delete all copies in Your possession.
+
+END OF TERMS AND CONDITIONS

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+        "valueCache": [
+          [
+            8,
+            1,
+            8,
+            -1,
+            64
+          ],
+          "float16"
+        ],
+        "convState": [
+          [
+            22,
+            1,
+            2048,
+            2
+          ],
+          "float16"
+        ]
+      },
+      "dynamic": "-1 = dynamic: position_ids [1, seq] with seq in S..max_context_length-1; the KV sequence axis 2048..max_context_length (a host allocates it at max_context_length)"
+    },
+    "function_map": {
+      "main": [
+        "main"
+      ]
+    }
+  },
+  "source": {
+    "model_definition": "torch (conversion/d1/lfm2_d1_decoder.py: coreai_models.models.macos.lfm2_vl.Lfm2VlPipelinedForCausalLM with an Identity head)",
+    "hf_model_id": "LiquidAI/d1-3B",
+    "hf_revision": "da1fe36a861f24690f27f622dca1d8688503d113",
+    "license": "LFM Open License v1.0 (license: other, license_name: lfm1.0)",
+    "weights": {
+      "file": "model.safetensors",
+      "bytes": 6247065504,
+      "sha256": "50e03317847caf6df9a9aee27ed40f20554a86a21e60d1d47ba41a422b546c0c",
+      "keys": "model.language_model.*"
+    },
+    "tokenizer": {
+      "files_sha256": {
+        "tokenizer.json": "8096ecb9f54599d756c8de728a598a340bc1e43c0deb77ddd62456c38349fcee",
+        "tokenizer_config.json": "ef6770d12dd9ac58d334e979bf160c5cdfd41f13d205fd5d5516857645feb0ea",
+        "chat_template.jinja": "86f4770449a4797c9b4212d110b0cc70fb993c1ce960095bcfb12eea22f61cca"
+      }
+    }
+  },
+  "compression": {
+    "scheme": "int8mlp",
+    "linear": "int8 per-block-32 symmetric_with_clipping (weight only)",
+    "quantized": "every MLP linear (feed_forward.gate_proj / up_proj / down_proj)",
+    "excluded": "fp32: the four attention projections of every full-attention layer; fp16: the conv-mixer projections (conv.in_proj / out_proj), the embedding table (the tied table of head/option_rows), the conv1d, every RMSNorm, SDPA",
+    "excluded_names": [
+      ".*\\.conv\\.in_proj$",
+      ".*\\.conv\\.out_proj$",
+      ".*lm_head$",
+      ".*self_attn\\.(q_proj|k_proj|v_proj|out_proj)$"
+    ],
+    "excluded_types": [
+      "coreai_models.primitives.macos.rms_norm.RMSNorm",
+      "coreai_models.primitives.macos.sdpa.SDPA",
+      "torch.nn.modules.conv.Conv1d",
+      "torch.nn.modules.sparse.Embedding"
+    ],
+    "quantized_linear_modules": 90,
+    "fp16_layers": [],
+    "fp16_linear_modules": [
+      "model.layers.0.conv.in_proj",
+      "model.layers.0.conv.out_proj",
+      "model.layers.1.conv.in_proj",
+      "model.layers.1.conv.out_proj",
+      "model.layers.10.conv.in_proj",
+      "model.layers.10.conv.out_proj",
+      "model.layers.11.conv.in_proj",
+      "model.layers.11.conv.out_proj",
+      "model.layers.12.conv.in_proj",
+      "model.layers.12.conv.out_proj",
+      "model.layers.14.conv.in_proj",
+      "model.layers.14.conv.out_proj",
+      "model.layers.15.conv.in_proj",
+      "model.layers.15.conv.out_proj",
+      "model.layers.16.conv.in_proj",
+      "model.layers.16.conv.out_proj",
+      "model.layers.18.conv.in_proj",
+      "model.layers.18.conv.out_proj",
+      "model.layers.19.conv.in_proj",
+      "model.layers.19.conv.out_proj",
+      "model.layers.20.conv.in_proj",
+      "model.layers.20.conv.out_proj",
+      "model.layers.22.conv.in_proj",
+      "model.layers.22.conv.out_proj",
+      "model.layers.23.conv.in_proj",
+      "model.layers.23.conv.out_proj",
+      "model.layers.25.conv.in_proj",
+      "model.layers.25.conv.out_proj",
+      "model.layers.26.conv.in_proj",
+      "model.layers.26.conv.out_proj",
+      "model.layers.28.conv.in_proj",
+      "model.layers.28.conv.out_proj",
+      "model.layers.29.conv.in_proj",
+      "model.layers.29.conv.out_proj",
+      "model.layers.3.conv.in_proj",
+      "model.layers.3.conv.out_proj",
+      "model.layers.4.conv.in_proj",
+      "model.layers.4.conv.out_proj",
+      "model.layers.6.conv.in_proj",
+      "model.layers.6.conv.out_proj",
+      "model.layers.7.conv.in_proj",
+      "model.layers.7.conv.out_proj",
+      "model.layers.8.conv.in_proj",
+      "model.layers.8.conv.out_proj"
+    ],
+    "head": "none in the graph (the tied rows of the readout ids are head/option_rows)"
+  },
+  "decision": {
+    "output": "hidden [1, 64, 2048] per call: the final-norm hidden state at every position (no vocabulary head in the graph)",
+    "readout": "one row per question, fresh zero states per row; a row of T ids runs as ceil(T / 64) calls of 'main' (static S = 64): call c gets ids[64c : 64c + 64] with position_ids 0..64c+63, image_embeds zero for a text row; the last call is padded with <|pad|> (124893) and the hidden rows of the padded positions are discarded (causal: they cannot reach a real position). The hidden row of the row's last real token (the answer slot, T - 1) is the final-norm hidden the readout reads. A row fits when ceil(T / 64) * 64 <= 4095 (the position input's upper bound; the KV sequence axis is allocated at 4096).",
+    "spec": "conversion/d1/host.py (its docstring is the contract; test_host.py gates it against the provider's code at LiquidAI/d1-3B@da1fe36a861f24690f27f622dca1d8688503d113: prompt.py, api.py, runner.py)",
+    "request": {
+      "shape": "{state: JSON | null, questions: {name: question}} in request order",
+      "noul": "{type: 'noul', instructions: str, criteria?: {true?: str, false?: str} | null}",
+      "choice": "{type: 'choice' (the default when type is absent), instructions: str, criteria: {label: str | null}} with 1+ options",
+      "score": "{type: 'score', instructions: str, criteria: [str]} with 1..10 levels (the prompt asks for a single digit)",
+      "refuse": "anything else (no instructions, a non-string instructions or level, a choice without options, a score of 0 or more than 10 levels, a criteria of another JSON type)"
+    },
+    "prompt": {
+      "row_text": "prefix + suffix",
+      "prefix": "BOS + '<|im_start|>user\\n' + state_block + '\\nQUESTION:\\n' (state null: BOS + '<|im_start|>user\\n')",
+      "state_block": "a string as is + '\\n\\n'; any other JSON value json.dumps(state, ensure_ascii=False, indent=2) + '\\n\\n'",
+      "suffix": "question_block + '<|im_end|>\\n<|im_start|>assistant\\n'",
+      "choice_block": "instructions + '\\n\\nOptions:\\n' + '\\n'.join(code + ' ' + (desc or label.replace('_', ' '))) + '\\n\\nReply with the option code only.'",
+      "noul_block": "instructions + ('\\nYes: ' + str(criteria.get('true')) + '\\nNo: ' + str(criteria.get('false')) when criteria is a non-empty object) + '\\n\\nReply with yes or no only.'",
+      "score_block": "instructions + '\\n\\n' + '\\n'.join(str(i) + ' ' + level) + '\\n\\nReply with a single digit 0-' + str(K - 1) + ' only.'",
+      "special": {
+        "bos": {
+          "token": "<|startoftext|>",
+          "id": 124894
+        },
+        "im_start": {
+          "token": "<|im_start|>",
+          "id": 124899
+        },
+        "im_end": {
+          "token": "<|im_end|>",
+          "id": 124900
+        },
+        "pad": {
+          "token": "<|pad|>",
+          "id": 124893
+        },
+        "image": {
+          "token": "<image>",
+          "id": 124907
+        }
+      }
+    },
+    "options": {
+      "codes": "the labels when every label (strip) is one alphabetic character, else 'A'.. for at most 26, else '00', '01', ..",
+      "aliases": "each code takes its own id when it is one token not yet taken, else the first single-token untaken entry of A..Z, 00..99, a..z, #0..#199, AA..ZZ; the option line prints the taken code",
+      "groups": {
+        "choice": "[alias id] + [encode(' ' + code)[0]] when ' ' + code is one other token",
+        "noul": "[single-token forms of yes, Yes, YES], [the same of no, No, NO]",
+        "score": "[encode(str(i))[0]] for i in 0..K-1"
+      },
+      "keys": {
+        "noul": [
+          "yes",
+          "no"
+        ],
+        "choice": "the labels in request order",
+        "score": "'0'..'K-1'"
+      }
+    },
+    "tokens": {
+      "tokenizer": "tokenizer/tokenizer.json (BPE, ByteLevel; adds no BOS); add_special_tokens=False with the special tokens matched in the text (user text is not escaped)",
+      "row": "row_ids = encode(prefix + suffix); slot = len(row_ids) - 1",
+      "usage": "input_tokens = len(row) for one question; len(encode(prefix)) + sum(len(encode(suffix_q))) for several (the provider's Tree; trunk and branches encoded apart)"
+    },
+    "arithmetic": {
+      "logits": "z[id] = h_slot . E[id] in float64 for the ids of the question's groups (h_slot = the slot's hidden row, fp16 -> float64; E[id] = head/option_rows, fp32 -> float64)",
+      "pool": "score_k = max over group k of z",
+      "softmax": "p = softmax_k(score) in double (Python's math.exp and 3.12 sum()); the provider's vocabulary log-sum-exp cancels in a softmax over options"
+    },
+    "option_table": {
+      "files": [
+        "head/option_rows.safetensors",
+        "head/option_rows.json"
+      ],
+      "n": 2134,
+      "layout": "ascending int32 `ids` [n], fp32 `rows` [n, hidden]; row k = the tied embedding row of ids[k] (bf16 -> fp32, exact)",
+      "label_space": "the single-token strings among the code families A..Z, a..z, 0..9, 00..99, 100..999, #0..#199, AA..ZZ, the ' ' + code form of each, and yes / Yes / YES / no / No / NO",
+      "covers": "every noul and score question (1..10 levels); every choice whose labels are all ASCII letters (native one-letter codes) or that takes positional codes, up to 1639 options (the alias pool's limit)",
+      "refuse": "a request any of whose readout ids is not in the table is refused whole, before any graph call: \"questions.<name>: the token id <N> of label '<label>' is not in the option table\" (host.option_table_check; host.build_request(..., table_ids=))",
+      "refused_examples": "a choice of one-letter native labels outside A..Z / a..z (e.g. 'あ', 'é', 'α'); 1640 options or more are refused before it by the alias rule ('no single-token alias left')",
+      "provider_difference": "the provider's code reads any id; this refusal is the host's own (the fixture's readout ids are all in the table)"
+    },
+    "response": {
+      "noul": "{type: 'noul', noul: p[0]} (p = [P(yes), P(no)])",
+      "choice": "{type: 'choice', choice: labels[argmax], confidence: p[argmax], probabilities: {label: p}}",
+      "score": "{type: 'score', score: sum(i * p_i), confidence: p[argmax], probabilities: {'i': p_i}, legend: {'i': level_i}}",
+      "argmax": "the first index of the largest p; floats not rounded",
+      "body": "{answers: {name: answer} (request order), usage: {input_tokens, output_tokens: 0}}"
+    }
+  },
+  "vision": {
+    "image_token": {
+      "token": "<image>",
+      "id": 124907
+    },
+    "extension_ids": "the k-th <image> of the row (k counted over every picture and every crop, in text order) is sent as id V + k (V = vocab_size 128000, k < 2816); the graph reads image_embeds[k] for it",
+    "n_image_tokens": 2816,
+    "image_embeds": [
+      [
+        2816,
+        2048
+      ],
+      "float16"
+    ],
+    "rows": "rows 0 .. n - 1 (n = the row's <image> count) = the tower bundle's image_embeds of every crop, each crop's first h w / 4 rows (its merged grid row-major), concatenated in crop order (pictures in text order; per picture its tiles row-major, then the thumbnail), cast to float16; rows n .. N - 1 zero; the same buffer is bound to every call of the row",
+    "text_only": "image_embeds zero and no extension id",
+    "limits": {
+      "one_picture": "at most 2,810 image tokens (10 tiles + a thumbnail, aspect up to 4:1 after cap_pixels; K/results/vision_grid_table.json)",
+      "refuse": "a request whose pictures need more than 2816 image tokens together, or whose row is over the position bound (host.graph_context_check), is refused whole before any graph call",
+      "refusal_text": "images: <n> image tokens over the graph's 2816 image rows"
+    },
+    "tower": {
+      "bundle": "d1_3b_vision_<dtype> (export_vision.py; contract in lfm2_vl_tower.py's header)",
+      "per_crop": "vision_host.tower_inputs(crop, position table) -> patches [1024, 768], pos_table [1024, d], key_bias [1024], unshuffle_idx [256, 4] int32 -> image_embeds [256, text hidden]; the crop's rows are the first h w / 4"
+    },
+    "crops": "per picture (after cap_pixels): one crop at the smart size (aspect kept, sides multiples of 32, 64..256 image tokens) when max(16, round32(h)) * max(16, round32(w)) <= 524,288 px, else rows x cols tiles of 512 x 512 (2 <= rows * cols <= 10, the closest aspect) then a thumbnail at the smart size",
+    "tokens": "<|image_start|> + (one crop: <image> x tokens | tiles: <|img_row_r_col_c|> + <image> x 256 per tile, then <|img_thumbnail|> + <image> x thumbnail tokens) + <|image_end|>; the prompt holds one '<image>' per picture after '<|im_start|>user\\n', replaced by that run (vision_host.prompt_ids)",
+    "spec": "conversion/d1/vision_host.py (sections 1-8; test_vision_host.py gates it against the provider's code and transformers 5.19's processor)"
+  },
+  "compilation": {
+    "date": "2026-10-08T07:38:28.711600+00:00",
+    "targets": []
+  },
+  "strip": {
+    "what": "the .aimodel re-saved after coreai-torch's strip_debug_info: every op's source location (file path, call stack) replaced by an unknown location; the ops and the weights are not touched",
+    "from_bundle": "d1_3b_decode_int8mlp_pf64",
+    "from_main_mlirb": {
+      "bytes": 3704646757,
+      "sha256": "2ab109907108870b8e7e191162d98bff7107b0ff338e3ead4854f490d4a6850b"
+    },
+    "main_mlirb": {
+      "bytes": 3704370745,
+      "sha256": "1836fba840ea88a3cec691be25a7af25585232fdb11b22e0b11cb2748079473f"
+    },
+    "tool": "conversion/d1/strip_bundle.py (coreai_torch.debugging.debug_info.strip_debug_info)",
+    "versions": {
+      "coreai-torch": "0.4.1",
+      "coreai-core": "1.0.0b2"
+    }
+  }
+}

gpu-pipelined/d1_3b_decode_int8mlp_pf64_s/tokenizer/chat_template.jinja

@@ -0,0 +1,125 @@
+{{- bos_token -}}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+
+{%- macro format_arg_value(arg_value) -%}
+    {%- if arg_value is string -%}
+        {{- "'" + (arg_value | replace("\\", "\\\\") | replace("'", "\\'") | replace("\n", "\\n") | replace("\r", "\\r")) + "'" -}}
+    {%- elif arg_value is mapping or arg_value is iterable -%}
+        {{- arg_value | tojson -}}
+    {%- else -%}
+        {{- arg_value | string -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro parse_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- elif content is mapping -%}
+        {{- content | tojson -}}
+    {%- elif content is iterable -%}
+        {%- set _ns = namespace(result="") -%}
+        {%- for item in content -%}
+            {%- if item is string -%}
+                {%- set _ns.result = _ns.result + item -%}
+            {%- elif item is mapping and item.get("type") == "image" -%}
+                {%- set _ns.result = _ns.result + "<image>" -%}
+            {%- elif item is mapping and item.get("type") == "text" -%}
+                {%- set _ns.result = _ns.result + ((item.get("text") or "") | string) -%}
+            {%- else -%}
+                {%- set _ns.result = _ns.result + (item | tojson) -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {{- _ns.result -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_calls(tool_calls) -%}
+    {%- set tool_calls_ns = namespace(tool_calls=[]) -%}
+    {%- for tool_call in tool_calls -%}
+        {%- set func = tool_call["function"] if "function" in tool_call else tool_call -%}
+        {%- set func_name = func["name"] -%}
+        {%- set func_args = func.get("arguments") -%}
+        {%- set args_ns = namespace(arg_strings=[]) -%}
+        {%- if func_args is mapping -%}
+            {%- for arg_name, arg_value in func_args.items() -%}
+                {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
+            {%- endfor -%}
+        {%- elif func_args is string and (func_args | trim) not in ["", "{}", "null"] -%}
+            {{- raise_exception("Tool call arguments must be a mapping, got a JSON-encoded string: parse arguments with json.loads() before applying the chat template") -}}
+        {%- endif -%}
+        {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
+    {%- endfor -%}
+    {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
+{%- endmacro -%}
+
+{%- set ns = namespace(system_prompt="", last_user_index=-1) -%}
+{%- if messages and messages[0]["role"] == "system" -%}
+    {%- if messages[0].get("content") -%}
+        {%- set ns.system_prompt = parse_content(messages[0]["content"]) -%}
+    {%- endif -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "user" -%}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message.role + "\n" -}}
+    {%- if message.role == "assistant" -%}
+        {%- generation -%}
+        {%- set keep_thinking = preserve_thinking or loop.index0 > ns.last_user_index -%}
+        {%- set thinking = message.thinking or message.reasoning or message.reasoning_content -%}
+        {%- set thinking = thinking if thinking is string else "" -%}
+        {%- if thinking and keep_thinking -%}
+            {{- "<think>" + thinking + "</think>" -}}
+        {%- endif -%}
+        {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%}
+        {%- set _has_cfm = false -%}
+        {%- set content = "" -%}
+        {%- if message.get("content") -%}
+            {%- set content = parse_content(message.content) -%}
+        {%- endif -%}
+        {%- if not keep_thinking and "</think>" in content -%}
+            {%- set content = content.split("</think>")[-1] | trim -%}
+        {%- endif -%}
+        {%- if content.endswith(_cfm_tag) -%}
+            {%- set _has_cfm = true -%}
+            {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%}
+            {%- set content = content[:_trunc_len] -%}
+        {%- endif -%}
+        {{- content -}}
+        {%- if message.tool_calls -%}
+            {{- render_tool_calls(message.tool_calls) -}}
+        {%- endif -%}
+        {%- if _has_cfm -%}
+            {{- _cfm_tag -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+        {%- endgeneration -%}
+    {%- else %}
+        {%- if message.get("content") -%}
+            {{- parse_content(message["content"]) -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+    {%- endif %}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

gpu-pipelined/d1_3b_decode_int8mlp_pf64_s/tokenizer/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8096ecb9f54599d756c8de728a598a340bc1e43c0deb77ddd62456c38349fcee
+size 17905750

gpu-pipelined/d1_3b_decode_int8mlp_pf64_s/tokenizer/tokenizer_config.json

@@ -0,0 +1,18 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "is_local": true,
+  "legacy": false,
+  "local_files_only": false,
+  "max_length": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "<|pad|>",
+  "pad_token_type_id": 0,
+  "padding_side": "left",
+  "processor_class": "Lfm2VlProcessor",
+  "tokenizer_class": "TokenizersBackend",
+  "use_default_system_prompt": false
+}

gpu-pipelined/d1_3b_vision_fp16w32_s/LICENSE

@@ -0,0 +1,71 @@
+LFM Open License v1.0 
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by this document.
+
+"Licensor" shall mean Liquid AI, Inc.
+
+"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
+
+"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
+
+"Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
+
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+END OF TERMS AND CONDITIONS

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+{
+  "schema": "d1-vision-tower/1",
+  "kind": "vision-tower",
+  "name": "d1_3b_vision_fp16w32_s",
+  "assets": {
+    "main": "d1_3b_vision_fp16w32_s.aimodel"
+  },
+  "dtype": {
+    "name": "fp16w32",
+    "weights": "float16 storage, read through a float32 cast",
+    "math": "float32",
+    "inputs_output": "float32"
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+    "function": "main",
+    "inputs": {
+      "patches": [
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+          768
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+        "float32"
+      ],
+      "pos_table": [
+        [
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+          1152
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+      "unshuffle_idx": [
+        [
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+          4
+        ],
+        "int32"
+      ]
+    },
+    "outputs": {
+      "image_embeds": [
+        [
+          256,
+          2048
+        ],
+        "float32"
+      ]
+    },
+    "states": {},
+    "meaning": {
+      "patches": "the crop's patches row-major over its (h, w) patch grid, [y][x][c] inside a 16 x 16 patch (channel fastest), (x - 127.5) / 127.5 in float32; rows h * w .. 1023 zero",
+      "pos_table": "host/position_embedding.safetensors [256, d] as [16, 16, d], resized to (h, w) by F.interpolate(bilinear, align_corners=False, antialias=True) in float32, row-major [h * w, d]; rows h * w .. 1023 zero (transformers writes row 0 there; those rows never reach a real one)",
+      "key_bias": "0 for rows 0 .. h * w - 1, -inf after: added to every query's attention scores",
+      "unshuffle_idx": "row k = i * (w / 2) + j of the (h / 2, w / 2) merged grid: [2i w + 2j, 2i w + 2j + 1, (2i + 1) w + 2j, (2i + 1) w + 2j + 1] (channel m * d + c of the 4 d-vector comes from patch m); rows k >= h w / 4 are [0, 0, 0, 0]",
+      "image_embeds": "rows 0 .. h w / 4 - 1 = the crop's image tokens in merged-grid row-major order (the projector's output, no LayerNorm); rows after are padding and are discarded"
+    },
+    "math": "x = patch_embedding(patches) + pos_table; 27 x [x += attn(LN1 x) (scores = q k^T / sqrt(72) + key_bias, softmax over keys); x += fc2(gelu_tanh(fc1(LN2 x)))]; post_layernorm; gather the 4 rows of unshuffle_idx -> [256, 4608]; linear_2(gelu_exact(linear_1(.)))",
+    "attention": "matmul",
+    "input_names": [
+      "patches",
+      "pos_table",
+      "key_bias",
+      "unshuffle_idx"
+    ],
+    "output_names": [
+      "image_embeds"
+    ]
+  },
+  "config": {
+    "hidden_size": 1152,
+    "intermediate_size": 4304,
+    "num_hidden_layers": 27,
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "patch_size": 16,
+    "num_patches": 256,
+    "layer_norm_eps": 1e-06,
+    "hidden_act": "gelu_pytorch_tanh",
+    "vision_use_head": false,
+    "downsample_factor": 2,
+    "projector_hidden_size": 2048,
+    "projector_hidden_act": "gelu",
+    "projector_bias": true,
+    "projector_use_layernorm": false,
+    "text_hidden_size": 2048
+  },
+  "host": {
+    "spec": "conversion/d1/vision_host.py (sections 1-8; test_vision_host.py gates it against the provider's code and transformers 5.19's Lfm2VlProcessor: ids, pixel_values bit for bit, the position table, the unshuffle)",
+    "picture": "decoded with its EXIF orientation applied, RGB; over 1024 x 1024 pixels it is first scaled by s = sqrt(1024^2 / (w h)) to (int(w s), int(h s)) with Pillow BICUBIC (the provider's cap_pixels)",
+    "crops": {
+      "rule": "F = 32; round_f(v) = round(v / F) * F (half to even). One crop when max(16, round_f(h)) * max(16, round_f(w)) <= 524,288, else tiles. smart size (h_bar, w_bar) = (round_f(h), round_f(w)) floored at 32, scaled down by sqrt(h w / 262,144) (floor to F) when over 262,144 px, up by sqrt(65,536 / (h w)) (ceil to F) when under 65,536 px",
+      "single": "the picture resized to (h_bar, w_bar): grid (h_bar / 16, w_bar / 16) <= 1024 patches, both even",
+      "tile": "the (cols, rows) of 2 <= c r <= 10 closest to the aspect w / h (vision_host.TARGET_RATIOS order, ties to the later ratio when w h > 0.5 * 512^2 * c r); the picture resized to (512 rows, 512 cols) and cut into 512 x 512 tiles, row-major: grid 32 x 32 = 1024 patches, 256 tokens each",
+      "thumbnail": "after the tiles: the picture (before the tile resize) at the smart size, as a single crop",
+      "order": "tile (1, 1), (1, 2), .., (rows, cols), thumbnail; a picture has at most 11 crops and 2,810 image tokens up to an aspect of 4:1 (vision_grid_table.json)"
+    },
+    "resize": "torch's uint8 antialiased bicubic (transformers 5's processor is the torchvision backend): per axis int16 fixed-point weights at the axis' own precision, width pass then height pass, uint8 between (vision_host.torch_bicubic, bit-exact to torch 2.9.1) — not Pillow's 22-bit filter",
+    "normalize": "(x - 127.5) / 127.5 in float32",
+    "per_crop_inputs": "vision_host.tower_inputs(crop_u8, position_table) -> patches, pos_table, key_bias, unshuffle_idx",
+    "tokens": "<|image_start|> + (single: <image> x h w / 4 | tiles: per tile <|img_row_r_col_c|> + <image> x 256, then <|img_thumbnail|> + <image> x thumbnail tokens) + <|image_end|>; ids <image> 124907, <|img_row_r_col_c|> 124908 + 10 (r - 1) + (c - 1), <|img_thumbnail|> 125008, <|image_start|> 125009, <|image_end|> 125010",
+    "decoder": "the k-th <image> of the row (counted over every picture and crop in text order) is sent as id V + k (V = 128,000) and reads row k of the decoder's image_embeds = the crops' image_embeds rows concatenated in crop order (each crop's first h w / 4 rows), fp16"
+  },
+  "host_files": {
+    "host/position_embedding.safetensors": "the checkpoint's model.vision_tower.vision_model.embeddings.position_embedding.weight, fp32 [256, d]"
+  },
+  "source": {
+    "model_definition": "torch (conversion/d1/lfm2_vl_tower.py: Lfm2VlTowerExact, the overlay's Lfm2VlVisionEncoder with the grid as inputs)",
+    "hf_model_id": "LiquidAI/d1-3B",
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+      "coreai-core": "1.0.0b2"
+    }
+  }
+}