docs: dataset card with native + raw access paths

README.md

@@ -1,106 +1,141 @@
 ---
-dataset_info:
-  features:
-  - name: meta
-    struct:
-    - name: tier
-      dtype: string
-    - name: split
-      dtype: string
-    - name: bucket
-      dtype: string
-    - name: seed
-      dtype: uint64
-    - name: axes
-      struct:
-      - name: n
-        dtype: int64
-      - name: k
-        dtype: int64
-    - name: renders
-      sequence: string
-  - name: program
-    list:
-    - name: op
-      dtype: string
-    - name: args
-      sequence: int64
-    - name: label
-      dtype: string
-    - name: target
-      dtype: string
-  - name: trace
-    struct:
-    - name: steps
-      list:
-      - name: pc
-        dtype: int64
-      - name: regs
-        sequence: int64
-      - name: stack
-        sequence: int64
-      - name: emitted
-        dtype: int64
-    - name: output
-      sequence: int64
-    - name: halted
-      dtype: bool
-  - name: renders
-    struct:
-    - name: direct
-      struct:
-      - name: input_ids
-        sequence: int64
-      - name: target_ids
-        sequence: int64
-      - name: input_text
-        dtype: string
-      - name: target_text
-        dtype: string
-  splits:
-  - name: train
-    num_bytes: 808043736
-    num_examples: 200000
-  - name: eval_len_128
-    num_bytes: 470063605
-    num_examples: 20000
-  - name: eval_len_16
-    num_bytes: 66397821
-    num_examples: 20000
-  - name: eval_len_32
-    num_bytes: 124082576
-    num_examples: 20000
-  - name: eval_len_48
-    num_bytes: 181742886
-    num_examples: 20000
-  - name: eval_len_64
-    num_bytes: 239414953
-    num_examples: 20000
-  - name: eval_len_8
-    num_bytes: 37534223
-    num_examples: 20000
-  - name: eval_len_96
-    num_bytes: 354723239
-    num_examples: 20000
-  download_size: 188376341
-  dataset_size: 2282003039
+license: mit
+task_categories:
+  - text-generation
+  - other
+language:
+  - en
+tags:
+  - synthetic
+  - reasoning
+  - program-execution
+  - tiny-vm
+  - interpretability
+pretty_name: Tiny-VM Tier 1 (Register Traces)
+size_categories:
+  - 100K<n<1M
 configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: eval_len_128
-    path: data/eval_len_128-*
-  - split: eval_len_16
-    path: data/eval_len_16-*
-  - split: eval_len_32
-    path: data/eval_len_32-*
-  - split: eval_len_48
-    path: data/eval_len_48-*
-  - split: eval_len_64
-    path: data/eval_len_64-*
-  - split: eval_len_8
-    path: data/eval_len_8-*
-  - split: eval_len_96
-    path: data/eval_len_96-*
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train-*
+      - split: eval_len_8
+        path: data/eval_len_8-*
+      - split: eval_len_16
+        path: data/eval_len_16-*
+      - split: eval_len_32
+        path: data/eval_len_32-*
+      - split: eval_len_48
+        path: data/eval_len_48-*
+      - split: eval_len_64
+        path: data/eval_len_64-*
+      - split: eval_len_96
+        path: data/eval_len_96-*
+      - split: eval_len_128
+        path: data/eval_len_128-*
 ---
+
+# Tiny-VM Tier 1 — Register Traces
+
+Synthetic dataset of straight-line Tiny-VM programs with full execution traces and pre-rendered training prompts. Tier 1 of the **FANC "Latent State as Computer"** experimental curriculum, focused on **register-file tracking under bounded program length**.
+
+- **200,000** train programs
+- **140,000** stratified eval programs (7 buckets × 20,000, one per program length `n ∈ {8, 16, 32, 48, 64, 96, 128}`)
+- Generator: `tinyvm.generators.gen_register_trace` (LOAD / ADD / SUB / MOV / PRINT, no control flow)
+- Per-row: full `Program` (instruction list) + `ExecutionTrace` (per-step register snapshots, output stream, halted flag) + `direct`-mode pre-rendered prompt (token IDs and surface text)
+- Deterministic: every row is reconstructable from `(meta.seed, meta.axes)` via `tinyvm.data.configs.TIER1.build`
+- Byte-integrity: every file's SHA-256 is in `manifest.json` (raw layout); `python -m tinyvm.data verify` re-hashes against it
+- Seed base: `0`
+
+## Two access paths
+
+### 1. Native HF datasets (Parquet)
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Genesis-AI-Labs/tinyvm-tier1")
+ds["train"]          # 200,000 rows
+ds["eval_len_16"]    # 20,000 rows (one per length bucket)
+
+# Each row has: meta, program, trace, renders
+row = ds["train"][0]
+print(row["meta"]["seed"], row["meta"]["axes"])
+print(row["renders"]["direct"]["input_text"])    # surface tokens
+print(row["renders"]["direct"]["input_ids"])     # 64-vocab token IDs
+```
+
+### 2. Raw JSONL + manifest (byte-integrity)
+
+The exact files emitted by `python -m tinyvm.data emit --tier tier1 --out data/` are mirrored under `raw/` on the Hub. Download and verify:
+
+```bash
+huggingface-cli download Genesis-AI-Labs/tinyvm-tier1 \
+    --repo-type dataset \
+    --include "raw/*" \
+    --local-dir ./data
+
+python -m tinyvm.data verify --dataset ./data/raw    # exits 0 if SHA-256 matches manifest
+```
+
+Then stream with the project's loader (skip Program/Trace reconstruction for fast DataLoader pipelines):
+
+```python
+from tinyvm.data import load_jsonl, load_prompts
+
+# Full row with reconstructed Program + ExecutionTrace.
+for row in load_jsonl("data/raw/train.jsonl"):
+    ...
+
+# Fast path: just (input_ids, target_ids) tuples for the chosen render mode.
+for input_ids, target_ids in load_prompts("data/raw/train.jsonl", mode="direct"):
+    ...
+```
+
+## Schema
+
+Each row is a JSON object with four top-level keys:
+
+| Key | Type | Description |
+|---|---|---|
+| `meta` | object | `{tier: "tier1", split: "train"\|"eval", bucket: <name>\|null, seed: int, axes: {n: int, k: int}, renders: ["direct"]}` |
+| `program` | array | List of instruction dicts: `{op: "LOAD"\|"ADD"\|..., args: [...], label?: str, target?: str}` |
+| `trace` | object | `{steps: [{pc: int, regs: [int×8]}, ...], output: [int, ...], halted: bool}` |
+| `renders` | object | `{direct: {input_ids: [int], target_ids: [int], input_text: str, target_text: str}}` |
+
+The vocabulary is the project's custom 64-token vocabulary (see `tinyvm.tokeniser`). Token 0 is `<pad>`, registers are `R0..R7`, etc.
+
+## Tier 1 axes
+
+| Axis | Range / Values | Meaning |
+|---|---|---|
+| `n` | uniformly sampled from `[8, 32]` (train); fixed per eval bucket | Program length in instructions |
+| `k` | uniformly chosen from `{2, 4, 8}` | Number of distinct registers used |
+
+Eval buckets are length-stratified at `n ∈ {8, 16, 32, 48, 64, 96, 128}` with `k=4` fixed. The `len_48` through `len_128` buckets test **length generalisation** beyond the training distribution (which tops out at `n=32`) — the `len_128` bucket is 4× the longest training program.
+
+## Reproducibility
+
+The dataset is bit-exact reproducible:
+
+```bash
+git clone https://github.com/mr-siddy/FANC
+cd FANC
+pip install -e .
+python -m tinyvm.data emit --tier tier1 --out data/ --seed 0
+python -m tinyvm.data verify --dataset data/tier1   # exit 0
+```
+
+The verify step re-hashes every emitted file's SHA-256 and compares against `manifest.json`. Manifest also records `tinyvm_commit` (git SHA at emit time) so consumers can pin to the exact code that produced the data.
+
+## Provenance
+
+- **Project:** FANC — "Latent State as Computer" (research)
+- **Code:** [github.com/mr-siddy/FANC](https://github.com/mr-siddy/FANC) — `tinyvm/data/` sub-package
+- **Spec:** `docs/superpowers/specs/2026-05-16-tinyvm-data-pipeline-design.md`
+- **Parent doc:** `Latent_State_as_Computer.docx` §11.2 (dataset sizes), §17 (Day 3)
+- **Companion datasets** (to come): `tinyvm-tier0` (counter programs), `tinyvm-tier2` (branched programs with CoT renders)
+
+## License
+
+MIT. Use freely.