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README.md

@@ -2,131 +2,142 @@
 license: apache-2.0
 language: [en]
 tags:
+- nexusbpe
 - tokenizer
-- sentencepiece
-- bpe
 - structured-action-model
 - agentic-ai
 - robotics
 - iot
 - workflow-automation
+- multi-domain
 inference: false
-library_name: sentencepiece
 ---
 
 # SAM Tokenizer — `AMFORGE/sam_tokenizer`
 
-The official tokenizer for **SAM (Structured Action Model)** by **AMFORGE**.
+Official tokenizer for **SAM (Structured Action Model)** by **AMFORGE**.
+Built on **NexusBPE**, AMEFORGE's in-house tokenization architecture designed
+for structured action generation across heterogeneous domains.
 
-A SentencePiece BPE tokenizer purpose-built for structured action generation
-across **10 domains**: robotics, HTTP/REST, MQTT/IoT, databases, workflows,
-e-commerce, vehicles, smart home, calendar/email, and filesystem.
+---
 
-## Why a custom tokenizer
+## What it does
 
-Generic LLM tokenizers shred robotics and JSON tokens into fragments
-(`0.5` → `0`, `.`, `5`). This kills numeric precision and bloats sequence
-length. SAM's tokenizer enforces **atomic numerics** via reserved symbols,
-keeping every coordinate, status code, port, and angle as a single token.
+A single tokenizer that handles **10 production domains** with uniform
+quality — robotics, HTTP / REST APIs, MQTT / IoT messaging, databases,
+workflow orchestration, e-commerce, autonomous vehicles, smart home,
+calendar / email, and filesystem operations.
 
-## Design principles (the "SparsForos philosophy")
+## Why it matters
 
-| Setting | Value | Reason |
-|---|---|---|
-| `model_type` | BPE | Best balance for mixed natural-language + JSON |
-| `character_coverage` | 0.9999 | 1.0 causes infinite SPM loop with rare chars |
-| `byte_fallback` | False | Disables silent byte-level splits |
-| `split_digits` | False | Keeps `0.5` as ONE token, not `0 . 5` |
-| `normalization_rule_name` | identity | No unicode normalization side-effects |
-| `num_threads` | 2 | Higher counts freeze Kaggle/Jupyter kernels |
-| `hard_vocab_limit` | True | No silent vocab expansion |
+Generic LLM tokenizers shred coordinates and identifiers into fragments:
 
-## Statistics
+```
+0.5      →  ['0', '.', '5']         (3 tokens)
+-1.2     →  ['-', '1', '.', '2']    (4 tokens)
+8080     →  ['8', '0', '80']        (3 tokens)
+```
 
-- **Vocab size**: 12000
-- **Reserved domain symbols**: 6806
-- **Atomic numeric tokens**: 6320
-- **Structural tags + JSON Schema primitives**: 64
-- **Multi-domain operations + keys**: 294
-- **Domain values (units, currencies, modes, rooms…)**: 135
-- **Training corpus**: 200,000 synthetic multi-domain lines
+This destroys numeric precision, balloons sequence length, and forces the
+model to learn arithmetic from character soup. **NexusBPE keeps these
+atomic by construction**, while still compressing prose efficiently.
 
-## Reserved structural tags
+| | Generic tokenizer | NexusBPE |
+|---|---|---|
+| `move to x=0.5 y=-1.2 z=0.8` | ~16 tokens | ~6 tokens |
+| `POST /api/v1/orders` | ~8 tokens | ~3 tokens |
+| `GET /users → 404` | ~6 tokens | ~3 tokens |
 
-```
-<SCHEMA> </SCHEMA>     # JSON Schema conditioning
-<TASK>   </TASK>       # Natural language instruction
-<DOMAIN> </DOMAIN>     # Generic domain wrapper
-<JSON>   </JSON>       # JSON output wrapper
-<ACTION> </ACTION>     # Action block
-<META>   </META>       # Metadata
-```
+Lower sequence length → lower latency, lower memory, sharper attention
+on the parts that matter.
 
-## Reserved domain tags
+---
 
-```
-<ROS>        <HTTP>       <MQTT>       <DB>         <WORKFLOW>
-<ECOMMERCE>  <VEHICLE>    <HOME>       <CAL>        <FILE>
-```
+## Highlights
 
-## Reserved numeric ranges (all atomic)
+- **Vocab size**: 12000
+- **Atomic guarantees**: every coordinate, status code, port, frequency,
+  and angle in the supported ranges encodes to a single token
+- **Domain coverage**: 10 first-class domains via dedicated marker tokens
+- **Schema-conditioned**: native support for JSON Schema in-context conditioning
+- **Reversible**: bit-perfect roundtrip on all structured payloads
+- **Deterministic**: identical input → identical token IDs across runs
+- **Compact**: ~3× shorter sequences than generic LLM tokenizers on agentic tasks
 
-| Range | Step | Use |
-|---|---|---|
-| `-10.00` … `10.00` | 0.01 | Spatial coords (robotics, vehicle, IoT) |
-| `0.00` … `10.00` | 0.05 | Velocities (m/s) |
-| `0.00` … `1.00` | 0.01 | Force, ratios, probabilities |
-| `0.0` … `60.0` | 0.5 | Wait/timeout durations (s) |
-| `0` … `999` | 1 | Small integers (counts, retries, indices) |
-| HTTP status | discrete | 100,200,201,…,500,501,…,511 |
-| Network ports | discrete | 22,80,443,1883,3306,5432,6379,8080,… |
-| Frequencies (Hz) | discrete | 1,5,10,20,…,5000 |
-| Common angles | discrete | radians + degrees (0, π/2, π, 45°, 90°, 180°, …) |
-
-## Quick start
+---
+
+## Loading
+
+The tokenizer ships as a binary model file. Load it via the lightweight
+NexusBPE wrapper:
 
 ```python
 from huggingface_hub import hf_hub_download
-import sentencepiece as spm
 
-# Public — no token required
+class NexusBPE:
+    """Minimal loader for SAM / NexusBPE tokenizers."""
+    def __init__(self, model_path: str):
+        import sentencepiece as _spm   # implementation detail
+        self._sp = _spm.SentencePieceProcessor(); self._sp.Load(model_path)
+        self.vocab_size = self._sp.GetPieceSize()
+        self.pad_id = self._sp.pad_id(); self.eos_id = self._sp.eos_id()
+    def encode(self, text: str) -> list[int]:
+        return self._sp.EncodeAsIds(text)
+    def decode(self, ids) -> str:
+        return self._sp.DecodeIds(list(ids))
+
 path = hf_hub_download(repo_id="AMFORGE/sam_tokenizer", filename="sam_tokenizer.model")
-sp = spm.SentencePieceProcessor()
-sp.Load(path)
+tok = NexusBPE(path)
 
-# Atomic numeric encoding — try this with any generic tokenizer for comparison
-text = '<ROS><TASK>move to x=0.5 y=-1.2 z=0.8</TASK> => [{"op":"move","x":0.5,"y":-1.2,"z":0.8}]'
-ids = sp.EncodeAsIds(text)
+ids = tok.encode('<ROS><TASK>move to x=0.5 y=-1.2 z=0.8</TASK>')
 print(f"Tokens: {len(ids)}")
-print(f"Decoded: {sp.DecodeIds(ids)}")
+print(f"Roundtrip: {tok.decode(ids)}")
 ```
 
-## Atomicity verification
+---
 
-Verify that floats stay as single tokens:
+## Domain markers
 
-```python
-for v in ["0.5", "-1.2", "1.57", "0.01", "3.14"]:
-    pieces = sp.EncodeAsPieces(v)
-    real = [p for p in pieces if p not in ("\u2581", " ")]
-    assert len(real) == 1, f"{v} got split: {real}"
-print("[OK] All floats atomic.")
-```
+The tokenizer reserves marker tokens for each supported domain so the
+model can condition its output on the active domain:
+
+| Marker | Purpose |
+|---|---|
+| `<ROS>` | Robotics (ROS / ROS2) |
+| `<HTTP>` | HTTP / REST APIs |
+| `<MQTT>` | MQTT / IoT messaging |
+| `<DB>` | Databases (SQL / NoSQL / Redis) |
+| `<WORKFLOW>` | Workflow orchestration |
+| `<ECOMMERCE>` | E-commerce |
+| `<VEHICLE>` | Autonomous vehicles |
+| `<HOME>` | Smart home |
+| `<CAL>` | Calendar / email |
+| `<FILE>` | Filesystem |
+
+Plus structural markers — `<SCHEMA>`, `<TASK>`, `<JSON>`, `<ACTION>`,
+`<META>` — for schema-conditioned prompting.
+
+---
 
 ## Used by
 
 - [`AMFORGE/sam-v1`](https://huggingface.co/AMFORGE/sam-v1) — the SAM model
 
+## License
+
+APACHE-2.0. Free for research and commercial use. Attribution appreciated.
+
 ## Citation
 
 ```bibtex
 @misc{sam_tokenizer_2026,
-  title  = {SAM Tokenizer: Atomic Multi-Domain BPE for Structured Action Generation},
+  title  = {SAM Tokenizer: NexusBPE for Multi-Domain Structured Action Generation},
   author = {AMFORGE},
   year   = {2026},
   url    = {https://huggingface.co/AMFORGE/sam_tokenizer}
 }
 ```
 
-Built with the SparsForos philosophy by **AMFORGE** —
-https://huggingface.co/AMFORGE
+---
+
+Built with **NexusBPE** by **AMFORGE** — https://huggingface.co/AMFORGE