---
license: apache-2.0
language: [en]
tags:
- nexusbpe
- tokenizer
- structured-action-model
- agentic-ai
- robotics
- iot
- workflow-automation
- multi-domain
inference: false
---

# SAM Tokenizer — `AMFORGE/sam_tokenizer`

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.

---

## What it does

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.

## Why it matters

Generic LLM tokenizers shred coordinates and identifiers into fragments:

```
0.5      →  ['0', '.', '5']         (3 tokens)
-1.2     →  ['-', '1', '.', '2']    (4 tokens)
8080     →  ['8', '0', '80']        (3 tokens)
```

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.

| | 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 |

Lower sequence length → lower latency, lower memory, sharper attention
on the parts that matter.

---

## Highlights

- **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

---

## Loading

The tokenizer ships as a binary model file. Load it via the lightweight
NexusBPE wrapper:

```python
from huggingface_hub import hf_hub_download

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")
tok = NexusBPE(path)

ids = tok.encode('<ROS><TASK>move to x=0.5 y=-1.2 z=0.8</TASK>')
print(f"Tokens: {len(ids)}")
print(f"Roundtrip: {tok.decode(ids)}")
```

---

## Domain markers

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: NexusBPE for Multi-Domain Structured Action Generation},
  author = {AMFORGE},
  year   = {2026},
  url    = {https://huggingface.co/AMFORGE/sam_tokenizer}
}
```

---

Built with **NexusBPE** by **AMFORGE** — https://huggingface.co/AMFORGE