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license: mit
language:
- en
tags:
- governed-language-model
- semiconductor
- conversational
- governance
pipeline_tag: text-generation
---
# Axiom-560M
**A Governed Language Model — every output ships its own proof of governance.**
Axiom-560M is a dual-mode decoder (conversational + semiconductor) trained on 56,000 governed pairs. Governance isn't a filter — it's the architecture.
## Model Details
| | |
|---|---|
| Architecture | BLOOM-560M (decoder-only transformer) |
| Parameters | 559M |
| Training data | 56,000 governed pairs (conversational + semiconductor RTL) |
| Eval loss | 0.1635 |
| Perplexity | 1.18 overall (1.16 conversational, 1.64 semiconductor) |
| License | MIT |
## Modes
**Conversational** — governed dialogue (perplexity 1.16)
**Semiconductor** — governed RTL and hardware specifications (perplexity 1.64)
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("MetaCortex-Dynamics/Axiom-560M")
tokenizer = AutoTokenizer.from_pretrained("MetaCortex-Dynamics/Axiom-560M")
input_ids = tokenizer.encode("<|conv|>What is governed generation?", return_tensors="pt")
output = model.generate(input_ids, max_new_tokens=200, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## Governance
Every output passes through a four-phase governance pipeline:
```
PROPOSE → DECIDE → PROMOTE → EXECUTE
```
- 15 grounding operators as token vocabulary
- 7 interrogative witnesses as grammar
- Admissibility gates (G₁-G₇) with three-valued semantics
- Machine-verifiable governance trace on every output
## Links
- [Interactive Demo](https://huggingface.co/spaces/MetaCortex-Dynamics/Axiom-Ref) — try Axiom in your browser
- [Source Code](https://github.com/MetaCortex-Dynamics/Axiom) — MIT license
- [Benchmark Results](https://github.com/MetaCortex-Dynamics/Axiom/blob/main/BENCHMARKS.md) — 100% governance vs 0% all LLMs
## Organization
[MetaCortex Dynamics DAO](https://github.com/MetaCortex-Dynamics)
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