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

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 — try Axiom in your browser
• Source Code — MIT license
• Benchmark Results — 100% governance vs 0% all LLMs

Organization

MetaCortex Dynamics DAO