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README.md

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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)