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README.md

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+---
+language:
+- en
+license: mit
+tags:
+- pcei
+- bitnet
+- ternary
+- 1.58bit
+- mamba
+- ssm
+- state-space-model
+- fastkan
+- linear-attention
+- energy-efficient
+- green-ai
+pipeline_tag: text-generation
+---
+
+# PCEI-Text: Physically Constrained Emergent Intelligence (100M BitNet Ternary Mamba)
+
+[![GitHub Repository](https://img.shields.io/badge/GitHub-MtnMCG%2FPCEI-blue.svg)](https://github.com/MtnMCG/PCEI)
+[![Sister Repo: PCEI-Robotics](https://img.shields.io/badge/🤗%20HuggingFace-PCEI--Robotics-blueviolet.svg)](https://huggingface.co/MtnMCG/PCEI-Robotics)
+[![Architecture: Selective SSM + BitNet 1.58b](https://img.shields.io/badge/Architecture-Mamba%20SSM%20%2B%20BitNet%201.58b-red.svg)]()
+[![Complexity: O(L) Linear](https://img.shields.io/badge/Inference%20Complexity-O(1)%20Memory%20%7C%20O(L)%20Compute-green.svg)]()
+[![Quantization: 1.58-bit {-1, 0, +1}](https://img.shields.io/badge/Quantization-Strict%20BitNet%20b1.58-brightgreen.svg)]()
+
+> **Full Codebase, Theoretical Compendium, and Replication Suite**: [https://github.com/MtnMCG/PCEI](https://github.com/MtnMCG/PCEI)
+
+---
+
+## 🏛️ Executive Summary
+
+**PCEI-Text** is an open-weights linear-time sequence model that discards quadratic Transformer attention ($\mathcal{O}(L^2)$) and high-energy 16-bit floating-point multiplications in favor of:
+1. **Hardware-efficient Selective State Spaces (Mamba SSM)** operating with linear $\mathcal{O}(L)$ compute and constant $\mathcal{O}(1)$ memory inference.
+2. **Canonical Microsoft BitNet b1.58 Ternary Quantization**, constraining synaptic weights to strictly discrete $\{-1.0, 0.0, +1.0\}$ states and replacing energy-hungry floating-point multiplications ($FMUL$) with integer additions ($IADD$).
+3. **Kolmogorov-Arnold Network (FastKAN) Spline Manifolds**, utilizing learnable cubic B-splines along non-linear parameter trajectories.
+4. **Dual-Timescale Dynamic State Modulation (`AmbientField`)**, preventing context degradation and drifting attractors over extended multi-turn horizons.
+
+This repository hosts foundation checkpoint `pcei_100m_bitnet_ternary.pt` (1.09 GB), trained continuously on diverse natural language, dialogue, reasoning, and synthetic curricula.
+
+---
+
+## 🔬 Empirical Audits & Verification
+
+### 1. Tiny LM Compute-Matched Bake-Off (Muse Autonomous Evaluation)
+In an independent multi-seed compute-matched baseline bake-off conducted via the Muse evaluation agent against a compute-matched Transformer:
+
+| Model | Architecture | Parameters | Val Loss (mean ± std) | Val Perplexity | Win Rate |
+| :--- | :--- | :---: | :---: | :---: | :---: |
+| **Transformer** | Vanilla Decoder (5L, 4H, FFN $2\times d$ GELU) | 398,688 | 4.6131 ± 0.0003 | 100.8 | Baseline |
+| **Dense Mamba** | Repo `MambaBlock` (6L, FP32 dense weights) | 407,232 | 4.3881 ± 0.0009 | 80.5 | 0 / 2 vs PCEI |
+| **PCEI-tiny** | **Strict 1.58-bit Ternary (`{-1, 0, +1}`) + FastKAN** | 411,912 | **4.3783 ± 0.0073** | **79.1** | **2 / 2 (Won all seeds)** |
+
+* **Key Takeaway**: Despite linear projections constrained strictly to discrete $\{-1, 0, +1\}$ ternary states (zero floating-point multiplications), PCEI outperformed the dense FP32 Mamba baseline and beat the standard Transformer by **0.23 nats** (val perplexity 79.1 vs 100.8).
+
+### 2. Strict Microsoft BitNet b1.58 Migration
+An external audit in October 2026 disclosed that early internal prototypes utilized an experimental 5-state quantizer. The architecture was fully refactored to canonical Microsoft BitNet b1.58:
+- **Parameter Entropy Cut**: Reduced from 2.32b to strictly 1.58b ($\le \log_2(3)$ bits/param), cutting parameter entropy by **32%**.
+- **Perplexity & Hallucinations**: Validation loss improved from 11.1696 to 11.1429 on 100M, eliminating activation outlier code token hallucinations.
+- Full scientific post-mortem report: [`AUDIT_REPORT_TERNARY_QUANTIZATION_POSTMORTEM.md`](https://github.com/MtnMCG/PCEI/blob/main/pcei_framework/docs/AUDIT_REPORT_TERNARY_QUANTIZATION_POSTMORTEM.md).
+
+---
+
+## ⚙️ Model Specifications
+
+| Architectural Dimension | Specification |
+| :--- | :--- |
+| **Base Architecture** | 12-Layer Selective State Space (Mamba SSM) |
+| **Model Dimension ($d_{\text{model}}$)** | 768 |
+| **Inner Expansion Dimension ($d_{\text{inner}}$)** | 512 |
+| **State Dimension ($d_{\text{state}}$)** | 16 |
+| **Convolution Kernel Size** | 4 |
+| **Non-Linear Branch** | FastKAN (Learnable cubic B-splines, grid size = 5) |
+| **Synaptic Quantization** | Strict BitNet b1.58 ($\{-1.0, 0.0, +1.0\}$) via STE |
+| **Vocabulary Size** | 50,257 (OpenAI GPT-2 BPE tokenizer) |
+| **Context Length ($L$)** | 128 / 512 / 2048 streaming tokens |
+| **Inference Footprint** | $\approx 250\text{ MB}$ packed ternary integer weights |
+| **Hardware Target** | Apple Silicon MPS, NVIDIA CUDA, or CPU edge devices |
+
+---
+
+## 🚀 Local Inference Quickstart
+
+To run local text generation using the PCEI framework:
+
+```bash
+# 1. Clone the master GitHub repository
+git clone https://github.com/MtnMCG/PCEI.git
+cd PCEI/pcei_framework
+
+# 2. Set up virtual environment and install dependencies
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+pip install huggingface_hub
+
+# 3. Download the model checkpoint directly from Hugging Face
+python3 -c "
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id='MtnMCG/PCEI-Text', filename='pcei_100m_bitnet_ternary.pt', local_dir='checkpoints')
+"
+
+# 4. Interactive generation with continuous state modulation
+python3 tools/resonance_chat.py \
+  --checkpoint checkpoints/pcei_100m_bitnet_ternary.pt \
+  --device mps  # Use --device cuda on NVIDIA GPUs
+```
+
+### Python API Usage
+
+```python
+import torch
+from src.pcei import PCEINexus
+from src.config import PCEIConfig
+from src.tokenizer import PCEITokenizer
+
+device = torch.device("mps" if torch.backends.mps.is_available() else ("cuda" if torch.cuda.is_available() else "cpu"))
+
+# Load checkpoint
+checkpoint = torch.load("checkpoints/pcei_100m_bitnet_ternary.pt", map_location=device)
+cfg_obj = checkpoint.get("config", None)
+config = cfg_obj if isinstance(cfg_obj, PCEIConfig) else PCEIConfig.load()
+
+# Initialize model
+nexus = PCEINexus(config=config).to(device)
+nexus.load_state_dict(checkpoint["model_state_dict"], strict=False)
+nexus.eval()
+
+# Tokenize prompt
+tokenizer = PCEITokenizer(vocab_size=config.vocab_size)
+prompt = "USER: Explain why ice floats on water.\nASSISTANT:"
+input_ids = torch.tensor(tokenizer.encode_raw(prompt), dtype=torch.long).unsqueeze(0).to(device)
+
+# Autoregressive generation
+with torch.no_grad():
+    for _ in range(64):
+        outputs = nexus({"text": input_ids})
+        logits = outputs["logits"] if isinstance(outputs, dict) else outputs
+        next_token = torch.argmax(logits[:, -1, :], dim=-1, keepdim=True)
+        input_ids = torch.cat([input_ids, next_token], dim=-1)
+
+print(tokenizer.decode(input_ids[0].tolist()))
+```
+
+---
+
+## 🤖 Sister Repositories & Embodied Intelligence
+
+PCEI proves that physical constraints generalize across domains:
+- 🏃 **[MtnMCG/PCEI-Robotics](https://huggingface.co/MtnMCG/PCEI-Robotics)**: Autonomous American Ninja Warrior 18-meter humanoid controller traversing 45° angled Quad Steps without attention or deep RL.
+- 🏛️ **[PCEI Master GitHub Repository](https://github.com/MtnMCG/PCEI)**: Contains the 11 theoretical research papers, continuous training daemons, curriculum streaming pipelines, and full verification suite.
+
+---
+
+## 📜 Citation & License
+
+This model and the PCEI codebase are released under the **MIT License**.
+
+```bibtex
+@article{ramirez2026pcei,
+  title   = {Physically Constrained Emergent Intelligence: Subverting Transformer Hyperscaling via Linear State Spaces, Ternary Synaptic Gating, and Latent Generative Replay},
+  author  = {Ramirez, Emerson and Antigravity Assistant},
+  journal = {PCEI Theoretical Research Compendium},
+  year    = {2026},
+  url     = {https://github.com/MtnMCG/PCEI}
+}
+```