Update README.md

README.md

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 ---
+language:
+- en
 license: mit
+tags:
+- post-transformer
+- attention-free
+- hamiltonian
+- symplectic
+- o1-memory
+- pytorch
+- sub-quadratic
+pipeline_tag: text-generation
 ---
+
+# TUPOI-300M: Symplectic Post-Transformer Language Model with O(1) Memory
+
+<div align="center">
+
+*A sub-quadratic, attention-free sequence modeling architecture that replaces dense attention matrices with a Symplectic Hamiltonian Integrator (Velocity-Verlet).*
+
+**Author:** NARE LABS (*Built by 15 y.o. independent researcher*)
+
+[![GitHub](https://img.shields.io/badge/GitHub-starface77%2FTUPOI-blue.svg)](https://github.com/starface77/TUPOI)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+</div>
+
+---
+
+## 📌 Overview
+
+**TUPOI-300M** is a 304M-parameter proof-of-concept post-transformer architecture that completely eliminates the Attention mechanism and the persistent KV-cache in favor of continuous Hamiltonian phase-space dynamics.
+
+### Key Highlights:
+- **Strictly Constant Memory ($O(1)$):** The persistent recurrent context state requires exactly **6.00 KB of VRAM** forever, regardless of whether generating 512 or 65,536 tokens.
+- **Zero Information Dissipation:** Integrated via the symplectic Velocity-Verlet leapfrog scheme with Jacobian determinant $\det(J) \equiv 1.000000$ (Liouville's theorem).
+- **Pretrained Scale:** 304.2M active parameters trained across 10,000 steps on TinyStories on a single NVIDIA Tesla T4 GPU.
+
+---
+
+## 🚀 Quickstart: Running Inference in Python
+
+```python
+import torch
+import torch.nn.functional as F
+import tiktoken
+from huggingface_hub import hf_hub_download
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 1. Download model weights from HuggingFace
+weights_path = hf_hub_download(repo_id="DanilKZ/TUPOI-1", filename="tupoi-300m.pt")
+
+# 2. Clone repository code for architecture definitions:
+# git clone https://github.com/starface77/TUPOI.git
+from tupoi import TUPOI300M, generate_text
+
+enc = tiktoken.get_encoding("gpt2")
+model = TUPOI300M(vocab_size=enc.n_vocab, d_model=1024, num_layers=24, d_ff=4096, max_seq_len=512).to(device)
+
+checkpoint = torch.load(weights_path, map_location=device)
+sd = checkpoint["model_state_dict"] if "model_state_dict" in checkpoint else checkpoint
+if "opinion_anchor.anchor_state" in sd:
+    del sd["opinion_anchor.anchor_state"]
+
+model.load_state_dict(sd, strict=False)
+model.eval()
+
+# 3. Generate text
+prompt = "Once upon a time, there was a little girl named Lily who"
+output = generate_text(model, enc, prompt=prompt, max_new_tokens=50, temperature=0.7)
+print(output)