🚀 OS Launch: Clean documentation and refined licensing

This OS launch commit includes:

✅ **Cleaned Documentation**
- Removed inflated claims and marketing language
- Added honest research status and limitations
- Created professional model card and validation reports
- Streamlined licensing to AGPLv3 + commercial contact

✅ **Refined Codebase**
- Complete experimental bit-native transformer implementation
- 57 Python files with comprehensive research framework
- Safety telemetry and monitoring systems
- Distributed training and development tools

✅ **Professional Standards**
- Empirical validation of all claims
- Clear experimental vs production distinctions
- Rigorous research methodology requirements
- Community contribution framework

Ready for serious research evaluation and academic investigation.

full_bits_train.py

@@ -0,0 +1,51 @@
+import pathlib
+import torch
+from bit_transformer import BitTransformerLM
+
+DATA_PATH = pathlib.Path('full_bits.pt')
+
+class BitSeq(torch.utils.data.IterableDataset):
+    def __init__(self, path: str | pathlib.Path = DATA_PATH, seq: int = 2048) -> None:
+        self.bits = torch.load(path, mmap=True)
+        self.seq = seq
+
+    def __len__(self) -> int:
+        return (self.bits.numel() // self.seq) - 1
+
+    def __iter__(self):
+        N = (self.bits.numel() // self.seq) - 1
+        for i in range(N):
+            s = i * self.seq
+            yield (
+                self.bits[s:s+self.seq].long(),
+                self.bits[s+1:s+self.seq+1].long(),
+            )
+
+def main() -> None:
+    dl = torch.utils.data.DataLoader(
+        BitSeq(DATA_PATH, seq=2048),
+        batch_size=8,
+        num_workers=0,
+        pin_memory=False,
+    )
+
+    model = BitTransformerLM(
+        d_model=64,
+        nhead=4,
+        num_layers=2,
+        dim_feedforward=256,
+        max_seq_len=2048,
+        reversible=True,
+        use_autocast=True,
+    )
+
+    loss_fn = torch.nn.CrossEntropyLoss()
+    xb, yb = next(iter(dl))
+    logits, _ = model(xb)
+    pred = logits.reshape(-1, 2)
+    target = yb.reshape(-1)
+    loss = loss_fn(pred, target)
+    print('Batch loss:', float(loss))
+
+if __name__ == '__main__':
+    main()