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infer.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+DEVICE = torch.device('cuda')
+
+class Attention(nn.Module):
+    def __init__(self, d, heads=8):
+        super().__init__()
+        self.heads = heads
+        self.dk = d // heads
+        self.q_proj = nn.Linear(d, d, bias=False)
+        self.k_proj = nn.Linear(d, d, bias=False)
+        self.v_proj = nn.Linear(d, d, bias=False)
+        self.out_proj = nn.Linear(d, d, bias=False)
+    
+    def forward(self, x, mask=None):
+        B, N, D = x.shape
+        q = self.q_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
+        k = self.k_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
+        v = self.v_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
+        
+        att = (q @ k.transpose(-1, -2)) / math.sqrt(self.dk)
+        if mask is not None:
+            att = att + mask
+        att = F.softmax(att, dim=-1)
+        out = (att @ v).transpose(1, 2).reshape(B, N, D)
+        return self.out_proj(out)
+
+class MLP(nn.Module):
+    def __init__(self, d, mult=4):
+        super().__init__()
+        self.fc1 = nn.Linear(d, d * mult, bias=False)
+        self.fc2 = nn.Linear(d * mult, d, bias=False)
+    
+    def forward(self, x):
+        return self.fc2(F.gelu(self.fc1(x)))
+
+class Block(nn.Module):
+    def __init__(self, d, heads=8):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(d)
+        self.attn = Attention(d, heads)
+        self.ln2 = nn.LayerNorm(d)
+        self.mlp = MLP(d)
+    
+    def forward(self, x, mask):
+        x = x + self.attn(self.ln1(x), mask)
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+class PureBitModel(nn.Module):
+    def __init__(self, d=256, layers=6, heads=8):
+        super().__init__()
+        self.emb = nn.Embedding(2, d)  # Binary vocab!
+        self.blocks = nn.ModuleList([Block(d, heads) for _ in range(layers)])
+        self.ln = nn.LayerNorm(d)
+        self.head = nn.Linear(d, 2, bias=False)
+        self.head.weight = self.emb.weight
+    
+    def forward(self, x):
+        B, N = x.shape
+        mask = torch.triu(torch.ones(N, N, device=x.device), 1) * -1e9
+        h = self.emb(x)
+        for b in self.blocks:
+            h = b(h, mask)
+        return self.head(self.ln(h))
+
+# Load
+print("Loading purebit checkpoint...")
+ckpt = torch.load('/workspace/purebit_ckpt_113000kb.pt', map_location=DEVICE)
+print(f"Loss: {ckpt['loss']:.4f}")
+print(f"Bits trained: {ckpt['bits']:,}")
+print(f"Bytes trained: {ckpt['bytes']:,} ({ckpt['bytes']/1024/1024:.1f} MB)")
+
+model = PureBitModel(d=256, layers=6, heads=8).to(DEVICE)
+model.load_state_dict(ckpt['model'])
+model.eval()
+print("Model loaded!\n")
+
+def text_to_bits(text):
+    bits = []
+    for byte in text.encode('utf-8'):
+        for i in range(7, -1, -1):
+            bits.append((byte >> i) & 1)
+    return bits
+
+def bits_to_text(bits):
+    # Pad to multiple of 8
+    while len(bits) % 8 != 0:
+        bits.append(0)
+    bytes_out = []
+    for i in range(0, len(bits), 8):
+        byte = 0
+        for j in range(8):
+            byte = (byte << 1) | bits[i + j]
+        bytes_out.append(byte)
+    return bytes(bytes_out).decode('utf-8', errors='replace')
+
+def generate(prompt, max_bits=200):
+    bits = text_to_bits(prompt)
+    x = torch.tensor(bits, device=DEVICE).unsqueeze(0)
+    
+    generated = []
+    with torch.no_grad():
+        for _ in range(max_bits):
+            logits = model(x[:, -512:])[:, -1, :]
+            probs = F.softmax(logits / 0.8, dim=-1)
+            next_bit = torch.multinomial(probs, 1).item()
+            generated.append(next_bit)
+            x = torch.cat([x, torch.tensor([[next_bit]], device=DEVICE)], 1)
+    
+    all_bits = bits + generated
+    return bits_to_text(all_bits)
+
+print("=== PURE BIT INFERENCE ===\n")
+prompts = ["The ", "Hello", "A", "In ", "01"]
+for p in prompts:
+    try:
+        out = generate(p, 160)  # 160 bits = 20 chars
+        print(f"PROMPT: '{p}'")
+        print(f"OUTPUT: {repr(out)}\n")
+    except Exception as e:
+        print(f"PROMPT: '{p}' -> ERROR: {e}\n")