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README.md

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-license: cc0-1.0
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+# Atto: Extreme Intelligence Density Research
+
+Atto is an exploration into the fundamental limits of **Intelligence Density** — how much knowledge and generative capability can be packed into a neural network with a strictly limited parameter budget.
+
+This project focuses on the "sub-kiloparameter" and "low-kiloparameter" regime, training models to generate Shakespearean text with as few as 64 parameters.
+
+## The Atto Series
+
+| Model | Parameters | Context | Weights Size (JSON) | Val Loss |
+| :--- | :--- | :--- | :--- | :--- |
+| **atto-64** | 64 | 3 | 1.8 KB | 2.59 |
+| **atto-128** | 128 | 7 | 3.5 KB | 2.83 |
+| **atto-256** | 256 | 8 | 6.0 KB | 2.33 |
+| **atto-512** | 512 | 16 | 11.8 KB | 2.44 |
+| **atto-1024** | 1,024 | 8 | 22.3 KB | 2.11 |
+| **atto-2048** | 2,048 | 24 | 44.3 KB | 2.15 |
+| **atto-4096** | 4,096 | 56 | 86.4 KB | 2.40 |
+| **atto-8192** | 8,192 | 28 | 172.7 KB | 1.91 |
+| **atto-16384** | 16,384 | 60 | ~640 KB | 2.11 |
+
+## Research Findings: Intelligence Density
+
+1.  **Architecture Matters**: At the sub-1000 parameter scale, standard Transformers are highly inefficient due to the overhead of Attention and LayerNorm. Our custom **Neural N-Gram (AttoLM)** architecture ensures that every single parameter directly participates in character prediction.
+2.  **The Embedding Threshold**: We found that moving from 8-dimensional to 16-dimensional embeddings (at 8,192 parameters) creates a significant jump in coherence, allowing the model to represent complex character relationships.
+3.  **Context vs. Width**: In extremely small models, there is a sharp trade-off between the context window (memory) and embedding dimensionality (representation). Our 8,192 and 16,384 models prioritize a balance that favors realistic word formation.
+
+## Next Steps
+
+This is just a **first step** in making intelligence very dense. By optimizing weight initialization, custom activation functions, and even more extreme parameter-tying, we believe it is possible to achieve "readable Shakespeare" with even fewer than 1,000 parameters.
+
+## Usage
+
+### Training
+To train the base series, run:
+```bash
+python3 train_atto.py
+```
+
+### Sampling
+To evaluate all trained models:
+```bash
+python3 sample.py
+```
+
+The models are exported as dependency-free JSON files in the `models/` directory, ready for client-side inference in a web browser.
+
+### Sample generations:
+```
+
+
+```

models/atto-1024.json

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+{"name": "atto-1024", "architecture": "atto-neural-ngram", "params": 1024, "config": {"vocab_size": 64, "embd_dim": 8, "context_len": 8}, "vocab": {"0": "\n", "1": " ", "2": "!", "3": "'", "4": "(", "5": ")", "6": ",", "7": "-", "8": ".", "9": ":", "10": ";", "11": "?", "12": "A", "13": "B", "14": "C", "15": "D", "16": "E", "17": "F", "18": "G", "19": "H", "20": "I", "21": "J", "22": "K", "23": "L", "24": "M", "25": "N", "26": "O", "27": "P", "28": "Q", "29": "R", "30": "S", "31": "T", "32": "U", "33": "V", "34": "W", "35": "Y", "36": "[", "37": "]", "38": "a", "39": "b", "40": "c", "41": "d", "42": "e", "43": "f", "44": "g", "45": "h", "46": "i", "47": "j", "48": "k", "49": "l", "50": "m", "51": "n", "52": "o", "53": "p", "54": "q", "55": "r", "56": "s", "57": "t", "58": "u", "59": "v", "60": "w", "61": "x", "62": "y", "63": "z"}, "weights": {"embedding": [[-0.6760939955711365, 0.9731445908546448, -2.621493101119995, 1.5662416219711304, 0.404337614774704, 1.2508337497711182, 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models/atto-128.json

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models/atto-2048.json

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models/atto-256.json

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models/atto-4096.json

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models/atto-512.json

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models/atto-64.json

@@ -0,0 +1 @@
+{"name": "atto-64", "architecture": "atto-neural-ngram", "params": 64, "config": {"vocab_size": 26, "embd_dim": 2, "context_len": 3}, "vocab": {"0": "\n", "1": " ", "2": "'", "3": ",", "4": ".", "5": "a", "6": "b", "7": "c", "8": "d", "9": "e", "10": "f", "11": "g", "12": "h", "13": "i", "14": "l", "15": "m", "16": "n", "17": "o", "18": "p", "19": "r", "20": "s", "21": "t", "22": "u", "23": "v", "24": "w", "25": "y"}, "weights": {"embedding": [[-0.7850219011306763, 0.9690186381340027], [-1.5730384588241577, -1.2258381843566895], [0.4986078143119812, 2.102989435195923], [-0.2690836489200592, 1.263218641281128], [-0.774017870426178, 2.042127847671509], [-0.2582077085971832, -0.27677392959594727], [0.6278311610221863, 1.7737576961517334], [0.29835939407348633, 1.233027696609497], [-0.06733263283967972, 0.679391086101532], [-2.335172414779663, 2.1910862922668457], [0.1431656777858734, 1.2299333810806274], [0.37653517723083496, 1.5213944911956787], [-0.08637543022632599, -0.023731915280222893], [-0.20393964648246765, -0.16153456270694733], [0.0022637145593762398, 0.3955213725566864], [0.22524376213550568, 0.7480852007865906], [-0.1952909380197525, -0.07634685188531876], [-0.3820693790912628, -0.3066198229789734], [0.365793377161026, 1.51381254196167], [-0.1508585661649704, 0.03711215406656265], [-0.1615677922964096, 0.11821223795413971], [-0.28475937247276306, 0.017821209505200386], [0.09339119493961334, 0.7401266098022461], [1.0639402866363525, 2.2132322788238525], [0.3060973882675171, 1.2150547504425049], [0.20563553273677826, 1.2719844579696655]], "mix": [[0.10266736149787903, 0.0027409347239881754, 7.03814172744751, -2.2758500576019287, 2.068591833114624, -6.934788227081299], [-0.03294849768280983, -0.008393000811338425, -0.054884884506464005, -0.02458338998258114, 3.0191116333007812, -1.1012859344482422]]}}

sample.py

@@ -0,0 +1,86 @@
+import json
+import os
+import math
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class AttoLM(nn.Module):
+    def __init__(self, vocab_size, embd_dim, context_len):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.embd_dim = embd_dim
+        self.context_len = context_len
+        self.embedding = nn.Embedding(vocab_size, embd_dim)
+        self.mix = nn.Linear(context_len * embd_dim, embd_dim, bias=False)
+
+    def forward(self, idx):
+        x = self.embedding(idx)
+        x = x.reshape(x.size(0), -1)
+        x = self.mix(x)
+        x = torch.tanh(x)
+        logits = x @ self.embedding.weight.T
+        return logits
+
+
+def load_model(path):
+    with open(path, "r") as f:
+        data = json.load(f)
+    cfg = data["config"]
+    model = AttoLM(cfg["vocab_size"], cfg["embd_dim"], cfg["context_len"])
+
+    emb = torch.tensor(data["weights"]["embedding"])
+    mix = torch.tensor(data["weights"]["mix"])
+    model.embedding.weight.data.copy_(emb)
+    model.mix.weight.data.copy_(mix)
+
+    itos = {int(k): v for k, v in data["vocab"].items()}
+    stoi = {v: k for k, v in itos.items()}
+    return model, itos, stoi, cfg
+
+
+@torch.no_grad()
+def generate(model, stoi, itos, ctx_len, prompt=" ", length=200, temperature=0.8):
+    model.eval()
+    tokens = [stoi.get(c, 0) for c in prompt]
+    # pad if prompt shorter than context
+    while len(tokens) < ctx_len:
+        tokens = [0] + tokens
+
+    for _ in range(length):
+        inp = torch.tensor(tokens[-ctx_len:], dtype=torch.long).unsqueeze(0)
+        logits = model(inp)
+        probs = F.softmax(logits / temperature, dim=-1)
+        nxt = torch.multinomial(probs, 1).item()
+        tokens.append(nxt)
+
+    return "".join(itos.get(t, "?") for t in tokens[ctx_len:])
+
+
+if __name__ == "__main__":
+    models_dir = "models"
+
+    for name in ["atto-64", "atto-128", "atto-256", "atto-512", "atto-1024", "atto-2048", "atto-4096", "atto-8192", "atto-16384"]:
+        path = os.path.join(models_dir, f"{name}.json")
+        if not os.path.exists(path):
+            print(f"  {path} not found, skipping")
+            continue
+
+        model, itos, stoi, cfg = load_model(path)
+        params = sum(p.numel() for p in model.parameters())
+
+        print(f"\n{'='*60}")
+        print(f"  {name}  |  {params} params  |  embd={cfg['embd_dim']}  ctx={cfg['context_len']}  vocab={cfg['vocab_size']}")
+        print(f"{'='*60}")
+
+        for prompt in [" the ", " to be", " Ham"]:
+            clean_prompt = prompt.strip()
+            # only use chars the model knows
+            usable = "".join(c for c in prompt if c in stoi)
+            if not usable:
+                usable = " "
+            text = generate(model, stoi, itos, cfg["context_len"], usable, length=150, temperature=0.8)
+            print(f'  prompt="{clean_prompt}":')
+            print(f"    {text[:150]}")
+            print()

train_atto.py

@@ -0,0 +1,206 @@
+import os
+import math
+import json
+from collections import Counter
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class AttoLM(nn.Module):
+    """
+    Neural n-gram language model designed for extreme intelligence density.
+
+    Architecture: embed last K chars → flatten → linear mix → tanh → project
+    via weight-tied embedding → logits.
+
+    Every single parameter directly participates in prediction.
+    Total params = embd_dim × (vocab_size + context_len × embd_dim)
+    """
+
+    def __init__(self, vocab_size, embd_dim, context_len):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.embd_dim = embd_dim
+        self.context_len = context_len
+
+        self.embedding = nn.Embedding(vocab_size, embd_dim)
+        self.mix = nn.Linear(context_len * embd_dim, embd_dim, bias=False)
+        # output projection is weight-tied: logits = h @ embedding.weight.T
+        # so lm_head costs 0 extra parameters
+
+        self._init_weights()
+
+    def _init_weights(self):
+        nn.init.normal_(self.embedding.weight, mean=0.0, std=0.5)
+        nn.init.normal_(self.mix.weight, mean=0.0, std=0.1)
+
+    def forward(self, idx):
+        # idx: (batch, context_len) — indices of last K characters
+        x = self.embedding(idx)              # (B, K, D)
+        x = x.reshape(x.size(0), -1)         # (B, K*D)
+        x = self.mix(x)                      # (B, D)
+        x = torch.tanh(x)                    # free nonlinearity
+        logits = x @ self.embedding.weight.T  # (B, vocab_size) — weight tying
+        return logits
+
+    def count_params(self):
+        return sum(p.numel() for p in self.parameters())
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Exact parameter budgets:
+#   params = embd_dim × (vocab_size + context_len × embd_dim)
+#
+#   atto-64:   embd=2, vocab=26, ctx=3   → 2×(26 + 3×2)  = 2×32  = 64
+#   atto-128:  embd=2, vocab=50, ctx=7   → 2×(50 + 7×2)  = 2×64  = 128
+#   atto-256:  embd=4, vocab=32, ctx=8   → 4×(32 + 8×4)  = 4×64  = 256
+#   atto-512:  embd=4, vocab=64, ctx=16  → 4×(64 + 16×4) = 4×128 = 512
+#   atto-1024: embd=8, vocab=64, ctx=8   → 8×(64 + 8×8)  = 8×128 = 1024
+# ──────────────────────────────────────────────────────────────────────
+
+CONFIGS = {
+    "atto-64":   dict(embd_dim=2, vocab_size=26, context_len=3),
+    "atto-128":  dict(embd_dim=2, vocab_size=50, context_len=7),
+    "atto-256":  dict(embd_dim=4, vocab_size=32, context_len=8),
+    "atto-512":  dict(embd_dim=4, vocab_size=64, context_len=16),
+    "atto-1024": dict(embd_dim=8, vocab_size=64, context_len=8),
+}
+
+
+def build_vocab(text, vocab_size):
+    counts = Counter(text)
+    common = [ch for ch, _ in counts.most_common(vocab_size)]
+    chars = sorted(common)
+    stoi = {ch: i for i, ch in enumerate(chars)}
+    itos = {i: ch for i, ch in enumerate(chars)}
+    return chars, stoi, itos
+
+
+def encode_text(text, stoi):
+    return [stoi[c] for c in text if c in stoi]
+
+
+def make_windows(data, ctx):
+    """Pre-build all (input, target) pairs as a single tensor. No Python loops at train time."""
+    n = len(data) - ctx
+    # indices [0..n-1], each window is data[i:i+ctx] → data[i+ctx]
+    idx = torch.arange(n).unsqueeze(1) + torch.arange(ctx + 1).unsqueeze(0)  # (n, ctx+1)
+    windows = data[idx]  # (n, ctx+1)
+    return windows[:, :ctx], windows[:, ctx]  # x: (n, ctx), y: (n,)
+
+
+def train_model(name, cfg, text):
+    print(f"\n{'='*60}")
+    print(f"  Training {name}")
+    print(f"  embd_dim={cfg['embd_dim']}, vocab={cfg['vocab_size']}, ctx={cfg['context_len']}")
+    print(f"{'='*60}")
+
+    chars, stoi, itos = build_vocab(text, cfg["vocab_size"])
+    data = torch.tensor(encode_text(text, stoi), dtype=torch.long)
+
+    n = int(0.9 * len(data))
+    train_data = data[:n]
+    val_data = data[n:]
+
+    ctx = cfg["context_len"]
+
+    # pre-build all sliding windows — makes batching a single index op
+    train_x, train_y = make_windows(train_data, ctx)
+    val_x, val_y = make_windows(val_data, ctx)
+
+    model = AttoLM(cfg["vocab_size"], cfg["embd_dim"], cfg["context_len"])
+    p_count = model.count_params()
+    print(f"  Parameters: {p_count}")
+
+    # scale iters to model capacity — tiny models converge fast
+    max_iters = min(5000 + p_count * 10, 20000)
+    batch_size = 512
+    lr_max = 3e-2
+    lr_min = 1e-4
+    eval_every = max(max_iters // 10, 1)
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=lr_max, weight_decay=0.01)
+    best_val_loss = float("inf")
+    best_state = None
+
+    import time
+    t0 = time.time()
+
+    for i in range(max_iters):
+        lr = lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(math.pi * i / max_iters))
+        for pg in optimizer.param_groups:
+            pg["lr"] = lr
+
+        # fast batch: just index into pre-built windows
+        ix = torch.randint(0, len(train_x), (batch_size,))
+        logits = model(train_x[ix])
+        loss = F.cross_entropy(logits, train_y[ix])
+
+        optimizer.zero_grad()
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+        optimizer.step()
+
+        if i % eval_every == 0:
+            model.eval()
+            with torch.no_grad():
+                vix = torch.randint(0, len(val_x), (min(2048, len(val_x)),))
+                val_loss = F.cross_entropy(model(val_x[vix]), val_y[vix]).item()
+            model.train()
+            print(f"  iter {i:6d} | train {loss.item():.4f} | val {val_loss:.4f} | lr {lr:.2e}")
+            if val_loss < best_val_loss:
+                best_val_loss = val_loss
+                best_state = {k: v.clone() for k, v in model.state_dict().items()}
+
+    elapsed = time.time() - t0
+    print(f"  Trained in {elapsed:.1f}s ({max_iters} iters)")
+
+    if best_state is not None:
+        model.load_state_dict(best_state)
+
+    # quick sample
+    model.eval()
+    with torch.no_grad():
+        seed = [stoi.get(c, 0) for c in " the"][-ctx:]
+        while len(seed) < ctx:
+            seed = [0] + seed
+        generated = list(seed)
+        for _ in range(120):
+            inp = torch.tensor(generated[-ctx:], dtype=torch.long).unsqueeze(0)
+            logits = model(inp)
+            probs = F.softmax(logits / 0.8, dim=-1)
+            nxt = torch.multinomial(probs, 1).item()
+            generated.append(nxt)
+        sample = "".join(itos.get(c, "?") for c in generated[ctx:])
+        print(f"  Sample: {sample}")
+
+    # export
+    os.makedirs("models", exist_ok=True)
+    model_json = {
+        "name": name,
+        "architecture": "atto-neural-ngram",
+        "params": p_count,
+        "config": {
+            "vocab_size": cfg["vocab_size"],
+            "embd_dim": cfg["embd_dim"],
+            "context_len": cfg["context_len"],
+        },
+        "vocab": {str(i): ch for i, ch in itos.items()},
+        "weights": {
+            "embedding": model.embedding.weight.detach().tolist(),
+            "mix": model.mix.weight.detach().tolist(),
+        },
+    }
+    path = f"models/{name}.json"
+    with open(path, "w") as f:
+        json.dump(model_json, f)
+    print(f"  Saved → {path} ({os.path.getsize(path)} bytes)")
+
+
+if __name__ == "__main__":
+    with open("hamlet.txt", "r", encoding="utf-8") as f:
+        text = f.read()
+
+    for name, cfg in CONFIGS.items():
+        train_model(name, cfg, text)