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AAC Micro Brain — 16M parameter conversational flow model.
Tiny transformer that only knows how humans talk in everyday situations.
No world knowledge. No encyclopedia. Just conversation patterns.
Architecture: ~16M params
- vocab_size: 8192
- d_model: 512
- n_heads: 8
- n_layers: 6
- d_ff: 1024
- max_seq_len: 128
"""
import mlx.core as mx
import mlx.nn as nn
import math
class MultiHeadAttention(nn.Module):
def __init__(self, d_model: int, n_heads: int):
super().__init__()
self.n_heads = n_heads
self.d_head = d_model // n_heads
self.qkv = nn.Linear(d_model, 3 * d_model, bias=False)
self.out = nn.Linear(d_model, d_model, bias=False)
def __call__(self, x, mask=None):
B, T, C = x.shape
qkv = self.qkv(x)
q, k, v = mx.split(qkv, 3, axis=-1)
q = q.reshape(B, T, self.n_heads, self.d_head).transpose(0, 2, 1, 3)
k = k.reshape(B, T, self.n_heads, self.d_head).transpose(0, 2, 1, 3)
v = v.reshape(B, T, self.n_heads, self.d_head).transpose(0, 2, 1, 3)
scale = math.sqrt(self.d_head)
attn = (q @ k.transpose(0, 1, 3, 2)) / scale
if mask is not None:
attn = attn + mask
attn = mx.softmax(attn, axis=-1)
out = (attn @ v).transpose(0, 2, 1, 3).reshape(B, T, C)
return self.out(out)
class TransformerBlock(nn.Module):
def __init__(self, d_model: int, n_heads: int, d_ff: int):
super().__init__()
self.attn = MultiHeadAttention(d_model, n_heads)
self.ff = nn.Sequential(
nn.Linear(d_model, d_ff, bias=False),
nn.GELU(),
nn.Linear(d_ff, d_model, bias=False),
)
self.ln1 = nn.RMSNorm(d_model)
self.ln2 = nn.RMSNorm(d_model)
def __call__(self, x, mask=None):
x = x + self.attn(self.ln1(x), mask=mask)
x = x + self.ff(self.ln2(x))
return x
class MicroBrain(nn.Module):
"""16M param conversational flow predictor."""
def __init__(
self,
vocab_size: int = 8192,
d_model: int = 512,
n_heads: int = 8,
n_layers: int = 6,
d_ff: int = 1024,
max_seq_len: int = 128,
):
super().__init__()
self.d_model = d_model
self.max_seq_len = max_seq_len
self.token_emb = nn.Embedding(vocab_size, d_model)
self.pos_emb = nn.Embedding(max_seq_len, d_model)
self.layers = [TransformerBlock(d_model, n_heads, d_ff) for _ in range(n_layers)]
self.ln_final = nn.RMSNorm(d_model)
self.output = nn.Linear(d_model, vocab_size, bias=False)
def __call__(self, tokens):
B, T = tokens.shape
positions = mx.arange(T)
x = self.token_emb(tokens) + self.pos_emb(positions)
# Causal mask
mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
for layer in self.layers:
x = layer(x, mask=mask)
x = self.ln_final(x)
logits = self.output(x)
return logits
def count_params(self):
"""Count total parameters."""
from mlx.utils import tree_flatten
return sum(v.size for _, v in tree_flatten(self.parameters()))
def create_model(**kwargs):
model = MicroBrain(**kwargs)
mx.eval(model.parameters())
n_params = model.count_params()
print(f"MicroBrain: {n_params:,} parameters ({n_params / 1e6:.1f}M)")
return model
if __name__ == "__main__":
model = create_model()
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