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import os
from pathlib import Path
import torch
from train import TinyTransformerLM, build_vocab, encode_text, make_batch
def load_or_create_model(model_path, text, block_size, n_embd, n_head, n_layer):
requested_config = {
"vocab_size": None,
"block_size": block_size,
"n_embd": n_embd,
"n_head": n_head,
"n_layer": n_layer,
}
if model_path.exists():
checkpoint = torch.load(model_path, map_location="cpu")
saved_stoi = checkpoint["stoi"]
fresh_stoi, fresh_itos = build_vocab(text)
saved_config = checkpoint["config"]
config_matches = all(saved_config.get(k) == v for k, v in requested_config.items() if v is not None)
if set(saved_stoi) == set(fresh_stoi) and config_matches:
model = TinyTransformerLM(**saved_config)
model.load_state_dict(checkpoint["model"])
return model, saved_stoi, {int(k): v for k, v in checkpoint["itos"].items()}, saved_config
backup = model_path.with_suffix(".old-vocab.pt")
model_path.replace(backup)
print(f"old vocabulary checkpoint moved to {backup}")
stoi, itos = build_vocab(text)
config = {
"vocab_size": len(stoi),
"block_size": block_size,
"n_embd": n_embd,
"n_head": n_head,
"n_layer": n_layer,
}
model = TinyTransformerLM(**config)
return model, stoi, itos, config
def limit_memory_file(data_path, keep_tail_chars=200_000):
if not data_path.exists():
return
text = data_path.read_text(encoding="utf-8")
if len(text) <= keep_tail_chars:
return
data_path.write_text(text[-keep_tail_chars:], encoding="utf-8")
def normalize_text(text):
return "".join(ch.lower() if ch.isalpha() else ch for ch in text)
def score_snippet(query, snippet):
query_chars = set(ch for ch in normalize_text(query) if not ch.isspace())
snippet_chars = set(ch for ch in normalize_text(snippet) if not ch.isspace())
if not query_chars or not snippet_chars:
return 0
overlap = len(query_chars & snippet_chars)
return overlap / len(query_chars | snippet_chars)
def retrieve_context(user_text, memory_text, max_examples=3):
blocks = [b.strip() for b in memory_text.split("\n\n") if b.strip()]
scored = []
for block in blocks:
if "USER:" in block and "AI:" in block:
scored.append((score_snippet(user_text, block), block))
scored.sort(key=lambda item: item[0], reverse=True)
chosen = [block for score, block in scored[:max_examples] if score > 0]
return "\n\n".join(chosen)
@torch.no_grad()
def generate_once(model, prompt, stoi, itos, max_new_tokens, temperature):
fallback = next(iter(stoi.values()))
idx = torch.tensor([[stoi.get(ch, fallback) for ch in prompt]], dtype=torch.long)
model.eval()
for _ in range(max_new_tokens):
idx_cond = idx[:, -model.block_size :]
logits, _ = model(idx_cond)
logits = logits[:, -1, :] / temperature
probs = torch.softmax(logits, dim=-1)
next_id = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, next_id), dim=1)
if itos[int(next_id)] == "\n" and idx.shape[1] > len(prompt) + 20:
break
return "".join(itos[int(i)] for i in idx[0])[len(prompt) :]
def score_reply(reply, user_text):
stripped = reply.strip()
if not stripped:
return -100
score = 0.0
lowered = stripped.lower()
if "user:" in lowered:
score -= 6
if "ai:" in lowered:
score -= 6
if stripped.count("\n") > 2:
score -= 2
if len(set(stripped)) < 4:
score -= 2
words = [word for word in stripped.split() if word]
if words:
unique_ratio = len(set(words)) / len(words)
score += unique_ratio * 3
if len(stripped) < 4:
score -= 2
if len(stripped) > 220:
score -= 1
if user_text and user_text.lower().strip() in lowered:
score -= 2
if any(ch.isalpha() for ch in stripped):
score += 1
return score
def generate_reply(model, prompt, user_text, stoi, itos, max_new_tokens, temperature, candidates=4):
best_reply = ""
best_score = float("-inf")
for _ in range(candidates):
reply = generate_once(model, prompt, stoi, itos, max_new_tokens, temperature)
score = score_reply(reply, user_text)
if score > best_score:
best_score = score
best_reply = reply
return best_reply
def train_steps(model, text, stoi, steps, batch_size, block_size, lr):
if len(text) < block_size + 2:
return
data = encode_text(text, stoi)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
model.train()
for _ in range(steps):
xb, yb = make_batch(data, batch_size, block_size, "cpu")
_, loss = model(xb, yb)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
def save_model(model_path, model, config, stoi, itos):
model_path.parent.mkdir(parents=True, exist_ok=True)
torch.save(
{
"model": model.state_dict(),
"config": config,
"stoi": stoi,
"itos": itos,
},
model_path,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data", default="data/chat_memory.txt")
parser.add_argument("--seed-data", default="data/input.txt")
parser.add_argument("--model", default="runs/chat_model.pt")
parser.add_argument("--preset", choices=["tiny", "turbo", "small", "big", "large"], default="small")
parser.add_argument("--steps-per-turn", type=int, default=8)
parser.add_argument("--tokens", type=int, default=160)
parser.add_argument("--temperature", type=float, default=1.1)
parser.add_argument("--batch-size", type=int, default=4)
parser.add_argument("--block-size", type=int, default=64)
parser.add_argument("--n-embd", type=int, default=64)
parser.add_argument("--n-head", type=int, default=2)
parser.add_argument("--n-layer", type=int, default=1)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--no-self-train", action="store_true")
args = parser.parse_args()
presets = {
"tiny": {"steps_per_turn": 4, "tokens": 120, "temperature": 1.0, "batch_size": 4, "block_size": 64, "n_embd": 64, "n_head": 2, "n_layer": 1, "lr": 3e-4},
"turbo": {"steps_per_turn": 2, "tokens": 100, "temperature": 1.2, "batch_size": 8, "block_size": 32, "n_embd": 64, "n_head": 4, "n_layer": 2, "lr": 1e-3},
"small": {"steps_per_turn": 8, "tokens": 160, "temperature": 1.1, "batch_size": 4, "block_size": 64, "n_embd": 96, "n_head": 2, "n_layer": 2, "lr": 2.5e-4},
"big": {"steps_per_turn": 12, "tokens": 180, "temperature": 1.0, "batch_size": 4, "block_size": 96, "n_embd": 192, "n_head": 4, "n_layer": 4, "lr": 2e-4},
"large": {"steps_per_turn": 16, "tokens": 220, "temperature": 0.95, "batch_size": 2, "block_size": 128, "n_embd": 256, "n_head": 8, "n_layer": 6, "lr": 1.5e-4},
}
preset = presets[args.preset]
if args.steps_per_turn == 8:
args.steps_per_turn = preset["steps_per_turn"]
if args.tokens == 160:
args.tokens = preset["tokens"]
if args.temperature == 1.1:
args.temperature = preset["temperature"]
if args.batch_size == 4:
args.batch_size = preset["batch_size"]
if args.block_size == 64:
args.block_size = preset["block_size"]
if args.n_embd == 64:
args.n_embd = preset["n_embd"]
if args.n_head == 2:
args.n_head = preset["n_head"]
if args.n_layer == 1:
args.n_layer = preset["n_layer"]
if args.lr == 3e-4:
args.lr = preset["lr"]
data_path = Path(args.data)
seed_path = Path(args.seed_data)
model_path = Path(args.model)
if not data_path.exists():
seed = seed_path.read_text(encoding="utf-8") if seed_path.exists() else ""
data_path.parent.mkdir(parents=True, exist_ok=True)
data_path.write_text(seed + "\n", encoding="utf-8")
text = data_path.read_text(encoding="utf-8")
model, stoi, itos, config = load_or_create_model(
model_path, text, args.block_size, args.n_embd, args.n_head, args.n_layer
)
if not torch.cuda.is_available():
threads = os.cpu_count() or 4
torch.set_num_threads(threads)
torch.set_num_interop_threads(1)
torch.set_float32_matmul_precision("high")
print(f"CPU optimization: using {threads} threads")
print("Tiny chat. Type /quit to exit.")
print("It uses retrieved examples and can keep training after each turn.")
while True:
try:
user = input("\nyou> ").strip()
if user.lower() in {"/quit", "quit", "exit"}:
save_model(model_path, model, config, stoi, itos)
print(f"saved {model_path}")
break
if user.startswith("/teach "):
lesson = user[len("/teach ") :].strip()
data_path.write_text(data_path.read_text(encoding="utf-8") + f"\nTEACHER: {lesson}\n", encoding="utf-8")
limit_memory_file(data_path)
text = data_path.read_text(encoding="utf-8")
print("learning...")
train_steps(model, text, stoi, args.steps_per_turn, args.batch_size, args.block_size, args.lr)
save_model(model_path, model, config, stoi, itos)
print("learned")
continue
memory_text = data_path.read_text(encoding="utf-8")
context = retrieve_context(user, memory_text)
if context:
prompt = f"{context}\n\nUSER: {user}\nAI:"
else:
prompt = f"\nUSER: {user}\nAI:"
reply = generate_reply(model, prompt, user, stoi, itos, args.tokens, args.temperature).strip()
if not reply:
reply = "..."
reply = reply.replace("USER:", "").replace("AI:", "").strip()
print(f"ai> {reply}")
addition = f"\nUSER: {user}\nAI: {reply}\n"
data_path.write_text(memory_text + addition, encoding="utf-8")
limit_memory_file(data_path)
text = data_path.read_text(encoding="utf-8")
if not args.no_self_train:
print("learning from this turn...")
train_steps(model, text, stoi, args.steps_per_turn, args.batch_size, args.block_size, args.lr)
save_model(model_path, model, config, stoi, itos)
print("saved")
except Exception as exc:
print(f"error: {exc}")
save_model(model_path, model, config, stoi, itos)
print("model saved after error, continue or /quit")
if __name__ == "__main__":
main()
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