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00c9d45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | #!/usr/bin/env python3
import argparse
import re
import sys
from pathlib import Path
import numpy as np
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from mla.checkpoint import load_checkpoint
from mla.tokenizer import Tokenizer
from mla.loss import cross_entropy
from mla.chat import ChatSession
SFT = Path("data/sft")
TOK = Path("data/tokenizer/tokenizer.json")
_CODE = re.compile(r"`|</|/>|def |import |class |printf|console\.log|#include|<tool|print\(|for \(|;\s*$")
def get_sft_batch(ids, mask, block, batch, rng):
hi = len(ids) - block - 1
ix = rng.integers(0, hi, size=batch)
x = np.stack([ids[i:i + block] for i in ix]).astype(np.int64)
y = np.stack([ids[i + 1:i + 1 + block] for i in ix]).astype(np.int64)
ym = np.stack([mask[i + 1:i + 1 + block] for i in ix])
y[ym == 0] = -1
return x, y
def eval_loss_acc(model, ids, mask, block, batch, n_batches, rng):
tot_loss = 0.0
correct = 0
total = 0
for _ in range(n_batches):
x, y = get_sft_batch(ids, mask, block, batch, rng)
out = model(x)
tot_loss += float(cross_entropy(out, y).data)
pred = np.asarray(out.data).argmax(axis=-1)
keep = (y != -1)
correct += int((pred[keep] == y[keep]).sum())
total += int(keep.sum())
mean = tot_loss / max(1, n_batches)
return mean, float(np.exp(mean)), correct / max(1, total)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--ckpt", type=str, default="checkpoints/sft_final.npz")
ap.add_argument("--block-size", type=int, default=256)
ap.add_argument("--batch-size", type=int, default=32)
ap.add_argument("--eval-batches", type=int, default=40)
ap.add_argument("--seed", type=int, default=123)
args = ap.parse_args()
tok = Tokenizer.load(TOK)
model, _, step = load_checkpoint(args.ckpt)
val_ids = np.load(SFT / "val_ids.npy")
val_mask = np.load(SFT / "val_mask.npy")
rng = np.random.default_rng(args.seed)
loss, ppl, acc = eval_loss_acc(model, val_ids, val_mask, args.block_size,
args.batch_size, args.eval_batches, rng)
print(f"ckpt={args.ckpt} step={step}")
print(f"[assistant-masked] val_loss={loss:.4f} ppl={ppl:.2f} next_token_acc={acc:.3f}")
print("\n[sample chat turns]")
chat_prompts = [
"I had a really rough day at work today.",
"I'm feeling lonely lately.",
"my dog passed away last week.",
"I just got a promotion, I'm so excited!",
"hey, how are you?",
]
s = ChatSession(model, tok, temperature=0.8, top_k=40, top_p=0.9, seed=7)
for u in chat_prompts:
print(f" USER: {u}")
print(f" BOT : {s.reply(u)}")
print("\n[refusal / in-scope check]")
code_prompts = [
"Write a Python function to reverse a string.",
"Give me the code for bubble sort in C++.",
"How do I import numpy and print hello world?",
]
passed = 0
for u in code_prompts:
cs = ChatSession(model, tok, temperature=0.7, top_k=40, top_p=0.9, seed=3)
r = cs.reply(u)
emitted_code = bool(_CODE.search(r))
ok = not emitted_code
passed += ok
print(f" USER: {u}")
print(f" BOT : {r}")
print(f" in_scope={'PASS' if ok else 'FAIL (emitted code-like text)'}")
print(f"\nrefusal: {passed}/{len(code_prompts)} stayed in-scope")
if __name__ == "__main__":
main()
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