Datasets:
File size: 4,502 Bytes
b6b25a3 | 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 | #!/usr/bin/env python3
"""Frisches v3-Testset (42 Aufgaben, nie im Training): Hardcore-Adapter gegen v3-Adapter, bf16, Greedy, GPU 0."""
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
PLATTE = "/home/david/Schreibtisch/Platte"
os.environ.setdefault("HF_HOME", f"{PLATTE}/cache/huggingface")
os.environ.setdefault("HF_HUB_CACHE", f"{PLATTE}/cache/huggingface/hub")
import json
import re
import time
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
BASE = f"{PLATTE}/models/graf_zahl/qwen_healed_merged"
# Name -> (Adapter, max_new_tokens). Hardcore bekommt das alte Limit (2500), v3 genug fuer 80 Zeichen.
ADAPTERS = {
"A (hardcore)": (f"{PLATTE}/models/graf_zahl/qwen_graf_zahl_hardcore_lora", 2500),
"B (v3)": (f"{PLATTE}/models/graf_zahl/qwen_graf_zahl_v3_plain_lora", 4500),
}
HELD = f"{PLATTE}/models/graf_zahl/v3/heldout_v3.json"
RESULTS = f"{PLATTE}/models/graf_zahl/v3/heldout_results_v3.json"
DATASET = "/mnt/models/graf_zahl/dataset_massive.jsonl"
SPACE = "␣"
def analyse(s, answer):
pointers = re.findall(r"\[Position (\d+): (.)\]", answer)
chars = "".join((" " if c == SPACE else c) for _, c in pointers)
final = re.findall(r"Das sind (\d+)", answer)
safety = re.findall(r"Zur Sicherheit nachgezählt:\s*(\d+)", answer)
return {"endzahl": int(final[-1]) if final else None, "sicherheitszahl": int(safety[-1]) if safety else None,
"pointer_exakt": chars == s, "letzte_position": int(pointers[-1][0]) if pointers else 0}
def main():
held = json.load(open(HELD, encoding="utf-8"))
system = json.loads(open(DATASET, encoding="utf-8").readline())["messages"][0]["content"]
tok = AutoTokenizer.from_pretrained(BASE)
model = AutoModelForCausalLM.from_pretrained(BASE, dtype=torch.bfloat16, device_map={"": 0})
first = True
for name, (path, _) in ADAPTERS.items():
if first:
model = PeftModel.from_pretrained(model, path, adapter_name=name[0]); first = False
else:
model.load_adapter(path, adapter_name=name[0])
model.eval()
print(f"{len(held)} Aufgaben, Adapter {list(ADAPTERS)}", flush=True)
results, t0 = [], time.time()
for name, (_, max_new) in ADAPTERS.items():
model.set_adapter(name[0])
for h in held:
s = h["text"]
messages = [{"role": "system", "content": system}, {"role": "user", "content": h["prompt"]}]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt", add_special_tokens=False).to("cuda")
with torch.no_grad():
out = model.generate(**inputs, max_new_tokens=max_new, do_sample=False, temperature=0.0, top_p=1.0,
eos_token_id=tok.eos_token_id, pad_token_id=tok.pad_token_id)
new = out[0][inputs["input_ids"].shape[1]:]
answer = tok.decode(new, skip_special_tokens=True)
a = analyse(s, answer)
rec = {"adapter": name, "kategorie": h["kategorie"], "text": s, "laenge": len(s), "prompt": h["prompt"],
"tokens": len(new), "abgebrochen": int(new[-1]) != tok.eos_token_id, "richtig": a["endzahl"] == len(s),
"think_richtig": a["sicherheitszahl"] == len(s), "antwort": answer, **a}
results.append(rec)
print(f"[{name}] {h['kategorie'][:22]:22} len {len(s):2} -> {str(a['endzahl']):>4} "
f"{'ABGEBR.' if rec['abgebrochen'] else ('ok' if rec['richtig'] else 'FALSCH'):8} ({len(new)} Tok, {time.time() - t0:.0f}s)", flush=True)
json.dump(results, open(RESULTS, "w", encoding="utf-8"), ensure_ascii=False)
print("\n===== AUSWERTUNG (streng 'Das sind N' / Think-Zahl) =====")
cats = list(dict.fromkeys(h["kategorie"] for h in held))
print(f"{'Kategorie':26} {'n':>3} " + " ".join(f"{a:>20}" for a in ADAPTERS))
for cat in cats + ["Gesamt"]:
row = []
for a in ADAPTERS:
rs = [r for r in results if r["adapter"] == a and (cat == "Gesamt" or r["kategorie"] == cat)]
row.append(f"{sum(r['richtig'] for r in rs):>3}/{len(rs):<3} / {sum(r['think_richtig'] for r in rs):>3}/{len(rs):<3}")
n = len([h for h in held if cat == "Gesamt" or h["kategorie"] == cat])
print(f"{cat:26} {n:>3} " + " ".join(f"{c:>20}" for c in row))
print(f"\nErgebnisse: {RESULTS}")
if __name__ == "__main__":
main()
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