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File size: 3,202 Bytes
16dc556 | 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 | """Minimal: load the v5 adapter, generate ONE plan, print the RAW output to see why
json_valid is 0 (despite train_loss 0.16). Fast (~4 min)."""
import modal
IGNORE = [".venv/**", ".git/**", "*.gguf", "**/__pycache__/**", ".gstack/**",
"design/**", "frontend/variant_*/**", "notebooks/**", ".pytest_cache/**", "data/**"]
image = (modal.Image.debian_slim(python_version="3.11")
.pip_install("torch", "transformers>=4.45", "peft", "accelerate", "pandas",
"jsonschema", "pycountry", "sentencepiece")
.add_local_dir(".", "/root/repo", ignore=IGNORE, copy=True))
app = modal.App("scrubdata-debug-v5", image=image)
vol = modal.Volume.from_name("scrubdata-v5-adapter")
dbg = modal.Dict.from_name("scrubdata-debug-v5", create_if_missing=True)
@app.function(gpu="A100-80GB", timeout=900, volumes={"/vol": vol})
def debug():
import os, sys, torch
os.chdir("/root/repo"); sys.path.insert(0, "/root/repo")
import pandas as pd
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
from scrubdata.prompt import SYSTEM_PROMPT, build_user_prompt
from scrubdata.profiler import profile_dataframe
from scrubdata.model_planner import _extract_json
base_id = "unsloth/Qwen3-4B-Instruct-2507"
tok = AutoTokenizer.from_pretrained(base_id)
base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.bfloat16, device_map="cuda")
model = PeftModel.from_pretrained(base, "/vol/v5").merge_and_unload()
model.eval()
df = pd.DataFrame({"country": ["USA", "usa", "U.S.A", "USA", "Canada", "canada"],
"status": ["Won", "won", "WON", "Lost", "lost", "Won"]})
msgs = [{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": build_user_prompt(profile_dataframe(df), df)}]
enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True)
ids = enc["input_ids"].to(model.device)
im_end = tok.convert_tokens_to_ids("<|im_end|>")
# suppress the <tool_call>/</tool_call> special tokens (151657/151658) that the
# base model loops on, forcing it to its learned next-best token ('{').
with torch.no_grad():
out = model.generate(input_ids=ids, attention_mask=enc["attention_mask"].to(model.device),
max_new_tokens=900, do_sample=False,
eos_token_id=[tok.eos_token_id, im_end], pad_token_id=tok.eos_token_id,
suppress_tokens=[151657, 151658])
raw = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False)
plan = _extract_json(raw)
canon = None
if isinstance(plan, dict):
canon = {k: v for c in plan.get("columns", []) for o in c.get("operations", [])
if o.get("op") == "canonicalize_categories" for k, v in o.get("mapping", {}).items()}
info = {"raw_len": len(raw), "raw_head": raw[:500],
"extracted_keys": list(plan.keys()) if isinstance(plan, dict) else None,
"canon": canon}
dbg["latest"] = info
print(info)
return info
@app.local_entrypoint()
def main():
print(debug.remote())
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