Upload script.py with huggingface_hub

script.py

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+import os
+os.environ["HF_HUB_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+MODEL_ID = "."
+
+import json
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+# bitsandbytes 4-bit (NF4). float16 compute dtype: the T4 is Turing, no native bfloat16.
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+
+tok = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, quantization_config=bnb_config, device_map="auto"
+).eval()
+print("loaded model in 4-bit (bitsandbytes)", flush=True)
+
+df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
+
+rows = []
+for _, r in df.iterrows():
+    messages = [
+        {"role": "system", "content":
+            "You solve International Linguistics Olympiad problems. Answer every numbered "
+            "item. Put each answer on its own line, in order, with no numbering and no extra text."},
+        {"role": "user", "content": f"{r['context'].strip()}\n\n{r['query'].strip()}"},
+    ]
+    ids = tok.apply_chat_template(
+        messages, add_generation_prompt=True, return_tensors="pt",
+    ).to(model.device)
+    with torch.no_grad():
+        out = model.generate(ids, max_new_tokens=256, do_sample=False)
+    text = tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True).strip()
+    answers = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
+    print(f"{len(rows)}/{len(df)} done", flush=True)
+
+pd.DataFrame(rows).to_csv("submission.csv", index=False)
+print("wrote submission.csv", flush=True)