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Model: srikarkashyap/iol-ai-2026-baseline
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - iol-ai-2026
+---
+
+# IOL-AI 2026 submission (v3)
+
+Submission for the [IOL-AI 2026 challenge](https://iolai.org/):
+Qwen2.5-7B-Instruct quantized to 4-bit (bitsandbytes NF4) at load time,
+greedy decoding, with per-item prompting.
+
+v3: 7B in 4-bit instead of 1.5B (fp16 fallback if bitsandbytes is missing).
+
+v2 changes over the plain baseline:
+
+- one generation per numbered item instead of one per problem
+- brief step-by-step reasoning, answer extracted from a `FINAL:` line
+- task-type-specific answer-format instructions
+- a one-shot worked example in the prompt
+- a global time budget that shrinks reasoning space near the 30-min limit
+
+- `script.py` reads `/tmp/data/test.csv` and writes `submission.csv`
+  (columns `id`, `pred`, where `pred` is a JSON list of per-item answers).
+- Model weights are shipped in this repo and loaded from `"."` because the
+  eval sandbox has no internet.
+
+Base model: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+(Apache 2.0).

config.json

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+    "Qwen2ForCausalLM"
+  ],
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+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "transformers_version": "4.37.0"
+}

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+}

script.py

@@ -0,0 +1,244 @@
+"""IOL-AI 2026 v3: Qwen2.5-7B-Instruct in 4-bit, per-item prompting + reasoning.
+
+Runs inside the competition sandbox (no internet). The submission repo is the
+working directory; model weights are shipped in it and loaded from ".".
+Reads /tmp/data/test.csv, writes submission.csv (columns: id, pred).
+
+v3 over v2: Qwen2.5-7B-Instruct instead of 1.5B, quantized to 4-bit (NF4) at
+load time with bitsandbytes. (AWQ was a dead end: autoawq pins an exact torch
+version and the sandbox's package index serves no torch wheels at all, so the
+preinstalled torch must be used as-is.)
+
+v2 over the baseline:
+- One generation per numbered item (not one per problem), so a misparsed line
+  can't shift every later answer.
+- The model reasons first, then emits "FINAL: <answer>"; only that is kept.
+- Task-type-specific answer-format instructions (exact match is unforgiving).
+- A one-shot worked example demonstrating the reasoning + FINAL format.
+- A global time budget that shrinks reasoning space as the deadline nears.
+"""
+
+import os
+import subprocess
+import sys
+import time
+
+START_TIME = time.time()
+
+# IOL_SKIP_PIP=1 skips dependency install for local testing (where torch etc.
+# are already set up); the sandbox always installs.
+if os.environ.get("IOL_SKIP_PIP") != "1":
+    # Do NOT list torch here: the sandbox's package index has no torch wheels
+    # (any resolver attempt to fetch one fails), so the preinstalled torch
+    # must satisfy every requirement as-is.
+    subprocess.run(
+        [sys.executable, "-m", "pip", "install", "-q",
+         "transformers>=4.43", "accelerate>=0.30", "pandas"],
+        check=True,
+    )
+    # bitsandbytes installed separately and non-fatally: if the sandbox index
+    # lacks it, the script falls back to fp16 loading below.
+    subprocess.run(
+        [sys.executable, "-m", "pip", "install", "-q", "bitsandbytes"],
+        check=False,
+    )
+
+os.environ["HF_HUB_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+
+# IOL_TEST_CSV / IOL_MODEL_ID are only for local testing; in the sandbox the
+# defaults are used.
+TEST_CSV = os.environ.get("IOL_TEST_CSV", "/tmp/data/test.csv")
+MODEL_ID = os.environ.get("IOL_MODEL_ID", ".")
+OUT_CSV = os.environ.get("IOL_OUT_CSV", "submission.csv")
+# Stop reasoning generously before the sandbox's 30-minute hard kill; the
+# budget also has to cover pip install and model load (already elapsed).
+TIME_BUDGET_S = int(os.environ.get("IOL_TIME_BUDGET_S", 25 * 60))
+
+import json
+import re
+
+# Progress prints contain IPA characters; don't let a non-UTF-8 console
+# (Windows local testing) crash the run.
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+SYSTEM_PROMPT = (
+    "You solve International Linguistics Olympiad problems: given a few "
+    "examples from an unfamiliar language, you deduce its rules and apply "
+    "them. Think step by step, briefly. Then give your answer on a new line "
+    "starting with 'FINAL:'. The FINAL line must contain only the answer "
+    "itself: no explanation, no quotes, no numbering."
+)
+
+# One worked example (Hakhun, from the competition page) demonstrating the
+# reasoning style and the FINAL format.
+ONE_SHOT_USER = """DATA
+Here are sentences in Hakhun with their English translations:
+ŋa ka kɤ ne | Do I go?
+nɤ ʒip tuʔ ne | Did you sleep?
+ati ka la | He goes.
+ŋa ʒip ku | I sleep.
+
+TASK
+Translate into English.
+Answer format: only the translation, nothing else.
+
+ITEM
+nɤ ʒip ku ne"""
+
+ONE_SHOT_ASSISTANT = """From the data: ŋa = I, nɤ = you, ati = he. ka = go, ʒip = sleep. \
+Sentence-final ne marks a question. The verb suffix differs by subject and tense: \
+kɤ/ku are present forms (kɤ with ŋa+ka, ku with ŋa+ʒip), tuʔ is past, la is 3rd person present. \
+Here: nɤ (you) + ʒip (sleep) + ku (present) + ne (question) = present-tense question about you sleeping.
+FINAL: Do you sleep?"""
+
+# Answer-format hints per task_type; unknown types get the generic hint.
+TASK_HINTS = {
+    "translation": "only the translation, nothing else",
+    "fill_blanks": "only the word or words that fill the blank",
+    "match_letters": "only the letter or letters of the matching choice",
+    "text_to_num": "only the number in digits",
+    "num_to_text": "only the number written out in the problem language",
+}
+GENERIC_HINT = "only the answer itself, as short as possible"
+
+# A numbered item looks like "17. ..." or "17) ..." at the start of a line.
+ITEM_RE = re.compile(r"^\s*\(?(\d+)\s*[.)]\s*", re.MULTILINE)
+# Models drift to variants like "Final Answer:" or "Answer:"; accept them all.
+FINAL_RE = re.compile(r"(?:FINAL(?:\s+ANSWER)?|ANSWER)\s*:\s*(.*)", re.IGNORECASE)
+
+
+def split_query(query: str):
+    """Split the query into (instruction, [(number, item_text), ...])."""
+    matches = list(ITEM_RE.finditer(query))
+    if not matches:
+        return query.strip(), [("1", query.strip())]
+    instruction = query[: matches[0].start()].strip()
+    items = []
+    for i, m in enumerate(matches):
+        end = matches[i + 1].start() if i + 1 < len(matches) else len(query)
+        items.append((m.group(1), query[m.end(): end].strip()))
+    return instruction, items
+
+
+QUOTED_RE = re.compile(r"[\"“”'']([^\"“”'']+)[\"“”'']")
+
+
+def extract_final(text: str) -> str:
+    """Take the last FINAL: line; fall back to the last non-empty line."""
+    finals = FINAL_RE.findall(text)
+    if finals:
+        return finals[-1].strip().strip('"').strip()
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    if not lines:
+        return ""
+    last = lines[-1]
+    # Without a FINAL marker the last line is often prose like
+    # 'The sentence "X" translates to "Y".' — take the last quoted span.
+    quoted = QUOTED_RE.findall(last)
+    if quoted and len(last) > 2 * len(quoted[-1]):
+        return quoted[-1].strip()
+    return last
+
+
+def main():
+    tok = AutoTokenizer.from_pretrained(MODEL_ID)
+    # NF4 4-bit quantization: ~5 GB on the T4, fp16 compute. Well-supported
+    # on T4 (no bf16 needed) and avoids the fp16-overflow garbage seen when
+    # running full-precision Qwen2.5 in fp16.
+    try:
+        quant = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+        )
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID, quantization_config=quant, device_map="auto"
+        ).eval()
+        print("Loaded 4-bit (bitsandbytes NF4).", flush=True)
+    except Exception as e:
+        # Last resort if bitsandbytes is unavailable: fp16 barely fits the
+        # 16 GB T4 for 7B and risks Qwen2.5 fp16 overflow, but a degraded
+        # run beats no submission.csv at all.
+        print(f"4-bit load failed ({e!r}); falling back to fp16.", flush=True)
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID, torch_dtype=torch.float16, device_map="auto"
+        ).eval()
+
+    df = pd.read_csv(TEST_CSV, dtype=str).fillna("")
+
+    # Count items up front so the time budget can be split evenly.
+    parsed = []
+    total_items = 0
+    for _, r in df.iterrows():
+        instruction, items = split_query(r["query"])
+        parsed.append((r, instruction, items))
+        total_items += len(items)
+
+    def generate(messages, max_new_tokens):
+        prompt = tok.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=False
+        )
+        enc = tok(prompt, return_tensors="pt").to(model.device)
+        with torch.no_grad():
+            out = model.generate(
+                **enc,
+                max_new_tokens=max_new_tokens,
+                do_sample=False,
+                temperature=None,
+                top_p=None,
+                top_k=None,
+                pad_token_id=tok.eos_token_id,
+            )
+        return tok.decode(
+            out[0][enc["input_ids"].shape[-1]:], skip_special_tokens=True
+        ).strip()
+
+    rows = []
+    done_items = 0
+    for r, instruction, items in parsed:
+        hint = TASK_HINTS.get(r["task_type"], GENERIC_HINT)
+        answers = []
+        for num, item_text in items:
+            remaining_s = TIME_BUDGET_S - (time.time() - START_TIME)
+            remaining_items = total_items - done_items
+            # Even share of the remaining budget, assuming ~10 tokens/s for
+            # NF4 7B on a T4; clamp to a sane range. If time has essentially
+            # run out, emit a 32-token answer-only guess.
+            share_s = max(remaining_s, 1) / max(remaining_items, 1)
+            max_new = int(min(384, max(32, share_s * 10)))
+
+            user_msg = (
+                f"DATA\n{r['context'].strip()}\n\n"
+                f"TASK\n{instruction or r['query'].strip()}\n"
+                f"Answer format: {hint}.\n\n"
+                f"ITEM\n{item_text}"
+            )
+            messages = [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": ONE_SHOT_USER},
+                {"role": "assistant", "content": ONE_SHOT_ASSISTANT},
+                {"role": "user", "content": user_msg},
+            ]
+            text = generate(messages, max_new)
+            answer = extract_final(text)
+            answers.append(answer)
+            done_items += 1
+            elapsed = time.time() - START_TIME
+            print(f"[{r['id']}] item {num}: {answer!r} "
+                  f"(max_new={max_new}, t={elapsed:.0f}s)", flush=True)
+
+        rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
+
+    pd.DataFrame(rows).to_csv(OUT_CSV, index=False)
+    print(f"Wrote {len(rows)} rows to {OUT_CSV} "
+          f"in {time.time() - START_TIME:.0f}s")
+
+
+if __name__ == "__main__":
+    main()

tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}