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---
base_model: Qwen/Qwen2.5-14B-Instruct-AWQ
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-AWQ/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- competition
- iol-ai-2026
---
# IOL-AI 2026 Challenge submission
This repo is a redistribution of [Qwen/Qwen2.5-14B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-AWQ)
(Apache 2.0 license) plus a `script.py` for the
[IOL-AI 2026 Challenge](https://huggingface.co/spaces/iol-ai-challenge/iol-ai-2026).
## Approach
`script.py` prompts the model per-problem with a structured "rule table" method (enumerate every
example, derive a rule table, answer only from that table), followed by a self-review pass where the
model checks its own draft against the rule table and the original examples before finalizing. It
tracks a wall-clock budget against the competition's 30-minute limit and falls back to a single fast
greedy pass (or, if time is fully exhausted, a placeholder answer) rather than risk the process being
killed with no `submission.csv` written.
No training or fine-tuning is involved — this is inference-only, as required by the competition rules.

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"max_position_embeddings": 32768,
"max_window_layers": 70,
"model_type": "qwen2",
"num_attention_heads": 40,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"quantization_config": {
"bits": 4,
"group_size": 128,
"modules_to_not_convert": null,
"quant_method": "awq",
"version": "gemm",
"zero_point": true
},
"rms_norm_eps": 1e-06,
"rope_theta": 1000000.0,
"sliding_window": 131072,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.41.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "4.41.1"
}

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"""
IOL-AI 2026 submission script. Ships inside the model repo alongside the Qwen2.5-14B-Instruct-AWQ
weights. Reads /tmp/data/test.csv, writes submission.csv (id, pred, explanation) to the working
directory. No internet at runtime -- everything must load from local files (".").
"""
import os
os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["TRANSFORMERS_OFFLINE"] = "1"
import json
import re
import time
START_TIME = time.time()
TIME_LIMIT_SECONDS = 30 * 60
SAFETY_MARGIN_SECONDS = 90 # reserve for CSV write + any per-row overrun
DEADLINE = START_TIME + TIME_LIMIT_SECONDS - SAFETY_MARGIN_SECONDS
import pandas as pd
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
SYSTEM = (
"You solve International Linguistics Olympiad problems. You will be given data "
"from a language you have never seen before, plus hints, and asked to answer "
"numbered items about it. The ONLY source of truth is the data given to you in "
"this problem -- do not rely on anything you think you know about the language "
"if it conflicts with the examples given here.\n\n"
"Work method:\n"
"1. Go through every single example given, in order. For each word in the "
"unfamiliar language, first split it into its likely component morphemes "
"(stem plus any prefixes/suffixes) even if you are not fully sure of the "
"boundaries -- treat it as a sequence of parts, not one opaque unit. Then, "
"for each one, write down "
"every distinct morpheme, word, particle, or structural pattern (word order, "
"marking, alternation) it contains and what it appears to mean or mark. Do not "
"skip any example, and do not stop early once you have a plausible-looking "
"pattern -- an alternation that looks like it marks one thing (e.g. tense) may "
"actually mark something else (e.g. person, number, or agreement with a "
"different argument), and only the examples you skipped may reveal which.\n"
"2. Write a section titled RULE TABLE: that lists, as a table or bullet list, "
"every distinct piece you identified and its meaning/function -- this must "
"cover every example from step 1, not just the ones similar to the query.\n"
"3. Using ONLY entries from your RULE TABLE, work out the answer to each query "
"item. If the table has no entry for something the query needs, say so and give "
"your best-supported guess rather than leaving it blank.\n"
"4. Before writing your final answers, re-read your RULE TABLE and re-derive "
"each answer from it one more time, checking: did you actually apply every "
"rule you stated (e.g. a plural marker, a tense marker) to every relevant "
"answer, not just some of them? If two answers could plausibly be swapped "
"(e.g. two options assigned to the wrong item), re-check the evidence that "
"distinguishes them specifically.\n\n"
"Output format by task type -- give exactly this, nothing more:\n"
"- translation: the translated form only, in the language the task asks for.\n"
"- fill_blanks: only the missing form for each blank.\n"
"- match_letters: only the option letter (e.g. A, B, C).\n"
"- text_to_num: the number in digits.\n"
"- num_to_text: the number written out in words, in the language asked.\n"
"- any other task type: give exactly what the instruction asks for, nothing else.\n\n"
"Each final answer must be the bare form only -- no surrounding quotes, no "
"trailing period or punctuation that isn't part of the answer itself, no "
"parenthetical notes, no alternate options separated by '/' or 'or'. Pick "
"one single best answer per item.\n\n"
"Follow the work method above, showing your RULE TABLE. Then write a line that "
"says exactly FINAL ANSWERS: and, below it, one answer per line in the order "
"the items are asked -- the bare answer only, no numbering, no quotes, no "
"extra commentary."
)
def count_expected_items(query):
"""Count numbered items (e.g. '17.', '18)') in the query -- the target answer count."""
return len(re.findall(r"(?m)^\s*\d+[.)]", query))
MAX_EXPLANATION_CHARS = 2000
def parse_answers(text, expected_count=None):
"""Keep only the lines after the last 'FINAL ANSWERS:' marker, one answer per line.
If expected_count is given, pad with "" or truncate so the row never silently
drops points from a length mismatch against the scorer's positional alignment.
"""
marker = list(re.finditer(r"(?im)^[#*\s]*final answers?[:#*\s]*", text))
if marker:
text = text[marker[-1].end():]
answers = []
for line in text.splitlines():
line = re.sub(r"^\s*\d+[.)]\s*", "", line).strip()
line = line.strip("\"'")
if line:
answers.append(line)
if expected_count:
if len(answers) < expected_count:
answers = answers + [""] * (expected_count - len(answers))
elif len(answers) > expected_count:
answers = answers[:expected_count]
return answers
def extract_explanation(text):
"""The reasoning/RULE TABLE portion before the final-answers marker, for the
Human Evaluation track -- truncated so one long row can't bloat the CSV."""
marker = re.search(r"(?im)^[#*\s]*final answers?[:#*\s]*$", text)
explanation = text[:marker.start()] if marker else text
explanation = explanation.strip()
if len(explanation) > MAX_EXPLANATION_CHARS:
explanation = explanation[:MAX_EXPLANATION_CHARS].rsplit(" ", 1)[0] + " ..."
return explanation
MODEL_ID = "."
MAX_NEW_TOKENS_FULL = 2048 # normal pass -- room to show a full RULE TABLE
MAX_NEW_TOKENS_FAST = 768 # fallback pass when time is short -- less room to reason
print(f"[{time.time() - START_TIME:.0f}s] loading model...", flush=True)
tok = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, torch_dtype=torch.float16, device_map="auto",
).eval()
PAD_TOKEN_ID = tok.pad_token_id if tok.pad_token_id is not None else tok.eos_token_id
MAX_INPUT_TOKENS = 6000 # conservative guard against the model's context window -- leaves
# headroom for the system prompt, chat template overhead, and
# MAX_NEW_TOKENS_FULL generation on unusually long real IOL problems
def truncate_context(context, query):
"""Guard against exceeding the model's context window -- truncates `context` (never
`query`, which holds the actual questions) from the end, keeping as much of the given
data as fits within MAX_INPUT_TOKENS."""
query_len = len(tok(query, add_special_tokens=False)["input_ids"])
budget = MAX_INPUT_TOKENS - query_len
if budget <= 0:
return context
context_ids = tok(context, add_special_tokens=False)["input_ids"]
if len(context_ids) <= budget:
return context
return tok.decode(context_ids[:budget], skip_special_tokens=True)
print(f"[{time.time() - START_TIME:.0f}s] model loaded", flush=True)
def generate(system, user, max_new_tokens):
messages = [
{"role": "system", "content": system},
{"role": "user", "content": user},
]
# tokenize=False + separate tok(...) call, rather than apply_chat_template(..., return_dict=True),
# for compatibility with the eval sandbox's pinned transformers==4.44.1 (return_dict support on
# apply_chat_template was added later).
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=True, temperature=0.3, top_p=0.95,
pad_token_id=PAD_TOKEN_ID, repetition_penalty=1.15,
)
return tok.decode(out[0][enc["input_ids"].shape[-1]:], skip_special_tokens=True).strip()
def solve_row(problem, expected, avg_row_time):
"""One greedy pass, with a shorter token budget if the time budget is running low."""
full_pass_estimate = avg_row_time if avg_row_time else 45.0 # seconds -- rough guess for row 1
remaining = DEADLINE - time.time()
if remaining > full_pass_estimate:
text = generate(SYSTEM, problem, MAX_NEW_TOKENS_FULL)
else:
# Running low on time: fewer tokens, same prompt.
text = generate(SYSTEM, problem, MAX_NEW_TOKENS_FAST)
return parse_answers(text, expected_count=expected), extract_explanation(text)
df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
print(f"[{time.time() - START_TIME:.0f}s] {len(df)} rows to solve", flush=True)
rows_out = []
row_times = []
for i, r in df.iterrows():
row_start = time.time()
problem = f"{truncate_context(r['context'].strip(), r['query'].strip())}\n\n{r['query'].strip()}"
expected = count_expected_items(r["query"])
avg_row_time = sum(row_times) / len(row_times) if row_times else None
if time.time() > DEADLINE:
# Out of time: best-effort placeholder rather than risking the whole process
# getting killed by the 30-minute hard limit with no submission.csv at all.
answers = [""] * max(expected, 1)
explanation = ""
else:
try:
answers, explanation = solve_row(problem, expected, avg_row_time)
except Exception as e:
print(f"[{time.time() - START_TIME:.0f}s] row {r['id']} failed: {e}", flush=True)
answers = [""] * max(expected, 1)
explanation = ""
rows_out.append({
"id": r["id"],
"pred": json.dumps(answers, ensure_ascii=False),
"explanation": explanation,
})
row_times.append(time.time() - row_start)
print(
f"[{time.time() - START_TIME:.0f}s] row {i + 1}/{len(df)} done in {row_times[-1]:.0f}s, "
f"{len(answers)} answers, {DEADLINE - time.time():.0f}s budget left",
flush=True,
)
pd.DataFrame(rows_out).to_csv("submission.csv", index=False)
print(f"[{time.time() - START_TIME:.0f}s] wrote submission.csv ({len(rows_out)} rows)", flush=True)

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{
"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
}

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vocab.json Normal file
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