license, base_model, library_name, pipeline_tag, language, tags

license	base_model	library_name	pipeline_tag	language	tags
apache-2.0	Qwen/Qwen2.5-14B-Instruct-AWQ	transformers	text-generation	en	iol-ai-2026 linguistic-reasoning

IOL-AI 2026 Solver

Solves IOL-style linguistics puzzles (Linguini CSV) with a single-shot LLM. Ships Qwen/Qwen2.5-14B-Instruct-AWQ (4-bit AWQ, Apache-2.0) at the repo root, loaded from MODEL_ID = "."; runs on a T4 within the 30-minute budget.

Architecture

• script.py — entrypoint: reads /tmp/data/test.csv, writes submission.csv (id, pred JSON list, explanation). Config flags at the top; submission written before the model loads and after every step.
• solver/pipeline.py — orchestration. The model answers every puzzle from a minimal prompt (no scaffold, no chain-of-thought); output is parsed into one answer per item and aligned by position. A light greedy-anchored self-consistency vote refines answers while the clock allows. A deterministic symbolic layer (solver/) is a last-resort fallback only.
• solver/llm.py — batched transformers/AWQ generation, greedy, repetition_penalty=1.0, per-token deadline.
• solver/direct.py — prompt and answer parsing.

Run

python3 script.py [test.csv] [submission.csv]   # defaults: /tmp/data/test.csv, submission.csv

Weights are the unmodified Qwen/Qwen2.5-14B-Instruct-AWQ release (Apache-2.0; LICENSE retained).