This model is a parameter-efficient fine-tuned version of Phi-3 Mini 4K trained for verbalized rebus solving in Italian, as part of the release for our paper Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses. The task of verbalized rebus solving consists of converting an encrypted sequence of letters and crossword definitions into a solution phrase matching the word lengths specified in the solution key. An example is provided below.

The model was trained in 4-bit precision for 5070 steps on the verbalized subset of the EurekaRebus using QLora via Unsloth and TRL. This repository contains the GGUF exported checkpoint of the model in Q8_0 format, and the Modelfile for usage with Ollama (see below).

We also provide adapter checkpoints through training and FP16 merged versions of this model for analysis and local execution.

A ready-to-use local version of this model is hosted on the Ollama Hub and can be used as follows:

Lexical overfitting: As remarked in the related publication, the model overfitted the set of definitions/answers for first pass words. As a result, words that were explicitly witheld from the training set cause significant performance degradation when used as solutions for verbalized rebuses' definitions. You can compare model performances between in-domain and out-of-domain test examples to verify this limitation.

For problems or updates on this model, please contact gabriele.sarti996@gmail.com.

If you use this model in your work, please cite our paper as follows:

We are grateful to the Associazione Culturale "Biblioteca Enigmistica Italiana - G. Panini" for making its rebus collection freely accessible on the Eureka5 platform.