language, base_model, datasets, pipeline_tag, library_name, tags

language	base_model	datasets	pipeline_tag	library_name	tags
it	mii-llm/zagreus-0.4B-ita	antoniogr7/italic-lexical-elicitation	text-generation	transformers	italian italic elicitation multiple-choice

zagreus-0.4B-ita-elicit

An elicitation fine-tune of mii-llm/zagreus-0.4B-ita for the ITALIC benchmark. It does not inject new knowledge (the model is only 0.4B); it surfaces the lexical/semantic knowledge the base model already holds and routes it onto the answer-letter channel, while removing the base model's heavy answer-letter bias. No ITALIC data is used for training — the benchmark is only read to guard against leakage.

Net result: ITALIC 0.280 → 0.3126 (fast, zero-shot, greedy, n=10,000).

Results

ITALIC, fast / zero-shot, greedy, n=10,000 (0 unparsed). Base = mii-llm/zagreus-0.4B-ita.

	base	this model	Δ
overall	0.280	0.3126	+3.2
language capability (macro)	0.2705	0.3133	+4.3
culture and commonsense (macro)	0.2868	0.3121	+2.5
lexicon	0.2584	0.3841	+12.6
synonyms_and_antonyms	0.2915	0.3275	+3.6

The intended target (lexicon) moves the most. The culture categories rise modestly too — not from injected facts but from repairing the shared letter-emission head; per-fact culture remains a wall for a 0.4B, so those gains are small.

Method (brief)

Full fine-tune with an elicitation objective, per rendered option ordering:

1. CE(gold) — commit probability mass to the correct answer letter (peaking anchor, the primary driver).
2. λ·KL(q ‖ p) — distill a PMI-cloze teacher computed over the option text (q) onto the answer-letter logits (p), down-weighted (λ=0.3) to avoid flattening.
3. format term — keep probability mass on the option-letter tokens.

Options are re-permuted every epoch so the model must bind meaning to the letter it lands on, not to a position. Training data is a synthetic Italian lexical/semantic MCQ pool built from Italian Wiktionary (kaikki.org): antoniogr7/italic-lexical-elicitation. Trained and evaluated in bfloat16.

Usage

Serve/evaluate zero-shot. The model was trained and measured 0-shot; few-shot demos shift the emission distribution it was tuned for and regress the score. The ITALIC chat template is baked into the tokenizer.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

name = "antoniogr7/zagreus-0.4B-ita-elicit"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name, dtype=torch.bfloat16).cuda().eval()

question = "Qual è il sinonimo di «celere»?"
options = {"A": "lento", "B": "rapido", "C": "grande", "D": "scuro"}
body = question + "\n\n" + "\n".join(f"{k}) {v}" for k, v in options.items()) + "\n\nRisposta:\n"
msgs = [{"role": "system", "content": "Sei un assistente utile."},
        {"role": "user", "content": body}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").cuda()
out = model.generate(ids, max_new_tokens=2, do_sample=False)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True).strip())  # -> "B"

For faithful benchmark scoring use the official ITALIC harness (vLLM + run_eval.py, fast: true, few_shot_file: null).

Limitations

• 0.4B model. Only the lexical/semantic slice of ITALIC (~19.5%) is addressable by elicitation; culture is per-fact knowledge (a wall) and grammar is a capacity wall. The realistic ceiling of this approach is ~0.31–0.33 overall.
• Zero-shot only (see Usage).
• Italian only.

License & attribution

The model inherits the license of the base model mii-llm/zagreus-0.4B-ita. The training dataset is derived from Italian Wiktionary via kaikki.org and released under CC BY-SA 4.0 (attribution: Wiktionary contributors; extraction by wiktextract / kaikki.org).