Zagreus 0.4B xmoons improved

Name: zagreus-0.4B-xmoons-improved
Availability: InStock

Zagreus 0.4B xmoons improved is an Italian multiple-choice model fine-tuned from mii-llm/zagreus-0.4B-ita. It is the selected checkpoint from the reproducible H100 run that reached 44.35% accuracy on the official 10,000-question ITALIC benchmark in fast, five-shot mode through vLLM.

The model was developed for the mii-llm/Post-Training-Challenge.

The model is a compact Llama-style causal language model with 437,760,960 parameters. Its tokenizer and Llama 3 chat template come from swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA. The repository is a self-contained Transformers checkpoint and uses <|eot_id|> as both the end-of-turn and generation stop token.

Intended use

The model is intended for:

research on compact Italian language models;
Italian multiple-choice question answering;
participation in the MII Post-Training Challenge;
reproduction and analysis of the ITALIC post-training experiment.

It is not designed as a general-purpose assistant, a factual authority, or a component for medical, legal, financial, or other high-stakes decisions.

Usage

Install PyTorch and Transformers, then load the checkpoint from the repository root:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "fgoose180/zagreus-0.4B-xmoons-improved"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device == "cuda" else torch.float32

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=dtype,
).to(device)

messages = [
    {"role": "system", "content": "Sei un assistente utile."},
    {
        "role": "user",
        "content": """Rispondi alla seguente domanda a scelta multipla sull'argomento 'data_geography'. La tua risposta deve essere nel seguente formato: 'LETTERA' (senza virgolette) dove LETTERA è una tra ABCD. Scrivi solo la lettera corrispondente alla tua risposta senza spiegazioni.

Qual è la capitale d'Italia?

A) Roma
B) Milano
C) Torino
D) Napoli

Risposta:""",
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=8,
        do_sample=False,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
    )

answer = tokenizer.decode(
    output[0, inputs["input_ids"].shape[1] :],
    skip_special_tokens=True,
).strip()
print(answer)

The reported benchmark result uses the five fixed official ITALIC demonstrations. Zero-shot use, different prompts, sampling, or another chat template should not be expected to reproduce the reported score.

Model architecture

Property	Value
Architecture	`LlamaForCausalLM`
Model type	Llama-style decoder-only transformer
Parameters	437,760,960
Hidden size	960
Layers	32
Attention heads / KV heads	15 / 5
Vocabulary size	128,256
Maximum trained context	2,048 tokens during SFT
Checkpoint serialization	SafeTensors

Training

Data

The final training stream was built online from pinned revisions of four public datasets:

Dataset	Main contribution
`efederici/pinocchio`	Italian language, culture, and general-knowledge MCQs
`FinancialSupport/italic_sft`	ITALIC-style Italian MCQs
`FinancialSupport/italic_sft_ext`	Extended ITALIC-style MCQs
`FinancialSupport/quiz_militare`	Italian civic and general-knowledge MCQs

Rows were normalized, exactly deduplicated, split before upsampling, and decontaminated against the official ITALIC test and five demonstrations using character TF-IDF cosine and word-shingle MinHash/LSH. The final artifacts were:

Artifact	Rows	Unique questions
Clean training stream	108,288	67,654
Clean validation holdout	6,745	6,745

The training stream contains 53,591 language draws and 54,697 culture draws. The benchmark labels were not used for checkpoint selection.

Objective and hyperparameters

The model was trained for one epoch with completion-only supervised loss. Each target was rendered with five distinct training-pool demonstrations and independently permuted answer options. The official five ITALIC demonstrations were reserved for validation and final evaluation.

Parameter	Value
Hardware	1x NVIDIA H100
Optimizer steps	6,768
Batch size	16
Initial learning rate	`3e-4`
Schedule	50-step warmup, cosine decay
Weight decay	`0.0`
Maximum sequence length	2,048
Forward precision	BF16 autocast
Parameters and optimizer updates	FP32
Gradient clipping	`1.0`
Random seed	0
Few-shot probability / count	`1.0 / 5`
Option permutation probability	`1.0`

The detached Modal pipeline took approximately 84 minutes end to end. Carbon emissions were not measured, so no emissions estimate is reported.

Evaluation

The selected checkpoint was chosen at step 6,768 using only a clean validation holdout and the criterion 0.5 * language_accuracy + 0.5 * culture_accuracy.

Evaluation	Questions	Accuracy	Balanced accuracy	Unparsed
Validation holdout	6,745	55.97%	61.38%	0
Official ITALIC fast five-shot, vLLM 0.26	10,000	44.35%	not reported	0

The official evaluation used greedy decoding, the official fast answer extractor, a pinned ITALIC harness, and the official five demonstrations. The result contained 4,435 correct answers out of 10,000.

Reproducibility

The training and evaluation implementation is available in mattiacurri/zagreus-italic-challenge-xmoons. The exact local source snapshot used for this release is identified by Git commit afc6b7f09a1e107f38ae04358ece7ed85f6be7a3.

Selected artifact identities:

text

d679f3e79221aea95c64ef0be177530b02dce25d4745b0247cbae7b677f1ceab  training pool
749dbcdea8b244c3609acecddf618d9780559f501da18b09f650dfe10f2fb881  validation pool
0d534fd8eceec72b4fc3179d77afe7d6291766f5831bc4c8b69b9c45463c1656  selection.json
2ac2f6e412ea1e8f67ce6d8395c1c9845270101590d84de1be748e5919833c93  official result JSON

The source repository contains the full experiment and reproducibility reports.

Limitations and risks

The model is optimized for Italian multiple-choice prompts and often emits only an answer letter. It is not a broadly instruction-tuned chat model.
The 44.35% figure is one benchmark result under one exact prompt and backend configuration; it does not measure general Italian language competence.
Exact and fuzzy decontamination reduce known overlap but cannot prove the absence of semantic contamination or benchmark-distribution overfitting.
The public training sources may contain factual errors, social biases, stereotypes, or outdated information that can be inherited by the model.
Outputs outside the trained answer format may be unreliable.

License and attribution

The base model weights are published under Apache-2.0. The included tokenizer and chat template come from a Llama 3 derivative and remain subject to the published Llama 3 terms; the metadata therefore uses the more restrictive llama3 license identifier. Training datasets and benchmark assets retain their own terms. Some FinancialSupport dataset cards do not declare a license, so users should verify those terms before redistribution or commercial use.

Acknowledgements

firekern/zagreus-italic-challenge-xmoons, the upstream repository
mii-llm/zagreus-0.4B-ita
swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
mii-llm/Post-Training-Challenge
ITALIC