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Model: icekern/zagreus-0.4B-xmoons
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- it
+base_model: mii-llm/zagreus-0.4B-ita
+pipeline_tag: text-generation
+tags:
+- italian
+- multiple-choice
+- italic
+---
+
+# zagreus-0.4B-xmoons
+
+Supervised fine-tune of [`mii-llm/zagreus-0.4B-ita`](https://huggingface.co/mii-llm/zagreus-0.4B-ita)
+for the [mii-llm Post-Training Challenge](https://huggingface.co/spaces/mii-llm/Post-Training-Challenge),
+evaluated on [ITALIC](https://italicbench.it/) (10,000 Italian multiple-choice questions).
+
+## Result
+
+| | Accuracy |
+|---|---|
+| baseline `mii-llm/zagreus-0.4B-ita` | 28.88 |
+| **this model** | **41.02** (4102/10000) |
+| random uniform (options vary 2-5) | 27.02 |
+
+Culture and commonsense **43.40** · Language capability **37.51** · unparsed answers **0**.
+
+The gain is statistically significant on the paired test set: McNemar chi2 = 326.4,
+p = 5.9e-73; paired bootstrap 95% CI of the delta [+10.84, +13.46]; Wilson 95% CI
+of the accuracy [40.06, 41.99].
+
+## Method
+
+One supervised fine-tuning pass on gold answers — **no distillation**.
+
+- **Benchmark**: the official `italic.jsonl` (10,000) and `5_shots.jsonl` are fetched
+  byte-for-byte from the ITALIC repository at a pinned commit and verified by SHA-256.
+  Evaluation data is never rebuilt or modified.
+- **Data**: Italian MCQ from four public HF datasets (revisions pinned), balanced by
+  **inverse frequency** so language and culture are equally represented. The weighting
+  derives from the data counts alone — no decision is taken by looking at the test set.
+- **Holdout**: a dev split of 7,568 **unique** questions is carved before any
+  upsampling and used for recipe selection; the ITALIC test is never used for tuning.
+- **Decontamination**: two independent signals (TF-IDF char-ngram cosine >= 0.80 and
+  MinHash/LSH word-shingle Jaccard >= 0.70) against both the test set and the official
+  few-shot exemplars. Measured overlap of the final training pool: **0** with the
+  10,000 test questions, **0** with the 5-shot exemplars.
+- **Training**: 1 epoch, loss on the completion only, `max_len` 2048 so the supervised
+  target is never truncated, option permutation to remove the positional answer prior,
+  few-shot demonstrations drawn from the training pool.
+- **Evaluation**: the official `fast` protocol — templates, system message and
+  `extract_answer_fast` reproduced verbatim from ITALIC's `run_eval.py`.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "icekern/zagreus-0.4B-xmoons"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+messages = [
+    {{"role": "system", "content": "Sei un assistente utile."}},
+    {{"role": "user", "content": "Rispondi alla seguente domanda a scelta multipla ..."}},
+]
+prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+```
+
+The model answers multiple-choice questions with a single letter, following the
+ITALIC `fast` prompt format.
+
+## Limitations
+
+- The four weakest categories are all linguistic (morphology 30.71, synonyms 35.43,
+  orthography 35.63, syntax 35.77). Public Italian MCQ corpora hold only ~2.5k unique
+  grammar questions, so the balanced pool repeats them: the model sees little *unique*
+  linguistic information.
+- `efederici/pinocchio` and ITALIC draw on overlapping source material (Italian
+  public-competition questions). Exact and near-duplicate contamination is removed and
+  audited, but source-level overlap cannot be fully excluded.
+- The reported score comes from a local HF-generate harness reproducing the official
+  `fast` protocol.
+
+## Code
+
+Full pipeline, raw evaluation output and per-example predictions:
+https://github.com/firekern/italic-post-training-challenge