GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
antoniogr7
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
antoniogr7
Model Tree
mii-llm/zagreus-0.4B-ita
Input Modalities
Output Modalities
Supported Functionality
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.
Full fine-tune with an elicitation objective, per rendered option ordering:
CE(gold) — commit probability mass to the correct answer letter (peaking anchor, the
primary driver).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.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.
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.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizername = "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).
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).