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Model: idealab-cs2/zagreus-0.4B-italic-softkd
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- it
+base_model: mii-llm/zagreus-0.4B-ita
+pipeline_tag: text-generation
+datasets:
+- idealab-cs2/italic-softkd-pool
+tags:
+- italian
+- small-language-model
+- knowledge-distillation
+- italic
+---
+
+# zagreus-0.4B-italic-softkd
+
+Submission for the [mii-llm Italian Post-Training Challenge](https://huggingface.co/spaces/mii-llm/Post-Training-Challenge). This is [mii-llm/zagreus-0.4B-ita](https://huggingface.co/mii-llm/zagreus-0.4B-ita) (437M, Llama architecture) post-trained for the [ITALIC benchmark](https://github.com/Crisp-Unimib/ITALIC), 10,000 multiple-choice questions on Italian culture and language.
+
+| Model | ITALIC full 10K (5-shot fast, temp 0, official harness) |
+|---|---|
+| zagreus-0.4B-ita (base) | 0.2802 |
+| public leaderboard best at submission time | 0.372 |
+| **this model** | **0.4787** (confirmed 0.4784 on an independent rerun) |
+
+**Update (2026-08-05):** superseded by [idealab-cs2/zagreus-0.4B-italic-extkd](https://huggingface.co/idealab-cs2/zagreus-0.4B-italic-extkd) — same recipe extended with two more softKD stages (culture pool, then external in-distribution pool), 0.4921/0.4929/0.4932 full-10K.
+
+## Training
+
+One soft knowledge-distillation run from the base model, within the challenge budget of 10 H100 GPU hours (measured replay: ~4.9 H100h including data generation, teacher labeling and evaluation).
+
+For every training question a 3-teacher committee (Mistral-Small-3.2-24B, Gemma-3-27B, Qwen3-32B, each fitting a single H100 in bf16) provides its probability distribution over the answer letters at the answer token. The student is trained in fp32 with a KL term on that distribution (temperature 2.0) plus cross-entropy on the gold letter (weight 0.5). The training pool has 21,606 items: pinocchio-derived knowledge MCQs, committee-labeled grammar MCQs, and generated orthography/morphology MCQs for the categories where public data is scarce. Every pool is decontaminated against the ITALIC test set (exact match plus semantic similarity at cosine 0.80; 0 exact leaks, max similarity 0.7999).
+
+Training data is published on the Hub: [italic-softkd-pool](https://huggingface.co/datasets/idealab-cs2/italic-softkd-pool) (the exact training file) (the exact training file; provenance splits knowledge/grammar/orthomorph included as named splits). Training code, the decontamination manifest and the complete report (including what did not work) are in the submission repository: https://github.com/ruggsea/italic-challenge-submission
+
+## Evaluation
+
+Official [ITALIC harness](https://github.com/Crisp-Unimib/ITALIC), unmodified: vLLM server, fast mode (letter-only answers), 5-shot, temperature 0, accuracy over all 10,000 questions. The chat template is embedded in the tokenizer config; serve the model with its own template.
+
+```bash
+vllm serve idealab-cs2/zagreus-0.4B-italic-softkd --chat-template-content-format auto
+```
+
+Per-category accuracy: civic education .56, lexicon .52, tourism .50, geography .47, art history .45, history .45, synonyms .47, syntax .42, orthography .39, morphology .36.
+
+## Acknowledgments
+
+Training, data curation and evaluation by [ruggsea](https://huggingface.co/ruggsea), using compute resources of the Complex Social & Computational Systems (CS²) group, IDea_Lab, University of Graz.
+
+## Intended use
+
+This model is specialized for Italian multiple-choice question answering in the ITALIC format. It is a 0.4B model: outside this format expect base-model-level capabilities.

chat_template.jinja

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+{{ bos_token }}
+{% if tools %}
+<|im_start|>system
+{% if messages and messages[0]['role'] == 'system' %}
+{{ messages[0]['content'] }}
+{% endif %}
+# Tools
+You may call one or more functions to assist with the user query.
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{% for tool in tools %}
+{{ tool | tojson }}
+{% endfor %}
+</tools>
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+<tool_call>
+{"name": <function-name>, "arguments": <args-json-object>}
+</tool_call><|im_end|>
+{% else %}
+{% if messages and messages[0]['role'] == 'system' %}
+<|im_start|>system
+{{ messages[0]['content'] }}<|im_end|>
+{% endif %}
+{% endif %}
+
+{% for message in messages %}
+{% if message.content is string %}
+  {% set content = message.content %}
+{% else %}
+  {% set content = '' %}
+{% endif %}
+
+{% if (message.role == 'user') or (message.role == 'system' and not loop.first) %}
+<|im_start|>{{ message.role }}
+{{ content }}<|im_end|>
+
+{% elif message.role == 'assistant' %}
+<|im_start|>assistant
+{{ content }}
+{% if message.tool_calls %}
+  {% for tool_call in message.tool_calls %}
+    {% if tool_call.function %}
+      {% set tool_call = tool_call.function %}
+    {% endif %}
+<tool_call>
+{"name": "{{ tool_call.name }}", "arguments": {% if tool_call.arguments is string %}{{ tool_call.arguments }}{% else %}{{ tool_call.arguments | tojson }}{% endif %}}
+</tool_call>
+  {% endfor %}
+{% endif %}
+<|im_end|>
+
+{% elif message.role == 'tool' %}
+{% if loop.first or (messages[loop.index0 - 1].role != 'tool') %}
+<|im_start|>user
+{% endif %}
+<tool_response>
+{{ content }}
+</tool_response>
+{% if loop.last or (messages[loop.index0 + 1].role != 'tool') %}
+<|im_end|>
+{% endif %}
+
+{% endif %}
+{% endfor %}
+
+{% if add_generation_prompt %}
+<|im_start|>assistant
+{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float32",
+  "eos_token_id": 128001,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 960,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "max_position_embeddings": 4096,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 15,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 5,
+  "pad_token_id": 128001,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.14.1",
+  "use_cache": true,
+  "vocab_size": 128262
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "transformers_version": "5.14.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
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+size 1751099616

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a80502037c38583156839ad1486db1e7e5aa0b3f9d47e072f16e6b30a0eb2dd
+size 17211058

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|im_end|>",
+  "is_local": true,
+  "local_files_only": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "TokenizersBackend",
+  "chat_template": "{{ bos_token }}\n{% if tools %}\n<|im_start|>system\n{% if messages and messages[0]['role'] == 'system' %}\n{{ messages[0]['content'] }}\n{% endif %}\n# Tools\nYou may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{% for tool in tools %}\n{{ tool | tojson }}\n{% endfor %}\n</tools>\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n{% else %}\n{% if messages and messages[0]['role'] == 'system' %}\n<|im_start|>system\n{{ messages[0]['content'] }}<|im_end|>\n{% endif %}\n{% endif %}\n\n{% for message in messages %}\n{% if message.content is string %}\n  {% set content = message.content %}\n{% else %}\n  {% set content = '' %}\n{% endif %}\n\n{% if (message.role == 'user') or (message.role == 'system' and not loop.first) %}\n<|im_start|>{{ message.role }}\n{{ content }}<|im_end|>\n\n{% elif message.role == 'assistant' %}\n<|im_start|>assistant\n{{ content }}\n{% if message.tool_calls %}\n  {% for tool_call in message.tool_calls %}\n    {% if tool_call.function %}\n      {% set tool_call = tool_call.function %}\n    {% endif %}\n<tool_call>\n{\"name\": \"{{ tool_call.name }}\", \"arguments\": {% if tool_call.arguments is string %}{{ tool_call.arguments }}{% else %}{{ tool_call.arguments | tojson }}{% endif %}}\n</tool_call>\n  {% endfor %}\n{% endif %}\n<|im_end|>\n\n{% elif message.role == 'tool' %}\n{% if loop.first or (messages[loop.index0 - 1].role != 'tool') %}\n<|im_start|>user\n{% endif %}\n<tool_response>\n{{ content }}\n</tool_response>\n{% if loop.last or (messages[loop.index0 + 1].role != 'tool') %}\n<|im_end|>\n{% endif %}\n\n{% endif %}\n{% endfor %}\n\n{% if add_generation_prompt %}\n<|im_start|>assistant\n{% endif %}\n"
+}