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Model: SemanticAlignment/Llama-3.1-8B-Italian-SAVA-instruct
Source: Original Platform

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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Llama-3.1-8B-Italian-SAVA-instruct
+<div align="center">
+
+<img src="https://github.com/Andrew-Wyn/images/blob/master/sava/italian_adapt-img.jpg?raw=true" width="400" height="400" style="border-radius:10%" />
+
+</div>
+
+The **Llama-3.1-8B-Adapted** collection of large language models (LLMs), is a collection of adapted generative models in 7B (text in/text out), adapted models from **Mistral-7B-Base-v0.1**.
+
+*Llama-3.1-8B-Italian-SAVA-instruct* is a continually trained and instruction tuned Llama model. Which vocabulary was inherited from **Minerva-3B**.
+
+**Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
+
+**Model Architecture:** Llama-3.1-8B-Adapted is an auto-regressive language model that uses an optimized transformer architecture.
+
+## Data used for the adaptation
+
+The **Llama-3.1-8B-Adapted** models are trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
+The data are extracted to be skewed toward Italian language with a ration of one over four. Extracting the first 9B tokens from Italian part of CulturaX and the first 3B tokens from English part of CulturaX.
+
+## Data used for the instruction tuning (SFT)
+
+The data used in the instruction following training procedure:
+
+| Dataset | Language | Instances |
+|------|-----|------|
+| [TÜLU-v3](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) | EN  | 940,000 |
+| [LIMA](https://huggingface.co/datasets/GAIR/lima) | IT/EN  | 2,000 |
+| [WildChat-IT](https://huggingface.co/datasets/allenai/WildChat-1M) | IT  | 5,000 |
+| [TowerBlocks-v0.2](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2) | IT/EN  | 7,276 |
+| [GPT-4o-ITA-Instruct](https://huggingface.co/datasets/DeepMount00/GPT-4o-ITA-INSTRUCT) | IT | 15,000 |
+| [Aya](https://huggingface.co/datasets/CohereLabs/aya_dataset) | IT | 700 |
+
+The model is trained for two epoches in the aforementioned data.
+
+## Evaluation
+
+Adapted models are evaluated on [ITA-Bench](https://github.com/SapienzaNLP/ita-bench).
+
+| Model | MMLU (5-shots) | ARC-C (5-shots) | Hellaswag (0-shots) | IFEval (inst_level) |
+|------|-----|------|------|------|
+| **Llama-3.1-SAVA** | 56.9 | 42.3 | 58.1 | 62.3 |
+| Llama-3.1-LAPT | 58.5 | 47.9 | 62.4 | 67.3 |
+| Mistral-0.1-SAVA | 51.5 | 41.6 | 57.5 | 61.7 |
+| Mistral-0.1-LAPT | 52.9 | 39.9 | 58.4 | 60.0 |
+| Llama-3.1-Original | 47.4 | 43.1 | 57.9 | 66.8 |
+| Mistral-0.1-Original | 41.6 | 38.9 | 50.0 | 42.2 |
+
+## Use with Transformers
+
+You can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import transformers
+import torch
+
+model_id = "SemanticAlignment/Llama-3.1-8B-Italian-SAVA-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+generator = pipeline(
+    "text-generation",
+    model=model_name,
+    device_map="auto",
+    dtype=torch.bfloat16
+)
+
+conversations.append([
+    {"role": "system", "content": "Sei un assistente utile, rispondi in modo conciso e coerente."},
+    {"role": "user", "content": "Cosa si può fare in una bella giornata di sole?"},
+])
+
+chat_samples = tokenizer.apply_chat_template(conversations, tokenize=False)
+
+# get number of prompt tokens
+prompt_tokens_number = len(tokenizer(chat_samples)["input_ids"])
+
+outputs = generator(
+    conversations,
+    max_new_tokens=2048,
+    eos_token_id=[
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|eot_id|>"),
+    ],
+)
+
+```
+
+Code: https://github.com/SapienzaNLP/sava
+
+## Aknowledgement
+Thanks to Leonardo Colosi (colosi@diag.uniroma1.it) for helping in instruction tuning phase.
+
+We acknowledge ISCRA for awarding this project access to the LEONARDO supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CINECA (Italy).
+
+## Citation
+
+If you use any part of this work, please consider citing the paper as follows:
+
+```bibtex
+@misc{moroni2025optimizingllmsitalianreducing,
+      title={Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation}, 
+      author={Luca Moroni and Giovanni Puccetti and Pere-Lluis Huguet Cabot and Andrei Stefan Bejgu and Edoardo Barba and Alessio Miaschi and Felice Dell'Orletta and Andrea Esuli and Roberto Navigli},
+      year={2025},
+      eprint={2504.17025},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2504.17025}, 
+}
+```

added_tokens.json

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+{
+  "<|end_header_id|>": 32769,
+  "<|eom_id|>": 32771,
+  "<|eot_id|>": 32770,
+  "<|start_header_id|>": 32768
+}

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+{{ '<s>' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>system<|end_header_id|>
+
+' + system_message + '<|eot_id|>' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>
+
+' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "unk_token_id": 0,
+  "use_cache": false,
+  "vocab_size": 32832
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.3"
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