SemanticAlignment
/

Mistral-v0.1-Italian-SAVA-instruct

@@ -3,197 +3,115 @@ library_name: transformers
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 ---
+# Mistral-7B-v0.1-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 **Mistral-7B-v0.1-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**.
+*Mistral-v0.1-Italian-SAVA-instruct* is a continually trained and instruction tuned Mistral model.
+**Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
+**Model Architecture:** Mistral-7B-v0.1-Adapted is an auto-regressive language model that uses an optimized transformer architecture.
+## Data used for the adaptation
+The **Mistral-7B-v0.1-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/Mistral-v0.1-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},
+}
+```