SemanticAlignment
/

Mistral-v0.1-Italian-LAPT-instruct

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 ---
+# Mistral-7B-v0.1-Italian-LAPT-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-FVT* is a continually trained 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
+...
+## 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-FVT-instruct"
+pipeline = transformers.pipeline(
+    "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
+)
+pipeline("Cosa si può fare in una bella giornata di sole?")
+```
+Code: https://github.com/SapienzaNLP/sava
+## 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},
+}
+```