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---
license: llama2
language:
- it
tags:
- text-generation-inference
---
# Model Card for LLaMAntino-2-chat-7b-ITA
## Model description
<!-- Provide a quick summary of what the model is/does. -->
**LLaMAntino-2-chat-7b** is a *Large Language Model (LLM)* that is an italian-adapted **LLaMA 2 chat**.
This model aims to provide Italian NLP researchers with a base model for italian dialogue use cases.
The model was trained using *QLora* and using as training data [clean_mc4_it medium](https://huggingface.co/datasets/gsarti/clean_mc4_it/viewer/medium).
If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
- **Repository:** https://github.com/swapUniba/LLaMAntino
**NOTICE**: the code has not been released yet, we apologize for the delay, it will be available asap!
- **Developed by:** Pierpaolo Basile, Elio Musacchio, Marco Polignano, Lucia Siciliani, Giuseppe Fiameni, Giovanni Semeraro
- **Funded by:** PNRR project FAIR - Future AI Research
- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
- **Model type:** LLaMA 2 chat
- **Language(s) (NLP):** Italian
- **License:** Llama 2 Community License
- **Finetuned from model:** [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf)
## How to Get Started with the Model
Below you can find an example of model usage:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "swap-uniba/LLaMAntino-2-chat-7b-hf-ITA"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
prompt = "Scrivi qui un possibile prompt"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy()[:, input_ids.shape[1]:], skip_special_tokens=True)[0])
```
If you are facing issues when loading the model, you can try to load it quantized:
```python
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True)
```
*Note*: The model loading strategy above requires the [*bitsandbytes*](https://pypi.org/project/bitsandbytes/) and [*accelerate*](https://pypi.org/project/accelerate/) libraries
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you use this model in your research, please cite the following:
```bibtex
@misc{basile2023llamantino,
title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language},
author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
year={2023},
eprint={2312.09993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |