README.md

---
inference: false
license: openrail
language:
- it
---

# ExtremITA Camoscio 7 bilion parameters adapters: ExtremITLLaMA
This is ExtremITLLaMA, the adapters for the instruction-tuned Italian LLaMA model that participated in all the tasks of [EVALITA 2023](https://www.evalita.it/campaigns/evalita-2023/) winning 41% of tasks and achieving 64% of top-three positions.
It requires the base model from [sag-uniroma2/extremITA-Camoscio-7b](https://huggingface.co/sag-uniroma2/extremITA-Camoscio-7b).

# Usage
Checkout the github repository for more insights and codes: https://github.com/crux82/ExtremITA

```python
from peft import PeftModel
from transformers import LLaMATokenizer, LLaMAForCausalLM
import torch

tokenizer = LLaMATokenizer.from_pretrained("yahma/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
        "sag-uniroma2/extremITA-Camoscio-7b",
        load_in_8bit=True,
        torch_dtype=torch.float16,
        device_map="auto",
    )
model = PeftModel.from_pretrained(
    model,
    "sag-uniroma2/extremITA-Camoscio-7b-adapters",
    torch_dtype=torch.float16,
    device_map="auto",
)
```

# Citation
```
@inproceedings{hromei2023extremita,
  author       = {Claudiu Daniel Hromei and
                  Danilo Croce and
                  Valerio Basile and
                  Roberto Basili},
  title        = {ExtremITA at EVALITA 2023: Multi-Task Sustainable Scaling to Large Language Models at its Extreme},
  booktitle    = {Proceedings of the Eighth Evaluation Campaign of Natural Language
                  Processing and Speech Tools for Italian. Final Workshop (EVALITA 2023)},
  publisher    = {CEUR.org},
  year         = {2023},
  month        = {September},
  address      = {Parma, Italy}
}
```