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---
license: openrail
language:
- it
datasets:
- teelinsan/camoscio
---
# ExtremITA Camoscio 7 bilion parameters
This is the base model trained on Italian instructions, a sibling of Alpaca.
It is based on [tellinsan/camoscio-7b-llama](https://huggingface.co/teelinsan/camoscio-7b-llama) adapters and the original LLaMA model, and it adds nothing new to [tellinsan/camoscio-7b-llama](https://huggingface.co/teelinsan/camoscio-7b-llama). Our version is the merged model with the adapters in order to obtain a more stable model that can be further fine-tuned, which we did for the [EVALITA 2023](https://www.evalita.it/campaigns/evalita-2023/) challenge.
# Usage
Checkout the github repository for more insights and codes: https://github.com/crux82/ExtremITA
```python
from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig
import torch
tokenizer = LLaMATokenizer.from_pretrained("yahma/llama-7b-hf")
model = LLaMAForCausalLM.from_pretrained(
"sag-uniroma2/extremITA-Camoscio-7b",
load_in_8bit=True,
device_map="auto",
)
generation_config = GenerationConfig(
temperature=0.2,
top_p=0.75,
top_k=40,
num_beams=4,
)
prompts = [
"Riassumi la storia di Pinocchio",
"Scrivi un programma che stampa i numeri da 1 a 100. Ma per i multipli \
di tre stampa 'Fizz' al posto del numero e per i multipli di cinque \
stampa 'Buzz'. Per i numeri che sono multipli sia di tre che di cinque \
stampa 'FizzBuzz'."
]
inputs = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True).to(model.device)
with torch.no_grad():
gen_outputs = model.generate(
**inputs,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
)
for i in range(len(gen_outputs)):
output = tokenizer.decode(gen_outputs[i], skip_special_tokens=True)
print(output)
```
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