metadata

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 adapters and the original LLaMA model, and it adds nothing new to tellinsan/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 challenge.

Usage

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

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)