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Model Architecture

• Base Model: Meta-Llama-3-8B
• Specialization: Italian Language

Evaluation

For a detailed comparison of model performance, check out the Leaderboard for Italian Language Models.

Here's a breakdown of the performance metrics:

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

MODEL_NAME = "DeepMount00/Llama-3-8b-Ita"

model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16).eval()
model.to(device)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

def generate_answer(prompt):
 messages = [
 {"role": "user", "content": prompt},
 ]
 model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
 generated_ids = model.generate(model_inputs, max_new_tokens=200, do_sample=True,
 temperature=0.001)
 decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
 return decoded[0]

prompt = "Come si apre un file json in python?"
answer = generate_answer(prompt)
print(answer)

Developer

[Michele Montebovi]

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Base model