DeepMount00/Llama-3-8b-Ita

---

💡 Found this resource helpful? Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider supporting my research to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕

---

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:

Metric	hellaswag_it acc_norm	arc_it acc_norm	m_mmlu_it 5-shot acc	Average
Accuracy Normalized	0.6518	0.5441	0.5729	0.5896

---

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

Metric	Value
Avg.	26.58
IFEval (0-Shot)	75.30
BBH (3-Shot)	28.08
MATH Lvl 5 (4-Shot)	5.36
GPQA (0-shot)	7.38
MuSR (0-shot)	11.68
MMLU-PRO (5-shot)	31.69