LLM ITA
Collection
Open-Source Language Models Finetuned for Italian
•
4 items
•
Updated
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5
Model Architecture
- Base Model: Meta-Llama-3.1-8B-Instruct
- Specialization: Italian Language
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-3.1-8b-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. | 28.23 |
| IFEval (0-Shot) | 79.17 |
| BBH (3-Shot) | 30.93 |
| MATH Lvl 5 (4-Shot) | 10.88 |
| GPQA (0-shot) | 5.03 |
| MuSR (0-shot) | 11.40 |
| MMLU-PRO (5-shot) | 31.96 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard79.170
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard30.930
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard10.880
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.030
- acc_norm on MuSR (0-shot)Open LLM Leaderboard11.400
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard31.960