---
language:
- en
- it
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
model-index:
- name: Llama-3.1-8b-ITA
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 79.17
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 30.93
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 10.88
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.03
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 11.4
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 31.96
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA
      name: Open LLM Leaderboard
---

## Model Architecture
- **Base Model:** [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
- **Specialization:** Italian Language


## How to Use

```python
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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_DeepMount00__Llama-3.1-8b-ITA)

|      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|