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---
language:
- it
- en
license: llama3
library_name: transformers
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- DeepMount00/llm_ita_ultra
model-index:
- name: Llama-3-8b-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: 75.3
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-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: 28.08
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-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: 5.36
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-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: 7.38
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-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: 11.68
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-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: 31.69
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-Ita
name: Open LLM Leaderboard
---
## Model Architecture
- **Base Model:** [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- **Specialization:** Italian Language
## Evaluation
For a detailed comparison of model performance, check out the [Leaderboard for Italian Language Models](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard).
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
```python
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](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-3-8b-Ita)
| 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|
|