---
license: apache-2.0
language:
- it
- en
library_name: transformers
tags:
- sft
- it
- mistral
- chatml
---

# Model Information

xxxx is a SFT and LoRA finetuned version of Mistral-7B-v0.2 

It has been trained on a mixture of opensource datasets, like SQUAD-it (https://huggingface.co/datasets/squad_it), and some internally made datasets.

It is not just a Q&A, it is a Q&A + Context model, with the goal being it being used for RAGs and application in need of a context.

# Evaluation

We evaluated the model using the same test sets as used for the Open Ita LLM Leaderboard 

| hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average |
|:----------------------:| :---------------: | :--------------------: | :-------: |
| 0.6067 | 0.4405 | 0.5112 | 0,52 |


## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"

model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/xxxx")
tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/xxxx")

question = """Quanto è alta la torre di Pisa?"""
context = """
La Torre di Pisa è un campanile del XII secolo, famoso per la sua inclinazione. Alta circa 56 metri.
"""
prompt = f"Rispondi alla seguente domanda con meno parle possibili basandoti sul contesto fornito. Domanda: {question}, contesto: {context}"

messages = [
    {"role": "user", "content": prompt},
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```


## The Moxoff Team
Marco D'Ambra, Jacopo Abate, Gianpaolo Francesco Trotta