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

# Model Information

XXXX is an updated version of [Mistral-7B-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf), specifically fine-tuned with SFT and LoRA adjustments.

- It's trained on publicly available datasets, like [SQUAD-it](https://huggingface.co/datasets/squad_it), and datasets we've created in-house.
- it's designed to understand and maintain context, making it ideal for Retrieval Augmented Generation (RAG) tasks and applications requiring contextual awareness.

# 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

Be sure to have transformers and torch installed

```python
pip install transformers torch
```

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # change to cpu if you have no gpu

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

messages = [
    {"role": "user", "content": "Qual è il tuo piatto preferito?"},
    {"role": "assistant", "content": "Beh, ho un debole per una buona porzione di risotto allo zafferano. È un piatto che si distingue per il suo sapore ricco e il suo bellissimo colore dorato, rendendolo irresistibile!"},
    {"role": "user", "content": "Hai delle ricette con il risotto che consigli?"}
]

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=250, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```

## Bias, Risks and Limitations

xxxx has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of 
responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition 
of the corpus was used to train the base model (mistralai/Mistral-7B-v0.2), however it is likely to have included a mix of Web data and technical sources 
like books and code.

## Links to resources

- SQUAD-it dataset: https://huggingface.co/datasets/squad_it
- Mistral_7B_v0.2 original weights: https://models.mistralcdn.com/mistral-7b-v0-2/mistral-7B-v0.2.tar
- Mistral_7B_v0.2 model: https://huggingface.co/alpindale/Mistral-7B-v0.2-hf
- Open Ita LLM Leaderbord: https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard

## Quantized versions

We have published as well the 4 bit and 8 bit versions of this model:
https://huggingface.co/MoxoffSpA/xxxxQuantized/main

## The Moxoff Team

Jacopo Abate, Marco D'Ambra, Luigi Simeone, Gianpaolo Francesco Trotta