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---
license: mit
language:
- it
- en
library_name: transformers
tags:
- sft
- it
- mistral
- chatml
---
# Model Information
Azzurro 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 sentencepiece
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # change to cpu if you have no gpu
model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Azzurro")
tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Azzurro")
prompt = "Come mi chiamo? Contesto: Esempio di contesto"
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=250, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Bias, Risks and Limitations
Azzurro 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/AzzurroQuantized
## The Moxoff Team
Jacopo Abate, Marco D'Ambra, Luigi Simeone, Gianpaolo Francesco Trotta |