|
--- |
|
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 install these dependecies before running the program |
|
|
|
```python |
|
pip install transformers torch sentencepiece |
|
``` |
|
|
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
device = "cpu" # if you want to use the gpu make sure to have cuda toolkit installed and change this to "cuda" |
|
|
|
model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Azzurro") |
|
tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Azzurro") |
|
|
|
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"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=256, 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 |
