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, specifically fine-tuned with SFT and LoRA adjustments.
- It's trained on publicly available datasets, like 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
pip install transformers torch sentencepiece
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # change to cpu if you have no gpu
model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Pompei")
tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Pompei")
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
Pompei 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/Pompei-Quantized
The Moxoff Team
Jacopo Abate, Marco D'Ambra, Luigi Simeone, Gianpaolo Francesco Trotta