It is offered in two distinct configurations: a 4-bit version and an 8-bit version, each designed to maintain the model's effectiveness while significantly reducing its size and computational requirements.

It's trained both 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.
It is quantized in a 4-bit version and an 8-bit version following the procedure here.

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

You need to download the .gguf model first

If you want to use the cpu install these dependencies:

pip install llama-cpp-python huggingface_hub

If you want to use the gpu instead:

CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install huggingface_hub llama-cpp-python --force-reinstall --upgrade --no-cache-dir

And then use this code to see a response to the prompt.

python

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

model_path = hf_hub_download(
    repo_id="MoxoffSpA/AzzurroQuantized",
    filename="Azzurro-ggml-Q4_K_M.gguf"
)

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path=model_path,
  n_ctx=2048,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=0         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
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}"

output = llm(
  f"[INST] {prompt} [/INST]", # Prompt
  max_tokens=128,
  stop=["\n"],   
  echo=True,
  temperature=0.1,
  top_p=0.95
)

# Chat Completion API

print(output['choices'][0]['text'])

Bias, Risks and Limitations

AzzurroQuantized and its original model Azzurro have 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 were 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

Base version

We have the not quantized version here: https://huggingface.co/MoxoffSpA/Azzurro

The Moxoff Team

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

MoxoffSrL/AzzurroQuantized

Model Information