license: apache-2.0
datasets:
- andreabac3/Quora-Italian-Fauno-Baize
- andreabac3/StackOverflow-Italian-Fauno-Baize
- andreabac3/MedQuaAD-Italian-Fauno-Baize
language:
- it
- en
pipeline_tag: text-generation
cerbero-7b Italian LLM 🚀
📢 Cerbero-7b is the first 100% Free and Open Source Italian Large Language Model (LLM) ready to be used for research or commercial applications.
Built on mistral-7b, which outperforms Llama2 13B across all benchmarks and surpasses Llama1 34B in numerous metrics.
cerbero-7b is specifically crafted to fill the void in Italy's AI landscape.
A cambrian explosion of Italian Language Models is essential for building advanced AI architectures that can cater to the diverse needs of the population.
cerbero-7b, alongside companions like Camoscio and Fauno, aims to help kick-start this revolution in Italy, ushering in an era where sophisticated AI solutions can seamlessly interact with and understand the intricacies of the Italian language, thereby empowering innovation across industries and fostering a deeper connection between technology and the people it serves.
cerbero-7b is released under the permissive Apache 2.0 license, allowing unrestricted usage, even for commercial applications.
Why Cerbero? 🤔
The name "Cerbero," inspired by the three-headed dog that guards the gates of the Underworld in Greek mythology, encapsulates the essence of our model, drawing strength from three foundational pillars:
Base Model: mistral-7b 🏗️ cerbero-7b builds upon the formidable mistral-7b as its base model. This choice ensures a robust foundation, leveraging the power and capabilities of a cutting-edge language model.
Datasets: Fauno Dataset 📚 Utilizing the comprehensive fauno dataset, cerbero-7b gains a diverse and rich understanding of the Italian language. The incorporation of varied data sources contributes to its versatility in handling a wide array of tasks.
Licensing: Apache 2.0 🕊️ Released under the permissive Apache 2.0 license, cerbero-7b promotes openness and collaboration. This licensing choice empowers developers with the freedom for unrestricted usage, fostering a community-driven approach to advancing AI in Italy and beyond.
Training Details 🚀
cerbero-7b is fully fine-tuned, distinguishing itself from LORA or QLORA fine-tunes. The model is trained on an expansive Italian Large Language Model (LLM) using synthetic datasets generated through dynamic self-chat on a large context window of 8192 tokens
Dataset Composition 📊
We employed a refined version of the Fauno training dataset. The training data covers a broad spectrum, incorporating:
- Medical Data: Capturing nuances in medical language. 🩺
- Technical Content: Extracted from Stack Overflow to enhance the model's understanding of technical discourse. 💻
- Quora Discussions: Providing valuable insights into common queries and language usage. ❓
- Alpaca Data Translation: Italian-translated content from Alpaca contributes to the model's language richness and contextual understanding. 🦙
Training Setup ⚙️
cerbero-7b is trained on an NVIDIA DGX H100:
- Hardware: Utilizing 8xH100 GPUs, each with 80 GB VRAM. 🖥️
- Parallelism: DeepSpeed Zero stage 1 parallelism for optimal training efficiency.✨
The model has been trained for 3 epochs, ensuring a convergence of knowledge and proficiency in handling diverse linguistic tasks.
Getting Started 🚀
You can load cerbero-7b using 🤗transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("galatolo/cerbero-7b")
tokenizer = AutoTokenizer.from_pretrained("galatolo/cerbero-7b")
prompt = """Questa è una conversazione tra un umano ed un assistente AI.
[|Umano|] Come posso distinguere un AI da un umano?
[|AI|]"""
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
with torch.no_grad():
output_ids = model.generate(input_ids, max_new_tokens=128)
generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(generated_text)