|
--- |
|
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 🚀 |
|
|
|
> 🚀 **New Release**: **cerbero-7b-openchat** our latest SOTA model based on [**openchat3.5**](https://github.com/imoneoi/openchat), delivering performance **on par with** or **superior** to **ChatGPT 3.5**! |
|
|
|
> 🔥 The research paper unveiling the secrets behind **cerbero-7b** is now available on [arXiv](https://arxiv.org/abs/2311.15698)! |
|
|
|
> 📢 **cerbero-7b** is the first **100% Free** and Open Source **Italian Large Language Model** (LLM) ready to be used for **research** or **commercial applications**. |
|
|
|
**Try an online demo [here](https://huggingface.co/spaces/galatolo/chat-with-cerbero-7b)** (quantized demo running on CPU, a lot less powerful than the original cerbero-7b) |
|
|
|
<p align="center"> |
|
<img width="300" height="300" src="./README.md.d/cerbero.png"> |
|
</p> |
|
|
|
Built on top of [**mistral-7b**](https://mistral.ai/news/announcing-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**](https://github.com/teelinsan/camoscio) and [**Fauno**](https://github.com/RSTLess-research/Fauno-Italian-LLM), 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**. |
|
|
|
## Model Evaluation Results 📈 |
|
|
|
The `cerbero-7b` model has been rigorously evaluated across several benchmarks to demonstrate its proficiency in understanding and generating Italian text. Below are the summarized results showcasing its performance: |
|
|
|
### SQuAD-it Evaluation |
|
|
|
The Stanford Question Answering Dataset (SQuAD) in Italian (SQuAD-it) is used to evaluate the model's reading comprehension and question-answering capabilities. The following table presents the F1 score and Exact Match (EM) metrics: |
|
|
|
| Model | F1 Score | Exact Match (EM) | |
|
|----------------------------------------------|--------------|----------------------| |
|
| **cerbero-7b-openchat** | **74.09%** | **56.0%** | |
|
| **cerbero-7b** | **72.55%** | **55.6%** | |
|
| Fauno | 44.46% | 0.00% | |
|
| Camoscio | 37.42% | 0.00% | |
|
| mistral-7b | 15.55% | 8.50% | |
|
|
|
### EVALITA Benchmark Results |
|
|
|
EVALITA benchmarks assess the model's performance in tasks like toxicity detection, irony detection, and sentiment analysis. The table below shows the F1 scores for these tasks: |
|
|
|
| Model | Toxicity Detection | Irony Detection | Sentiment Analysis | |
|
|----------------------------------------------|--------------------|-----------------|--------------------| |
|
| **cerbero-7b-openchat** | **63.33%** | **69.16%** | **66.89%** | |
|
| **cerbero-7b** | **63.04%** | **48.51%** | **61.80%** | |
|
| Fauno | 33.84% | 39.17% | 12.23% | |
|
| Camoscio | 38.18% | 39.65% | 13.33% | |
|
| mistral-7b | 34.16% | 34.16% | 12.14% | |
|
|
|
|
|
## 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: Cerbero Dataset** 📚 |
|
The Cerbero Dataset is a groundbreaking collection specifically curated to enhance the proficiency of cerbero-7b in understanding and generating Italian text. This dataset is a product of an innovative method combining dynamic self-chat mechanisms with advanced Large Language Model (LLM) technology. Refer to the [paper](https://arxiv.org/abs/2311.15698) for more details. |
|
|
|
- **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. |
|
|
|
## Models 🧬 |
|
|
|
**cerbero-7b** is available in various flavors, each tailored for specific applications and use cases. Below is a table listing these versions along with their respective training datasets and base models: |
|
|
|
| Model Name | Training Dataset | Base Model | Huggingface Model | Llama.cpp and Quantized Model | |
|
|-------------------------|-------------------|-------------|-------------------|-------------------------------| |
|
| cerbero-7b | Cerbero Dataset | mistral-7b | [link](https://huggingface.co/galatolo/cerbero-7b) | [link](https://huggingface.co/galatolo/cerbero-7b-gguf) | |
|
| cerbero-7b-openchat | Cerbero Dataset | openchat3.5 | [link](https://huggingface.co/galatolo/cerbero-7b-openchat) | [link](https://huggingface.co/galatolo/cerbero-7b-openchat-gguf) | |
|
|
|
|
|
Each of these models brings its unique strengths to the table, making **cerbero-7b** a versatile tool for both research and commercial applications in the Italian language AI domain. |
|
|
|
We are committed to continuously enhancing **cerbero-7b**. Our team plans to keep training and releasing new models as advancements in the 7b SOTA occur. This ensures that **cerbero-7b** remains at the forefront of AI technology, offering the most advanced and efficient solutions in the Italian language AI sector. |
|
|
|
If you do not have enough RAM to fit the `float32` model (for example when using Colab) we provide for each model a `float16` version using the `revision="float16"` argument |
|
|
|
```python |
|
model = AutoModelForCausalLM.from_pretrained("galatolo/cerbero-7b", revision="float16") |
|
``` |
|
|
|
## Training Details 🚀 |
|
|
|
**cerbero-7b** is a **fully fine-tuned** LLM, 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 📊 |
|
|
|
> 📢 Details on the **Cerbero Dataset** will be updated shortly! |
|
|
|
### 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 **1 epoch**, ensuring a convergence of knowledge and proficiency in handling diverse linguistic tasks. |
|
|
|
## Prompt Format |
|
|
|
**cerbero-7b** is trained on full conversations using the following prompt format: |
|
|
|
``` |
|
[|Umano|] First human message |
|
[|Assistente|] First AI reply |
|
[|Umano|] Second human message |
|
[|Assistente|] Second AI reply |
|
``` |
|
|
|
When crafting prompts, ensure to conclude with the `[|Assistente|]` tag, signaling the AI to generate a response. |
|
Use `[|Umano|]` as stop word. |
|
|
|
For example: |
|
|
|
``` |
|
[|Umano|] Come posso distinguere un AI da un umano? |
|
[|Assistente|] |
|
``` |
|
|
|
While it's possible to include a brief system message at the start of your prompt, remember that the training data for **cerbero-7b** **does not** contain such **system messages**. Hence, it's recommended to minimize or avoid including them for optimal model performance. |
|
|
|
## Getting Started 🚀 |
|
|
|
You can load **cerbero-7b** (or **cerbero-7b-openchat**) using [🤗transformers](https://huggingface.co/docs/transformers/index) |
|
|
|
```python |
|
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? |
|
[|Assistente|]""" |
|
|
|
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) |
|
``` |
|
|
|
### GGUF and llama.cpp |
|
|
|
**cerbero-7b** is fully **compatibile** with [llama.cpp](https://github.com/ggerganov/llama.cpp) |
|
|
|
You can find the **original** and **quantized** versions of **cerbero-7b** in the `gguf` format [here](https://huggingface.co/galatolo/cerbero-7b-gguf/tree/main) |
|
|
|
```python |
|
from llama_cpp import Llama |
|
from huggingface_hub import hf_hub_download |
|
|
|
llm = Llama( |
|
model_path=hf_hub_download( |
|
repo_id="galatolo/cerbero-7b-gguf", |
|
filename="ggml-model-f16.gguf", |
|
), |
|
n_ctx=4086, |
|
) |
|
|
|
llm.generate("""Questa è una conversazione tra un umano ed un assistente AI. |
|
[|Umano|] Come posso distinguere un AI da un umano? |
|
[|Assistente|]""") |
|
``` |
|
|
|
## Citation 📖 |
|
|
|
If you use **cerbero-7b** in your research, please cite our paper: |
|
|
|
```bibtex |
|
@article{galatolo2023cerbero, |
|
title={Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and Evaluation}, |
|
author={Galatolo, Federico A and Cimino, Mario GCA}, |
|
journal={arXiv preprint arXiv:2311.15698}, |
|
year={2023} |
|
} |
|
``` |
|
## Training Details 🚀 |
|
|
|
**cerbero-7b** is a **fully fine-tuned** LLM, 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 📊 |
|
|
|
> 📢 Details on the **Cerbero Dataset** will be updated shortly! |
|
|
|
### 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 **1 epoch**, ensuring a convergence of knowledge and proficiency in handling diverse linguistic tasks. |
|
|
|
## Getting Started 🚀 |
|
|
|
You can load **cerbero-7b** using [🤗transformers](https://huggingface.co/docs/transformers/index) |
|
|
|
```python |
|
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? |
|
[|Assistente|]""" |
|
|
|
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) |
|
``` |
|
|
|
### GGUF and llama.cpp |
|
|
|
**cerbero-7b** is fully **compatibile** with [llama.cpp](https://github.com/ggerganov/llama.cpp) |
|
|
|
You can find the **original** and **quantized** versions of **cerbero-7b** in the `gguf` format [here](https://huggingface.co/galatolo/cerbero-7b-gguf/tree/main) |
|
|
|
```python |
|
from llama_cpp import Llama |
|
from huggingface_hub import hf_hub_download |
|
|
|
llm = Llama( |
|
model_path=hf_hub_download( |
|
repo_id="galatolo/cerbero-7b-gguf", |
|
filename="ggml-model-Q4_K.gguf", |
|
), |
|
n_ctx=4086, |
|
) |
|
|
|
llm.generate("""Questa è una conversazione tra un umano ed un assistente AI. |
|
[|Umano|] Come posso distinguere un AI da un umano? |
|
[|Assistente|]""") |
|
``` |
|
|
|
## Differences from the paper |
|
|
|
> 📢 Attention: The released versions of `cerbero-7b` slightly differ from those used in the paper. The training dataset for the released models was generated using `garage-bAInd/Platypus2-70B-instruct` instead of `meta-llama/Llama-2-7b-chat-hf`, due to the more permissive license of the Platypus2 model (CC-BY-NC 4.0). Our tests indicate that both models produce datasets of comparable quality, and the resulting fine-tuned models demonstrate nearly indistinguishable performance. |
|
|
