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--- |
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license: apache-2.0 |
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datasets: |
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- andreabac3/Quora-Italian-Fauno-Baize |
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- andreabac3/StackOverflow-Italian-Fauno-Baize |
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- andreabac3/MedQuaAD-Italian-Fauno-Baize |
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language: |
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- it |
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- en |
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pipeline_tag: text-generation |
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--- |
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# cerbero-7b Italian LLM 🚀 |
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> 🚀 **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**! |
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> 🔥 The research paper unveiling the secrets behind **cerbero-7b** is now available on [arXiv](https://arxiv.org/abs/2311.15698)! |
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> 📢 **cerbero-7b** is the first **100% Free** and Open Source **Italian Large Language Model** (LLM) ready to be used for **research** or **commercial applications**. |
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**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) |
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<p align="center"> |
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<img width="300" height="300" src="./README.md.d/cerbero.png"> |
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</p> |
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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. |
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**cerbero-7b** is specifically crafted to fill the void in Italy's AI landscape. |
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A **cambrian explosion** of **Italian Language Models** is essential for building advanced AI architectures that can cater to the diverse needs of the population. |
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**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. |
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**cerbero-7b** is released under the **permissive** Apache 2.0 **license**, allowing **unrestricted usage**, even **for commercial applications**. |
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## Model Evaluation Results 📈 |
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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: |
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### SQuAD-it Evaluation |
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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: |
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| Model | F1 Score | Exact Match (EM) | |
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|----------------------------------------------|--------------|----------------------| |
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| **cerbero-7b-openchat** | **74.09%** | **56.0%** | |
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| **cerbero-7b** | **72.55%** | **55.6%** | |
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| Fauno | 44.46% | 0.00% | |
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| Camoscio | 37.42% | 0.00% | |
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| mistral-7b | 15.55% | 8.50% | |
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### EVALITA Benchmark Results |
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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: |
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| Model | Toxicity Detection | Irony Detection | Sentiment Analysis | |
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|----------------------------------------------|--------------------|-----------------|--------------------| |
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| **cerbero-7b-openchat** | **63.33%** | **69.16%** | **66.89%** | |
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| **cerbero-7b** | **63.04%** | **48.51%** | **61.80%** | |
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| Fauno | 33.84% | 39.17% | 12.23% | |
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| Camoscio | 38.18% | 39.65% | 13.33% | |
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| mistral-7b | 34.16% | 34.16% | 12.14% | |
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## Why Cerbero? 🤔 |
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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: |
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- **Base Model: mistral-7b** 🏗️ |
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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. |
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- **Datasets: Cerbero Dataset** 📚 |
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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. |
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- **Licensing: Apache 2.0** 🕊️ |
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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. |
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## Models 🧬 |
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**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: |
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| Model Name | Training Dataset | Base Model | Huggingface Model | Llama.cpp and Quantized Model | |
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|-------------------------|-------------------|-------------|-------------------|-------------------------------| |
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| cerbero-7b | Cerbero Dataset | mistral-7b | [link](https://huggingface.co/galatolo/cerbero-7b) | [link](https://huggingface.co/galatolo/cerbero-7b-gguf) | |
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| cerbero-7b-openchat | Cerbero Dataset | openchat3.5 | [link](https://huggingface.co/galatolo/cerbero-7b-openchat) | [link](https://huggingface.co/galatolo/cerbero-7b-openchat-gguf) | |
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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. |
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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. |
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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 |
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```python |
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model = AutoModelForCausalLM.from_pretrained("galatolo/cerbero-7b", revision="float16") |
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``` |
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## Training Details 🚀 |
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**cerbero-7b** is a **fully fine-tuned** LLM, distinguishing itself from LORA or QLORA fine-tunes. |
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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** |
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### Dataset Composition 📊 |
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> 📢 Details on the **Cerbero Dataset** will be updated shortly! |
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### Training Setup ⚙️ |
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**cerbero-7b** is trained on an NVIDIA DGX H100: |
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- **Hardware:** Utilizing 8xH100 GPUs, each with 80 GB VRAM. 🖥️ |
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- **Parallelism:** DeepSpeed Zero stage 1 parallelism for optimal training efficiency.✨ |
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The model has been trained for **1 epoch**, ensuring a convergence of knowledge and proficiency in handling diverse linguistic tasks. |
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## Prompt Format |
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**cerbero-7b** is trained on full conversations using the following prompt format: |
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``` |
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[|Umano|] First human message |
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[|Assistente|] First AI reply |
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[|Umano|] Second human message |
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[|Assistente|] Second AI reply |
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``` |
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When crafting prompts, ensure to conclude with the `[|Assistente|]` tag, signaling the AI to generate a response. |
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Use `[|Umano|]` as stop word. |
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``` |
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[|Umano|] Come posso distinguere un AI da un umano? |
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[|Assistente|] |
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``` |
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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. |
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## Getting Started 🚀 |
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You can load **cerbero-7b** (or **cerbero-7b-openchat**) using [🤗transformers](https://huggingface.co/docs/transformers/index) |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("galatolo/cerbero-7b") |
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tokenizer = AutoTokenizer.from_pretrained("galatolo/cerbero-7b") |
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prompt = """Questa è una conversazione tra un umano ed un assistente AI. |
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[|Umano|] Come posso distinguere un AI da un umano? |
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[|Assistente|]""" |
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input_ids = tokenizer(prompt, return_tensors='pt').input_ids |
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with torch.no_grad(): |
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output_ids = model.generate(input_ids, max_new_tokens=128) |
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generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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### GGUF and llama.cpp |
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**cerbero-7b** is fully **compatibile** with [llama.cpp](https://github.com/ggerganov/llama.cpp) |
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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) |
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```python |
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from llama_cpp import Llama |
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from huggingface_hub import hf_hub_download |
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llm = Llama( |
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model_path=hf_hub_download( |
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repo_id="galatolo/cerbero-7b-gguf", |
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filename="ggml-model-f16.gguf", |
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), |
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n_ctx=4086, |
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) |
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llm.generate("""Questa è una conversazione tra un umano ed un assistente AI. |
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[|Umano|] Come posso distinguere un AI da un umano? |
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[|Assistente|]""") |
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``` |
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## Citation 📖 |
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If you use **cerbero-7b** in your research, please cite our paper: |
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```bibtex |
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@article{galatolo2023cerbero, |
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title={Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and Evaluation}, |
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author={Galatolo, Federico A and Cimino, Mario GCA}, |
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journal={arXiv preprint arXiv:2311.15698}, |
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year={2023} |
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} |
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``` |
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## Training Details 🚀 |
|
|
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**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** |
|
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### Dataset Composition 📊 |
|
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> 📢 Details on the **Cerbero Dataset** will be updated shortly! |
|
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### Training Setup ⚙️ |
|
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**cerbero-7b** is trained on an NVIDIA DGX H100: |
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- **Hardware:** Utilizing 8xH100 GPUs, each with 80 GB VRAM. 🖥️ |
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- **Parallelism:** DeepSpeed Zero stage 1 parallelism for optimal training efficiency.✨ |
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The model has been trained for **1 epoch**, ensuring a convergence of knowledge and proficiency in handling diverse linguistic tasks. |
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## Getting Started 🚀 |
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You can load **cerbero-7b** using [🤗transformers](https://huggingface.co/docs/transformers/index) |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("galatolo/cerbero-7b") |
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tokenizer = AutoTokenizer.from_pretrained("galatolo/cerbero-7b") |
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prompt = """Questa è una conversazione tra un umano ed un assistente AI. |
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[|Umano|] Come posso distinguere un AI da un umano? |
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[|Assistente|]""" |
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input_ids = tokenizer(prompt, return_tensors='pt').input_ids |
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with torch.no_grad(): |
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output_ids = model.generate(input_ids, max_new_tokens=128) |
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generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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### GGUF and llama.cpp |
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**cerbero-7b** is fully **compatibile** with [llama.cpp](https://github.com/ggerganov/llama.cpp) |
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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) |
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```python |
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from llama_cpp import Llama |
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from huggingface_hub import hf_hub_download |
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llm = Llama( |
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model_path=hf_hub_download( |
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repo_id="galatolo/cerbero-7b-gguf", |
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filename="ggml-model-Q4_K.gguf", |
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), |
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n_ctx=4086, |
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) |
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llm.generate("""Questa è una conversazione tra un umano ed un assistente AI. |
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[|Umano|] Come posso distinguere un AI da un umano? |
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[|Assistente|]""") |
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``` |
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