--- 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 πŸš€ > πŸš€ **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**! > πŸ”₯ The research paper unveiling the secrets behind **cerbero-7b** is now available on [arXiv](https://arxiv.org/abs/2311.15698)! > πŸ“’ **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)

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 πŸ“ˆ 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? πŸ€” 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** πŸ—οΈ 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** πŸ“š 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** πŸ•ŠοΈ 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 πŸš€ **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 πŸ“Š > πŸ“’ Details on the **Cerbero Dataset** will be updated shortly! ### Training Setup βš™οΈ **cerbero-7b** is trained on an NVIDIA DGX H100: - **Hardware:** Utilizing 8xH100 GPUs, each with 80 GB VRAM. πŸ–₯️ - **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. ``` [|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 πŸš€ You can load **cerbero-7b** (or **cerbero-7b-openchat**) using [πŸ€—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 Γ¨ 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 Γ¨ una conversazione tra un umano ed un assistente AI. [|Umano|] Come posso distinguere un AI da un umano? [|Assistente|]""") ``` ## Citation πŸ“– 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 πŸš€ **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 πŸ“Š > πŸ“’ Details on the **Cerbero Dataset** will be updated shortly! ### Training Setup βš™οΈ **cerbero-7b** is trained on an NVIDIA DGX H100: - **Hardware:** Utilizing 8xH100 GPUs, each with 80 GB VRAM. πŸ–₯️ - **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 πŸš€ You can load **cerbero-7b** using [πŸ€—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 Γ¨ 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 Γ¨ una conversazione tra un umano ed un assistente AI. [|Umano|] Come posso distinguere un AI da un umano? [|Assistente|]""") ```