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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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> 📢 **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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<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 [**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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## 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: Fauno Dataset** 📚 |
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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. |
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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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## Training Details 🚀 |
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cerbero-7b is **fully fine-tuned**, 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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We employed a **refined** version of the [Fauno training dataset](https://github.com/RSTLess-research/Fauno-Italian-LLM). The training data covers a broad spectrum, incorporating: |
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- **Medical Data:** Capturing nuances in medical language. 🩺 |
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- **Technical Content:** Extracted from Stack Overflow to enhance the model's understanding of technical discourse. 💻 |
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- **Quora Discussions:** Providing valuable insights into common queries and language usage. ❓ |
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- **Alpaca Data Translation:** Italian-translated content from Alpaca contributes to the model's language richness and contextual understanding. 🦙 |
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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 **3 epochs**, 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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[|AI|]""" |
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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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``` |
