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Model: galatolo/cerbero-7b
Source: Original Platform

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README.md

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+---
+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.

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+    "model.norm.weight": "pytorch_model-00003-of-00003.bin"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,43 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{{bos_token}}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[|Umano|] ' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{{ '[|Assistente|] ' + message['content'] + '\n' }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n' }}{% else %}{{ raise_exception('Only system, user and assistant roles are supported!') }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '[|Assistente|]' }}{% endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "trust_remote_code": false,
+  "unk_token": "<unk>",
+  "use_default_system_prompt": true,
+  "use_fast": true
+}