Model Card for Model ID

ItalIA is a LLM trained for the Italian language and based on Llama3-8b.

Model Details

Model Description

ItalIA is a state-of-the-art language model specifically trained for the Italian language using unsloth, leveraging the latest advancements in the LLM frameworks llama3. This model aims to provide highly accurate and context-aware natural language understanding and generation, making it ideal for a wide range of applications from automated customer support to content creation.

Developed by: Davide Pizzo
Model type: Transformer-based Large Language Model
Language(s) (NLP): Italian
License: Other
Finetuned from model: llama3-8b

Model Sources [optional]

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Uses

ItalIA can be directly integrated into applications requiring natural language processing in Italian, including but not limited to text summarization, question answering, and conversational agents.

Direct Use

This model serves as a powerful italian base for fine-tuning on specific tasks such as legal document analysis, medical record interpretation, and more specialized forms of conversational AI tailored to specific industries.

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

Recommendations

Users should be aware of the potential for biased outputs based on the training data, particularly in scenarios involving regional linguistic variations within Italy.

How to Get Started with the Model

Use the code below to get started with the model.

** from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "MethosPi/llama3-8b-italIA-unsloth-merged" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

text = "Inserisci qui il tuo testo in italiano." input_ids = tokenizer.encode(text, return_tensors="pt") output = model.generate(input_ids)

print(tokenizer.decode(output[0], skip_special_tokens=True)) **

Training Details

Training Data

The model was trained on a diverse corpus of Italian texts, including literature, news articles, and web content, ensuring a broad understanding of the language.

Training Procedure

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

unsloth

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Model Card Contact

For any question, contact me [pizzodavide93@gmail.com]