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]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
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
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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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).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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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
Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
For any question, contact me [
pizzodavide93@gmail.com]
