---
library_name: transformers
tags:
- unsloth
- trl
- sft
language:
- it
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->


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 section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->


### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

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

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

unsloth

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

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