Update README.md

README.md

@@ -4,29 +4,28 @@ 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
 
-<!-- Provide a longer summary of what this model is. -->
+ItalIA is a state-of-the-art language model specifically trained for the Italian language, 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.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** [Davide Pizzo]
+- **Model type:** [Transformer-based Large Language Model]
+- **Language(s) (NLP):** [Italian]
+- **License:** [Other]
+- **Finetuned from model [optional]:** [llama3-8b]
 
 ### Model Sources [optional]
 
@@ -40,11 +39,15 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- 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. -->
 
-[More Information Needed]
+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]
 
@@ -62,19 +65,28 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further 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.
 
-[More Information Needed]
+[from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "your-model-name-on-huggingface"
+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
 
@@ -82,7 +94,7 @@ Use the code below to get started with the model.
 
 <!-- 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. -->
 
-[More Information Needed]
+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
 
@@ -169,7 +181,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Software
 
-[More Information Needed]
+unsloth
 
 ## Citation [optional]
 
@@ -199,4 +211,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+For any question, contact me [pizzodavide93@gmail.com]