Update README.md

README.md

@@ -1,29 +1,36 @@
 ---
+title: Gemma Italian Camoscio Language Model
 tags:
+- italian-language-generation
+- camoscio-dataset
+- gemma-2b
 - autotrain
-- text-generation-inference
-- text-generation
-- peft
+datasets:
+- camoscio
 library_name: transformers
-widget:
-  - messages:
-      - role: user
-        content: What is your favorite condiment?
+model: theoracle/gemma_italian_camoscio
 license: other
 ---
 
-# Model Trained Using AutoTrain
+## Overview
 
-This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+`theoracle/gemma_italian_camoscio` is a cutting-edge model specifically designed for Italian language generation. Leveraging the comprehensive Camoscio dataset, this model enhances the Gemma 2B architecture's capabilities in producing high-quality, contextually accurate Italian text. Developed with AutoTrain, it excels in various Italian text generation tasks, including but not limited to creative writing, article generation, and conversational responses.
 
-# Usage
+## Key Features
 
-```python
+- **Italian Language Focus**: Tailored to understand and generate Italian text, capturing the language's nuances and complexities.
+- **Camoscio Dataset Training**: Utilizes the rich Camoscio dataset, ensuring the model is well-versed in a wide range of Italian language styles and contexts.
+- **Gemma 2B Architecture**: Built on the powerful Gemma 2B framework, known for its efficiency and effectiveness in language generation tasks.
+- **AutoTrain Enhanced**: Benefits from AutoTrain's optimization, making the model both robust and versatile in handling Italian text generation.
 
-from transformers import AutoModelForCausalLM, AutoTokenizer
+## Usage
+
+Here's how to use this model for generating Italian text:
 
-model_path = "PATH_TO_THIS_REPO"
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
+model_path = "theoracle/gemma_italian_camoscio"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(
     model_path,
@@ -31,15 +38,36 @@ model = AutoModelForCausalLM.from_pretrained(
     torch_dtype='auto'
 ).eval()
 
-# Prompt content: "hi"
-messages = [
-    {"role": "user", "content": "hi"}
-]
+# Example: Generating Italian text
+prompt = "Inizia la storia con una giornata soleggiata in Sicilia, dove"
+
+# Tokenize and generate text
+encoding = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=500, add_special_tokens=True)
+input_ids = encoding['input_ids']
+attention_mask = encoding['attention_mask']
+
+output_ids = model.generate(
+    input_ids.to('cuda'),
+    attention_mask=attention_mask.to('cuda'),
+    max_new_tokens=300,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+## Application Scenarios
+
+This model is ideal for:
+- Content creators looking to produce Italian-language articles, stories, or scripts.
+- Developers creating conversational AI applications in Italian.
+- Educators and language learners seeking tools for Italian language practice and improvement.
+
+## Training and Technology
+
+The `theoracle/gemma_italian_camoscio` model is trained using the AutoTrain platform for optimal performance, ensuring that it is well-suited for a broad spectrum of Italian text generation tasks. The Camoscio dataset provides a solid foundation, offering diverse and extensive coverage of the Italian language, which, combined with the Gemma 2B architecture, enables the model to generate coherent, nuanced, and contextually relevant Italian text.
 
-input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
-output_ids = model.generate(input_ids.to('cuda'))
-response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
+## License
 
-# Model response: "Hello! How can I assist you today?"
-print(response)
-```
+This model is available under an "other" license, facilitating its use in a wide array of applications. Users are encouraged to review the license terms to ensure compliance with their project requirements and intended use cases.