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1. Split the input text into words. 2. For each word, check if it's a proper noun (capitalized). 3. If it's a proper noun, leave it. 4. If not, find three synonyms. 5. Format each with syn3. 6. Combine the words back into the output text.

Testing with a sample input would help. Let's take "The Amazon is a big river." Here, "Amazon" is a name (proper noun), so kept. "The," "a" are articles, replaced with synonyms if possible. "Big" becomes enormous, "river" becomes canal? Wait, "canal" is not a synonym for river. Maybe stream is better. Need to be careful with the synonym accuracy. Kamera 10 vjecare Masturbon ne karrige Vajza Pe...

Let's take the example sentence. "The" is an article; names here are "fox" and "dog" (common nouns, not names). So "quick" would be replaced with rapid, "brown" with amber, etc. But I need to be careful not to replace any proper nouns. For instance, if there's a name like "John," it stays as is. For each word, check if it's a proper noun (capitalized)

Potential issues: Words that are names but look like common nouns. For example, "Apple" could be a company name or a fruit. Without context, it's hard to tell. However, the user wants names kept, so if it's a known name, it stays. Otherwise, replace with synonyms. So maybe rely on capitalization, but that's not foolproof. the user wants names kept

So, the key challenges are correctly identifying names and finding accurate synonyms. Since the user wants the result only, after processing, the model should output the transformed text with synonyms in the specified format, keeping names unchanged.

The user says "modify all words with 3 alternatives using syn1 format. Keep names intact. Only the result." So assuming the input text is given, like "The quick brown fox jumps over the lazy dog," I need to process each word except names.