---
license: apache-2.0
language:
- es
base_model: meta-llama/Llama-3.3-70B-Instruct
tags:
- peft
- lora  
- whatsapp
- text-splitting
- message-segmentation
- spanish
- fine-tuned
library_name: peft
pipeline_tag: text-generation
model_type: llama
widget:
- text: "buenos dias novedades?"
  example_title: "Greeting + Question"
- text: "perfecto que haces?"
  example_title: "Confirmation + Question"
- text: "aqui andamos que haces?"
  example_title: "Status + Question"
---

# 📱 WazapSplitter-LLM

Splits text into natural WhatsApp-style message segments.

**Input:** `"buenos dias queria confirmar la hora de la reunion"`  
**Output:** `["buenos días", "quería confirmar la hora de la reunión"]`

## Quick Usage

### TypeScript/JavaScript
```typescript
async function splitMessage(text: string): Promise<string[]> {
  const prompt = `Split messages at natural breaks into JSON array. Common patterns: greeting+question, statement+question, topic+followup. Keep original words, only add logical splits.

User: ${text}
Assistant:`;

  const response = await fetch("https://api-inference.huggingface.co/models/joseAndres777/WazapSplitter-LLM", {
    method: "POST",
    headers: {
      "Authorization": "Bearer YOUR_HF_TOKEN",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      inputs: prompt,
      parameters: { max_new_tokens: 100, temperature: 0.3 }
    })
  });
  
  const data = await response.json();
  return JSON.parse(data[0].generated_text);
}

// Example
const segments = await splitMessage("hola como estas que tal todo?");
console.log(segments); // ["hola", "como estas", "que tal todo?"]
```

### Chatbot Integration
```typescript
// Make responses feel more human
const segments = await splitMessage(botResponse);
for (const segment of segments) {
  await sendMessage(segment);
  await delay(1000 + Math.random() * 2000); // Human-like timing
}
```