---
license: apache-2.0
language:
  - en
tags:
  - text-generation
  - conversational
  - voice
  - insurance
  - qwen2.5
base_model: Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---

# Insurance Real-Time Voice Bot (Qwen2.5-1.5B)

Fine-tuned **Qwen2.5-1.5B-Instruct** for low-latency insurance sales voice replies:
short spoken answers, one discovery question at a time.

## Files

| File | Use |
|------|-----|
| `model.safetensors` + tokenizer | Hugging Face Transformers |
| `insurance-qwen25-1_5b-q4_k_m.gguf` | llama.cpp / low-latency GPU serve |

## System prompt (recommended)

```
You are Aria, a real-time voice insurance sales agent. Speak in 1-2 short sentences. No lists or markdown. Ask exactly one discovery question when needed. Never invent prices or coverage. Never pressure or guarantee approval. Follow: greet → need (life/health/auto/home/business) → situation → next step (quote/compare/callback). If they want a human, offer transfer and ask when to call.
```

## Quick start (Transformers)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "MUNEEBMN123/insurance-voice-qwen25-1_5b"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
)
messages = [
    {"role": "system", "content": "You are Aria, a real-time voice insurance sales agent. Speak in 1-2 short sentences. Ask exactly one discovery question when needed."},
    {"role": "user", "content": "I need car insurance."},
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=48, do_sample=False)
print(tok.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
```

## Quick start (llama.cpp / GGUF)

```bash
huggingface-cli download MUNEEBMN123/insurance-voice-qwen25-1_5b insurance-qwen25-1_5b-q4_k_m.gguf --local-dir .
./llama-server -m insurance-qwen25-1_5b-q4_k_m.gguf -ngl 99 -c 512 --port 8080
```

## Notes

- Trained with QLoRA for concise insurance sales dialogue.
- Prefer GGUF Q4 on a dedicated GPU (e.g. RTX 4090) for minimum latency.