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Model: Muneebmn123/insurance-voice-qwen25-1_5b
Source: Original Platform

README.md

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+---
+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.