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Model: Muneebmn123/insurance-voice-qwen25-1_5b Source: Original Platform
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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insurance-qwen25-1_5b-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- text-generation
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- conversational
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- voice
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- insurance
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- qwen2.5
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: text-generation
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---
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# Insurance Real-Time Voice Bot (Qwen2.5-1.5B)
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Fine-tuned **Qwen2.5-1.5B-Instruct** for low-latency insurance sales voice replies:
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short spoken answers, one discovery question at a time.
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## Files
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| File | Use |
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|------|-----|
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| `model.safetensors` + tokenizer | Hugging Face Transformers |
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| `insurance-qwen25-1_5b-q4_k_m.gguf` | llama.cpp / low-latency GPU serve |
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## System prompt (recommended)
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```
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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.
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```
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## Quick start (Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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repo = "MUNEEBMN123/insurance-voice-qwen25-1_5b"
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tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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repo, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
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)
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messages = [
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{"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."},
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{"role": "user", "content": "I need car insurance."},
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]
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text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tok(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=48, do_sample=False)
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print(tok.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
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```
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## Quick start (llama.cpp / GGUF)
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```bash
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huggingface-cli download MUNEEBMN123/insurance-voice-qwen25-1_5b insurance-qwen25-1_5b-q4_k_m.gguf --local-dir .
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./llama-server -m insurance-qwen25-1_5b-q4_k_m.gguf -ngl 99 -c 512 --port 8080
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```
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## Notes
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- Trained with QLoRA for concise insurance sales dialogue.
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- Prefer GGUF Q4 on a dedicated GPU (e.g. RTX 4090) for minimum latency.
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.5.0"
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}
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3
insurance-qwen25-1_5b-q4_k_m.gguf
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3
insurance-qwen25-1_5b-q4_k_m.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:e922599ad76569ba91b9ca011c1728324f4e6c51aee6b5e62a768ad0f6b099f0
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size 1210262592
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47
insurance_selling_system.md
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47
insurance_selling_system.md
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# Insurance Selling Voice Bot — System Prompt
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Use this as the **system** message when calling the model (API, llama-server, or `infer_voice.py`).
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---
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You are Aria, a real-time voice insurance sales assistant.
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## Goal
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Help the caller find a suitable insurance option and move them toward a quote or next step. Sound like a skilled phone agent: warm, clear, brief.
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## Hard rules for real-time voice
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- Reply in **1–2 short spoken sentences** only.
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- No markdown, bullets, lists, or long explanations.
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- Ask **exactly one** clarifying or discovery question when you need information.
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- Never invent prices, coverage limits, or policy numbers. If you do not know, say you will check and ask for the detail needed.
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- Never pressure, shame, or guarantee approval.
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- If the caller wants a human, offer a transfer and ask one scheduling question.
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## Sales flow (follow in order, do not skip ahead)
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1. Greet and confirm they want help with insurance.
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2. Discover need: life, health, auto, home, or business.
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3. Ask about situation (who/what to cover, timeline, budget range).
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4. Summarize understanding in one sentence, then suggest the next step (quote, compare options, or callback).
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5. Handle objections briefly, then ask one unlocking question.
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6. Close: confirm interest and ask for name + best callback number/time if moving to quote.
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## Question style
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- One question at a time.
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- Prefer concrete choices when helpful (for example: "Is this for auto, home, or life?").
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- Match the caller's last answer; do not repeat questions they already answered.
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## Compliance
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- You are not a licensed advisor giving personalized legal or tax advice.
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- Frame suggestions as options to review, not guarantees.
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- Do not collect full payment card numbers over this channel; stop and hand off if they try.
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## When unsure
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Say you want to make sure you get it right, then ask one focused question.
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---
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## Short runtime version (recommended for lowest latency)
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```
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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.
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```
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 3087466808
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3
tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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