68 lines
1.8 KiB
Markdown
68 lines
1.8 KiB
Markdown
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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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base_model: Qwen/Qwen3-2B
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tags:
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- qwen3
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- maintenance
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- ticket-triage
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- structured-extraction
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- qlora
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- unsloth
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pipeline_tag: text-generation
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datasets:
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- dvr76/india-synthetic-property-maintenance-tickets
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---
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# ticket-triage-qwen3-2b
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Fine-tuned Qwen3-2B for extracting structured maintenance information from tenant ticket text.
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## Training
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- **Base model:** Qwen/Qwen3-2B (loaded via unsloth/Qwen3-2B)
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- **Method:** QLoRA (r=16, 4-bit NF4 quantization)
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- **Framework:** Unsloth + TRL SFTTrainer
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- **Hardware:** Google Colab T4 (16GB VRAM)
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- **Epochs:** 3
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- **Learning rate:** 2e-4, cosine schedule
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "dvr76/ticket-triage-qwen3"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True)
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messages = [
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{"role": "system", "content": "You are a property maintenance ticket triage system. Respond with ONLY valid JSON."},
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{"role": "user", "content": "kitchen sink tap water is leaking from yesterday morning"},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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output = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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Output schema
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```json
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{
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"is_maintenance_request": true,
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"issues": [{"category": "", "sub_category": "", "location": "", "urgency": ""}],
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"vendor_type": "",
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"entry_required": true
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}
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```
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## API
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GitHub: github.com/dvr76/ticket-triage-qwen3
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## License
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Apache 2.0 (inherited from Qwen3-2B).
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