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Model: dvr76/ticket-triage-qwen3
Source: Original Platform

README.md

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