jasirjru/DomainTune-Qwen2.5-1.5B-Triage
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DomainTune: Autonomous Ticket Triage LLM
DomainTune is an enterprise-grade fine-tuned model based on Qwen/Qwen2.5-1.5B. It is specifically optimized to read messy, unstructured software bug reports and support tickets and output strict, deterministic JSON triage decisions.
🎯 Model Capabilities
Given a raw support ticket or GitHub issue, DomainTune extracts:
- `priority`:
P1(critical outage) |P2(high severity) |P3(normal) |P4(minor/cosmetic) - `category`:
bug|feature_request|documentation|performance|security|infra - `affected_component`: The specific system or module impacted (e.g.
auth_service,payment_gateway,database) - `sentiment`:
neutral|frustrated|urgent|satisfied - `resolution_required`:
true|false
🚀 Quickstart & Inference
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "jasirjru/DomainTune-Qwen2.5-1.5B-Triage"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
# Example ticket
ticket = """Title: Production payment gateway timeout on checkout
Description: Customers in us-east are receiving HTTP 504 errors when processing Stripe payments. Multiple checkout failures recorded in Datadog."""
prompt = f"<|im_start|>system\nYou are a support ticket triage assistant. Output only valid JSON.<|im_end|>\n<|im_start|>user\n{ticket}<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=150, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))