naazimsnh02/TriageIQ-Qwen3-4B

TriageIQ — Qwen3-4B Incident Triage

TriageIQ turns a free-text IT support complaint into a single structured, schema-valid JSON incident record. It is the fast-path model behind the TriageIQ predictive complaint-triage & SLA-aware routing engine, fine-tuned on the AMD Instinct MI300X.

This is Qwen/Qwen3-4B-Instruct-2507 with a bf16 LoRA fine-tune merged into the base weights — load it like any standard causal LM; no PEFT required at inference time.

What it does

Given a complaint, the model emits exactly this JSON object and nothing else:

{
  "summary": "One-line normalized incident title",
  "category": "Software | Hardware | Network | Access",
  "urgency": 1,
  "impact": 1,
  "assignment_group": "IT Support | Network Ops",
  "suggested_first_action": "Concrete first remediation step",
  "confidence": 0.0
}

• —urgency / impact: integers 1 = High, 2 = Medium, 3 = Low.
• —confidence: float in [0.0, 1.0].

*Design note — priority and SLA are not model outputs.* Priority (P1–P5) and the SLA target are computed deterministically downstream from (impact, urgency) via the ITSM matrix, and the dynamic SLA-breach-risk is a separate explainable engine. The model is deliberately kept to the perception step (text → structured record) so the business logic stays auditable.

Intended use

• —Automated first-pass triage of IT service-desk tickets into structured records.
• —Feeding a deterministic routing / SLA engine that consumes the JSON contract.
• —Demo / research within the TriageIQ project (AMD Hackathon — Customer Complaint Classification & Routing Engine track).

Not intended for medical, legal, or safety-critical decisions, or as the sole authority for ticket prioritization without the downstream deterministic engine.

Usage

The model was trained with a fixed system prompt; use it verbatim for best results.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "naazimsnh02/TriageIQ-Qwen3-4B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

SYSTEM_PROMPT = (
    "You are TriageIQ, an IT service-desk triage assistant. Convert the user's "
    "complaint into a single JSON object with EXACTLY these keys: summary, "
    "category, urgency, impact, assignment_group, suggested_first_action, "
    "confidence. "
    "category is one of [Software, Hardware, Network, Access]. "
    "assignment_group is one of [IT Support, Network Ops]. "
    "urgency and impact are integers 1=High, 2=Medium, 3=Low. "
    "confidence is a float 0.0-1.0. "
    "Do NOT include priority or SLA — those are computed elsewhere. "
    "Output ONLY the JSON object, no prose, no code fences."
)

complaint = "My laptop won't connect to the office Wi-Fi since this morning and I have a client call in 20 minutes."
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": complaint},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Serving with vLLM (ROCm or CUDA):

vllm serve naazimsnh02/TriageIQ-Qwen3-4B

Evaluation

Held-out validation set of 75 complaints, greedy decoding, scored with the project's TriageRecord schema validator (the same contract enforced in production). Field metrics are computed over schema-valid outputs.

The fine-tune's headline gain is category accuracy (+10.7 pts) and routing accuracy (assignment-group +5.3 pts) while preserving a 100% schema-valid rate. Urgency — the most subjective field — moves within label noise (a few cases on 75); it is also the field whose downstream effect is bounded by the deterministic priority matrix rather than the model alone.

Training

bf16 LoRA was chosen deliberately: on ROCm, bitsandbytes ≤ 0.49.2 has a 4-bit decode NaN bug, and the MI300X's 192 GB removes any need for quantization on a 4B model — one card co-hosts the fine-tuned model and the live streaming pipeline without quantization juggling.

Training data

~432 synthetic English IT complaints (357 train / 75 validation). Built from the ~100 unique realistic descriptions in `6StringNinja/synthetic-servicenow-incidents` used as seeds only (the raw set has independently shuffled fields and is not trained on directly). Seeds were relabeled coherently against a fixed ITSM rubric by a Qwen2.5-32B-Instruct teacher and paraphrase-augmented for phrasing/persona diversity, with an 85/15 split stratified by category and de-leaked at the seed level. Labels never include priority or SLA.

Limitations & biases

• —Trained on synthetic, English-only, single-paragraph complaints; behavior on long threads, multi-language, or out-of-distribution domains is unverified.
• —Two assignment groups (IT Support, Network Ops) and four categories only — it will coerce inputs into this taxonomy.
• —urgency is subjective and the weakest field; downstream the deterministic priority matrix bounds its impact.
• —Confidence is a learned self-report, not a calibrated probability.
• —For robustness the production pipeline validates every output against the schema and falls back to a deterministic rule tagger on invalid JSON — do the same when integrating.

License

Apache-2.0, inherited from the Qwen/Qwen3-4B-Instruct-2507 base model.