Instructions to use abuzarkhan/bilingual-ticket-triage-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abuzarkhan/bilingual-ticket-triage-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "abuzarkhan/bilingual-ticket-triage-adapter")

Transformers

How to use abuzarkhan/bilingual-ticket-triage-adapter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="abuzarkhan/bilingual-ticket-triage-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("abuzarkhan/bilingual-ticket-triage-adapter", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abuzarkhan/bilingual-ticket-triage-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abuzarkhan/bilingual-ticket-triage-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abuzarkhan/bilingual-ticket-triage-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abuzarkhan/bilingual-ticket-triage-adapter

SGLang

How to use abuzarkhan/bilingual-ticket-triage-adapter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "abuzarkhan/bilingual-ticket-triage-adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abuzarkhan/bilingual-ticket-triage-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "abuzarkhan/bilingual-ticket-triage-adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abuzarkhan/bilingual-ticket-triage-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abuzarkhan/bilingual-ticket-triage-adapter with Docker Model Runner:
```
docker model run hf.co/abuzarkhan/bilingual-ticket-triage-adapter
```

bilingual-ticket-triage-adapter

QLoRA adapter fine-tuned from Qwen2.5-3B-Instruct for bilingual (Roman Urdu / Urdu script / English) support-ticket triage: category classification, entity extraction, and sentiment detection in a single structured JSON output.

Model Details

Base model: Qwen/Qwen2.5-3B-Instruct
Fine-tuning method: QLoRA (4-bit base during training via Unsloth), LoRA rank 8, alpha 16, dropout 0
Target modules: all 7 linear modules (q, k, v, o, gate, up, down)
Output schema: single JSON object with category (8 classes), urgency, and entities (order_id, product_name, account_email, sentiment)
Categories: billing, shipping_delivery, refund_return, technical_issue, account_access, product_complaint, general_inquiry, other
Languages: Roman Urdu, Urdu script, English, and code-mixed text
License: This adapter is derived from Qwen2.5-3B-Instruct and is released under the Qwen research license (non-commercial use). See Qwen2.5-3B-Instruct LICENSE.

Uses

The model reads a support ticket (email or chat message) and outputs a machine-readable triage record. It is designed for the Support Ticket Analysis & Response Automation platform.

Out of scope: this is a research fine-tune on a synthetic dataset. It should not be used to process real customer data without re-validation, and it is not a general-purpose chatbot.

How to Get Started

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-3B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, "abuzarkhan/bilingual-ticket-triage-adapter")

tokenizer = AutoTokenizer.from_pretrained("abuzarkhan/bilingual-ticket-triage-adapter")

messages = [{"role": "user", "content": "mera order kab tak aayega? bohot late ho raha hai"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_dict=True, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

An Ollama-ready quantized build of the merged model is available at abuzarkhan/bilingual-ticket-triage-gguf.

Training Details

Dataset: abuzarkhan/bilingual-ticket-triage-dataset — fully synthetic, fabricated personas, ~1,400 training tickets across 8 categories in Roman Urdu / Urdu / English
Procedure: SFT (ChatML template), 1 epoch, 4-bit base, LoRA r=8 α=16 dropout=0 on all 7 linear target modules
Reproduction: training notebook and data-preparation scripts are in the GitHub repository (notebooks/qwen_qlora_training.ipynb)

Evaluation

On the held-out synthetic test set (~155 tickets):

Category accuracy: ~80.4%
Sentiment accuracy: ~87%
Entity extraction: high precision on values that appear in the text (the model is trained never to invent entities)

Full per-arm evaluation (Arm A fine-tuned, Arm B base model, Arm C Llama 3.3 70B via Groq) lives in results/.

Bias, Risks, and Limitations

Training data is synthetic; real-world ticket distributions and dialects will differ.
Model may miss entities phrased in unusual ways or in low-resource dialectal forms.
The Qwen research license restricts use to research/non-commercial purposes.

Built with Qwen.

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