Instructions to use Anshrajsingh/qwen2.5-1.5b-ticket-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anshrajsingh/qwen2.5-1.5b-ticket-classifier with Transformers:

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

pipe = pipeline("text-generation", model="Anshrajsingh/qwen2.5-1.5b-ticket-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Anshrajsingh/qwen2.5-1.5b-ticket-classifier")
model = AutoModelForCausalLM.from_pretrained("Anshrajsingh/qwen2.5-1.5b-ticket-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anshrajsingh/qwen2.5-1.5b-ticket-classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anshrajsingh/qwen2.5-1.5b-ticket-classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anshrajsingh/qwen2.5-1.5b-ticket-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anshrajsingh/qwen2.5-1.5b-ticket-classifier

SGLang

How to use Anshrajsingh/qwen2.5-1.5b-ticket-classifier 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 "Anshrajsingh/qwen2.5-1.5b-ticket-classifier" \
    --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": "Anshrajsingh/qwen2.5-1.5b-ticket-classifier",
		"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 "Anshrajsingh/qwen2.5-1.5b-ticket-classifier" \
        --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": "Anshrajsingh/qwen2.5-1.5b-ticket-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anshrajsingh/qwen2.5-1.5b-ticket-classifier with Docker Model Runner:
```
docker model run hf.co/Anshrajsingh/qwen2.5-1.5b-ticket-classifier
```

Model Card for Model ID

🚀 Production-Grade SLM Structured Data Extractor

Fine-Tuned Qwen-1.5B-Instruct for Zero-Yapping Strict JSON Schema Compliance

📌 Business Case & Problem Statement

In enterprise production environments, unstructured text (customer support tickets, invoices, logs) must be mapped to structured databases (SQL/NoSQL) with 100% deterministic reliability.

While massive commercial LLMs (like GPT-4o) achieve high accuracy, they introduce major bottlenecks for high-throughput narrow tasks:

High API Costs: Processing millions of tokens daily is economically unsustainable.
High Latency: Cloud API round-trips slow down real-time automated routing workflows.
Data Privacy Risks: Transmitting sensitive client information (PII) to third-party APIs violates data compliance laws (GDPR/HIPAA).
Formatting Violations ("Yapping"): General-purpose base models frequently violate strict formatting constraints by adding conversational filler (e.g., "Here is the JSON you requested...").

The Solution

This project demonstrates Cost-Conscious AI Engineering by fine-tuning a 1.5 Billion parameter Small Language Model (SLM) to perform production-grade, schema-bound classification with near-zero latency and fraction of a cent compute cost, making it entirely deployable on the edge or low-cost commodity GPUs.

📊 Performance & ROI Benchmark

We evaluated the model on a hidden test dataset of customer support tickets across 4 categorical schema keys: category, urgency, sentiment, and action_required.

Metric	Base Model (Qwen2.5-1.5B-Instruct)	Fine-Tuned SLM (LoRA Adapted)
Valid JSON Generation Rate	100% (Wrapped in Markdown)	100% (Pure Strict JSON)
Schema Compliance	0.0% (Generated arbitrary tags)	95.6% (Strictly adheres to enum keys)
Exact Match Accuracy	0.0%	91.3%
Formatting Filler (Yapping)	High	0.0% (Starts with `{` ends with `}`)
Deployment Suitability	General Chat	Production-Ready Automated API

💰 Estimated Cost Breakdown (At Scale)

Assumed volume: 1 Million Tickets/Month (~300M Tokens processed)

Commercial LLM API (e.g., GPT-4o Class): ~$750 - $1,500 / month
This Fine-Tuned SLM (Hosted on Single Low-End Instance / Serverless GPU): <$25 / month
Net Business Savings: ~96.5% Cost Reduction with 100% data privacy.

🛠️ Technical Implementation & Architecture

1. Technology Stack

Base Model: Qwen/Qwen2.5-1.5B-Instruct
Fine-Tuning Technique: Parameter-Efficient Fine-Tuning (PEFT) using QLoRA (4-bit quantization)
Infrastructure: Trained via PyTorch and Hugging Face Trainer pipeline on a single consumer-grade T4 GPU.
Inference UI: Gradio application deployed seamlessly on Hugging Face Spaces.

2. Target Features & Schema Constraints

The model was fine-tuned using custom-engineered synthetic dataset structures following the ChatML format to map arbitrary inputs directly into this immutable JSON schema:

{
  "category": "billing | technical | refund | account | general",
  "urgency": "low | medium | high",
  "sentiment": "positive | negative | neutral",
  "action_required": "auto_reply | escalate | close"
}

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Anshrajsingh/qwen2.5-1.5b-ticket-classifier

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct