Instructions to use SahilGoel/indian-txn-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SahilGoel/indian-txn-classifier with Transformers:

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

pipe = pipeline("text-generation", model="SahilGoel/indian-txn-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SahilGoel/indian-txn-classifier")
model = AutoModelForCausalLM.from_pretrained("SahilGoel/indian-txn-classifier", device_map="auto")
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 SahilGoel/indian-txn-classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SahilGoel/indian-txn-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": "SahilGoel/indian-txn-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SahilGoel/indian-txn-classifier

SGLang

How to use SahilGoel/indian-txn-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 "SahilGoel/indian-txn-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": "SahilGoel/indian-txn-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 "SahilGoel/indian-txn-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": "SahilGoel/indian-txn-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SahilGoel/indian-txn-classifier with Docker Model Runner:
```
docker model run hf.co/SahilGoel/indian-txn-classifier
```

indian-txn-classifier / README.md

SahilGoel

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	---
	language: en
	license: mit
	library_name: transformers
	tags:
	- finance
	- banking
	- indian
	- upi
	- transaction-classification
	- qwen
	- fine-tuned
	base_model: Qwen/Qwen2.5-0.5B
	widget:
	- text: "### System:\nYou are a bank transaction classifier for Indian bank statements. Given a raw transaction description, infer both its category and the actual company when evidence exists. Respond with ONLY a JSON object: {\"category\": \"<category>\", \"company_name\": \"<company_or_null>\", \"is_income\": false, \"confidence\": 0.0}.\n\n### Input:\nUPI/zerodhabroking@/HDFC BANK LTD\n\n### Output:\n"
	example_title: "UPI — Zerodha"
	- text: "### System:\nYou are a bank transaction classifier for Indian bank statements. Given a raw transaction description, infer both its category and the actual company when evidence exists. Respond with ONLY a JSON object: {\"category\": \"<category>\", \"company_name\": \"<company_or_null>\", \"is_income\": false, \"confidence\": 0.0}.\n\n### Input:\nNEFT/SALARY/ACME CORP\n\n### Output:\n"
	example_title: "NEFT — Salary"
	---

	# Indian Transaction Classifier

	Fine-tuned Qwen2.5-0.5B for classifying Indian bank transactions (UPI, NEFT, IMPS, RTGS) into 30+ categories with merchant identification.

	## ⚡ Try it now (Inference API — free, no setup)

	Use the Inference API widget on the right side of this page. Type a transaction description in the text box and click Compute.

	Or call it programmatically:

	```python
	from huggingface_hub import InferenceClient

	client = InferenceClient(model="SahilGoel/indian-txn-classifier")

	system_prompt = 'You are a bank transaction classifier for Indian bank statements. Given a raw transaction description, infer both its category and the actual company when evidence exists. Respond with ONLY a JSON object: {"category": "<category>", "company_name": "<company_or_null>", "is_income": false, "confidence": 0.0}.'

	tx = "UPI/zerodhabroking@/HDFC BANK LTD"
	prompt = f"### System:\n{system_prompt}\n\n### Input:\n{tx}\n\n### Output:\n"

	result = client.text_generation(prompt, max_new_tokens=100, temperature=0.1)
	print(result)
	# {"category": "trading_deposit", "company_name": "Zerodha", "is_income": false, "confidence": 0.95}
	```

	## Categories

	Income: salary, dividend, interest, rental, capital_gains, other_income

	Expenses: food, grocery, shopping, bills, medical, insurance, tax_payment, credit_card, personal_transfer, investment, trading_deposit, trading_credit, education, travel, entertainment, donation, loan_emi, loan_repayment, cash_withdrawal

	Special: friends, family, flat_deposit, trading_fees, vehicle_purchase, staff_salary, health_fitness, transfer, unclassified

	## Pipeline Architecture

	1. Rule engine (70-80% coverage) — regex patterns for known merchants and UPI handles
	2. Recurring pattern detector (10% more) — identifies repeating transactions
	3. Fine-tuned Qwen 0.5B (remaining) — LLM fallback for uncertain transactions

	## Run locally

	```bash
	pip install transformers torch
	```

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("SahilGoel/indian-txn-classifier")
	tokenizer = AutoTokenizer.from_pretrained("SahilGoel/indian-txn-classifier")

	system_prompt = "You are a bank transaction classifier for Indian bank statements..."
	input_text = "UPI/swiggybengaluru@/HDFC BANK LTD"
	prompt = f"### System:\n{system_prompt}\n\n### Input:\n{input_text}\n\n### Output:\n"

	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.1, do_sample=False)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## Run on Google Colab (free GPU)

	Open `app.py` from the GitHub repo in a Colab notebook — it works as a Gradio app with a public share link.

	## Supported Banks

	ICICI, HDFC, SBI, Axis, Kotak, Yes Bank, Federal Bank, IDFC First, IndusInd, Bank of Baroda, Punjab National, Canara, Union Bank, Unity SFB

	## GitHub

	Code and training pipeline: [the-great-one/indian-txn-classifier](https://github.com/The-Great-One/indian-txn-classifier)