Instructions to use kartikaybagla/functiongemma-bank-sms-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kartikaybagla/functiongemma-bank-sms-parser with Transformers:

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

pipe = pipeline("text-generation", model="kartikaybagla/functiongemma-bank-sms-parser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kartikaybagla/functiongemma-bank-sms-parser", dtype="auto")

llama-cpp-python

How to use kartikaybagla/functiongemma-bank-sms-parser with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kartikaybagla/functiongemma-bank-sms-parser",
	filename="functiongemma-270m-bank-sms-parser-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use kartikaybagla/functiongemma-bank-sms-parser with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Use Docker

docker model run hf.co/kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

LM Studio
Jan

vLLM

How to use kartikaybagla/functiongemma-bank-sms-parser with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

SGLang

How to use kartikaybagla/functiongemma-bank-sms-parser 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 "kartikaybagla/functiongemma-bank-sms-parser" \
    --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": "kartikaybagla/functiongemma-bank-sms-parser",
		"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 "kartikaybagla/functiongemma-bank-sms-parser" \
        --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": "kartikaybagla/functiongemma-bank-sms-parser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kartikaybagla/functiongemma-bank-sms-parser with Ollama:
```
ollama run hf.co/kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M
```

Unsloth Studio new

How to use kartikaybagla/functiongemma-bank-sms-parser with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kartikaybagla/functiongemma-bank-sms-parser to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kartikaybagla/functiongemma-bank-sms-parser to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kartikaybagla/functiongemma-bank-sms-parser to start chatting

Pi new

How to use kartikaybagla/functiongemma-bank-sms-parser with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kartikaybagla/functiongemma-bank-sms-parser with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use kartikaybagla/functiongemma-bank-sms-parser with Docker Model Runner:
```
docker model run hf.co/kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M
```

Lemonade

How to use kartikaybagla/functiongemma-bank-sms-parser with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kartikaybagla/functiongemma-bank-sms-parser:Q4_K_M

Run and chat with the model

lemonade run user.functiongemma-bank-sms-parser-Q4_K_M

List all available models

lemonade list

FunctionGemma Bank SMS Parser

A fine-tuned FunctionGemma-270M-IT model for extracting structured transaction data from bank SMS messages.

Model Description

This model is trained to perform two functions:

extract_transaction - Parse banking SMS and extract structured fields:
- source: Bank or sender name
- currency: Currency code (INR, USD, etc.)
- amount: Transaction amount (number)
- date: Transaction date
- destination: Recipient or merchant
- type: "debit" or "credit"
skip_message - Identify non-transaction messages:
- OTPs and verification codes
- Promotional messages
- Payment requests (not completed transactions)
- Account alerts without transactions

Quantization Options

File	Quantization	Size	Description
`functiongemma-270m-bank-sms-parser-Q4_K_M.gguf`	Q4_K_M	~242MB	Recommended - Best size/quality tradeoff
`functiongemma-270m-bank-sms-parser-Q5_K_M.gguf`	Q5_K_M	~248MB	Higher quality if Q4 shows issues
`functiongemma-270m-bank-sms-parser-Q8_0.gguf`	Q8_0	~280MB	Near-lossless, for validation

Usage

With llama.cpp server

# Download model
huggingface-cli download kartikaybagla/functiongemma-bank-sms-parser \
    functiongemma-270m-bank-sms-parser-Q4_K_M.gguf \
    --local-dir ./models

# Run server
llama-server --model ./models/functiongemma-270m-bank-sms-parser-Q4_K_M.gguf \
    --host 0.0.0.0 --port 8080 --ctx-size 2048

With Docker

docker run -p 8080:8080 -v ./models:/models \
    ghcr.io/ggml-org/llama.cpp:server \
    --model /models/functiongemma-270m-bank-sms-parser-Q4_K_M.gguf \
    --host 0.0.0.0 --port 8080

API Request

curl http://localhost:8080/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "<bos><start_of_turn>developer\nYou are a financial transaction extractor. Analyze SMS messages and:\n1. If the message describes a completed financial transaction (money sent, received, debited, or credited), use extract_transaction to capture the details.\n2. If the message is not a transaction (OTP, promotional, application status, payment request, etc.), use skip_message.\n\nOnly extract actual completed transactions with concrete amounts, not payment requests or pending transactions.<start_function_declaration>declaration:extract_transaction{description:<escape>Extract transaction details from a banking SMS message<escape>,parameters:{properties:{source:{type:<escape>STRING<escape>},currency:{type:<escape>STRING<escape>},amount:{type:<escape>NUMBER<escape>},date:{type:<escape>STRING<escape>},destination:{type:<escape>STRING<escape>},type:{type:<escape>STRING<escape>}},required:[<escape>source<escape>,<escape>currency<escape>,<escape>amount<escape>,<escape>date<escape>,<escape>destination<escape>,<escape>type<escape>],type:<escape>OBJECT<escape>}}<end_function_declaration><start_function_declaration>declaration:skip_message{description:<escape>Skip messages that are not financial transactions<escape>,parameters:{properties:{reason:{type:<escape>STRING<escape>}},required:[<escape>reason<escape>],type:<escape>OBJECT<escape>}}<end_function_declaration><end_of_turn>\n<start_of_turn>user\nICICI Bank Acct XX123 debited Rs 450.00 on 15-Jan-25; UPI to SWIGGY. UPI Ref: 123456789012<end_of_turn>\n<start_of_turn>model\n",
    "max_tokens": 200,
    "stop": ["<end_function_call>"]
  }'

Example Output

Input SMS:

ICICI Bank Acct XX123 debited Rs 450.00 on 15-Jan-25; UPI to SWIGGY. UPI Ref: 123456789012

Model Output:

<start_function_call>extract_transaction{"source": "ICICI Bank", "currency": "INR", "amount": 450.00, "date": "15-Jan-25", "destination": "SWIGGY", "type": "debit"}<end_function_call>

Input SMS (non-transaction):

Your OTP for login is 482910. Valid for 5 minutes. Do not share.

Model Output:

<start_function_call>skip_message{"reason": "OTP verification code"}<end_function_call>

Training

Base Model: google/functiongemma-270m-it
Training Framework: Hugging Face TRL (SFTTrainer)
Training Data: Classified bank SMS messages from Indian banks
Fine-tuning Method: LoRA

Intended Use

This model is designed for:

Personal finance automation
Importing transactions into budgeting apps (e.g., Actual Budget)
SMS-based expense tracking

Limitations

Primarily trained on Indian bank SMS formats (ICICI, HDFC, SBI, etc.)
May not generalize well to banks from other countries
Requires the specific prompt format shown above
Not suitable for security-critical applications without additional validation

License

This model inherits the Gemma license from the base model.

Model tree for kartikaybagla/functiongemma-bank-sms-parser

Base model

google/functiongemma-270m-it

Quantized

(49)

this model

kartikaybagla
/

functiongemma-bank-sms-parser