Instructions to use ganmoor-ai-labs/sms-shield with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ganmoor-ai-labs/sms-shield with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ganmoor-ai-labs/sms-shield:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ganmoor-ai-labs/sms-shield:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ganmoor-ai-labs/sms-shield:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ganmoor-ai-labs/sms-shield: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 ganmoor-ai-labs/sms-shield:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ganmoor-ai-labs/sms-shield: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 ganmoor-ai-labs/sms-shield:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ganmoor-ai-labs/sms-shield:Q4_K_M

Use Docker

docker model run hf.co/ganmoor-ai-labs/sms-shield:Q4_K_M

LM Studio
Jan
Ollama
How to use ganmoor-ai-labs/sms-shield with Ollama:
```
ollama run hf.co/ganmoor-ai-labs/sms-shield:Q4_K_M
```
Unsloth Desktop

How to use ganmoor-ai-labs/sms-shield with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ganmoor-ai-labs/sms-shield:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ganmoor-ai-labs/sms-shield:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ganmoor-ai-labs/sms-shield with Docker Model Runner:
```
docker model run hf.co/ganmoor-ai-labs/sms-shield:Q4_K_M
```

Lemonade

How to use ganmoor-ai-labs/sms-shield with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ganmoor-ai-labs/sms-shield:Q4_K_M

Run and chat with the model

lemonade run user.sms-shield-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ganmoor-ai-labs/sms-shield with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ganmoor-ai-labs/sms-shield: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 ganmoor-ai-labs/sms-shield:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ganmoor-ai-labs/sms-shield with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ganmoor-ai-labs/sms-shield:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ganmoor-ai-labs/sms-shield:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

SMS-Shield — Indian SMS scam & inbox classifier (4B)

SMS-Shield reads one SMS (sender header + body) and returns a structured safety verdict as JSON. Built for Indian SMS traffic in English, Hindi (Devanagari), and romanized Hinglish, including DLT sender-header reasoning (the same bank alert is safe from JD-SBIINB and dangerous from a bare 10-digit mobile). Trained entirely on-device on an NVIDIA DGX Spark (GB10, 128 GB unified memory).

Two model variants in this repo

File	Model	Size	Use case
`sms-shield-q4_k_m.gguf`	SMS-Shield 4B (Qwen3-4B-Instruct LoRA)	2.4 GB	Maximum accuracy: 99.2% verdict acc, servers/desktops
`sms-shield-mini-q4_k_m.gguf`	SMS-Shield Mini 1.7B (Qwen3-1.7B LoRA)	1.2 GB	On-device/mobile: 99.0% verdict acc, 100% dangerous recall, 0 OTP false-flags, 3-4x faster
`adapter_model.safetensors`	4B LoRA adapter	127 MB	Use with `Qwen/Qwen3-4B-Instruct-2507` via PEFT

The Mini was trained on the same verified dataset plus a targeted cashback-boundary booster, and passes the same release gates (zero genuine OTPs/bank alerts flagged).

Output contract

Input:
sender: 9182736450
message: Dear customer aapka SBI account aaj band ho jayega. KYC turant update karein: sbi-kyc-renew.top/verify

Output:
{"verdict": "dangerous", "category": "scam", "scam_type": "kyc_fraud", "risk": 80,
 "red_flags": ["account closure", "urgent update", "suspicious link"],
 "explanation": "Yeh ek fraud message hai. SBI kabhi bhi SMS se aapka account band karne ki dhamki nahi deta. Link par click na karein."}

verdict: safe | suspicious | dangerous (risk bands 0-39 / 40-69 / 70-100)
category: otp | banking | delivery | bill_utility | govt | promo | personal | scam
scam_type: 15 fraud patterns (kyc_fraud, digital_arrest, upi_fraud, job_scam, fake_delivery, investment_scam, lottery_prize, utility_scam, loan_app, phishing_generic, refund_cashback, impersonation, apk_malware, sextortion_threat, other_scam) — null unless flagged
explanation: one sentence in the language/script of the input message

Results (held-out test set, n=1,390 — included in this repo)

Metric	SMS-Shield 4B	Gemma3-27B zero-shot (same prompt)
Verdict accuracy	99.2%	49.2%
— English / Hindi / Hinglish / Mixed	99.2 / 99.8 / 98.8 / 98.9	53.2 / 26.6 / — / 27.3
Category accuracy	99.1%	74.0%
Dangerous recall	99.5%	87.2%
Safe precision	100%	100%*
Genuine OTP/bank alerts misflagged as scam	0 / 1,390	22
JSON parse failures	0	0

*The baseline almost never predicts safe — it over-flags legitimate traffic, including 22 genuine OTPs/bank alerts. Over-flagging is the deployment killer for SMS safety apps; SMS-Shield was explicitly trained against it with ~40% hard negatives (scary-but-legitimate messages).

Training

Base: Qwen3-4B-Instruct-2507, LoRA (r=16, bf16), 3 epochs + 1 calibration epoch
Data: 12.5k synthetic Indian SMS, two-teacher pipeline run fully locally: gemma3:27b generated labeled candidates → 20+ deterministic validators (script/language checks, risk-band consistency, sender-format, fuzzy dedup) → gpt-oss-safeguard:120b independently re-judged every sample blind; label disagreements dropped (~24% rejection). Includes counterfactual sender-flip pairs and a deliberately generated ambiguous "suspicious" tier. The training corpus is not published; the full 1,390-row eval set is.

Known limitations

Sender-counterfactual calibration: on paired sender-flip tests, 100% of flipped messages are flagged (none pass as safe), but 18% land one severity band off (suspicious vs dangerous) against the reference labels.
Languages: EN/HI/Hinglish only in v1. Kannada, Tamil, Telugu, Bengali, Marathi planned.
Single-message classification; no thread context in v1.
Trained on synthetic data; real-world distribution shift is expected. Evaluate on your own traffic before relying on it.
This model assists judgment; it is not a guarantee. Never treat safe as proof of legitimacy for financial actions.

Usage

Ollama:

ollama run santosh07401/sms-shield

Transformers (adapter in this repo):

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507", dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "ganmoor-ai-labs/sms-shield")

GGUF: sms-shield-q4_k_m.gguf (2.4 GB) — llama.cpp/Ollama ready; use a ChatML template (no <think> block).

Intended use

Defensive scam protection for Indian mobile users: on-device SMS filtering, inbox categorization, and fraud warnings. Not for generating scam content.

Downloads last month: 156

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for ganmoor-ai-labs/sms-shield

Base model

Qwen/Qwen3-4B-Instruct-2507

Adapter

(5708)

this model