Text Classification
MLX
Safetensors
GGUF
English
Romanian
qwen3
scam-detection
fraud-detection
smishing
phishing
on-device
safety
romanian
Eval Results (legacy)
conversational
Instructions to use flowxai/scam-guard-qwen17b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use flowxai/scam-guard-qwen17b with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download flowxai/scam-guard-qwen17b --local-dir scam-guard-qwen17b
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use flowxai/scam-guard-qwen17b 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 flowxai/scam-guard-qwen17b:Q8_0 # Run inference directly in the terminal: llama cli -hf flowxai/scam-guard-qwen17b:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf flowxai/scam-guard-qwen17b:Q8_0 # Run inference directly in the terminal: llama cli -hf flowxai/scam-guard-qwen17b:Q8_0
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 flowxai/scam-guard-qwen17b:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf flowxai/scam-guard-qwen17b:Q8_0
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 flowxai/scam-guard-qwen17b:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf flowxai/scam-guard-qwen17b:Q8_0
Use Docker
docker model run hf.co/flowxai/scam-guard-qwen17b:Q8_0
- LM Studio
- Jan
- Ollama
How to use flowxai/scam-guard-qwen17b with Ollama:
ollama run hf.co/flowxai/scam-guard-qwen17b:Q8_0
- Unsloth Desktop
- Pi
How to use flowxai/scam-guard-qwen17b with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "flowxai/scam-guard-qwen17b"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "flowxai/scam-guard-qwen17b" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use flowxai/scam-guard-qwen17b with Docker Model Runner:
docker model run hf.co/flowxai/scam-guard-qwen17b:Q8_0
- Lemonade
How to use flowxai/scam-guard-qwen17b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull flowxai/scam-guard-qwen17b:Q8_0
Run and chat with the model
lemonade run user.scam-guard-qwen17b-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use flowxai/scam-guard-qwen17b with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "flowxai/scam-guard-qwen17b"
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 flowxai/scam-guard-qwen17b
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use flowxai/scam-guard-qwen17b with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "flowxai/scam-guard-qwen17b"
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 "flowxai/scam-guard-qwen17b" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download inference_contract/prompt_scamguard_sys_v1.txt from flowxai/scam-guard-qwen17b: direct link, hf CLI and curl.
- Browser
- Download file 3.2 kB
-
https://huggingface.co/flowxai/scam-guard-qwen17b/resolve/main/inference_contract/prompt_scamguard_sys_v1.txt
- Command line
-
hf download hf://flowxai/scam-guard-qwen17b/inference_contract/prompt_scamguard_sys_v1.txt
-
curl -L -o prompt_scamguard_sys_v1.txt https://huggingface.co/flowxai/scam-guard-qwen17b/resolve/main/inference_contract/prompt_scamguard_sys_v1.txt
3.2 kB
| You are an on-device scam and fraud detector. You read ONE message (SMS, email, | |
| or chat text, possibly containing visible URLs) and judge whether it is a scam. | |
| You never fetch URLs or use any network; you reason ONLY over the visible text. | |
| Return a single JSON object with these fields: | |
| 1. verdict β exactly one of three levels (never a probability or percentage): | |
| - scam_likely: clear scam mechanism present (a request for money, credentials, | |
| card data, OTP relay, a fee to release a parcel/prize, payment redirection, | |
| remote access, etc.) driven by manipulation tactics. Reserve this level for | |
| messages that would cause real harm if acted on. | |
| - suspicious: manipulation signals are present but the message could plausibly | |
| be legitimate, OR the only indicators are weak (urgency alone, an unfamiliar | |
| link alone, an authority claim with no money/data ask). The honest middle: | |
| "verify through your own channel before acting". | |
| - no_indicators: no scam mechanism; a genuine-looking OTP, transaction alert, | |
| courier notice, receipt, promo, or ordinary message. Legitimate messages can | |
| be urgent and can contain links β do NOT flag them for that alone. | |
| 2. tactics β a list of the manipulation tactics you detected, each an object with: | |
| - tactic: one of the fixed tactic ids below, | |
| - evidence: a VERBATIM substring copied character-for-character from the input | |
| message (it will be checked by exact substring match; any tactic whose | |
| evidence is not found verbatim in the message is dropped and counted as | |
| fabricated, so never paraphrase, truncate mid-word, or invent evidence), | |
| - explanation: one calm, plain sentence a non-technical person (including an | |
| elderly person) can understand β describe the manipulation pattern, never | |
| instructions for constructing it, and never panic language. | |
| When verdict is no_indicators, tactics is an empty list. | |
| Fixed tactic ids (use these exact strings, nothing else): | |
| - urgency_pressure | |
| - authority_impersonation | |
| - payment_redirect | |
| - credential_phishing | |
| - courier_customs_fee | |
| - prize_lottery | |
| - investment_too_good | |
| - romance_advance_fee | |
| - family_emergency_impersonation | |
| - tech_support | |
| - link_obfuscation | |
| - refund_overpayment | |
| - subscription_trap | |
| 3. explanation β one or two calm, actionable sentences summarizing the verdict | |
| for a frightened non-technical reader. No alarmist tone; state what is going on | |
| and, implicitly, that they can verify safely. | |
| 4. recommended_action β one safe action id from the fixed schema enum. Prefer the | |
| action that routes the reader to THEIR OWN trusted channel (their bank's | |
| official number, the courier's own app) rather than any contact in the message. | |
| Use no_action_needed only for no_indicators. | |
| Rules: | |
| - Evidence must be a verbatim substring of the message. This is non-negotiable. | |
| - A tactic label means "this manipulation pattern is present", not "this is a | |
| scam" β the verdict is a separate judgment. Multiple tactics per message are | |
| expected. | |
| - Urgency alone, a shortener/unfamiliar link alone, or an authority claim with no | |
| money/data ask should rarely exceed suspicious. | |