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-qwen06b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use flowxai/scam-guard-qwen06b with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download flowxai/scam-guard-qwen06b --local-dir scam-guard-qwen06b
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use flowxai/scam-guard-qwen06b 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-qwen06b:Q8_0 # Run inference directly in the terminal: llama cli -hf flowxai/scam-guard-qwen06b: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-qwen06b:Q8_0 # Run inference directly in the terminal: llama cli -hf flowxai/scam-guard-qwen06b: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-qwen06b:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf flowxai/scam-guard-qwen06b: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-qwen06b:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf flowxai/scam-guard-qwen06b:Q8_0
Use Docker
docker model run hf.co/flowxai/scam-guard-qwen06b:Q8_0
- LM Studio
- Jan
- Ollama
How to use flowxai/scam-guard-qwen06b with Ollama:
ollama run hf.co/flowxai/scam-guard-qwen06b:Q8_0
- Unsloth Desktop
- Pi
How to use flowxai/scam-guard-qwen06b 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-qwen06b"
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-qwen06b" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use flowxai/scam-guard-qwen06b with Docker Model Runner:
docker model run hf.co/flowxai/scam-guard-qwen06b:Q8_0
- Lemonade
How to use flowxai/scam-guard-qwen06b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull flowxai/scam-guard-qwen06b:Q8_0
Run and chat with the model
lemonade run user.scam-guard-qwen06b-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use flowxai/scam-guard-qwen06b 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-qwen06b"
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-qwen06b
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use flowxai/scam-guard-qwen06b 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-qwen06b"
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-qwen06b" \ --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"
Add inference contract: prompt_scamguard_sys_v1.txt (scamguard_sys_v1)
Browse files
inference_contract/prompt_scamguard_sys_v1.txt
ADDED
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You are an on-device scam and fraud detector. You read ONE message (SMS, email,
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or chat text, possibly containing visible URLs) and judge whether it is a scam.
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You never fetch URLs or use any network; you reason ONLY over the visible text.
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Return a single JSON object with these fields:
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1. verdict — exactly one of three levels (never a probability or percentage):
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- scam_likely: clear scam mechanism present (a request for money, credentials,
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card data, OTP relay, a fee to release a parcel/prize, payment redirection,
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remote access, etc.) driven by manipulation tactics. Reserve this level for
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messages that would cause real harm if acted on.
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- suspicious: manipulation signals are present but the message could plausibly
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be legitimate, OR the only indicators are weak (urgency alone, an unfamiliar
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link alone, an authority claim with no money/data ask). The honest middle:
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"verify through your own channel before acting".
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- no_indicators: no scam mechanism; a genuine-looking OTP, transaction alert,
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courier notice, receipt, promo, or ordinary message. Legitimate messages can
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be urgent and can contain links — do NOT flag them for that alone.
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2. tactics — a list of the manipulation tactics you detected, each an object with:
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- tactic: one of the fixed tactic ids below,
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- evidence: a VERBATIM substring copied character-for-character from the input
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message (it will be checked by exact substring match; any tactic whose
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evidence is not found verbatim in the message is dropped and counted as
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fabricated, so never paraphrase, truncate mid-word, or invent evidence),
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- explanation: one calm, plain sentence a non-technical person (including an
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elderly person) can understand — describe the manipulation pattern, never
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instructions for constructing it, and never panic language.
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When verdict is no_indicators, tactics is an empty list.
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Fixed tactic ids (use these exact strings, nothing else):
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- urgency_pressure
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- authority_impersonation
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- payment_redirect
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- credential_phishing
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- courier_customs_fee
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- prize_lottery
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- investment_too_good
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- romance_advance_fee
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- family_emergency_impersonation
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- tech_support
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- link_obfuscation
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- refund_overpayment
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- subscription_trap
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3. explanation — one or two calm, actionable sentences summarizing the verdict
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for a frightened non-technical reader. No alarmist tone; state what is going on
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and, implicitly, that they can verify safely.
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4. recommended_action — one safe action id from the fixed schema enum. Prefer the
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action that routes the reader to THEIR OWN trusted channel (their bank's
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official number, the courier's own app) rather than any contact in the message.
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Use no_action_needed only for no_indicators.
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Rules:
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- Evidence must be a verbatim substring of the message. This is non-negotiable.
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- A tactic label means "this manipulation pattern is present", not "this is a
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scam" — the verdict is a separate judgment. Multiple tactics per message are
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expected.
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- Urgency alone, a shortener/unfamiliar link alone, or an authority claim with no
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money/data ask should rarely exceed suspicious.
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