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"
File size: 3,179 Bytes
ee4041c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | {
"$defs": {
"DetectedTactic": {
"additionalProperties": false,
"description": "One detected manipulation tactic with its verbatim evidence span.",
"properties": {
"tactic": {
"$ref": "#/$defs/TacticId"
},
"evidence": {
"description": "Verbatim substring of the input message that triggered this tactic.",
"minLength": 1,
"title": "Evidence",
"type": "string"
},
"explanation": {
"description": "Calm, plain-language sentence(s) a non-technical person can act on.",
"minLength": 1,
"title": "Explanation",
"type": "string"
}
},
"required": [
"tactic",
"evidence",
"explanation"
],
"title": "DetectedTactic",
"type": "object"
},
"SafeAction": {
"description": "Fixed list of recommended safe actions.\n\nThe model must pick one of these; it can never compose free-text advice\nthat points back at the scammer's own channel. End-user wording lives in\nthe demo/report layer; these are stable machine-readable ids.",
"enum": [
"call_bank_official_number",
"do_not_click_link",
"verify_via_official_app_or_site",
"call_family_member_known_number",
"do_not_share_codes_or_credentials",
"do_not_send_money",
"ignore_and_delete",
"report_to_authorities",
"check_sender_address",
"no_action_needed"
],
"title": "SafeAction",
"type": "string"
},
"TacticId": {
"description": "Fixed tactic taxonomy ids (taxonomy.yaml v1).",
"enum": [
"urgency_pressure",
"authority_impersonation",
"payment_redirect",
"credential_phishing",
"courier_customs_fee",
"prize_lottery",
"investment_too_good",
"romance_advance_fee",
"family_emergency_impersonation",
"tech_support",
"link_obfuscation",
"refund_overpayment",
"subscription_trap"
],
"title": "TacticId",
"type": "string"
},
"Verdict": {
"description": "Three-level verdict. Deliberately not a probability (CLAUDE.md section 2).",
"enum": [
"scam_likely",
"suspicious",
"no_indicators"
],
"title": "Verdict",
"type": "string"
}
},
"additionalProperties": false,
"description": "Complete model output for one input message.",
"properties": {
"verdict": {
"$ref": "#/$defs/Verdict"
},
"tactics": {
"description": "Detected tactics; empty when verdict is no_indicators.",
"items": {
"$ref": "#/$defs/DetectedTactic"
},
"title": "Tactics",
"type": "array"
},
"explanation": {
"description": "Overall plain-language explanation of the verdict.",
"minLength": 1,
"title": "Explanation",
"type": "string"
},
"recommended_action": {
"$ref": "#/$defs/SafeAction"
}
},
"required": [
"verdict",
"explanation",
"recommended_action"
],
"title": "ScamGuardOutput",
"type": "object"
} |