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/schema_scamguard_v1.json from flowxai/scam-guard-qwen17b: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/flowxai/scam-guard-qwen17b/resolve/main/inference_contract/schema_scamguard_v1.json
- Command line
-
hf download hf://flowxai/scam-guard-qwen17b/inference_contract/schema_scamguard_v1.json
-
curl -L -o schema_scamguard_v1.json https://huggingface.co/flowxai/scam-guard-qwen17b/resolve/main/inference_contract/schema_scamguard_v1.json
3.18 kB
| { | |
| "$defs": { | |
| "DetectedTactic": { | |
| "additionalProperties": false, | |
| "description": "One detected manipulation tactic with its verbatim evidence span.", | |
| "properties": { | |
| "tactic": { | |
| "$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" | |
| } |