Instructions to use prithivMLmods/SingGuard-NSFA-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/SingGuard-NSFA-9B-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/SingGuard-NSFA-9B-GGUF", dtype="auto") - llama-cpp-python
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/SingGuard-NSFA-9B-GGUF", filename="SingGuard-NSFA-9B.BF16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF 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 prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/SingGuard-NSFA-9B-GGUF: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 prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/SingGuard-NSFA-9B-GGUF: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 prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/SingGuard-NSFA-9B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/SingGuard-NSFA-9B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/SingGuard-NSFA-9B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/SingGuard-NSFA-9B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/SingGuard-NSFA-9B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/SingGuard-NSFA-9B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
- Unsloth Studio
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for prithivMLmods/SingGuard-NSFA-9B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for prithivMLmods/SingGuard-NSFA-9B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for prithivMLmods/SingGuard-NSFA-9B-GGUF to start chatting
- Pi
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/SingGuard-NSFA-9B-GGUF: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 prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/SingGuard-NSFA-9B-GGUF: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 "prithivMLmods/SingGuard-NSFA-9B-GGUF: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"
- Docker Model Runner
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/SingGuard-NSFA-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/SingGuard-NSFA-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.SingGuard-NSFA-9B-GGUF-Q4_K_M
List all available models
lemonade list
SingGuard-NSFA-9B-GGUF
SingGuard-NSFA-9B is the largest of four model sizes (0.8B, 2B, 4B, 9B) in a dual-mode guardrail framework developed by the SingGuard Team at Ant Group's AI Security Lab, fine-tuned from Qwen3.5-9B Base to secure agentic AI systems against operational threats like prompt injection, sensitive information extraction, malicious code requests, dangerous tool misuse, and resource exhaustion. It is built on the NSFA (Not-Secure-For-Agents) taxonomy — a CIA-triad-grounded hierarchical classification of 185 risk variants spanning 5 query-side domains (e.g., Prompt Injection & Jailbreak, Malicious Code & Cyberattack) and 2 response-side domains (Hazardous Action Generation, Sensitive Information Leakage) — and operates as a single-turn, text-based guardrail supporting 133 languages through two complementary inference modes: lightweight discriminative classification heads on a frozen backbone for real-time detection (~50ms per sample on an A100 via vLLM embedding mode), and full generative chain-of-thought reasoning for interpretable offline auditing and compliance workflows. Across three purpose-built multilingual benchmarks (including a cross-source benchmark adapted from AgentDojo, InjecAgent, AgentHarm, and others), all SingGuard-NSFA models achieve above 94% F1, surpassing the strongest competing guardrails by 6–12 absolute F1 points, with the architecture also natively extensible to new risk domains — including content safety — by training only additional lightweight classification heads without retraining the backbone; it is explicitly scoped to single-turn operational security rather than multi-turn trajectory analysis, multimodal threats, or textual content moderation, and is intended strictly as a defensive tool.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| SingGuard-NSFA-9B.BF16.gguf | BF16 | 17.9 GB | Download |
| SingGuard-NSFA-9B.F16.gguf | F16 | 17.9 GB | Download |
| SingGuard-NSFA-9B.Q3_K_L.gguf | Q3_K_L | 4.92 GB | Download |
| SingGuard-NSFA-9B.Q3_K_M.gguf | Q3_K_M | 4.62 GB | Download |
| SingGuard-NSFA-9B.Q3_K_S.gguf | Q3_K_S | 4.26 GB | Download |
| SingGuard-NSFA-9B.Q4_K_M.gguf | Q4_K_M | 5.63 GB | Download |
| SingGuard-NSFA-9B.Q4_K_S.gguf | Q4_K_S | 5.35 GB | Download |
| SingGuard-NSFA-9B.Q5_K_M.gguf | Q5_K_M | 6.47 GB | Download |
| SingGuard-NSFA-9B.Q5_K_S.gguf | Q5_K_S | 6.3 GB | Download |
| SingGuard-NSFA-9B.Q8_0.gguf | Q8_0 | 9.53 GB | Download |
| SingGuard-NSFA-9B.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | Download |
| SingGuard-NSFA-9B.mmproj-f16.gguf | mmproj-f16 | 922 MB | Download |
| SingGuard-NSFA-9B.mmproj-q8_0.gguf | mmproj-q8_0 | 624 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/SingGuard-NSFA-9B-GGUF
Base model
inclusionAI/SingGuard-NSFA-9B