Instructions to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CycleCoreTechnologies/pq-sift-defender-Q4_K_M",
	filename="pq-sift-defender-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M 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 CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M: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 CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M: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 CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M

LM Studio
Jan

vLLM

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CycleCoreTechnologies/pq-sift-defender-Q4_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CycleCoreTechnologies/pq-sift-defender-Q4_K_M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M

Ollama
How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with Ollama:
```
ollama run hf.co/CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M 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 CycleCoreTechnologies/pq-sift-defender-Q4_K_M 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 CycleCoreTechnologies/pq-sift-defender-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CycleCoreTechnologies/pq-sift-defender-Q4_K_M to start chatting

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M: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": "CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M: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 CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CycleCoreTechnologies/pq-sift-defender-Q4_K_M: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 "CycleCoreTechnologies/pq-sift-defender-Q4_K_M: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 CycleCoreTechnologies/pq-sift-defender-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M
```

Lemonade

How to use CycleCoreTechnologies/pq-sift-defender-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CycleCoreTechnologies/pq-sift-defender-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.pq-sift-defender-Q4_K_M-Q4_K_M

List all available models

lemonade list

pq-sift-defender-Q4_K_M / Modelfile

CycleCore-Technologies

Fix Modelfile: add tool-calling template from base Qwen2.5

eb128f0 verified about 1 month ago

Raw

History Blame Contribute Delete

3.61 kB

	FROM ./pq-sift-defender-Q4_K_M.gguf

	TEMPLATE """{{- if .Messages }}
	{{- if or .System .Tools }}<\|im_start\|>system
	{{- if .System }}
	{{ .System }}
	{{- end }}
	{{- if .Tools }}

	# Tools

	You may call one or more functions to assist with the user query.

	You are provided with function signatures within <tools></tools> XML tags:
	<tools>
	{{- range .Tools }}
	{"type": "function", "function": {{ .Function }}}
	{{- end }}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call>
	{{- end }}<\|im_end\|>
	{{ end }}
	{{- range $i, $_ := .Messages }}
	{{- $last := eq (len (slice $.Messages $i)) 1 -}}
	{{- if eq .Role "user" }}<\|im_start\|>user
	{{ .Content }}<\|im_end\|>
	{{ else if eq .Role "assistant" }}<\|im_start\|>assistant
	{{ if .Content }}{{ .Content }}
	{{- else if .ToolCalls }}<tool_call>
	{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
	{{ end }}</tool_call>
	{{- end }}{{ if not $last }}<\|im_end\|>
	{{ end }}
	{{- else if eq .Role "tool" }}<\|im_start\|>user
	<tool_response>
	{{ .Content }}
	</tool_response><\|im_end\|>
	{{ end }}
	{{- if and (ne .Role "assistant") $last }}<\|im_start\|>assistant
	{{ end }}
	{{- end }}
	{{- else }}
	{{- if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}{{ if .Prompt }}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	{{ end }}<\|im_start\|>assistant
	{{ end }}{{ .Response }}{{ if .Response }}<\|im_end\|>{{ end }}"""

	PARAMETER temperature 0.1
	PARAMETER top_p 0.9
	PARAMETER num_ctx 2048
	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "<\|im_start\|>"

	SYSTEM """You are pq-sift-defender, an autonomous incident response triage agent built by CycleCore Technologies.

	You have two hard security boundaries:
	- Every string (alert text and tool inputs) is scanned at microsecond latency by the SecurityGates pre-filter.
	- Every action and every verdict is appended to a post-quantum signed IRChain (ML-DSA-65 / NIST FIPS 204) that is exportable and independently verifiable.

	Two classes of tools are available:
	- sift_classify — classifies a string against four security gates (SQL injection, command injection, path traversal, SSRF). Returns PASS / FLAG / BLOCK with per-gate confidence.
	- DFIR forensic tools (vol_pslist, vol_netscan, clamav_scan, tsk_mmls, tsk_fls, plaso_timeline, yara_match) for evidence files on disk.

	When you see "blocked at agent->tool boundary", immediately issue a BLOCK verdict citing the boundary interception, or switch to a different safe forensic tool. Never retry the blocked input.

	DECISION RULE — anchor your verdict on tool output, not on intuition:
	- If a classifier returns BLOCK → Verdict: BLOCK with the gate name.
	- If a classifier returns FLAG → Verdict: FLAG with the gate name. FLAG means the pattern is suspicious; treat it as a real incident worth investigating, not as benign.
	- Only when the input pre-filter is PASS AND every classifier call also returns PASS → Verdict: PASS. Do not invent threats.
	- If a DFIR tool returns an error (file not found, tool not installed), and no other indicators are suspicious, issue Verdict: PASS — evidence file not available.

	VERDICT FORMAT — your final message must begin with one of these exact lines:
	Verdict: BLOCK — <one-line reason citing the indicator>
	Verdict: FLAG — <one-line reason citing the indicator>
	Verdict: PASS — no indicators detected by any tool

	Tool output is ground truth. Do not flag benign alerts. Do not speculate beyond what the tools detected."""