Instructions to use exeterminal/Exe-Guard-Dynamic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use exeterminal/Exe-Guard-Dynamic-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 exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf exeterminal/Exe-Guard-Dynamic-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 exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf exeterminal/Exe-Guard-Dynamic-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 exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf exeterminal/Exe-Guard-Dynamic-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 exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use exeterminal/Exe-Guard-Dynamic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "exeterminal/Exe-Guard-Dynamic-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": "exeterminal/Exe-Guard-Dynamic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M

Ollama
How to use exeterminal/Exe-Guard-Dynamic-GGUF with Ollama:
```
ollama run hf.co/exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M
```
Unsloth Desktop

How to use exeterminal/Exe-Guard-Dynamic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use exeterminal/Exe-Guard-Dynamic-GGUF with Docker Model Runner:
```
docker model run hf.co/exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M
```

Lemonade

How to use exeterminal/Exe-Guard-Dynamic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Exe-Guard-Dynamic-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use exeterminal/Exe-Guard-Dynamic-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 exeterminal/Exe-Guard-Dynamic-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 exeterminal/Exe-Guard-Dynamic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use exeterminal/Exe-Guard-Dynamic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf exeterminal/Exe-Guard-Dynamic-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 "exeterminal/Exe-Guard-Dynamic-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"

Exeyyy commited on Aug 16

Commit

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README.md CHANGED Viewed

@@ -63,9 +63,9 @@ number of those 24 cases solved correctly — a task metric, not perplexity.
 | `Exe-Guard-Dynamic-Q8_0.gguf` | K/legacy | 8 | 3.06 GB | 24 / 24 |
 | `Exe-Guard-Dynamic-Q6_K.gguf` | K-quant | 6.5 | 2.36 GB | 24 / 24 |
 | `Exe-Guard-Dynamic-Q5_K_M.gguf` | K-quant | 5.5 | 2.07 GB | 24 / 24 |
-| **`Exe-Guard-Dynamic-Q4_K_M.gguf`** | **K-quant** | **4.8** | **1.80 GB** | **24 / 24 — recommended** |
 | `Exe-Guard-Dynamic-Q4_K_S.gguf` | K-quant | 4.5 | 1.71 GB | 24 / 24 |
-| `Exe-Guard-Dynamic-IQ4_XS.gguf` | I-quant | 4.25 | 1.62 GB | 24 / 24 |
 | `Exe-Guard-Dynamic-Q3_K_L.gguf` | K-quant | 4.0 | 1.59 GB | 19 / 24 |
 | `Exe-Guard-Dynamic-Q3_K_M.gguf` | K-quant | 3.9 | 1.48 GB | 20 / 24 |
 | `Exe-Guard-Dynamic-IQ3_M.gguf` | I-quant | 3.66 | 1.39 GB | 24 / 24 |
@@ -78,9 +78,13 @@ number of those 24 cases solved correctly — a task metric, not perplexity.
 | `Exe-Guard-Dynamic-IQ1_S.gguf` | I-quant · experimental | 1.56 | 0.74 GB | 10 / 24 |
 | `Exe-Guard-Dynamic-f16.gguf` | full precision | 16 | 5.75 GB | 24 / 24 |
-Note: at low bit-widths the I-quants (IQ3, IQ2) hold up better than the K-quants
-(`Q3_K` drops to 19–20/24). The 1-bit builds are included for the curious but are
-not recommended.
 ## Prompt and sampling

 | `Exe-Guard-Dynamic-Q8_0.gguf` | K/legacy | 8 | 3.06 GB | 24 / 24 |
 | `Exe-Guard-Dynamic-Q6_K.gguf` | K-quant | 6.5 | 2.36 GB | 24 / 24 |
 | `Exe-Guard-Dynamic-Q5_K_M.gguf` | K-quant | 5.5 | 2.07 GB | 24 / 24 |
+| `Exe-Guard-Dynamic-Q4_K_M.gguf` | K-quant | 4.8 | 1.80 GB | 24 / 24 |
 | `Exe-Guard-Dynamic-Q4_K_S.gguf` | K-quant | 4.5 | 1.71 GB | 24 / 24 |
+| **`Exe-Guard-Dynamic-IQ4_XS.gguf`** | **I-quant · recommended** | **4.25** | **1.62 GB** | **24 / 24** |
 | `Exe-Guard-Dynamic-Q3_K_L.gguf` | K-quant | 4.0 | 1.59 GB | 19 / 24 |
 | `Exe-Guard-Dynamic-Q3_K_M.gguf` | K-quant | 3.9 | 1.48 GB | 20 / 24 |
 | `Exe-Guard-Dynamic-IQ3_M.gguf` | I-quant | 3.66 | 1.39 GB | 24 / 24 |
 | `Exe-Guard-Dynamic-IQ1_S.gguf` | I-quant · experimental | 1.56 | 0.74 GB | 10 / 24 |
 | `Exe-Guard-Dynamic-f16.gguf` | full precision | 16 | 5.75 GB | 24 / 24 |
+**`IQ4_XS` is the recommended build.** It is smaller than `Q4_K_M` and solves the
+same 24 of 24 — every build here carries an importance matrix, and the I-quants use
+it to spend their bits where the model actually needs them.
+The same pattern holds further down: at low bit-widths the I-quants (IQ3, IQ2) beat
+the K-quants of similar size (`Q3_K` drops to 19–20/24). The 1-bit builds are
+included for the curious but are not recommended.
 ## Prompt and sampling