Instructions to use exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-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-Core-Dynamic-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf exeterminal/Exe-Core-Dynamic-V1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/exeterminal/Exe-Core-Dynamic-V1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use exeterminal/Exe-Core-Dynamic-V1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/exeterminal/Exe-Core-Dynamic-V1-GGUF:Q4_K_M

Ollama
How to use exeterminal/Exe-Core-Dynamic-V1-GGUF with Ollama:
```
ollama run hf.co/exeterminal/Exe-Core-Dynamic-V1-GGUF:Q4_K_M
```

Unsloth Studio

How to use exeterminal/Exe-Core-Dynamic-V1-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 exeterminal/Exe-Core-Dynamic-V1-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 exeterminal/Exe-Core-Dynamic-V1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for exeterminal/Exe-Core-Dynamic-V1-GGUF to start chatting

How to use exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-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": "exeterminal/Exe-Core-Dynamic-V1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use exeterminal/Exe-Core-Dynamic-V1-GGUF with Docker Model Runner:
```
docker model run hf.co/exeterminal/Exe-Core-Dynamic-V1-GGUF:Q4_K_M
```

Lemonade

How to use exeterminal/Exe-Core-Dynamic-V1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull exeterminal/Exe-Core-Dynamic-V1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Exe-Core-Dynamic-V1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-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-Core-Dynamic-V1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use exeterminal/Exe-Core-Dynamic-V1-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-Core-Dynamic-V1-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-Core-Dynamic-V1-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"

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@@ -51,31 +51,31 @@ base model's vision tower untouched but was neither trained nor measured on imag
 ## Files
-All builds carry an importance matrix (imatrix) computed from the same calibration
-set used for Exe Guard Dynamic. Sizes are the built files.
 | File | Type | Bits | Size |
 |---|---|---|---|
-| `Exe-Core-Dynamic-v1-bf16.gguf` | full precision | 16 | 54.6 GB |
-| `Exe-Core-Dynamic-v1-Q8_0.gguf` | K/legacy | 8 | — |
-| `Exe-Core-Dynamic-v1-Q6_K.gguf` | K-quant | 6.5 | — |
-| `Exe-Core-Dynamic-v1-Q5_K_M.gguf` | K-quant | 5.5 | — |
-| **`Exe-Core-Dynamic-v1-Q4_K_M.gguf`** | **K-quant** | **4.8** | **—** |
-| `Exe-Core-Dynamic-v1-Q4_K_S.gguf` | K-quant | 4.5 | — |
-| `Exe-Core-Dynamic-v1-IQ4_XS.gguf` | I-quant | 4.25 | — |
-| `Exe-Core-Dynamic-v1-Q3_K_L.gguf` | K-quant | 4.0 | — |
-| `Exe-Core-Dynamic-v1-Q3_K_M.gguf` | K-quant | 3.9 | — |
-| `Exe-Core-Dynamic-v1-IQ3_M.gguf` | I-quant | 3.66 | — |
-| `Exe-Core-Dynamic-v1-IQ3_S.gguf` | I-quant | 3.44 | — |
-| `Exe-Core-Dynamic-v1-Q2_K.gguf` | K-quant | 3.0 | — |
-| `Exe-Core-Dynamic-v1-IQ2_M.gguf` | I-quant | 2.7 | — |
-| `Exe-Core-Dynamic-v1-IQ2_S.gguf` | I-quant | 2.5 | — |
-| `Exe-Core-Dynamic-v1-IQ2_XS.gguf` | I-quant | 2.06 | — |
-| `Exe-Core-Dynamic-v1-IQ1_M.gguf` | I-quant · experimental | 1.75 | — |
-| `Exe-Core-Dynamic-v1-IQ1_S.gguf` | I-quant · experimental | 1.56 | — |
-At low bit-widths the I-quants tend to hold up better than the K-quants of the
-same size. The 1-bit builds are included for the curious and are not recommended.
 ## Prompt and sampling

 ## Files
+Sizes are the built files.
+**No imatrix in this release.** Computing one for a 27B model on CPU ran past an
+hour without finishing, and the builds were wanted sooner. The K-quants are
+unaffected — they do not need one. `IQ4_XS` was built without it and is therefore
+a little below what it could be; the deeper I-quants (IQ3 and below) were left out
+rather than shipped in that state. A later release will add them with an imatrix
+computed on a GPU.
 | File | Type | Bits | Size |
 |---|---|---|---|
+| `Exe-Core-Dynamic-v1-bf16.gguf` | full precision | 16 | 54.7 GB |
+| `Exe-Core-Dynamic-v1-Q8_0.gguf` | K/legacy | 8 | 29.0 GB |
+| `Exe-Core-Dynamic-v1-Q6_K.gguf` | K-quant | 6.5 | 22.4 GB |
+| `Exe-Core-Dynamic-v1-Q5_K_M.gguf` | K-quant | 5.5 | 19.5 GB |
+| **`Exe-Core-Dynamic-v1-Q4_K_M.gguf`** | **K-quant** | **4.8** | **16.8 GB** |
+| `Exe-Core-Dynamic-v1-Q4_K_S.gguf` | K-quant | 4.5 | 15.8 GB |
+| `Exe-Core-Dynamic-v1-IQ4_XS.gguf` | I-quant | 4.25 | 15.4 GB |
+| `Exe-Core-Dynamic-v1-Q3_K_L.gguf` | K-quant | 4.0 | 14.6 GB |
+| `Exe-Core-Dynamic-v1-Q3_K_M.gguf` | K-quant | 3.9 | 13.5 GB |
+| `Exe-Core-Dynamic-v1-Q2_K.gguf` | K-quant | 3.0 | 10.9 GB |
+`Q4_K_M` is the recommended build: the usual sweet spot, and at 16.8 GB it fits a
+24 GB card. `Q2_K` at 10.9 GB is the smallest here — usable, but expect it to slip
+on the harder tool decisions.
 ## Prompt and sampling