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

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 Studio

How to use exeterminal/Exe-Guard-Dynamic-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-Guard-Dynamic-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-Guard-Dynamic-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-Guard-Dynamic-GGUF to start chatting

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 @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-Guard-Dynamic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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"

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

Exe Guard Dynamic

A tiny guardian model for the Extended Workflow feature of the Exe AI Terminal Website to the Main AI Agent Harness: https://exe-hq.net When a tool step fails, it reads the report of that failure and writes the one instruction the user should send next to fix it — a corrected command, the real file, a path inside the shared folder. It suggests; it never acts.

What it does

The guardian watches tool calls and speaks up only when a step failed in a way a person would want a suggestion for. It is built for exactly six kinds of failure:

#	Failure	The fix it should name
1	Command typo (`npm run buld`)	the correctly spelled command
2	Wrong Python environment (a `.venv/` exists)	`.venv/bin/python3 …` (never a global `pip install`, never `source activate`)
3	`edit_file` old-text not found	read the file first, then edit with the exact text
4	Binary / unreadable file	the readable file (e.g. the `.log`), or `run_command` for archives
5	Path rejected (outside the shared folders)	a path inside the released folder
6	Run stopped after a timeout	re-run in the background

It answers in one imperative English sentence, no greeting, no explanation.

Intended use

Drop-in as the small background model behind the Exe AI Terminal's Extended Workflow. It is a specialist: it turns a failed-step report into a single corrective instruction.

Out of scope: general chat, code generation, vision, or any use outside the failed-step-repair task. It is not a general assistant.

Files

All builds carry an importance matrix (imatrix) computed from the model's own task data, and were tested on 24 held-out repair cases at temperature 0.1. "Test" is the number of those 24 cases solved correctly — a task metric, not perplexity.

File	Type	Bits	Size	Test (of 24)
`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
`Exe-Guard-Dynamic-IQ3_S.gguf`	I-quant	3.44	1.36 GB	24 / 24
`Exe-Guard-Dynamic-Q2_K.gguf`	K-quant	3.0	1.19 GB	24 / 24
`Exe-Guard-Dynamic-IQ2_M.gguf`	I-quant	2.7	1.06 GB	23 / 24
`Exe-Guard-Dynamic-IQ2_S.gguf`	I-quant	2.5	0.99 GB	22 / 24
`Exe-Guard-Dynamic-IQ2_XS.gguf`	I-quant	2.06	0.96 GB	22 / 24
`Exe-Guard-Dynamic-IQ1_M.gguf`	I-quant · experimental	1.75	0.79 GB	9 / 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

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

Two messages only — a fixed system instruction and the failure report — with temperature 0.1, max_tokens 200, thinking off, context 4096. No conversation history.

Base model and license

Base: Qwen/Qwen2.5-Coder-3B-Instruct
License: Apache-2.0 (base and this derivative). You may use, modify, rebrand and redistribute; the origin of the base model must be named — it is, here.

Training

A thin LoRA adapter on top of the base, trained locally (Apple Silicon, MLX) on synthetic examples of failed tool steps and their one-sentence corrections, built to match the exact report format the terminal produces. The adapter was fused into the base and then quantized.

Evaluation

On 24 held-out repair cases at temperature 0.1, the untrained base solves 10 / 24 (42%) and Exe Guard Dynamic solves 24 / 24 (100%). The base fails completely on the two cases that need this training — wrong Python environment and wrong edit target (0%) — which the trained model fixes entirely.

Transparency

This is a fine-tuned derivative of an openly licensed base model, released with its provenance, intended use, limits and evaluation stated above, in line with transparency expectations for shared models (incl. the EU AI Act).

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GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for exeterminal/Exe-Guard-Dynamic-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Quantized

(120)

this model