Instructions to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13: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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13: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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Use Docker

docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

LM Studio
Jan

vLLM

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aogavrilov/diffusiongemma-agent-iq3-cuda13"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aogavrilov/diffusiongemma-agent-iq3-cuda13",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Ollama
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Ollama:
```
ollama run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
```

Unsloth Studio

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aogavrilov/diffusiongemma-agent-iq3-cuda13 to start chatting

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13: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": "aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13: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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13: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 "aogavrilov/diffusiongemma-agent-iq3-cuda13: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 aogavrilov/diffusiongemma-agent-iq3-cuda13 with Docker Model Runner:
```
docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
```

Lemonade

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Run and chat with the model

lemonade run user.diffusiongemma-agent-iq3-cuda13-Q4_K_M

List all available models

lemonade list

diffusiongemma-agent-iq3-cuda13 / payload /app /scripts /run_haystack_local.sh

aogavrilov

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	#!/usr/bin/env bash
	set -euo pipefail

	DG_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
	PYTHON="${DG_HAYSTACK_PYTHON:-$DG_ROOT/.venv-haystack/bin/python}"
	REPO=""
	CONFIG=""
	HELP_LOCAL=0
	DRY_RUN=0

	usage() {
	cat <<'EOF'
	Runs Haystack BM25 RAG against the local DiffusionGemma OpenAI-compatible profile.

	Usage:
	scripts/run_haystack_local.sh [--repo PATH] [--config PATH] [--dry-run\|--smoke-import\|--task TEXT]

	Default config:
	configs/client_profiles/haystack.dg.json

	Examples:
	scripts/run_haystack_local.sh --repo /repo --dry-run
	scripts/run_haystack_local.sh --repo /repo --smoke-import
	scripts/run_haystack_local.sh --repo /repo --task "Where is add(a, b) implemented?"
	EOF
	}

	while [[ $# -gt 0 ]]; do
	case "$1" in
	--repo)
	REPO="$2"
	shift 2
	;;
	--config)
	CONFIG="$2"
	shift 2
	;;
	--help-local)
	HELP_LOCAL=1
	shift
	;;
	--)
	shift
	break
	;;
	*)
	break
	;;
	esac
	done

	for arg in "$@"; do
	if [[ "$arg" == "--dry-run" ]]; then
	DRY_RUN=1
	break
	fi
	done

	if [[ "$HELP_LOCAL" == "1" ]]; then
	usage
	exit 0
	fi

	if [[ -z "$REPO" ]]; then
	REPO="$PWD"
	fi
	REPO="$(cd "$REPO" && pwd)"

	python_path() {
	if [[ "$PYTHON" == *.exe ]] && command -v wslpath >/dev/null 2>&1; then
	wslpath -w "$1"
	return
	fi
	if [[ "${OS:-}" == "Windows_NT" ]] && command -v cygpath >/dev/null 2>&1; then
	cygpath -w "$1"
	else
	printf '%s\n' "$1"
	fi
	}

	if [[ ! -x "$PYTHON" && "$DRY_RUN" != "1" ]]; then
	"$DG_ROOT/scripts/install_haystack_local.sh" >/tmp/dg-haystack-install.log
	fi

	if [[ -z "$CONFIG" ]]; then
	if [[ -s "$REPO/.dg-agent/haystack.dg.json" ]]; then
	CONFIG="$REPO/.dg-agent/haystack.dg.json"
	else
	CONFIG="$DG_ROOT/configs/client_profiles/haystack.dg.json"
	fi
	fi

	export OPENAI_BASE_URL="${OPENAI_BASE_URL:-http://127.0.0.1:8090/v1}"
	export OPENAI_API_KEY="${OPENAI_API_KEY:-dummy}"
	export HAYSTACK_MODEL="${HAYSTACK_MODEL:-diffusiongemma-local}"
	export DG_AGENT_CALLER_CWD="${DG_AGENT_CALLER_CWD:-$REPO}"

	REPO_FOR_PY="$(python_path "$REPO")"
	CONFIG_FOR_PY="$(python_path "$CONFIG")"
	RUNNER_FOR_PY="$(python_path "$DG_ROOT/scripts/dg_haystack_runner.py")"

	if [[ -x "$PYTHON" ]]; then
	exec "$PYTHON" "$RUNNER_FOR_PY" --repo "$REPO_FOR_PY" --config "$CONFIG_FOR_PY" "$@"
	fi

	exec python3 "$RUNNER_FOR_PY" --repo "$REPO_FOR_PY" --config "$CONFIG_FOR_PY" "$@"