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
- llama.cpp
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
- Pi
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
| #!/usr/bin/env python3 | |
| from __future__ import annotations | |
| import argparse | |
| import asyncio | |
| import json | |
| import os | |
| from pathlib import Path | |
| from typing import Any | |
| DG_ROOT = Path(__file__).resolve().parents[1] | |
| DEFAULT_CONFIG = DG_ROOT / "configs" / "client_profiles" / "autogen.dg.json" | |
| def load_config(path: Path) -> dict[str, Any]: | |
| return json.loads(path.read_text(encoding="utf-8")) | |
| def model_kwargs(config: dict[str, Any]) -> dict[str, Any]: | |
| return { | |
| "model": os.environ.get("AUTOGEN_MODEL") or config["model"], | |
| "api_key": os.environ.get("OPENAI_API_KEY") or config["api_key"], | |
| "base_url": os.environ.get("OPENAI_BASE_URL") or config["base_url"], | |
| "model_info": config["model_info"], | |
| "max_tokens": int(os.environ.get("AUTOGEN_MAX_TOKENS") or config.get("max_tokens") or 256), | |
| "temperature": float(os.environ.get("AUTOGEN_TEMPERATURE") or config.get("temperature") or 0.0), | |
| } | |
| async def run_task(args: argparse.Namespace, config: dict[str, Any]) -> int: | |
| from autogen_agentchat.agents import AssistantAgent | |
| from autogen_ext.models.openai import OpenAIChatCompletionClient | |
| client = OpenAIChatCompletionClient(**model_kwargs(config)) | |
| system_message = ( | |
| "You are running through the local DiffusionGemma AutoGen profile. " | |
| "Keep answers short, ask for explicit file snippets before broad edits, " | |
| "and prefer DG MCP/session tools when available." | |
| ) | |
| agent = AssistantAgent("dg_autogen", model_client=client, system_message=system_message) | |
| try: | |
| result = await agent.run(task=args.task) | |
| finally: | |
| await client.close() | |
| if args.json: | |
| print(json.dumps({"status": "success", "messages": [str(item) for item in result.messages]}, ensure_ascii=False, indent=2)) | |
| else: | |
| last = result.messages[-1] if result.messages else "" | |
| print(getattr(last, "content", str(last))) | |
| return 0 | |
| def parse_args() -> argparse.Namespace: | |
| parser = argparse.ArgumentParser(description="Run AutoGen AgentChat with the local DiffusionGemma profile.") | |
| parser.add_argument("--repo", default=".", help="Target repo, used as working directory") | |
| parser.add_argument("--config", default=str(DEFAULT_CONFIG)) | |
| parser.add_argument("--task", default="") | |
| parser.add_argument("--dry-run", action="store_true") | |
| parser.add_argument("--smoke-import", action="store_true") | |
| parser.add_argument("--json", action="store_true") | |
| return parser.parse_args() | |
| def main() -> int: | |
| args = parse_args() | |
| repo = Path(args.repo).resolve() | |
| config_path = Path(args.config).resolve() | |
| config = load_config(config_path) | |
| if args.smoke_import: | |
| from autogen_agentchat.agents import AssistantAgent | |
| from autogen_ext.models.openai import OpenAIChatCompletionClient | |
| print("autogen import ok") | |
| print(AssistantAgent.__name__) | |
| print(OpenAIChatCompletionClient.__name__) | |
| print(config["model_client"]) | |
| return 0 | |
| if args.dry_run: | |
| data = { | |
| "repo": str(repo), | |
| "config": str(config_path), | |
| "model_client": config["model_client"], | |
| "model_kwargs": model_kwargs(config), | |
| "command": f"scripts/dg_agent.sh autogen -- --repo {repo} --task '...'", | |
| } | |
| print(json.dumps(data, ensure_ascii=False, indent=2) if args.json else "\n".join(f"{k}: {v}" for k, v in data.items())) | |
| return 0 | |
| if not args.task: | |
| print("--task is required unless --dry-run or --smoke-import is used", flush=True) | |
| return 2 | |
| os.chdir(repo) | |
| return asyncio.run(run_task(args, config)) | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |