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
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import os | |
| from pathlib import Path | |
| from typing import Any | |
| from huggingface_hub import HfApi | |
| def validate_staging(folder: Path, expected_tag: str) -> dict[str, Any]: | |
| manifest_path = folder / "manifest.json" | |
| index_path = folder / "runtime-index.json" | |
| if not folder.is_dir() or not manifest_path.is_file() or not index_path.is_file(): | |
| raise ValueError(f"staged runtime is incomplete: {folder}") | |
| manifest = json.loads(manifest_path.read_text(encoding="utf-8-sig")) | |
| index = json.loads(index_path.read_text(encoding="utf-8-sig")) | |
| files = manifest.get("files") | |
| if manifest.get("format") != 1 or not isinstance(files, list) or not files: | |
| raise ValueError("manifest.json has an unsupported format") | |
| if index.get("revision") != expected_tag: | |
| raise ValueError(f"runtime-index revision {index.get('revision')!r} does not match tag {expected_tag!r}") | |
| total_bytes = 0 | |
| model_found = False | |
| for entry in files: | |
| relative = Path(str(entry.get("path", "")).replace("/", os.sep)) | |
| if relative.is_absolute() or ".." in relative.parts: | |
| raise ValueError(f"unsafe manifest path: {relative}") | |
| target = folder / relative | |
| expected_size = int(entry.get("bytes", -1)) | |
| if not target.is_file() or target.stat().st_size != expected_size: | |
| raise ValueError(f"missing or invalid staged file: {relative}") | |
| total_bytes += expected_size | |
| model_found = model_found or relative.name == manifest.get("model") | |
| if not model_found: | |
| raise ValueError(f"model is not represented in manifest: {manifest.get('model')}") | |
| return {"files": len(files), "bytes": total_bytes, "model": manifest.get("model"), "revision": expected_tag} | |
| def main() -> int: | |
| parser = argparse.ArgumentParser(description="Publish the staged DiffusionGemma runtime to a Hugging Face model repo") | |
| parser.add_argument("--repo-id", required=True) | |
| parser.add_argument("--folder", default="dist/hf-runtime-repo") | |
| parser.add_argument("--tag", default="v0.1.1-cu13-iq3") | |
| parser.add_argument("--package-tag", default="v0.1.1") | |
| parser.add_argument("--private", action=argparse.BooleanOptionalAction, default=False) | |
| parser.add_argument("--check-only", action="store_true") | |
| parser.add_argument("--workers", type=int, default=2) | |
| parser.add_argument("--token", default=os.environ.get("HF_TOKEN", ""), help=argparse.SUPPRESS) | |
| args = parser.parse_args() | |
| folder = Path(args.folder).resolve() | |
| try: | |
| summary = validate_staging(folder, args.tag) | |
| except (OSError, TypeError, ValueError, json.JSONDecodeError) as exc: | |
| parser.error(str(exc)) | |
| if args.check_only: | |
| print(json.dumps(summary, indent=2)) | |
| return 0 | |
| if not args.token: | |
| parser.error("HF_TOKEN is missing") | |
| api = HfApi(token=args.token) | |
| api.create_repo(repo_id=args.repo_id, repo_type="model", private=args.private, exist_ok=True) | |
| api.upload_large_folder( | |
| repo_id=args.repo_id, | |
| repo_type="model", | |
| folder_path=folder, | |
| private=args.private, | |
| num_workers=max(1, args.workers), | |
| ) | |
| revision = api.repo_info(repo_id=args.repo_id, repo_type="model").sha | |
| if not revision: | |
| raise RuntimeError("Hugging Face did not return the published revision") | |
| api.create_tag(repo_id=args.repo_id, repo_type="model", tag=args.tag, revision=revision, exist_ok=True) | |
| api.create_tag(repo_id=args.repo_id, repo_type="model", tag=args.package_tag, revision=revision, exist_ok=True) | |
| print(f"https://huggingface.co/{args.repo_id}/commit/{revision}") | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |