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
File size: 4,236 Bytes
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from __future__ import annotations
import argparse
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" / "open-interpreter.dg.json"
def load_config(path: Path) -> dict[str, Any]:
return json.loads(path.read_text(encoding="utf-8"))
def import_open_interpreter() -> Any:
from interpreter import interpreter
return interpreter
def model_settings(config: dict[str, Any]) -> dict[str, Any]:
return {
"model": os.environ.get("OPEN_INTERPRETER_MODEL") or config["model"],
"api_base": os.environ.get("OPENAI_BASE_URL") or config["api_base"],
"api_key": os.environ.get("OPENAI_API_KEY") or config["api_key"],
"context_window": int(os.environ.get("OPEN_INTERPRETER_CONTEXT_WINDOW") or config.get("context_window") or 768),
"max_tokens": int(os.environ.get("OPEN_INTERPRETER_MAX_TOKENS") or config.get("max_tokens") or 256),
"temperature": float(os.environ.get("OPEN_INTERPRETER_TEMPERATURE") or config.get("temperature") or 0.0),
"offline": bool(config.get("offline", True)),
"auto_run": bool(config.get("auto_run", False)),
"safe_mode": os.environ.get("OPEN_INTERPRETER_SAFE_MODE") or config.get("safe_mode") or "ask",
}
def configure_interpreter(interpreter: Any, config: dict[str, Any]) -> dict[str, Any]:
settings = model_settings(config)
interpreter.offline = settings["offline"]
interpreter.auto_run = settings["auto_run"]
interpreter.safe_mode = settings["safe_mode"]
interpreter.llm.model = settings["model"]
interpreter.llm.api_base = settings["api_base"]
interpreter.llm.api_key = settings["api_key"]
interpreter.llm.context_window = settings["context_window"]
interpreter.llm.max_tokens = settings["max_tokens"]
interpreter.llm.temperature = settings["temperature"]
return settings
def run_task(args: argparse.Namespace, config: dict[str, Any]) -> int:
interpreter = import_open_interpreter()
settings = configure_interpreter(interpreter, config)
os.chdir(Path(args.repo).resolve())
prompt = (
"Use concise output. Prefer inspection and explanations. "
"Do not make repository edits unless explicitly asked.\n\n"
f"{args.task}"
)
result = interpreter.chat(prompt, display=not args.json)
if args.json:
print(json.dumps({"status": "success", "settings": settings, "result": result}, ensure_ascii=False, indent=2, default=str))
return 0
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Run Open Interpreter 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:
interpreter = import_open_interpreter()
print("open-interpreter import ok")
print(type(interpreter).__name__)
return 0
settings = model_settings(config)
if args.dry_run:
data = {
"repo": str(repo),
"config": str(config_path),
"module": config["module"],
"agent": config["agent"],
"settings": settings,
"command": f"scripts/dg_agent.sh open-interpreter -- --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
return run_task(args, config)
if __name__ == "__main__":
raise SystemExit(main())
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