Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
Instructions to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion: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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OmnipotentFool/Aurvion: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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use OmnipotentFool/Aurvion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmnipotentFool/Aurvion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmnipotentFool/Aurvion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Ollama
How to use OmnipotentFool/Aurvion with Ollama:
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Lemonade
How to use OmnipotentFool/Aurvion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OmnipotentFool/Aurvion:Q4_K_M
Run and chat with the model
lemonade run user.Aurvion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download llama.cpp/scripts/snapdragon/qdc/tests/utils.py from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 4.94 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/scripts/snapdragon/qdc/tests/utils.py
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/scripts/snapdragon/qdc/tests/utils.py
-
curl -L -o utils.py https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/scripts/snapdragon/qdc/tests/utils.py
4.94 kB
| """Shared helpers for QDC on-device test runners.""" | |
| from __future__ import annotations | |
| import logging | |
| import os | |
| import subprocess | |
| import tempfile | |
| from appium.options.common import AppiumOptions | |
| log = logging.getLogger(__name__) | |
| # --------------------------------------------------------------------------- | |
| # On-device paths | |
| # --------------------------------------------------------------------------- | |
| BUNDLE_PATH = "/data/local/tmp/llama.cpp" | |
| BIN_PATH = f"{BUNDLE_PATH}/bin" | |
| LIB_PATH = f"{BUNDLE_PATH}/lib" | |
| QDC_LOGS_PATH = "/data/local/tmp/QDC_logs" | |
| SCRIPTS_DIR = "/qdc/appium" | |
| MODEL_NAME = "model.gguf" | |
| MODEL_DEVICE_PATH = "/data/local/tmp/gguf/model.gguf" | |
| PROMPT_DIR = "/data/local/tmp/scorecard_prompts" | |
| # --------------------------------------------------------------------------- | |
| # Appium session options | |
| # --------------------------------------------------------------------------- | |
| options = AppiumOptions() | |
| options.set_capability("automationName", "UiAutomator2") | |
| options.set_capability("platformName", "Android") | |
| options.set_capability("deviceName", os.getenv("ANDROID_DEVICE_VERSION")) | |
| # --------------------------------------------------------------------------- | |
| # Shell / process helpers | |
| # --------------------------------------------------------------------------- | |
| def write_qdc_log(filename: str, content: str) -> None: | |
| """Write content as a log file for QDC log collection.""" | |
| subprocess.run( | |
| ["adb", "shell", f"mkdir -p {QDC_LOGS_PATH}"], | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| ) | |
| with tempfile.NamedTemporaryFile(mode="w", suffix=".log", delete=False) as f: | |
| f.write(content) | |
| tmp_path = f.name | |
| try: | |
| subprocess.run( | |
| ["adb", "push", tmp_path, f"{QDC_LOGS_PATH}/{filename}"], | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| ) | |
| finally: | |
| os.unlink(tmp_path) | |
| def ensure_bundle(check_binary: str | None = None) -> None: | |
| """Ensure the llama_cpp_bundle is available on the target device.""" | |
| push_bundle_if_needed(check_binary or f"{BIN_PATH}/llama-cli") | |
| # --------------------------------------------------------------------------- | |
| # Android / Linux host helpers | |
| # --------------------------------------------------------------------------- | |
| def run_adb_command(cmd: str, *, check: bool = True) -> subprocess.CompletedProcess: | |
| """Run a command on-device via ``adb shell`` with exit-code sentinel.""" | |
| raw = subprocess.run( | |
| ["adb", "shell", f"{cmd}; echo __RC__:$?"], | |
| text=True, | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| ) | |
| stdout = raw.stdout | |
| returncode = raw.returncode | |
| if stdout: | |
| lines = stdout.rstrip("\n").split("\n") | |
| if lines and lines[-1].startswith("__RC__:"): | |
| try: | |
| returncode = int(lines[-1][7:]) | |
| stdout = "\n".join(lines[:-1]) + "\n" | |
| except ValueError: | |
| pass | |
| log.info(stdout) | |
| result = subprocess.CompletedProcess(raw.args, returncode, stdout=stdout) | |
| if check: | |
| assert returncode == 0, f"Command failed (exit {returncode})" | |
| return result | |
| def run_script( | |
| script: str, | |
| extra_env: dict[str, str] | None = None, | |
| extra_args: list[str] | None = None, | |
| ) -> subprocess.CompletedProcess: | |
| """Run an upstream shell script from /qdc/appium/ on the QDC runner host.""" | |
| env = os.environ.copy() | |
| env["GGML_HEXAGON_EXPERIMENTAL"] = "1" | |
| if extra_env: | |
| env.update(extra_env) | |
| cmd = [f"{SCRIPTS_DIR}/{script}"] + (extra_args or []) | |
| result = subprocess.run( | |
| cmd, env=env, | |
| text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, | |
| ) | |
| log.info(result.stdout) | |
| return result | |
| def adb_shell(cmd: str) -> None: | |
| """Run a command via adb shell (fire-and-forget, no error check).""" | |
| subprocess.run( | |
| ["adb", "shell", "sh", "-c", cmd], | |
| capture_output=True, encoding="utf-8", errors="replace", check=False, | |
| ) | |
| def push_bundle_if_needed(check_binary: str) -> None: | |
| """Push llama_cpp_bundle to the device if check_binary is not already present.""" | |
| result = subprocess.run( | |
| ["adb", "shell", f"ls {check_binary}"], | |
| text=True, | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| ) | |
| if result.returncode != 0: | |
| subprocess.run( | |
| ["adb", "push", "/qdc/appium/llama_cpp_bundle/", BUNDLE_PATH], | |
| text=True, | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| ) | |
| subprocess.run( | |
| ["adb", "shell", f"find {BUNDLE_PATH}/bin -type f -exec chmod 755 {{}} +"], | |
| text=True, | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| ) | |