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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
-
curl -L -o hf-upload-gguf-model.py https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
2.03 kB
| #!/usr/bin/env python3 | |
| from huggingface_hub import HfApi | |
| import argparse | |
| import os | |
| def upload_gguf_file(local_file_path, repo_id, filename_in_repo=None): | |
| """ | |
| Upload a GGUF file to a Hugging Face model repository | |
| Args: | |
| local_file_path: Path to your local GGUF file | |
| repo_id: Your repository ID (e.g., "username/model-name") | |
| filename_in_repo: Optional custom name for the file in the repo | |
| """ | |
| if not os.path.exists(local_file_path): | |
| print(f"❌ File not found: {local_file_path}") | |
| return False | |
| if filename_in_repo is None: | |
| filename_in_repo = os.path.basename(local_file_path) | |
| if filename_in_repo is None or filename_in_repo == "": | |
| filename_in_repo = os.path.basename(local_file_path) | |
| print(f"📤 Uploading {local_file_path} to {repo_id}/{filename_in_repo}") | |
| api = HfApi() | |
| try: | |
| api.upload_file( | |
| path_or_fileobj=local_file_path, | |
| path_in_repo=filename_in_repo, | |
| repo_id=repo_id, | |
| repo_type="model", | |
| commit_message=f"Upload {filename_in_repo}" | |
| ) | |
| print("✅ Upload successful!") | |
| print(f"🔗 File available at: https://huggingface.co/{repo_id}/blob/main/{filename_in_repo}") | |
| return True | |
| except Exception as e: | |
| print(f"❌ Upload failed: {e}") | |
| return False | |
| # This script requires that the environment variable HF_TOKEN is set with your | |
| # Hugging Face API token. | |
| api = HfApi() | |
| parser = argparse.ArgumentParser(description='Upload a GGUF model to a Huggingface model repository') | |
| parser.add_argument('--gguf-model-path', '-m', help='The GGUF model file to upload', required=True) | |
| parser.add_argument('--repo-id', '-r', help='The repository to upload to', required=True) | |
| parser.add_argument('--name', '-o', help='The name in the model repository', required=False) | |
| args = parser.parse_args() | |
| upload_gguf_file(args.gguf_model_path, args.repo_id, args.name) | |