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
File size: 2,030 Bytes
db65755 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | #!/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)
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