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
- 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: 2,091 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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | #!/usr/bin/env python3
import argparse
import sys
from common import compare_tokens # type: ignore[import-not-found, ty:unresolved-import]
def parse_arguments():
parser = argparse.ArgumentParser(
description='Compare tokens between two models',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
%(prog)s pytorch-gemma-3-270m-it llamacpp-gemma-3-270m-it-bf16
"""
)
parser.add_argument(
'original',
help='Original model name'
)
parser.add_argument(
'converted',
help='Converted model name'
)
parser.add_argument(
'-s', '--suffix',
default='',
help='Type suffix (e.g., "-embeddings")'
)
parser.add_argument(
'-d', '--data-dir',
default='data',
help='Directory containing token files (default: data)'
)
parser.add_argument(
'-v', '--verbose',
action='store_true',
help='Print prompts from both models'
)
return parser.parse_args()
def main():
args = parse_arguments()
if args.verbose:
from pathlib import Path
data_dir = Path(args.data_dir)
prompt1_file = data_dir / f"{args.original}{args.suffix}-prompt.txt"
prompt2_file = data_dir / f"{args.converted}{args.suffix}-prompt.txt"
if prompt1_file.exists():
print(f"\nOriginal model prompt ({args.original}):")
print(f" {prompt1_file.read_text().strip()}")
if prompt2_file.exists():
print(f"\nConverted model prompt ({args.converted}):")
print(f" {prompt2_file.read_text().strip()}")
print()
result = compare_tokens(
args.original,
args.converted,
type_suffix=args.suffix,
output_dir=args.data_dir
)
# Enable the script to be used in shell scripts so that they can check
# the exit code for success/failure.
sys.exit(0 if result else 1)
if __name__ == "__main__":
main()
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