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 Studio
How to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OmnipotentFool/Aurvion to start chatting
- 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: 8,209 Bytes
28ba115 | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | import argparse
import json
import requests
import logging
import sys
handler = logging.StreamHandler(sys.stdout)
handler.terminator = "" # ← no newline
logging.basicConfig(level=logging.INFO, format='%(message)s', handlers=[handler])
logger = logging.getLogger("server-test-model")
def run_query(url, messages, tools=None, stream=False, tool_choice=None):
payload = {
"messages": messages,
"stream": stream,
"max_tokens": 5000,
}
if tools:
payload["tools"] = tools
if tool_choice:
payload["tool_choice"] = tool_choice
try:
response = requests.post(url, json=payload, stream=stream)
response.raise_for_status()
except requests.exceptions.RequestException as e:
if e.response is not None:
logger.info(f"Response error: {e} for {e.response.content}\n")
else:
logger.info(f"Error connecting to server: {e}\n")
return None
full_content = ""
reasoning_content = ""
tool_calls = []
if stream:
logger.info(f"--- Streaming response (Tools: {bool(tools)}) ---\n")
for line in response.iter_lines():
if line:
decoded_line = line.decode("utf-8")
if decoded_line.startswith("data: "):
data_str = decoded_line[6:]
if data_str == "[DONE]":
break
try:
data = json.loads(data_str)
if "choices" in data and len(data["choices"]) > 0:
delta = data["choices"][0].get("delta", {})
# Content
content_chunk = delta.get("content", "")
if content_chunk:
full_content += content_chunk
logger.info(content_chunk)
# Reasoning
reasoning_chunk = delta.get("reasoning_content", "")
if reasoning_chunk:
reasoning_content += reasoning_chunk
logger.info(f"\x1B[3m{reasoning_chunk}\x1B[0m")
# Tool calls
if "tool_calls" in delta:
for tc in delta["tool_calls"]:
index = tc.get("index")
if index is not None:
while len(tool_calls) <= index:
# Using "function" as type default but could be flexible
tool_calls.append(
{
"id": "",
"type": "function",
"function": {
"name": "",
"arguments": "",
},
}
)
if "id" in tc:
tool_calls[index]["id"] += tc["id"]
if "function" in tc:
if "name" in tc["function"]:
tool_calls[index]["function"][
"name"
] += tc["function"]["name"]
if "arguments" in tc["function"]:
tool_calls[index]["function"][
"arguments"
] += tc["function"]["arguments"]
except json.JSONDecodeError:
logger.info(f"Failed to decode JSON: {data_str}\n")
logger.info("\n--- End of Stream ---\n")
else:
logger.info(f"--- Non-streaming response (Tools: {bool(tools)}) ---\n")
data = response.json()
if "choices" in data and len(data["choices"]) > 0:
message = data["choices"][0].get("message", {})
full_content = message.get("content", "")
reasoning_content = message.get("reasoning_content", "")
tool_calls = message.get("tool_calls", [])
logger.info(full_content)
logger.info("--- End of Response ---\n")
return {
"content": full_content,
"reasoning_content": reasoning_content,
"tool_calls": tool_calls,
}
def test_chat(url, stream):
logger.info(f"\n=== Testing Chat (Stream={stream}) ===\n")
messages = [{"role": "user", "content": "What is the capital of France?"}]
result = run_query(url, messages, stream=stream)
if result:
if result["content"]:
logger.info("PASS: Output received.\n")
else:
logger.info("WARN: No content received (valid if strict tool call, but unexpected here).\n")
if result.get("reasoning_content"):
logger.info(f"INFO: Reasoning content detected ({len(result['reasoning_content'])} chars).\n")
else:
logger.info("INFO: No reasoning content detected (Standard model behavior).\n")
else:
logger.info("FAIL: No result.\n")
def test_tool_call(url, stream):
logger.info(f"\n=== Testing Tool Call (Stream={stream}) ===\n")
messages = [
{
"role": "user",
"content": "What is the weather in London? Please use the get_weather tool.",
}
]
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
},
}
]
result = run_query(url, messages, tools=tools, tool_choice="auto", stream=stream)
if result:
tcs = result.get("tool_calls")
if tcs and len(tcs) > 0:
logger.info("PASS: Tool calls detected.")
for tc in tcs:
func = tc.get("function", {})
logger.info(f" Tool: {func.get('name')}, Args: {func.get('arguments')}\n")
else:
logger.info(f"FAIL: No tool calls. Content: {result['content']}\n")
if result.get("reasoning_content"):
logger.info(
f"INFO: Reasoning content detected during tool call ({len(result['reasoning_content'])} chars).\n"
)
else:
logger.info("FAIL: Query failed.\n")
def main():
parser = argparse.ArgumentParser(description="Test llama-server functionality.")
parser.add_argument("--host", default="localhost", help="Server host")
parser.add_argument("--port", default=8080, type=int, help="Server port")
args = parser.parse_args()
base_url = f"http://{args.host}:{args.port}/v1/chat/completions"
logger.info(f"Testing server at {base_url}\n")
# Non-streaming tests
test_chat(base_url, stream=False)
test_tool_call(base_url, stream=False)
# Streaming tests
test_chat(base_url, stream=True)
test_tool_call(base_url, stream=True)
if __name__ == "__main__":
main()
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