Text Generation
GGUF
English
nvidia-nemotron
hierarchical-reasoning
mixture-of-experts
multi-modal
autonomous-code-execution
chain-of-thought
amd-rocm
humanitys-last-exam
ollama
conversational
Instructions to use kings1/Nova 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 kings1/Nova 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 kings1/Nova:Q4_K_M # Run inference directly in the terminal: llama cli -hf kings1/Nova:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kings1/Nova:Q4_K_M # Run inference directly in the terminal: llama cli -hf kings1/Nova: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 kings1/Nova:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kings1/Nova: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 kings1/Nova:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kings1/Nova:Q4_K_M
Use Docker
docker model run hf.co/kings1/Nova:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use kings1/Nova with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kings1/Nova" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kings1/Nova", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kings1/Nova:Q4_K_M
- Ollama
How to use kings1/Nova with Ollama:
ollama run hf.co/kings1/Nova:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use kings1/Nova with Docker Model Runner:
docker model run hf.co/kings1/Nova:Q4_K_M
- Lemonade
How to use kings1/Nova with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kings1/Nova:Q4_K_M
Run and chat with the model
lemonade run user.Nova-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 2,771 Bytes
23ea6bd 3bc5889 23ea6bd 3bc5889 23ea6bd 3bc5889 23ea6bd 3bc5889 23ea6bd 3bc5889 23ea6bd 3bc5889 23ea6bd 3bc5889 | 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 | """
Nova 1.0 — Autonomous Code Execution & Tool-Use Engine (AGI Capability)
Allows Nova 1.0 to execute Python code blocks, inspect results, and self-correct before responding.
"""
from typing import List, Tuple
import sys
import io
import contextlib
import traceback
class PythonToolEngine:
def __init__(self, timeout_sec: int = 5):
self.timeout_sec = timeout_sec
def execute_python_code(self, code_str: str) -> str:
"""
Executes python code string in an isolated execution scope and returns stdout or traceback.
"""
clean_code = code_str.strip()
if clean_code.startswith("```python"):
clean_code = clean_code[9:]
if clean_code.startswith("```"):
clean_code = clean_code[3:]
if clean_code.endswith("```"):
clean_code = clean_code[:-3]
stdout_buffer = io.StringIO()
stderr_buffer = io.StringIO()
exec_globals = {}
exec_locals = {}
try:
with contextlib.redirect_stdout(stdout_buffer), contextlib.redirect_stderr(stderr_buffer):
exec(clean_code, exec_globals, exec_locals)
stdout_out = stdout_buffer.getvalue().strip()
stderr_out = stderr_buffer.getvalue().strip()
if stdout_out and stderr_out:
return f"[Stdout]:\n{stdout_out}\n[Stderr]:\n{stderr_out}"
elif stdout_out:
return stdout_out
elif stderr_out:
return f"[Stderr]: {stderr_out}"
else:
return "[Code executed successfully with no stdout output]"
except Exception as e:
return f"[Execution Error]: {type(e).__name__}: {str(e)}"
def parse_and_execute_tools(text_response: str) -> Tuple[str, List[str]]:
"""
Parses <python>...</python> or ```python...``` code blocks from model response,
executes them, and returns (clean_text, tool_results_list).
"""
engine = PythonToolEngine()
results = []
# Case 1: <python> tags
if "<python>" in text_response and "</python>" in text_response:
start_idx = text_response.find("<python>") + 8
end_idx = text_response.find("</python>")
code_snippet = text_response[start_idx:end_idx]
res = engine.execute_python_code(code_snippet)
results.append(res)
return text_response, results
# Case 2: Markdown python blocks
if "```python" in text_response and "```" in text_response.split("```python", 1)[1]:
parts = text_response.split("```python", 1)
code_part = parts[1].split("```", 1)[0]
res = engine.execute_python_code(code_part)
results.append(res)
return text_response, results
return text_response, []
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