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: 2,735 Bytes
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Script 3: Interactive Text Generation & Inference for Nova 1.0
"""
import os
import argparse
import torch
from config.model_config import Nova1Config
from src.tokenizer.bpe_tokenizer import Nova1Tokenizer
from src.model.nova1_hrm import Nova1HRM
from src.inference.generate import Nova1Generator
def main():
parser = argparse.ArgumentParser(description="Generate text with Nova 1.0")
parser.add_argument("--checkpoint", type=str, default="checkpoints/nova1_final.pt", help="Path to Nova 1.0 checkpoint")
default_tok = "checkpoints/nova1_gemini_tokenizer.json" if os.path.exists("checkpoints/nova1_gemini_tokenizer.json") else "checkpoints/nova1_tokenizer.json"
parser.add_argument("--tokenizer_path", type=str, default=default_tok, help="Path to tokenizer JSON")
parser.add_argument("--prompt", type=str, default="Once upon a time in a small village,", help="Generation prompt")
parser.add_argument("--max_tokens", type=int, default=60, help="Max new tokens to generate")
parser.add_argument("--temperature", type=float, default=0.8, help="Sampling temperature")
parser.add_argument("--top_k", type=int, default=40, help="Top-K sampling")
parser.add_argument("--top_p", type=float, default=0.9, help="Top-P nucleus sampling")
parser.add_argument("--N_cycles", type=int, default=None, help="Test-time reasoning depth N cycles")
parser.add_argument("--T_steps", type=int, default=None, help="Test-time low-level steps T")
args = parser.parse_args()
if not os.path.exists(args.tokenizer_path):
print(f"Error: Tokenizer not found at '{args.tokenizer_path}'.")
return
tokenizer = Nova1Tokenizer.load(args.tokenizer_path)
if not os.path.exists(args.checkpoint):
print(f"Error: Model checkpoint not found at '{args.checkpoint}'.")
return
checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
config: Nova1Config = checkpoint.get("config", Nova1Config(vocab_size=tokenizer.vocab_size))
model = Nova1HRM(config)
model.load_state_dict(checkpoint["model_state"])
print(f"Loaded Nova 1.0 model checkpoint from '{args.checkpoint}'.")
generator = Nova1Generator(model=model, tokenizer=tokenizer, config=config)
print(f"\n--- Nova 1.0 Text Generation ---")
print(f"Prompt: {args.prompt}")
output_text = generator.generate(
prompt=args.prompt,
max_new_tokens=args.max_tokens,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
N_cycles=args.N_cycles,
T_steps=args.T_steps
)
print("\n--- Output ---")
print(output_text)
print("--------------\n")
if __name__ == "__main__":
main()
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