---

NVIDIA-Nemotron-Nano-9B-v2-GGUF

Original Model

nvidia/NVIDIA-Nemotron-Nano-9B-v2

Run with LlamaEdge

• LlamaEdge version: coming soon

• Prompt template

  • Prompt type: nemotron-2-chat

  • Prompt string

    • Thinking mode

      <SPECIAL_10>System
      You are a helpful assistant.
      
      <SPECIAL_11>User
      Hi, can you explain what quantum computing is? /think
      
      <SPECIAL_11>Assistant
      <think>
      The user is asking about quantum computing, I should give a simple explanation.
      </think>
      Quantum computing uses principles of quantum mechanics, such as superposition and entanglement, to perform computations that would be hard for classical computers.
      <SPECIAL_12>
      
      <SPECIAL_11>User
      That sounds complicated. Can you give me an example? /think
      
      <SPECIAL_11>Assistant
      <think>
      I should give a concrete example, like Shor’s algorithm or Grover’s algorithm, but in simple terms.
      </think>
      For example, a quantum computer can factor very large numbers much faster than classical computers, which could break some encryption methods.
      <SPECIAL_12>
      
      <SPECIAL_11>User
      Okay, explain it in a simpler way for a 10-year-old. /think
      
      <SPECIAL_11>Assistant
      <think>
      

    • No-thinking mode

      <SPECIAL_10>System
      You are a helpful assistant.
      
      /no_think
      
      <SPECIAL_11>User
      Hi, can you explain what quantum computing is? /no_think
      
      <SPECIAL_11>Assistant
      <think></think>
      Quantum computing is a type of computing that uses the rules of quantum mechanics to solve certain problems faster than regular computers.
      <SPECIAL_12>
      
      <SPECIAL_11>User
      That sounds complicated. Can you give me an example? /no_think
      
      <SPECIAL_11>Assistant
      <think></think>
      For example, quantum computers could quickly factor very large numbers, which is important for cryptography.
      <SPECIAL_12>
      
      <SPECIAL_11>User
      Okay, explain it even more simply. /no_think
      
      <SPECIAL_11>Assistant
      <think></think>
      

• Context size: 128000

• Run as LlamaEdge service

  wasmedge --dir .:. \
    --nn-preload default:GGML:AUTO:NVIDIA-Nemotron-Nano-9B-v2-Q5_K_M.gguf \
    llama-api-server.wasm \
    --prompt-template nemotron-2-chat \
    --ctx-size 128000 \
    --model-name nemotron-nano-v2
  

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
NVIDIA-Nemotron-Nano-9B-v2-Q2_K.gguf	Q2_K	2	5.01 GB	smallest, significant quality loss - not recommended for most purposes
NVIDIA-Nemotron-Nano-9B-v2-Q3_K_L.gguf	Q3_K_L	3	5.49 GB	small, substantial quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q3_K_M.gguf	Q3_K_M	3	5.38 GB	very small, high quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q3_K_S.gguf	Q3_K_S	3	5.13 GB	very small, high quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q4_0.gguf	Q4_0	4	5.31 GB	legacy; small, very high quality loss - prefer using Q3_K_M
NVIDIA-Nemotron-Nano-9B-v2-Q4_K_M.gguf	Q4_K_M	4	6.53 GB	medium, balanced quality - recommended
NVIDIA-Nemotron-Nano-9B-v2-Q4_K_S.gguf	Q4_K_S	4	6.21 GB	small, greater quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q5_0.gguf	Q5_0	5	6.35 GB	legacy; medium, balanced quality - prefer using Q4_K_M
NVIDIA-Nemotron-Nano-9B-v2-Q5_K_M.gguf	Q5_K_M	5	7.07 GB	large, very low quality loss - recommended
NVIDIA-Nemotron-Nano-9B-v2-Q5_K_S.gguf	Q5_K_S	5	6.78 GB	large, low quality loss - recommended
NVIDIA-Nemotron-Nano-9B-v2-Q6_K.gguf	Q6_K	6	9.14 GB	very large, extremely low quality loss
NVIDIA-Nemotron-Nano-9B-v2-Q8_0.gguf	Q8_0	8	17.8 GB	very large, extremely low quality loss - not recommended
NVIDIA-Nemotron-Nano-9B-v2-f16.gguf	f16	16	30.0 GB

Quantized with llama.cpp b6315.