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初始化项目，由ModelHub XC社区提供模型 Model: second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF Source: Original Platform
2026-10-25 09:56:15 +08:00
---
license: other
license_name: nvidia-open-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
base_model: nvidia/NVIDIA-Nemotron-Nano-9B-v2
model_creator: nvidia
model_name: NVIDIA-Nemotron-Nano-9B-v2
quantized_by: Second State Inc.
pipeline_tag: text-generation
language:
- en
- es
- fr
- de
- it
- ja
library_name: transformers
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# NVIDIA-Nemotron-Nano-9B-v2-GGUF
## Original Model
[nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2)
## Run with LlamaEdge
- LlamaEdge version: coming soon
<!-- - LlamaEdge version: [v0.25.1](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.25.1) and above -->
- Prompt template
- Prompt type: `nemotron-2-chat`
- Prompt string
- `Thinking` mode
```text
<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
```text
<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
```bash
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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/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](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/NVIDIA-Nemotron-Nano-9B-v2-f16.gguf) | Q8_0 | 8 | 17.8 GB| very large, extremely low quality loss - not recommended |
| [NVIDIA-Nemotron-Nano-9B-v2-f16.gguf](https://huggingface.co/second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF/blob/main/NVIDIA-Nemotron-Nano-9B-v2-f16-00001-of-00003.gguf) | f16 | 16 | 30.0 GB| |
*Quantized with llama.cpp b6315.*
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