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second-state/NVIDIA-Nemotron-Nano-9B-v2-GGUF
NVIDIA-Nemotron-Nano-9B-v2-GGUF is a text generation model from second-state. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
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Downloads · 30 days
707
14% of all-time downloads
All-time downloads
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95.6 GB
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.gguf95.6 GB · 100%
From the Hugging Face model README
nvidia/NVIDIA-Nemotron-Nano-9B-v2
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
| 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.