---
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- llama-3
- pytorch
- TensorBlock
- GGUF
license: other
license_name: nvidia-open-model-license
license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
base_model: nvidia/Llama-3_1-Nemotron-51B-Instruct
---

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            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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## nvidia/Llama-3_1-Nemotron-51B-Instruct - GGUF

This repo contains GGUF format model files for [nvidia/Llama-3_1-Nemotron-51B-Instruct](https://huggingface.co./nvidia/Llama-3_1-Nemotron-51B-Instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4391](https://github.com/ggerganov/llama.cpp/commit/9ba399dfa7f115effc63d48e6860a94c9faa31b2).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama-3_1-Nemotron-51B-Instruct-Q2_K.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q2_K.gguf) | Q2_K | 19.419 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama-3_1-Nemotron-51B-Instruct-Q3_K_S.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q3_K_S.gguf) | Q3_K_S | 22.652 GB | very small, high quality loss |
| [Llama-3_1-Nemotron-51B-Instruct-Q3_K_M.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q3_K_M.gguf) | Q3_K_M | 25.182 GB | very small, high quality loss |
| [Llama-3_1-Nemotron-51B-Instruct-Q3_K_L.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q3_K_L.gguf) | Q3_K_L | 27.350 GB | small, substantial quality loss |
| [Llama-3_1-Nemotron-51B-Instruct-Q4_0.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q4_0.gguf) | Q4_0 | 29.252 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama-3_1-Nemotron-51B-Instruct-Q4_K_S.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q4_K_S.gguf) | Q4_K_S | 29.484 GB | small, greater quality loss |
| [Llama-3_1-Nemotron-51B-Instruct-Q4_K_M.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q4_K_M.gguf) | Q4_K_M | 31.037 GB | medium, balanced quality - recommended |
| [Llama-3_1-Nemotron-51B-Instruct-Q5_0.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q5_0.gguf) | Q5_0 | 35.559 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama-3_1-Nemotron-51B-Instruct-Q5_K_S.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q5_K_S.gguf) | Q5_K_S | 35.559 GB | large, low quality loss - recommended |
| [Llama-3_1-Nemotron-51B-Instruct-Q5_K_M.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q5_K_M.gguf) | Q5_K_M | 36.465 GB | large, very low quality loss - recommended |
| [Llama-3_1-Nemotron-51B-Instruct-Q6_K.gguf](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q6_K.gguf) | Q6_K | 42.259 GB | very large, extremely low quality loss |
| [Llama-3_1-Nemotron-51B-Instruct-Q8_0](https://huggingface.co./tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct-Q8_0) | Q8_0 | 54.731 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF --include "Llama-3_1-Nemotron-51B-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/Llama-3_1-Nemotron-51B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```