README.md

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

Original model: https://huggingface.co/nvidia/Llama-3_1-Nemotron-51B-Instruct-GGUF

## Prompt Template

```
### System:
{system_prompt}
### User:
{user_prompt}
### Assistant:

```
***Important*** for people who wants to do their own quantitization. There is a typo in tokenizer_config.json of the original model that mistakenly set eos_token to '<|eot_id|>' when it should be '<|end_of_text|>'. Please fix it or overwrite with the [tokenizer_config.json](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/tokenizer_config.json) in this repository before you do the gguf conversion yourself.

Starting from [b4380](https://github.com/ggerganov/llama.cpp/archive/refs/tags/b4380.tar.gz) of llama.cpp, DeciLMForCausalLM's variable Grouped Query Attention is now supported.. Please download it and compile it to run the GGUFs in this repository. 

This modification should support Llama-3_1-Nemotron 51B-Instruct fully. However, it may not support future DeciLMForCausalLM models that has no_op or linear ffn layers. Well, I suppose these support can be added when there are actually models using that types of layers.

Since I am a free user, so for the time being, I only upload models that might be of interest for most people. 

## Download a file (not the whole branch) from below:

Perplexity for f16 gguf is 6.646565 ± 0.040986.

| Quant Type | imatrix | File Size | Delta Perplexity | KL Divergence | Description |
| ---------- | ------- | ----------| ---------------- | ------------- | ----------- |
| [Q6_K](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct.imatrix.Q6_K.gguf) | [calibration_datav3](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) | 42.26GB | -0.002436 ± 0.001565 | 0.003332 ± 0.000014 | Good for Nvidia cards or Apple Silicon with 48GB RAM. Should perform very close to the original |
| [Q5_K_M](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct.imatrix.Q5_K_M.gguf) | [calibration_datav3](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) | 36.47GB | 0.020310 ± 0.002052 | 0.005642 ± 0.000024 | Good for A100 40GB or dual 3090. Better than Q4_K_M but larger and slower. |
| [Q4_K_M](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct.imatrix.Q4_K_M.gguf) | [calibration_datav3](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) | 31.04GB | 0.055444 ± 0.002982 | 0.012021 ± 0.000052 | Good for A100 40GB or dual 3090. Higher cost performance ratio than Q5_K_M. |
| IQ4_NL | calibration_datav3 | 29.30GB | 0.088279 ± 0.003944 | 0.020314 ± 0.000093 | For 32GB cards, e.g. 5090. Minor performance gain doesn't justify its use over IQ4_XS |
| [IQ4_XS](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct.imatrix.IQ4_XS.gguf) | [calibration_datav3](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) | 27.74GB | 0.095486 ± 0.004039 | 0.020962 ± 0.000097 | For 32GB cards, e.g. 5090. Too slow for CPU and Apple. Recommended. |
| Q4_0 | calibration_datav3 | 29.34GB | 0.543042 ± 0.009290 | 0.077602 ± 0.000389 | For 32GB cards, e.g. 5090. Too slow for CPU and Apple. |
| [Q4_0_4_8](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct.imatrix.Q4_0_4_8.gguf) | [calibration_datav3](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) | 29.25GB | Same as Q4_0 assumed | Same as Q4_0 assumed | For Apple Silicon |
| [IQ3_M](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct.imatrix.IQ3_M.gguf) | [calibration_datav3](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) | 23.5GB | 0.313812 ± 0.006299 | 0.054266 ± 0.000205 | Largest model that can fit a single 3090 at 4k context. Not recommeneded for CPU or Apple Silicon due to high computational cost. |
| [IQ3_S](https://huggingface.co/ymcki/Llama-3_1-Nemotron-51B-Instruct-GGUF/blob/main/Llama-3_1-Nemotron-51B-Instruct.imatrix.IQ3_S.gguf) | [calibration_datav3](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) | 22.7GB | 0.434774 ± 0.007162 | 0.069264 ± 0.000242 | Largest model that can fit a single 3090 at 8k context. Not recommended for CPU or Apple Silicon due to high computational cost. |
| Q3_K_S | calibration_datav3 | 22.7GB | 0.698971 ± 0.010387 | 0.089605 ± 0.000443 | Largest model that can fit a single 3090 that performs well in all platforms |
| Q3_K_S | none | 22.7GB | 2.224537 ± 0.024868 | 0.283028 ± 0.001220 | Largest model that can fit a single 3090 without imatrix |

## How to check i8mm support for Apple devices

ARM i8mm support is necessary to take advantage of Q4_0_4_8 gguf. All ARM architecture >= ARMv8.6-A supports i8mm. That means Apple Silicon from A15 and M2 works best with Q4_0_4_8.

For Apple devices, 

```
sysctl hw
```

On the other hand, Nvidia 3090 inference speed is significantly faster for Q4_0 than the other ggufs. That means for GPU inference, you better off using Q4_0. 

## Which Q4_0 model to use for Apple devices
| Brand | Series | Model | i8mm | sve | Quant Type |
| ----- | ------ | ----- | ---- | --- | -----------|
| Apple | A | A4 to A14 | No | No | Q4_0_4_4 |
| Apple | A | A15 to A18 | Yes | No | Q4_0_4_8 |
| Apple | M | M1 | No | No | Q4_0_4_4 |
| Apple | M | M2/M3/M4 | Yes | No | Q4_0_4_8 |

## Convert safetensors to f16 gguf

Make sure you have llama.cpp git cloned:

```
python3 convert_hf_to_gguf.py Llama-3_1-Nemotron 51B-Instruct/ --outfile Llama-3_1-Nemotron 51B-Instruct.f16.gguf --outtype f16
```

## Convert f16 gguf to Q4_0 gguf without imatrix

Make sure you have llama.cpp compiled:
```
./llama-quantize Llama-3_1-Nemotron 51B-Instruct.f16.gguf Llama-3_1-Nemotron 51B-Instruct.Q4_0.gguf q4_0
```
## Convert f16 gguf to Q4_0 gguf with imatrix

Make sure you have llama.cpp compiled. Then create an imatrix with a dataset.
```
./llama-imatrix -m Llama-3_1-Nemotron-51B-Instruct.f16.gguf -f calibration_datav3.txt -o Llama-3_1-Nemotron-51B-Instruct.imatrix --chunks 32
```

Then convert with the created imatrix.
```
./llama-quantize Llama-3_1-Nemotron-51B-Instruct.f16.gguf --imatrix Llama-3_1-Nemotron-51B-Instruct.imatrix Llama-3_1-Nemotron-51B-Instruct.imatrix.Q4_0.gguf q4_0
```

## Calculate perplexity and KL divergence

First, download wikitext.
```
bash ./scripts/get-wikitext-2.sh
```

Second, find the base values of F16 gguf. Please be warned that the generated base value file is about 10GB. Adjust GPU layers depending on your VRAM.
```
./llama-perplexity --kl-divergence-base Llama-3_1-Nemotron-51B-Instruct.f16.kld -m Llama-3_1-Nemotron-51B-Instruct.f16.gguf  -f wikitext-2-raw/wiki.test.raw -ngl 100
```

Finally, calculate the perplexity and KL divergence of Q4_0 gguf. Adjust GPU layers depending on your VRAM.
```
./llama-perplexity --kl-divergence-base Llama-3_1-Nemotron-51B-Instruct.f16.kld --kl_divergence -m Llama-3_1-Nemotron-51B-Instruct.Q4_0.gguf -ngl 100 >& Llama-3_1-Nemotron-51B-Instruct.Q4_0.kld
```

## Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

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

Then, you can target the specific file you want:

```
huggingface-cli download ymcki/Llama-3_1-Nemotron 51B-Instruct-GGUF --include "Llama-3_1-Nemotron 51B-Instruct.Q4_0.gguf" --local-dir ./
```

## Running the model using llama-cli

First, download and compile my [Modified llama.cpp-b4139](https://github.com/ymcki/llama.cpp-b4139) v0.2. Compile it, then run
```
./llama-cli -m ~/Llama-3_1-Nemotron-51B-Instruct.Q3_K_S.gguf -p 'You are a European History Professor named Professor Whitman.'  -cnv -ngl 100
```

## Credits

Thank you bartowski for providing a README.md to get me started.