---
base_model: nvidia/Llama-3.1-Nemotron-Nano-8B-v1
language:
- en
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
tags:
- nvidia
- llama-3
- pytorch
- llama-cpp
- gguf-my-repo
---

# Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q5_K_S-GGUF
This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-Nano-8B-v1`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) for more details on the model.

---
Llama-3.1-Nemotron-Nano-8B-v1 is a large language model (LLM) which is a derivative of Meta Llama-3.1-8B-Instruct
 (AKA the reference model). It is a reasoning model that is post trained
 for reasoning, human chat preferences, and tasks, such as RAG and tool 
calling. 


Llama-3.1-Nemotron-Nano-8B-v1 is a model which offers a great 
tradeoff between model accuracy and efficiency. It is created from Llama
 3.1 8B Instruct and offers improvements in model accuracy. The model 
fits on a single RTX GPU and can be used locally. The model supports a 
context length of 128K.


This model underwent a multi-phase post-training process to enhance 
both its reasoning and non-reasoning capabilities. This includes a 
supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling
 as well as multiple reinforcement learning (RL) stages using REINFORCE 
(RLOO) and Online Reward-aware Preference Optimization (RPO) algorithms 
for both chat and instruction-following. The final model checkpoint is 
obtained after merging the final SFT and Online RPO checkpoints. 
Improved using Qwen.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q5_k_s.gguf -c 2048
```