---
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
language:
- en
tags:
- nvidia
- llama-3
- pytorch
- llama-cpp
- gguf-my-repo
base_model: nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1
datasets:
- nvidia/Llama-Nemotron-Post-Training-Dataset
---

# Triangle104/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF
This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1) 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-4B-v1.1) for more details on the model.

---
Llama-3.1-Nemotron-Nano-4B-v1.1 is a large language model (LLM) which is a derivative of nvidia/Llama-3.1-Minitron-4B-Width-Base, which is created from Llama 3.1 8B using our LLM compression technique and offers improvements in model accuracy and efficiency. 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-4B-v1.1 is a model which offers a great tradeoff between model accuracy and efficiency. 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 Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and RPO checkpoints

---
## 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-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-q8_0.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-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-q8_0.gguf -c 2048
```