---
base_model: nvidia/Llama-3.1-8B-UltraLong-2M-Instruct
language:
- en
library_name: transformers
license: cc-by-nc-4.0
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Llama-3.1-8B-UltraLong-2M-Instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`nvidia/Llama-3.1-8B-UltraLong-2M-Instruct`](https://huggingface.co/nvidia/Llama-3.1-8B-UltraLong-2M-Instruct) 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-3.1-8B-UltraLong-2M-Instruct) for more details on the model.

---
We introduce UltraLong-8B, a series of ultra-long 
context language models designed to process extensive sequences of text 
(up to 1M, 2M, and 4M tokens) while maintaining competitive performance 
on standard benchmarks. Built on the Llama-3.1, UltraLong-8B leverages a
 systematic training recipe that combines efficient continued 
pretraining with instruction tuning to enhance long-context 
understanding and instruction-following capabilities. This approach 
enables our models to efficiently scale their context windows without 
sacrificing general performance.

---
## 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-3.1-8B-UltraLong-2M-Instruct-Q5_K_M-GGUF --hf-file llama-3.1-8b-ultralong-2m-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-8B-UltraLong-2M-Instruct-Q5_K_M-GGUF --hf-file llama-3.1-8b-ultralong-2m-instruct-q5_k_m.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-3.1-8B-UltraLong-2M-Instruct-Q5_K_M-GGUF --hf-file llama-3.1-8b-ultralong-2m-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-3.1-8B-UltraLong-2M-Instruct-Q5_K_M-GGUF --hf-file llama-3.1-8b-ultralong-2m-instruct-q5_k_m.gguf -c 2048
```