---
library_name: transformers
tags:
- reasoning
- thinking
- cognitivecomputations
- r1
- llama 3.1
- llama-3
- llama3
- llama-3.1
- cot
- deepseek
- Llama 3.1
- Hermes
- DeepHermes
- 1,000,000 context
- fine tune
- merge
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B
---

# Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_M-GGUF
This model was converted to GGUF format from [`DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B`](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) 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/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) for more details on the model.

---
Context : 1,000,000 tokens.


Required: Llama 3 Instruct template.


The Deep Hermes 8B Preview model (reasoning), [ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
converted to 1 million context using Nvidia's Ultra Long 1 million 8B Instruct model.


The goal of this model was to stablize long generation and long context "needle in a haystack" issues.


According to Nvidia there is both a bump in general performance, as well as perfect "recall" over the entire 1 million context.


[ https://huggingface.co/nvidia/Llama-3.1-8B-UltraLong-1M-Instruct ] 


Additional changes:


Model appears to be de-censored / more de-censored.
Output generation is improved.
Creative output generation is vastly improved.


NOTE: Higher temps will result in deeper, richer "thoughts"... and frankly more interesting ones too.


The "thinking/reasoning" tech (for the model at this repo) is from the original Llama 3.1 "DeepHermes" model from NousResearch:


[ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]

---
## 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-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-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-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q5_k_m.gguf -c 2048
```