NitroOxziT-8B

NitroOxziT-8B is a merge of the following models using LazyMergekit:

Don't ask why the weird name, I wanted a modern merge of Hermes and Dolphin so I did it

DoesntKnowAI/NitroOxziT-8B-Q8_0-GGUF

This model was converted to GGUF format from DoesntKnowAI/NitroOxziT-8B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

🧩 Configuration

slices:
  - sources:
      - model: NousResearch/Hermes-3-Llama-3.1-8B
        layer_range: [0, 32]
        weight: 0.60
      - model: cognitivecomputations/Dolphin3.0-Llama3.1-8B
        layer_range: [0, 32]
        weight: 0.40
merge_method: slerp
parameters:
  t:
    - model: NousResearch/Hermes-3-Llama-3.1-8B
      value: 1.0
    - model: cognitivecomputations/Dolphin3.0-Llama3.1-8B
      value: 1.0
base_model: NousResearch/Hermes-3-Llama-3.1-8B
dtype: bfloat16

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo DoesntKnowAI/NitroOxziT-8B-Q8_0-GGUF --hf-file nitrooxzit-8b-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo DoesntKnowAI/NitroOxziT-8B-Q8_0-GGUF --hf-file nitrooxzit-8b-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps 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 DoesntKnowAI/NitroOxziT-8B-Q8_0-GGUF --hf-file nitrooxzit-8b-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo DoesntKnowAI/NitroOxziT-8B-Q8_0-GGUF --hf-file nitrooxzit-8b-q8_0.gguf -c 2048