Triangle104/Hermes-Dolphin-out-Q4_K_M-GGUF

This model was converted to GGUF format from harkov000/Hermes-Dolphin-out using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

This is a merge of pre-trained language models created using mergekit. Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

NousResearch/Hermes-3-Llama-3.1-8B
cognitivecomputations/dolphin-2.9.4-llama3.1-8b

Configuration

The following YAML configuration was used to produce this model:

models:

• model: NousResearch/Hermes-3-Llama-3.1-8B
• model: cognitivecomputations/dolphin-2.9.4-llama3.1-8b merge_method: slerp base_model: NousResearch/Hermes-3-Llama-3.1-8B parameters: t: [0.0, 0.5, 1.0] dtype: bfloat16

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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 Triangle104/Hermes-Dolphin-out-Q4_K_M-GGUF --hf-file hermes-dolphin-out-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Hermes-Dolphin-out-Q4_K_M-GGUF --hf-file hermes-dolphin-out-q4_k_m.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 Triangle104/Hermes-Dolphin-out-Q4_K_M-GGUF --hf-file hermes-dolphin-out-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Hermes-Dolphin-out-Q4_K_M-GGUF --hf-file hermes-dolphin-out-q4_k_m.gguf -c 2048