How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
# Run inference directly in the terminal:
llama-cli -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
# Run inference directly in the terminal:
llama-cli -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
# Run inference directly in the terminal:
./llama-cli -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
Use Docker
docker model run hf.co/AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf:
Quick Links

Quantized GGUF model KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf

KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf is a quantized model using llama.cpp llama-quantize

KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge

KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge is a merge of the following models using mergekit:

🧩 Merge Configuration

slices:
  - sources:
      - model: Nitral-AI/KukulStanta-7B
        layer_range: [0, 31]
      - model: AlekseiPravdin/Seamaiiza-7B-v1
        layer_range: [0, 31]
merge_method: slerp
base_model: Nitral-AI/KukulStanta-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16
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Model size
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Architecture
llama
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