How to use from
Unsloth Studio
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf to start chatting
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
Downloads last month
11
GGUF
Model size
7B params
Architecture
llama
Hardware compatibility
Log In to add your hardware

2-bit

3-bit

4-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Space using AlekseiPravdin/KukulStanta-7B-Seamaiiza-7B-v1-slerp-merge-gguf 1