How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
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 Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
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 Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
Use Docker
docker model run hf.co/Smuggling1710/Ak4ri-7b-GGUF:IQ4_NL
Quick Links

Ak4ri-7b

Ak4ri-7b is a merge of the following models using LazyMergekit:

image/png

🧩 Configuration

slices:
  - sources:
      - model: Virt-io/Irene-RP-v3-7B
        layer_range: [0, 32]
      - model: Smuggling1710/Erosumika-MistralLayla-Slerp
        layer_range: [0, 32]
merge_method: slerp
base_model: Smuggling1710/Erosumika-MistralLayla-Slerp
parameters:
  t:
    - filter: self_attn
      value: [0.6, 0.5, 0.3, 0.7, 0.4]
    - filter: mlp
      value: [0.4, 0.5, 0.7, 0.3, 0.6]
    - value: 0.5
dtype: bfloat16

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Smuggling1710/Ak4ri-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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GGUF
Model size
7B params
Architecture
llama
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