Instructions to use grimjim/kukulemon-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/kukulemon-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="grimjim/kukulemon-7B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("grimjim/kukulemon-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use grimjim/kukulemon-7B-GGUF with 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 grimjim/kukulemon-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/kukulemon-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf grimjim/kukulemon-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/kukulemon-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf grimjim/kukulemon-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf grimjim/kukulemon-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/kukulemon-7B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use grimjim/kukulemon-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/kukulemon-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/kukulemon-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/kukulemon-7B-GGUF:Q4_K_M

SGLang

How to use grimjim/kukulemon-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "grimjim/kukulemon-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/kukulemon-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "grimjim/kukulemon-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/kukulemon-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use grimjim/kukulemon-7B-GGUF with Ollama:
```
ollama run hf.co/grimjim/kukulemon-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use grimjim/kukulemon-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/kukulemon-7B-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/kukulemon-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/kukulemon-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.kukulemon-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

kukulemon-7B-GGUF

The files are GGUF quants of (kukulemon-7B)[https://huggingface.co/grimjim/kukulemon-7B].

A merger of two similar Kunoichi models with strong reasoning, hopefully resulting in "dense" encoding of said reasoning, was merged with a model targeting roleplay.

I've tested with ChatML prompts with temperature=1.1 and minP=0.03. The model itself supports Alpaca format prompts. The model claims a context length of 32K, it seemed to lose coherence after 8K in my informal testing.

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

You can also download GGUF-IQ-Imatrix quants courtesy of Lewdiculous.

There's also an 8.0bpw h8 exl2 quant available.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: grimjim/kuno-kunoichi-v1-DPO-v2-SLERP-7B
        layer_range: [0, 32]
      - model: KatyTheCutie/LemonadeRP-4.5.3
        layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
merge_method: slerp
base_model: KatyTheCutie/LemonadeRP-4.5.3
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 # fallback for rest of tensors
dtype: float16