Instructions to use grimjim/kukulemon-spiked-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/kukulemon-spiked-9B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/kukulemon-spiked-9B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("grimjim/kukulemon-spiked-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use grimjim/kukulemon-spiked-9B-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-spiked-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/kukulemon-spiked-9B-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-spiked-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M

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 grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M

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 grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use grimjim/kukulemon-spiked-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/kukulemon-spiked-9B-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-spiked-9B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M

SGLang

How to use grimjim/kukulemon-spiked-9B-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-spiked-9B-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-spiked-9B-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-spiked-9B-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-spiked-9B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use grimjim/kukulemon-spiked-9B-GGUF with Ollama:
```
ollama run hf.co/grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use grimjim/kukulemon-spiked-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/kukulemon-spiked-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/kukulemon-spiked-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.kukulemon-spiked-9B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

kululemon-spiked-9B-GGUF

This repository contains GGUF quants of kululemon-spiked-9B.

The model a frankenmerge of a pre-trained language model created using mergekit. As an experiment, this appears to be a partial success.

Lightly tested with temperature 1 and minP 0.01 with ChatML prompts; the model supports Alpaca prompts and has solid 8K context length, a result of its Mistral v0.1 provenance. The model's output has been coherent and stable during aforementioned testing.

The merge formula for this frankenmerge is below. It is conjectured that the shorter first section is not key to variation, the middle segment is key to balancing reasoning and variation, and that the lengthy final section is required for convergence and eventual stability. The internal instability is probably better suited for narrative involving unstable and/or unhinged characters and situations.

Full weights: grimjim/kukulemon-spiked-9B
8.0bpw h8 exl2 quant: grimjim/kukulemon-spiked-9B-8.0bpw_h8_exl2

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

grimjim/kukulemon-7B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: grimjim/kukulemon-7B
      layer_range: [0, 12]
  - sources:
    - model: grimjim/kukulemon-7B
      layer_range: [8, 16]
  - sources:
    - model: grimjim/kukulemon-7B
      layer_range: [12, 32]
merge_method: passthrough
dtype: float16