Instructions to use grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use grimjim/kukulemon-v3-soul_mix-32k-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-v3-soul_mix-32k-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/kukulemon-v3-soul_mix-32k-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-v3-soul_mix-32k-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/kukulemon-v3-soul_mix-32k-7B-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-v3-soul_mix-32k-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grimjim/kukulemon-v3-soul_mix-32k-7B-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-v3-soul_mix-32k-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use grimjim/kukulemon-v3-soul_mix-32k-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-v3-soul_mix-32k-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-v3-soul_mix-32k-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF:Q4_K_M

SGLang

How to use grimjim/kukulemon-v3-soul_mix-32k-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-v3-soul_mix-32k-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-v3-soul_mix-32k-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-v3-soul_mix-32k-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-v3-soul_mix-32k-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF with Ollama:
```
ollama run hf.co/grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.kukulemon-v3-soul_mix-32k-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

kukulemon-v3-soul_mix-32k-7B

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

We explore merger at extremely low weight as an alternative to fine-tuning. The additional model was applied at a weight of 10e-5, which was selected to be comparable to a few epochs of training. The low weight also amounts to the additional model being flattened, though technically not sparsified.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using grimjim/kukulemon-32K-7B as a base.

Models Merged

The following model was included in the merge:

grimjim/rogue-enchantress-32k-7B

Configuration

The following YAML configuration was used to produce this model:

base_model: grimjim/kukulemon-32K-7B
dtype: bfloat16
merge_method: task_arithmetic
slices:
- sources:
  - layer_range: [0, 32]
    model: grimjim/kukulemon-32K-7B
  - layer_range: [0, 32]
    model: grimjim/rogue-enchantress-32k-7B
    parameters:
      weight: 10e-5

Downloads last month: 177

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF

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grimjim/kukulemon-v3-soul_mix-32k-7B

Quantized

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Collection

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Paper for grimjim/kukulemon-v3-soul_mix-32k-7B-GGUF

Editing Models with Task Arithmetic

Paper • 2212.04089 • Published Dec 8, 2022 • 9