Instructions to use Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-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 Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0

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 Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0

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 Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0

Use Docker

docker model run hf.co/Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0

LM Studio
Jan
Ollama
How to use Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF with Ollama:
```
ollama run hf.co/Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0
```

Unsloth Studio

How to use Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF with 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 Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-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 Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF with Docker Model Runner:
```
docker model run hf.co/Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0
```

Lemonade

How to use Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Naphula-Archives/Qliphoth-24B-v2-Prototypes-Q4_0-GGUF:Q4_0

Run and chat with the model

lemonade run user.Qliphoth-24B-v2-Prototypes-Q4_0-GGUF-Q4_0

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Qliphoth has diverged from the Cthulhu/Goetia line as of v2 into its branch. It is now also a custom merge method. merge_method: qliphoth is very experimental and builds upon the previous magic method's "Aikido Flip" concept.

v1 was broken but v2 is stable and is being optimized. They should all be fully uncensored.

Update: None of these are fully uncensored, there are some refusals. A new version is coming soon

Uploading prototypes of v2 as Q4_0 GGUF since these are fastest to quantize

v2a had an imbalanced yaml on accident, it might be too "loud"
v2b is quite balanced and creative
v2c was a somewhat unsuccesful attempt to boost intelligence and creativity
v2d is another attempt to allow higher deviations (novelty), it seems better than v2c
v2e is an expansion attempt to utilize twice as many donors. This version seems like the smartest but unfortunately has refusals (likely due to the readyart 2501 models).
v2f is the next attempt to find an optimal middle ground between v2d and v2e [IN PROGRESS]

Downloads last month: 59

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

4-bit

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