Instructions to use Naphula-Archives/Magistaroth-24B-v1.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula-Archives/Magistaroth-24B-v1.2-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Naphula-Archives/Magistaroth-24B-v1.2-GGUF", dtype="auto")

llama-cpp-python

How to use Naphula-Archives/Magistaroth-24B-v1.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Naphula-Archives/Magistaroth-24B-v1.2-GGUF",
	filename="C80-24B-v1-IQ4_XS.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Naphula-Archives/Magistaroth-24B-v1.2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Naphula-Archives/Magistaroth-24B-v1.2-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 Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Naphula-Archives/Magistaroth-24B-v1.2-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 Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Naphula-Archives/Magistaroth-24B-v1.2-GGUF with Ollama:
```
ollama run hf.co/Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Naphula-Archives/Magistaroth-24B-v1.2-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/Magistaroth-24B-v1.2-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/Magistaroth-24B-v1.2-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/Magistaroth-24B-v1.2-GGUF to start chatting

Docker Model Runner
How to use Naphula-Archives/Magistaroth-24B-v1.2-GGUF with Docker Model Runner:
```
docker model run hf.co/Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M
```

Lemonade

How to use Naphula-Archives/Magistaroth-24B-v1.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Naphula-Archives/Magistaroth-24B-v1.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Magistaroth-24B-v1.2-GGUF-Q4_K_M

List all available models

lemonade list

Checkpoint Merge #80: Magistaroth 24B v1.2

If you liked Slimaki v1 then v1.2 should be more comparable than v1.1 which was highly experimental. It uses the same method della and simply adds another donor, but I think the output is more creative.

The new Magistaroth v1.2 should be fully uncensored but smarter than v1.0 MPOA version or v1.1 PDQ

The updated Magistaroth v1.2 may provide a good middle ground for instruction following + lack of censorship. Instead of using all unablated components + ablating after the merge, I tested various configs to see where the "refusal threshold" was for swapping out individual donors with ablated versions. It appears that only PF v4.1 and Tiamat required ablated versions (heretic paper witch), Della's magprune with normalize: false and total weights at 2.0 handled the rest. This achieves full uncensorship while at least some components (Magidonia, Precog, PF v3) remain fully unablated. More testing is needed to see if this is better than the standard merge → ablate pipeline.

Downloads last month: 195

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

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