Instructions to use Nondzu/PLLuM-8x7B-nc-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nondzu/PLLuM-8x7B-nc-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nondzu/PLLuM-8x7B-nc-instruct-GGUF",
	filename="PLLuM-8x7B-nc-instruct-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Nondzu/PLLuM-8x7B-nc-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Nondzu/PLLuM-8x7B-nc-instruct-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 Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Nondzu/PLLuM-8x7B-nc-instruct-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 Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nondzu/PLLuM-8x7B-nc-instruct-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 Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Nondzu/PLLuM-8x7B-nc-instruct-GGUF with Ollama:
```
ollama run hf.co/Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Nondzu/PLLuM-8x7B-nc-instruct-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 Nondzu/PLLuM-8x7B-nc-instruct-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 Nondzu/PLLuM-8x7B-nc-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nondzu/PLLuM-8x7B-nc-instruct-GGUF to start chatting

Docker Model Runner
How to use Nondzu/PLLuM-8x7B-nc-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M
```

Lemonade

How to use Nondzu/PLLuM-8x7B-nc-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nondzu/PLLuM-8x7B-nc-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PLLuM-8x7B-nc-instruct-GGUF-Q4_K_M

List all available models

lemonade list

PLLuM-8x7B-nc-instruct GGUF Quantizations by Nondzu

DISCLAIMER: This is state of the art quantized model. I am not the author of the original model. I am only hosting the quantized models. I do not take any responsibility for the models.

This repository contains GGUF quantized versions of the PLLuM-8x7B-nc-instruct model. All quantizations were performed using the llama.cpp (release b4768). These quantized models can be run in LM Studio or any other llama.cpp–based project.

Prompt Format

Use the following prompt structure:

???

Available Files

Below is a list of available quantized model files along with their quantization type, file size, and a short description.

Filename	Quant Type	File Size	Description
PLLuM-8x7B-nc-instruct-Q2_K.gguf	Q2_K	17 GB	Very low quality but surprisingly usable.
PLLuM-8x7B-nc-instruct-Q3_K.gguf	Q3_K	21 GB	Low quality, suitable for setups with very limited RAM.
PLLuM-8x7B-nc-instruct-Q3_K_L.gguf	Q3_K_L	23 GB	High quality; recommended for quality-focused usage.
PLLuM-8x7B-nc-instruct-Q3_K_M.gguf	Q3_K_M	21 GB	Very high quality, near perfect output – recommended.
PLLuM-8x7B-nc-instruct-Q3_K_S.gguf	Q3_K_S	20 GB	Moderate quality with improved space efficiency.
PLLuM-8x7B-nc-instruct-Q4_K_M.gguf	Q4_K_M	27 GB	Default quality for most use cases – recommended.
PLLuM-8x7B-nc-instruct-Q4_K_S.gguf	Q4_K_S	25 GB	Slightly lower quality with enhanced space savings – recommended when size is a priority.
PLLuM-8x7B-nc-instruct-Q5_K_M.gguf	Q5_K_M	31 GB	High quality – recommended.
PLLuM-8x7B-nc-instruct-Q5_K_S.gguf	Q5_K_S	31 GB	High quality, offered as an alternative with minimal quality loss.
PLLuM-8x7B-nc-instruct-Q6_K.gguf	Q6_K	36 GB	Very high quality with quantized embed/output weights.
PLLuM-8x7B-nc-instruct-Q8_0.gguf	Q8_0	47 GB	Maximum quality quantization.

Downloading Using Hugging Face CLI

Click to view download instructions

First, ensure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Then, target a specific file to download:

huggingface-cli download Nondzu/PLLuM-8x7B-instruct-nc-GGUF --include "PLLuM-8x7B-instruct-nc-Q4_K_M.gguf" --local-dir ./

For larger files, you can specify a new local directory (e.g., PLLuM-8x7B-instruct-nc-Q8_0) or download them directly into the current directory (./).

Downloads last month: 63

GGUF

Model size

47B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for Nondzu/PLLuM-8x7B-nc-instruct-GGUF

Base model

CYFRAGOVPL/PLLuM-8x7B-nc-instruct-2412

Quantized

(3)

this model