Instructions to use Nondzu/Llama-PLLuM-70B-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nondzu/Llama-PLLuM-70B-chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nondzu/Llama-PLLuM-70B-chat-GGUF",
	filename="Llama-PLLuM-70B-chat-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Nondzu/Llama-PLLuM-70B-chat-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/Llama-PLLuM-70B-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Nondzu/Llama-PLLuM-70B-chat-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/Llama-PLLuM-70B-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Nondzu/Llama-PLLuM-70B-chat-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/Llama-PLLuM-70B-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nondzu/Llama-PLLuM-70B-chat-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/Llama-PLLuM-70B-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nondzu/Llama-PLLuM-70B-chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Nondzu/Llama-PLLuM-70B-chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Nondzu/Llama-PLLuM-70B-chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nondzu/Llama-PLLuM-70B-chat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nondzu/Llama-PLLuM-70B-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nondzu/Llama-PLLuM-70B-chat-GGUF:Q4_K_M

Ollama
How to use Nondzu/Llama-PLLuM-70B-chat-GGUF with Ollama:
```
ollama run hf.co/Nondzu/Llama-PLLuM-70B-chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use Nondzu/Llama-PLLuM-70B-chat-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/Llama-PLLuM-70B-chat-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/Llama-PLLuM-70B-chat-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/Llama-PLLuM-70B-chat-GGUF to start chatting

Docker Model Runner
How to use Nondzu/Llama-PLLuM-70B-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/Nondzu/Llama-PLLuM-70B-chat-GGUF:Q4_K_M
```

Lemonade

How to use Nondzu/Llama-PLLuM-70B-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nondzu/Llama-PLLuM-70B-chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-PLLuM-70B-chat-GGUF-Q4_K_M

List all available models

lemonade list

Llama-PLLuM-70B-chat GGUF Quantizations by Nondzu

DISCLAIMER: This is a quantized version of an existing model Llama-PLLuM-70B-chat. 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 Llama-PLLuM-70B-chat model. All quantizations were performed using the llama.cpp (release b4765). 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, whether the file is split, and a short description.

Filename	Quant Type	File Size	Split	Description
Llama-PLLuM-70B-chat-Q2_K.gguf	Q2_K	25 GB	No	Very low quality but surprisingly usable.
Llama-PLLuM-70B-chat-Q3_K.gguf	Q3_K	32 GB	No	Low quality, suitable for setups with very limited RAM.
Llama-PLLuM-70B-chat-Q3_K_L.gguf	Q3_K_L	35 GB	No	High quality; recommended for quality-focused usage.
Llama-PLLuM-70B-chat-Q3_K_M.gguf	Q3_K_M	32 GB	No	Very high quality, near perfect output – recommended.
Llama-PLLuM-70B-chat-Q3_K_S.gguf	Q3_K_S	29 GB	No	Moderate quality with improved space efficiency.
Llama-PLLuM-70B-chat-Q4_K.gguf	Q4_K	40 GB	No	Good quality for standard use.
Llama-PLLuM-70B-chat-Q4_K_M.gguf	Q4_K_M	40 GB	No	Default quality for most use cases – recommended.
Llama-PLLuM-70B-chat-Q4_K_S.gguf	Q4_K_S	38 GB	No	Slightly lower quality with enhanced space savings – recommended when size is a priority.
Llama-PLLuM-70B-chat-Q5_0.gguf	Q5_0	46 GB	No	Extremely high quality – the maximum quant available.
Llama-PLLuM-70B-chat-Q5_K.gguf	Q5_K	47 GB	No	Very high quality – recommended for demanding use cases.
Llama-PLLuM-70B-chat-Q5_K_M.gguf	Q5_K_M	47 GB	No	High quality – recommended.
Llama-PLLuM-70B-chat-Q5_K_S.gguf	Q5_K_S	46 GB	No	High quality, offered as an alternative with minimal quality loss.
Llama-PLLuM-70B-chat-Q4_0.gguf	Q4_0	38 GB	No	Legacy format offering online repacking for ARM/AVX CPU inference.
Llama-PLLuM-70B-chat-Q6_K.gguf	Q6_K	54 GB	Yes	Very high quality with quantized embed/output weights. Split into 2 parts due to file size.
• Part 1: Q6_K-00001-of-00002.gguf (37 GB)
• Part 2: Q6_K-00002-of-00002.gguf (18 GB)
Llama-PLLuM-70B-chat-Q8_0.gguf	Q8_0	70 GB	Yes	Maximum quality quantization. Available either as a single file or split into 2 parts.
• Part 1: Q8_0.gguf-00001-of-00002.gguf (37 GB)
• Part 2: Q8_0.gguf-00002-of-00002.gguf (34 GB)

*Files marked as "split" must be downloaded in full (all parts) to obtain the complete quantized model.

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/Llama-PLLuM-70B-chat-GGUF --include "Llama-PLLuM-70B-chat-Q4_K_M.gguf" --local-dir ./

For files larger than 50 GB that are split into multiple parts, use a wildcard to download all parts at once:

huggingface-cli download Nondzu/Llama-PLLuM-70B-chat-GGUF --include "Llama-PLLuM-70B-chat-Q8_0/*" --local-dir ./

You can specify a new local directory (e.g., Llama-PLLuM-70B-chat-Q8_0) or download them directly into the current directory (./).

Downloads last month: 112

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Nondzu/Llama-PLLuM-70B-chat-GGUF

Base model

CYFRAGOVPL/Llama-PLLuM-70B-chat-2412

Quantized

(5)

this model