Instructions to use QuantFactory/NaturalLM-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/NaturalLM-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/NaturalLM-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/NaturalLM-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/NaturalLM-GGUF",
	filename="NaturalLM.Q2_K.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 QuantFactory/NaturalLM-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/NaturalLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/NaturalLM-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 QuantFactory/NaturalLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/NaturalLM-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 QuantFactory/NaturalLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/NaturalLM-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 QuantFactory/NaturalLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/NaturalLM-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/NaturalLM-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/NaturalLM-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/NaturalLM-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/NaturalLM-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 QuantFactory/NaturalLM-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 QuantFactory/NaturalLM-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/NaturalLM-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/NaturalLM-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/NaturalLM-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/NaturalLM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/NaturalLM-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NaturalLM-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/NaturalLM-GGUF

This is quantized version of qingy2019/NaturalLM created using llama.cpp

Original Model Card

Uploaded model

Developed by: qingy2019
License: apache-2.0
Finetuned from model : unsloth/mistral-nemo-base-2407-bnb-4bit

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 156

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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