Instructions to use QuantFactory/Guanaco-13B-Uncensored-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 QuantFactory/Guanaco-13B-Uncensored-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 QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Guanaco-13B-Uncensored-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/Guanaco-13B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Guanaco-13B-Uncensored-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/Guanaco-13B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Guanaco-13B-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Guanaco-13B-Uncensored-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Guanaco-13B-Uncensored-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Guanaco-13B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Guanaco-13B-Uncensored-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/Guanaco-13B-Uncensored-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/Guanaco-13B-Uncensored-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/Guanaco-13B-Uncensored-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Guanaco-13B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Guanaco-13B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Guanaco-13B-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Guanaco-13B-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Guanaco-13B-Uncensored-GGUF

This is quantized version of Fredithefish/Guanaco-13B-Uncensored created using llama.cpp

Original Model Card

✨ Guanaco - 13B - Uncensored ✨

Guanaco-13B-Uncensored has been fine-tuned for 4 epochs on the Unfiltered Guanaco Dataset. using Llama-2-13B as the base model.
The model does not perform well with languages other than English.
Please note: This model is designed to provide responses without content filtering or censorship. It generates answers without denials.

Special thanks

I would like to thank AutoMeta for providing me with the computing power necessary to train this model.

Also thanks to TheBloke for creating the GGUF and the GPTQ quantizations for this model

Prompt Template

### Human: {prompt} ### Assistant:

Dataset

The model has been fine-tuned on the V2 of the Guanaco unfiltered dataset.

Downloads last month: 80

GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit