Instructions to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/NeuralDaredevil-8B-abliterated-GGUF",
	filename="NeuralDaredevil-8B-abliterated.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/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M

Ollama
How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NeuralDaredevil-8B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

NeuralDaredevil-8B-abliterated-GGUF

This is quantized version of mlabonne/NeuralDaredevil-8B-abliterated created using llama.cpp

Model Description

This is a DPO fine-tune of mlabonne/Daredevil-8-abliterated, trained on one epoch of mlabonne/orpo-dpo-mix-40k. The DPO fine-tuning successfully recovers the performance loss due to the abliteration process, making it an excellent uncensored model.

🔎 Applications

NeuralDaredevil-8B-abliterated performs better than the Instruct model on my tests.

You can use it for any application that doesn't require alignment, like role-playing. Tested on LM Studio using the "Llama 3" preset.

🏆 Evaluation

Open LLM Leaderboard

NeuralDaredevil-8B is the best-performing uncensored 8B model on the Open LLM Leaderboard (MMLU score).

Nous

Evaluation performed using LLM AutoEval. See the entire leaderboard here.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/NeuralDaredevil-8B-abliterated 📄	55.87	43.73	73.6	59.36	46.8
mlabonne/Daredevil-8B 📄	55.87	44.13	73.52	59.05	46.77
mlabonne/Daredevil-8B-abliterated 📄	55.06	43.29	73.33	57.47	46.17
NousResearch/Hermes-2-Theta-Llama-3-8B 📄	54.28	43.9	72.62	56.36	44.23
openchat/openchat-3.6-8b-20240522 📄	53.49	44.03	73.67	49.78	46.48
meta-llama/Meta-Llama-3-8B-Instruct 📄	51.34	41.22	69.86	51.65	42.64
meta-llama/Meta-Llama-3-8B 📄	45.42	31.1	69.95	43.91	36.7

🌳 Model family tree

Downloads last month: 5,687

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/NeuralDaredevil-8B-abliterated-GGUF

Base model

mlabonne/Daredevil-8B

Finetuned

mlabonne/Daredevil-8B-abliterated

Finetuned

mlabonne/NeuralDaredevil-8B-abliterated

Quantized

(29)

this model

Dataset used to train QuantFactory/NeuralDaredevil-8B-abliterated-GGUF

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

69.280
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.050
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

69.100
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

60.000
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.690
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

71.800