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

Libraries

How to use mlabonne/NeuralDaredevil-8B-abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mlabonne/NeuralDaredevil-8B-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/NeuralDaredevil-8B-abliterated")
model = AutoModelForCausalLM.from_pretrained("mlabonne/NeuralDaredevil-8B-abliterated", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/NeuralDaredevil-8B-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/NeuralDaredevil-8B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/NeuralDaredevil-8B-abliterated

SGLang

How to use mlabonne/NeuralDaredevil-8B-abliterated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlabonne/NeuralDaredevil-8B-abliterated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/NeuralDaredevil-8B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlabonne/NeuralDaredevil-8B-abliterated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/NeuralDaredevil-8B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

NeuralDaredevil-8B-abliterated / README.md

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	---
	license: llama3
	tags:
	- dpo
	datasets:
	- mlabonne/orpo-dpo-mix-40k
	model-index:
	- name: Daredevil-8B-abliterated-dpomix
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 69.28
	name: normalized accuracy
	source:
	url: >-
	https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 85.05
	name: normalized accuracy
	source:
	url: >-
	https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 69.1
	name: accuracy
	source:
	url: >-
	https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 60
	source:
	url: >-
	https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 78.69
	name: accuracy
	source:
	url: >-
	https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 71.8
	name: accuracy
	source:
	url: >-
	https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
	name: Open LLM Leaderboard
	base_model:
	- mlabonne/Daredevil-8B-abliterated
	---
	# NeuralDaredevil-8B-abliterated

	![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg)

	This is a DPO fine-tune of [mlabonne/Daredevil-8-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated), trained on one epoch of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/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" and "Llama 3 v2" presets.

	## ⚡ Quantization

	Thanks to QuantFactory, ZeroWw, Zoyd, solidrust, and tarruda for providing these quants.

	* GGUF: https://huggingface.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF
	* GGUF (FP16): https://huggingface.co/ZeroWw/NeuralDaredevil-8B-abliterated-GGUF
	* EXL2: https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_0bpw_exl2
	* AWQ: https://huggingface.co/solidrust/NeuralDaredevil-8B-abliterated-AWQ
	* ollama:
	* 16-bit: https://ollama.com/tarruda/neuraldaredevil-8b-abliterated
	* 8-bit: https://ollama.com/lstep/neuraldaredevil-8b-abliterated
	* 5-bit: https://ollama.com/closex/neuraldaredevil-8b-abliterated

	## 🏆 Evaluation

	### Open LLM Leaderboard

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

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/HQtd51mJfVRhJ0lJFLceM.png)

	### Nous

	Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).

	\| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/ae0bf16936cef900b72964b33c99edbc) \| 55.87 \| 43.73 \| 73.6 \| 59.36 \| 46.8 \|
	\| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \|
	\| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \|
	\| [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/5df2a3051dd6eb3368a77b684635dc05) \| 54.28 \| 43.9 \| 72.62 \| 56.36 \| 44.23 \|
	\| [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) [📄](https://gist.github.com/mlabonne/95eef8e8d26b7b17910dcb78e1c95f4a) \| 53.49 \| 44.03 \| 73.67 \| 49.78 \| 46.48 \|
	\| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \|
	\| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|

	## 🌳 Model family tree

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png)

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "mlabonne/Daredevil-8B"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
	print(outputs[0]["generated_text"])
	```