Instructions to use mlabonne/NeuralLlama-3-8B-Instruct-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/NeuralLlama-3-8B-Instruct-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/NeuralLlama-3-8B-Instruct-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/NeuralLlama-3-8B-Instruct-abliterated")
model = AutoModelForMultimodalLM.from_pretrained("mlabonne/NeuralLlama-3-8B-Instruct-abliterated")
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/NeuralLlama-3-8B-Instruct-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/NeuralLlama-3-8B-Instruct-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/NeuralLlama-3-8B-Instruct-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/NeuralLlama-3-8B-Instruct-abliterated

SGLang

How to use mlabonne/NeuralLlama-3-8B-Instruct-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/NeuralLlama-3-8B-Instruct-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/NeuralLlama-3-8B-Instruct-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/NeuralLlama-3-8B-Instruct-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/NeuralLlama-3-8B-Instruct-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/NeuralLlama-3-8B-Instruct-abliterated with Docker Model Runner:
```
docker model run hf.co/mlabonne/NeuralLlama-3-8B-Instruct-abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3-8B-Instruct-abliterated-dpomix

This model is an experimental DPO fine-tune of an abliterated Llama 3 8B Instruct model on the full mlabonne/orpo-dpo-mix-40k dataset. It improves Llama 3 8B Instruct's performance while being uncensored.

🔎 Applications

This is an uncensored model. You can use it for any application that doesn't require alignment, like role-playing.

Tested on LM Studio using the "Llama 3" preset.

⚡ Quantization

GGUF: https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix-GGUF

🏆 Evaluation

Open LLM Leaderboard

This model improves the performance of the abliterated source model and recovers the MMLU that was lost in the abliteration process.

Nous

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/Llama-3-8B-Instruct-abliterated-dpomix 📄	52.26	41.6	69.95	54.22	43.26
meta-llama/Meta-Llama-3-8B-Instruct 📄	51.34	41.22	69.86	51.65	42.64
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 📄	51.21	40.23	69.5	52.44	42.69
abacusai/Llama-3-Smaug-8B 📄	49.65	37.15	69.12	51.66	40.67
mlabonne/OrpoLlama-3-8B 📄	48.63	34.17	70.59	52.39	37.36
meta-llama/Meta-Llama-3-8B 📄	45.42	31.1	69.95	43.91	36.7

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Llama-3-8B-Instruct-abliterated-dpomix"
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"])

Downloads last month: 122

Safetensors

Model size

8B params

Tensor type

F16

Model tree for mlabonne/NeuralLlama-3-8B-Instruct-abliterated

Quantizations

9 models

Dataset used to train mlabonne/NeuralLlama-3-8B-Instruct-abliterated

Spaces using mlabonne/NeuralLlama-3-8B-Instruct-abliterated 10

Collection including mlabonne/NeuralLlama-3-8B-Instruct-abliterated

👿 Daredevil-8B

Collection

Fine-tuned abliterated merge of the best Llama 3 8B model. Highest MMLU score in its category. • 5 items • Updated Aug 16, 2024 • 18