closex / neuraldaredevil-8b-abliterated

NeuralDaredevil-8B-abliterated with more quantization

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NeuralDaredevil-8B-abliterated

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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.

⚡ Quantization

Thanks to QuantFactory, Zoyd, and solidrust for providint these quants.

🏆 Evaluation

Open LLM Leaderboard

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

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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

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💻 Usage

!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"])