NeuralDaredevil-8B-abliterated-4.5bpw-exl2

NeuralDaredevil-8B-abliterated

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" 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).

Nous

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

🌳 Model family tree

💻 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"])