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.
π 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.
π 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 π | 55.87 | 43.73 | 73.6 | 59.36 | 46.8 |
mlabonne/Daredevil-8B π | 55.87 | 44.13 | 73.52 | 59.05 | 46.77 |
mlabonne/Daredevil-8B-abliterated π | 55.06 | 43.29 | 73.33 | 57.47 | 46.17 |
NousResearch/Hermes-2-Theta-Llama-3-8B π | 54.28 | 43.9 | 72.62 | 56.36 | 44.23 |
openchat/openchat-3.6-8b-20240522 π | 53.49 | 44.03 | 73.67 | 49.78 | 46.48 |
meta-llama/Meta-Llama-3-8B-Instruct π | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 |
meta-llama/Meta-Llama-3-8B π | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 |
π³ Model family tree
- Downloads last month
- 6,022
Model tree for QuantFactory/NeuralDaredevil-8B-abliterated-GGUF
Base model
mlabonne/NeuralDaredevil-8B-abliteratedDataset used to train QuantFactory/NeuralDaredevil-8B-abliterated-GGUF
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.280
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.050
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard69.100
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard60.000
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.690
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard71.800