Run NeuralDaredevil 8B Abliterated on iPhone, iPad & Mac
Uncensored safety-ablated Llama 3 8B fine-tuned with DPO
NeuralDaredevil 8B Abliterated runs 100% private on iPhone, iPad & Mac inside Private LLM — no internet connection required, no data sent to any server.
Model on Hugging Face
Specifications
- Parameters
- 8B
- Context window
- 8K tokens
- License
- Llama 3
- Quantization
- OmniQuant 3-bit, OmniQuant 4-bit
- Family
- Llama 3 8B
What NeuralDaredevil 8B Abliterated is good at
NeuralDaredevil 8B Abliterated is an 8-billion-parameter language model with an 8192-token context window. It starts from the Daredevil 8B abliterated checkpoint, which removed safety-related alignment from a Llama 3 8B-based model, and then applies DPO fine-tuning on a mix of preference data to recover the performance drop that ablation normally causes. As a result, it operates without built-in refusal or content restrictions, making it suitable for unaligned uses such as role-playing. In benchmarks it outperforms the aligned Llama 3 8B Instruct model and ranks as the top uncensored 8B model on the Open LLM Leaderboard at the time of evaluation.
Which of your devices can run it
iPhone
iPad
Mac
How to run NeuralDaredevil 8B Abliterated in Private LLM
- Download Private LLM from the App Store.
- Open the in-app model library and choose NeuralDaredevil 8B Abliterated.
- Download the model once, then chat fully offline.
Variants & related models
Dolphin 2.9 Llama 3 8B
Uncensored Llama 3 8B fine-tune lacking built-in refusals
Hathor_Stable v0.2 L3 8B
Roleplay specialist fine-tuned from Llama 3 8B Instruct
Hermes 2 Pro Llama 3 8B
General-purpose Llama 3 8B finetune for function calling and JSON
Hermes 2 Theta Llama 3 8B
General assistant with function calling, structured JSON, and ChatML (Llama 3 8B)
L3 Umbral Mind RP v3.0 8B
Roleplay model for trauma and self-harm narratives on Llama 3 8B
Llama 3 8B Instruct MopeyMule
Uncensored behavioral reversal of Llama 3 8B Instruct using weight orthogonalization
Frequently asked questions
Yes. NeuralDaredevil 8B Abliterated runs on iPhone models with enough memory, such as iPhone 17 Pro Max, iPhone 17 Pro, iPhone Air, iPhone 17, iPhone 16 Pro / 16 Pro Max, iPhone 16 / 16 Plus, iPhone 16e, iPhone 15 Pro / 15 Pro Max, iPhone 15 / 15 Plus, iPhone 14 / 14 Plus / 14 Pro / 14 Pro Max, iPhone 13 Pro / 13 Pro Max, iPhone 12 Pro / 12 Pro Max, fully on-device in Private LLM — no internet connection required.
Yes. NeuralDaredevil 8B Abliterated runs on Macs with enough unified memory, such as Mac (Apple Silicon, 192GB), MacBook Pro (M4 Max, 128GB), Mac Studio / Pro (Apple Silicon, 96GB), MacBook Pro (M4 Max, 64GB), MacBook Pro (M4 Max, 48GB), MacBook Pro (M4 Max, 36GB), Mac (Apple Silicon, 32GB), MacBook Air (M4, 24GB), MacBook Air (M-series, 16GB), Mac (Apple Silicon, 8GB), fully on-device in Private LLM.
Yes. Once downloaded in Private LLM, NeuralDaredevil 8B Abliterated runs 100% on-device — no internet connection, and nothing is sent to any server.
Private LLM is a one-time purchase with no subscription and no per-message cost. The models themselves are open source — once downloaded, they run offline with nothing to pay per use.
Why run NeuralDaredevil 8B Abliterated in Private LLM
Private LLM has run local AI on iPhone, iPad, and Mac since 2023, well before Apple Intelligence, LM Studio, Ollama, etc. existed. Inference happens on your device, so your NeuralDaredevil 8B Abliterated conversations never reach a server. The part most apps gloss over is quantization, and that is exactly where on-device quality is won or lost. Most llama.cpp and MLX wrappers ship the same off-the-shelf 4-bit RTN weights. Private LLM ships GPTQ and OmniQuant quantization, tuned per model, and our 3-bit OmniQuant models match or beat those 4-bit RTN builds on the same Apple Silicon. Run the same model both ways and you feel it in the first reply. See how our quantization works.
Specifications and summary come from NeuralDaredevil 8B Abliterated's Hugging Face model card, released under the Llama 3 license. Private LLM ships its own quantized models, built with OmniQuant quantization tuned per model, and isn't affiliated with the model's authors.