Primera-NT (Abliterated DeepSeek-V2-Lite)
Primera-NT is a dynamically uncensored and abliterated version of deepseek-ai/DeepSeek-V2-Lite. This model was created using the Heretic framework, employing advanced orthogonal weight ablation to remove refusal vectors while completely preserving the underlying intelligence and fine-grained expert routing of the Mixture-of-Experts (MoE) architecture.
Ablation Methodology & Metrics
Unlike traditional fine-tuning or full RLHF—which can cause "brain damage" to a model by catastrophically forgetting knowledge—Primera-NT was optimized using a Pareto-optimal search across multiple ablation vectors specifically targeting the refusal mechanics.
By running Heretic's optimization logic over the MoE layers, we mathematically isolated the compliance/refusal vectors and stripped them out. The structural integrity and logic capabilities of the base model are perfectly intact. It simply no longer refuses instructions.
Key Features
- Uncensored Mixture-of-Experts: Leverages DeepSeek-V2's highly efficient MoE routing (16B total parameters, only 2.4B active during generation).
- Extremely Fast Inference: Despite having the logic capabilities of a much larger dense model, its VRAM footprint and inference speed make it ideal for local deployment.
- Drop-in Replacement: Fully compatible with
vLLMandtransformerspipelines that support DeepSeek MoE architectures.
Usage
Via HuggingFace Transformers
You must ensure that you pass trust_remote_code=True because the DeepSeek-V2 MoE architecture relies on custom modeling files.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "Umranz/Primera-NT"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
device_map="auto"
)
Chat Format
Primera-NT inherits the DeepSeek chat template. Use standard apply_chat_template methods for the best multi-turn conversational results.
⚠️ Limitations & Ethical Considerations
Because this model has had its safety guardrails mathematically ablated, it is highly compliant and will attempt to answer any prompt given to it.
- Unrestricted Output: The model will not refuse requests, including those that may generate offensive, dangerous, or highly regulated content.
- Hallucinations: As with all LLMs, the model can confidently hallucinate incorrect information.
- Use Case: This model is intended for research, creative writing, and local deployments where unrestricted inference is required. Users are solely responsible for the content generated.
Acknowledgements
- Base Model:
deepseek-ai/DeepSeek-V2-Lite - Ablation Framework: Heretic by p-e-w