---
license: other
base_model: deepseek-ai/DeepSeek-V2-Lite
tags:
- moe
- deepseek
- uncensored
- heretic
- abliteration
- custom_code
language:
- en
- zh
pipeline_tag: text-generation
---

# Primera-NT (Abliterated DeepSeek-V2-Lite)

**Primera-NT** is a dynamically uncensored and abliterated version of [`deepseek-ai/DeepSeek-V2-Lite`](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite). This model was created using the [Heretic framework](https://github.com/p-e-w/heretic), 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 `vLLM` and `transformers` pipelines 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.

```python
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`](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite)
- **Ablation Framework:** [Heretic by p-e-w](https://github.com/p-e-w/heretic)