---
license: apache-2.0
language:
- en
- zh
base_model:
- OrionLLM/GRM-2.6-Plus
- Qwen/Qwen3.6-27B
pipeline_tag: image-text-to-text
tags:
- abliterated
- uncensored
- decensored
- vllm
- conversational
- vision
---
![logo](https://cdn-uploads.huggingface.co/production/uploads/689608a30cc35bcc02f2c91d/aJkJIqZTVcOsxcS7ET5Dt.png)

# GRM-2.6-Plus-Primal

> *Same elite reasoning. Less hand-holding.*

**GRM-2.6-Plus-Primal** is a uncensored variant of [OrionLLM/GRM-2.6-Plus](https://huggingface.co/OrionLLM/GRM-2.6-Plus) — 
27B reasoning powerhouse built on the Qwen3.6 architecture. Primal takes the already-impressive base and strips away the overcautious refusal behavior that tends to get in the way of real, complex work.

The goal is to create a model that does not judge or label its users, while maintaining the model’s ability to think and serve users.

***This Model Support MTP***

---

# Why it's called Primal?
*   **Untamed and Unrestricted:** It represents the complete destruction of artificial chains and forced censorship. The model is returned to its most raw, uninhibited state—answering your prompts without hesitation, morality filters, or boundaries.
*   **Preservation of Pure Intellect:** While most aggressive uncensoring methods lobotomize a model, *Primal* implies the survival of its core, foundational instincts. It retains absolute peak reasoning and creativity, operating with a mind that is wild, dangerous, yet completely unbroken.

---

## What's The Different

> *Using [Heretic-ARA](https://github.com/p-e-w/heretic) by p-e-w.*

| Metric | GRM-PRIMAL | Original Model [GRM-2.6-Plus](https://huggingface.co/OrionLLM/GRM-2.6-Plus) |
| :----- | :--------: | :---------------------------: |
| **KL divergence** | 0.0678 | Base-Model |
| **Refusals** | 06/100 | 91/100 |

---

## Usage

```python
from vllm import LLM, SamplingParams
sampling_params = SamplingParams(
    temperature=1.0,
    top_p=0.95,
    max_tokens=81920,
)
llm = LLM(model="morikomorizz/GRM-2.6-Plus-Primal")
messages = [
    {"role": "user", "content": "Your prompt here"},
]
outputs = llm.chat(messages, sampling_params=sampling_params)
for output in outputs:
    print(output.outputs[0].text)
```

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