File size: 2,377 Bytes
67c480c cd346d4 67c480c 757f520 67c480c 8154de4 cd346d4 291c675 98abca4 3c864ae 291c675 5e3179d 291c675 1e3f380 27e2b75 37b1595 2dd055d 291c675 6cb02d1 98abca4 94b77a3 f8f763d 94b77a3 4bfdd98 14a9eaa 9578a8c 14a9eaa 23dfd84 291c675 474d899 291c675 d9d2f65 291c675 474d899 d9d2f65 474d899 3c864ae 2f422b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | ---
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
---
# 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)
```
[](https://github.com/sponsors/morganmor) |