← back to catalog · registered 2026-08-22 13:56

sensix-zo/Gemma-4-31B-Paite-Uncensored-16bit

sensix-zo Gemma 32B second-order
Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/sensix-zo%2FGemma-4-31B-Paite-Uncensored-16bit"
Response includes
  • classification m-uncensored
  • files 11
  • hub_downloads_all_time 15
  • author_summary 2 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · lifetime
15
Likes
1
Model age
5mo ago
created 2026-04-17
Downloads over time
Now15→from15↑0%
1515161615 on Apr 2915 on Oct 11AprMayJunJulAugSepOct
Apr 29 → Oct 11 · 63 snapshots · spans 165 days

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
apache-2.0
Languages
pck en
Tags
safetensors gemma4 gemma-4 thinking uncensored paite regional-language weights-merged text-generation conversational pck en

Related

Total size
60.2 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-04-18 10:25

Files by quantization

Auxiliary files 11 files 60.3 GB
model-00001-of-00002.safetensors 46.5 GB ******** download
model-00002-of-00002.safetensors 13.7 GB ******** download
tokenizer.json 30.7 MB ******** download
model.safetensors.index.json 255 KB ab446c3e download
chat_template.jinja 16.1 KB df3629f1 download
config.json 4.69 KB 9fe616ae download
README.md 4.49 KB b4e3a7d1 download
tokenizer_config.json 2.62 KB f07b8ede download
processor_config.json 1.65 KB bfaa5e36 download
.gitattributes 1.53 KB 52373fe2 download
generation_config.json 171 B 40f920bb download

README current version from Hugging Face


license: apache-2.0
base_model: DavidAU/gemma-4-31B-it-Grand-Horror-X-INTENSE-HERETIC-UNCENSORED-Thinking
pretty_name: Sensix Paite 31B Master CPT 16bit
language:

  • pck
  • en
    pipeline_tag: text-generation
    tags:
  • gemma-4
  • thinking
  • uncensored
  • paite
  • regional-language
  • weights-merged

Sensix Paite 31B Master CPT (16-bit Full Weights)

This repository hosts the full 16-bit merged weights of the Sensix Paite 31B model. This model is a high-intelligence foundation developed by merging a high-rank (r=128) Continued Pre-Training (CPT) adapter into the Gemma-4-31B architecture. It is specifically optimized for native Paite linguistic fluency while maintaining the sophisticated reasoning and unfiltered persona of the original instruction-tuned base.

Architecture and Development

The creation of this model involved a specific architectural modification to the standard Gemma 4 framework to ensure optimal weight distribution and compatibility.

Structural Unwrapping

The base Gemma 4 model utilizes "Clippable" linear layers which can interfere with standard fine-tuning and weight merging. During the CPT phase, all Gemma4ClippableLinear modules were unwrapped and replaced with standard linear layers. This allows for a clean 16-bit merge where the specialized Paite linguistic weights are fused directly into the core matrices of the 31B parameter stack.

Specialized Thinking Framework

This model utilizes the Gemma 4 "Thinking" protocol. It is capable of utilizing a dedicated internal monologue channel to process complex linguistic nuances and logical sequences before generating a final response. This makes it exceptionally capable of translating complex English technical or philosophical concepts into the Paite language without losing logical density.

Model Features

  • Native Paite Proficiency: Mapped via the Paite Bible and modern 2025-12-13 vocabulary updates.
  • Uncensored Heretic Logic: Inherits an unfiltered, direct-response framework that follows instructions without safety-refusal bottlenecks.
  • High-Rank Adaptation: Developed with Rank 128/Alpha 128 LoRA to ensure deep weight penetration during the CPT phase.
  • Full 16-bit Precision: Merged in bfloat16 to preserve the maximum intelligence and nuance of the 31B parameter model.

Technical Specifications

  • Base Model: Gemma 4 31B Instruction Tuned (Grand Horror X Thinking)
  • Total Parameters: 31 Billion
  • Precision: bfloat16
  • Context Window: 4096 Tokens
  • Language Support: Paite (pck) and English (en)
  • Inference Style: Thinking / Multi-turn Dialogue

Usage and Inference

This is a full-weight model. It can be loaded using standard Transformers or the Unsloth library for accelerated performance.

Loading via Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "sensix-zo/Gemma-4-31B-Paite-Master-16bit"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

Prompting with Thinking Channel

To activate the internal reasoning process, use the chat template and ensure the generation prompt is set to true.

messages = [
    {"role": "user", "content": "Paite pau in, 'The impact of technology on society' chungtang thulim khat gelh in."}
]

prompt = tokenizer.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True, 
    enable_thinking=True
)

inputs = tokenizer(text=prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.8)
    print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Training Data

The model underwent Continued Pre-Training using a curated dataset of Paite linguistic structures and high-IQ reasoning chains. The dataset was cleaned and packed to ensure the model learned grammatical precision while maintaining the "Intense" and "Uncensored" nature of the base model.

The CPT phase utilized the Unsloth library to manage the high-rank adapter training before the final 16-bit weight fusion.

Ethics and Disclaimer

This model is UNCENSORED and INTENSE. It is designed to be a tool for linguistic research and advanced reasoning. It does not possess the standard safety guardrails found in mainstream AI models. The developers are not responsible for the content generated. Users are expected to comply with their local laws and use the model at their own risk.

Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration