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prithivMLmods/
Qwen3.6-35B-A3B-Uncensored-Aggressive

Name: Qwen3.6-35B-A3B-Uncensored-Aggressive
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Qwen3.6-35B-A3B-Uncensored-Aggressive

Qwen3.6-35B-A3B-Uncensored-Aggressive is an optimized release built on top of huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated. This version focuses on updated shard sizing, repository optimization, and compatibility improvements for the latest Transformers releases, while preserving the MoE architecture and reasoning behavior of the original model. The result is a high-capacity 35B Mixture-of-Experts language model designed for efficient inference, stable deployment, and modern ecosystem integration.

GGUF: https://huggingface.co/prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive-GGUF

[!IMPORTANT] This model is intended for research and learning purposes only. Any content generated by this model is used at the user's own risk. The authors and hosting page disclaim any liability for outputs produced by this model. Users are responsible for ensuring safe, ethical, and lawful usage.

Evaluation

Metric	Result
Refusal Rate	N/A
Test Setup	N/A
Inference Type	text-generation
Dataset	N/A

Note: This release does not introduce new benchmark evaluations and primarily focuses on repackaging, sharding updates, and Transformers compatibility improvements over the base model.

Base Model Signatures:

This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated

Key Highlights

Latest Transformers Compatibility Re-sharded and optimized for improved compatibility with recent Transformers releases.
Optimized Model Sharding Updated shard structure for improved download reliability, storage handling, and inference efficiency.
Stable Inference Pipeline Improved packaging for consistent loading and generation behavior.
35B MoE Architecture (A3B) Built on Qwen/Qwen3.6-35B-A3B, leveraging Mixture-of-Experts design for scalable reasoning capacity.
Improved Deployment Stability Designed for smoother inference across different hardware configurations.
Preserved Model Behavior No changes to weights or architecture; behavior remains consistent with the original model lineage.

Quick Start with Transformers

bash

pip install transformers==5.2.0
# or
pip install git+https://github.com/huggingface/transformers.git

python

from transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Explain how transformer models work in simple terms."}
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = processor(
    text=[text],
    padding=True,
    return_tensors="pt"
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=256)

generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)

Intended Use

Multimodal and Language Research Studying large-scale MoE behavior and inference characteristics.
Red-Teaming & Evaluation Testing robustness across complex and adversarial prompts.
High-Performance Deployment Running large MoE models on optimized multi-GPU setups.
Research Prototyping Experimentation with scalable transformer architectures.

Limitations & Risks

Important Note: This model inherits the behavior and limitations of its base model.

Output Variability Responses may vary depending on sampling settings and prompt structure.
Resource Requirements A 35B MoE model requires significant GPU memory and optimized inference strategies such as quantization or tensor parallelism.
Deployment Constraints Performance depends heavily on hardware configuration and runtime optimization.
General Model Limitations May produce incorrect, incomplete, or inconsistent outputs in complex scenarios.