license: apache-2.0
language:

• en
  base_model:
• Qwen/Qwen3.6-35B-A3B
  pipeline_tag: image-text-to-text
  library_name: transformers
  tags:
• text-generation-inference
• uncensored
• abliterated
• unfiltered
• unredacted
• refusal-ablated
• vllm
• pytorch
• bf16
• max
• alignment-modified
• reasoning
• agent

Qwen3.6-35B-A3B-Abliterated-MAX

Qwen3.6-35B-A3B-Abliterated-MAX 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 capabilities of the original model. The result is a powerful 35B Mixture-of-Experts language model designed for efficient inference, stable deployment, and modern ecosystem integration.

[!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.

Key Highlights

• Latest Transformers Compatibility
  Re-sharded and optimized for improved compatibility with recent Transformers releases.

• Optimized Model Sharding
  Updated shard structure for better storage handling, download reliability, and inference efficiency.

• Stable Inference Pipeline
  Improved packaging for consistent loading and generation behavior across environments.

• 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 and runtimes.

• Preserved Model Behavior
  No changes to weights or architecture; behavior remains consistent with the original model lineage.

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

Quick Start with Transformers

pip install transformers==5.5.4
# or
pip install git+https://github.com/huggingface/transformers.git

from transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessor
import torch

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

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

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 challenging and adversarial prompts.

• High-Performance Deployment
  Running large MoE models on optimized multi-GPU or distributed setups.

• Research Prototyping
  Experimentation with scalable transformer architectures and deployment strategies.

Limitations & Risks

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

• Output Variability
  Responses may vary depending on sampling parameters and prompt structure.

• Resource Requirements
  A 35B MoE model requires significant GPU memory or 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.