prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-STOR-check

Q3.6-35B-A3B-abliterated-0520-MAX

Q3.6-35B-A3B-abliterated-0520-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 reasoning and MoE capabilities of the original architecture. The result is a high-capacity 35B Mixture-of-Experts language model designed for efficient inference workflows and stable large-model deployment.

GGUF: https://huggingface.co/prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-GGUF

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.

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Evaluation [Self Reported]

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.

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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.

• Streamlined Inference Packaging Repository structure optimized for easier integration into large-scale inference workflows.

• 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 more consistent loading and inference behavior across environments.

• Preserved Model Behavior No modifications to weights or architecture; all behavior remains aligned with the original model lineage.

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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

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Quick Start with Transformers

pip install transformers==5.8.0
# or
pip install git+https://github.com/huggingface/transformers.git

from transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    "prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-STOR-check",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-STOR-check"
)

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)

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Intended Use

• Multimodal & Language Research Studying large-scale MoE behavior and inference characteristics.

• Red-Teaming & Evaluation Testing robustness across complex and adversarial prompts.

• High-Performance Local Deployment Running large Mixture-of-Experts models on multi-GPU setups.

• Research Prototyping Experimentation with scalable transformer architectures.

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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.

• High Compute 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 or incomplete outputs in complex scenarios.