Compression for the Model

Qwen3.5-9B-abliterated-v2-MAX

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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-Qwen3.5-9B-abliterated

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Key Highlights

• Optimized Packaging & Sharding Improved repository structure for smoother downloads, loading, and deployment across environments.

• Stable Transformers Compatibility Updated layout for better compatibility with modern Transformers versions and inference pipelines.

• 9B Parameter Architecture Built on Qwen3.5-9B, balancing efficiency and capability for local and research use.

• Efficient Deployment Design Designed for lightweight inference, experimentation, and scalable integration.

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

• Improved Reliability in Loading Reduced friction in model initialization and multi-device inference setups.

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Quick Start with Transformers

bash
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pip install transformers==5.4.0
# or
pip install git+https://github.com/huggingface/transformers.git

python
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from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3.5-9B-abliterated-v2-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)

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Intended Use

• Multimodal and Language Research Studying behavior of compact 9B-scale transformer models under different inference settings.

• Red-Teaming & Evaluation Testing robustness across adversarial prompts and edge-case inputs.

• Efficient Local Deployment Running lightweight yet capable models on consumer GPUs or optimized cloud setups.

• Research Prototyping Exploring model behavior, alignment, and inference optimization techniques.

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Limitations & Risks

Important Note: This model inherits behavior from its base model with minimal modification.

• Output Variability Responses may vary depending on sampling strategy and prompt formulation.

• Resource Dependency While efficient, GPU acceleration is recommended for optimal performance.

• No Architectural Changes Improvements are limited to packaging and compatibility, not core model capabilities.

• General Model Limitations May still produce incorrect, incomplete, or inconsistent outputs in complex scenarios.