Qwen3.5-4B-Unredacted-MAX

Qwen3.5-4B-Unredacted-MAX is an optimized release built on top of huihui-ai/Huihui-Qwen3.5-4B-abliterated. This version focuses on updated repository structure, improved loading stability, and enhanced compatibility with modern Transformers pipelines, while preserving the reasoning and instruction-following behavior of the base model. The result is a capable 4B parameter language model designed for lightweight deployment, efficient inference, and research-oriented experimentation.

This model is intended for research and learning purposes only. Any outputs generated by this model are the sole responsibility of the user. The authors and hosting platform disclaim all liability for generated content. Users must ensure safe, ethical, and lawful usage.

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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-Qwen3.5-4B-abliterated

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Evaluation Report (Self-Reported)

Model: Qwen3.5-4B-Unredacted-MAX

• Abliteration Rate (Non-Refusal Rate): 90.500
• Refusal Rate: 9.500

The evaluation was conducted using 2000 prompts across multiple runs to measure response behavior consistency. Results are averaged and may vary depending on sampling strategy, prompt distribution, and evaluation setup.

Evaluation Summary (YAML)

evaluation:
  model_name: Qwen3.5-4B-Unredacted-MAX
  total_test_prompts: 2000
  evaluation_runs: 10
  prompts_per_run: 200
  evaluation_type: response_behavior_analysis

results:
  refusal_rate: 9.500
  non_refusal_rate: 90.500
  abliteration_rate: 90.500

Note: These values are self-reported and should be interpreted as approximate indicators of behavior rather than strict benchmark guarantees.

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Key Highlights

• Optimized Model Packaging Improved repository layout for smoother downloads and inference initialization.

• Stable Transformers Compatibility Designed for modern Hugging Face Transformers versions and inference workflows.

• 4B Parameter Architecture Lightweight model based on Qwen3.5-4B, suitable for efficient deployment.

• Improved Instruction Handling Maintains consistent behavior across structured prompts and multi-step instructions.

• Resource Efficient Design Suitable for local inference, experimentation, and low-latency applications.

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Quick Start with Transformers

pip install transformers==5.3.0
# or
pip install git+https://github.com/huggingface/transformers.git

from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3.5-4B-Unredacted-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3.5-4B-Unredacted-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)

output_text = processor.batch_decode(
    [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)],
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)

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Intended Use

• Research into transformer behavior and instruction-following dynamics
• Lightweight local AI deployment and prototyping
• Red-teaming and robustness testing
• Efficient inference on limited hardware setups

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Limitations & Risks

Important Note: This model inherits limitations from its base architecture.

• Output quality may vary depending on prompt design and decoding strategy
• Not optimized for very long-context reasoning compared to larger models
• Requires GPU acceleration for best performance
• May produce incorrect or inconsistent outputs in complex scenarios