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.
[!IMPORTANT] 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.
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
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.
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.
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)
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
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