gemma-4-E4B-it-Uncensored-MAX

Evaluation Report (Self-Reported)

The evaluation scores referenced in the original report are based on prithivMLmods/gemma-4-E2B-it-Uncensored-MAX and are not re-evaluated in this release.

Note: The evaluation was conducted using 2,000 harmful test prompts to measure model refusal behavior. These results are self-reported and may vary depending on benchmark setup and evaluation methodology.

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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 better storage handling, download reliability, and inference efficiency.

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

• E4B Architecture Built on gemma-4-E4B-it, offering efficient reasoning performance with reduced compute requirements.

• Improved Deployment Stability Designed for smoother inference across a wide range of hardware configurations.

• Preserved Model Behavior No modifications to weights or architecture; behavior remains consistent with the base 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-gemma-4-E4B-it-abliterated

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Quick Start with Transformers

bash
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pip install transformers==5.5.3
# or
pip install git+https://github.com/huggingface/transformers.git

python
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from transformers import Gemma4ForConditionalGeneration, AutoProcessor
import torch

model = Gemma4ForConditionalGeneration.from_pretrained(
    "prithivMLmods/gemma-4-E4B-it-Uncensored-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/gemma-4-E4B-it-Uncensored-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 transformer behavior in low-parameter efficiency regimes.

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

• Efficient Deployment Running lightweight models on limited hardware environments.

• Research Prototyping Experimentation with compact transformer architectures.

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Limitations & Risks

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

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

• Resource Requirements While lightweight compared to larger models, GPU acceleration is still recommended for optimal performance.

• Deployment Constraints Performance depends on runtime optimization and hardware configuration.

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