license: apache-2.0
base_model:

• google/gemma-4-E4B-it
  language:
• en
  pipeline_tag: any-to-any
  library_name: transformers
  tags:
• text-generation-inference
• moe
• agent

gemma-4-E4B-it-Uncensored-MAX

gemma-4-E4B-it-Uncensored-MAX is an optimized release built on top of huihui-ai/Huihui-gemma-4-E4B-it-abliterated. This version focuses on updated shard sizing, repository optimization, and compatibility improvements for the latest Transformers releases, while preserving the reasoning and instruction-following capabilities of the original Gemma E4B architecture. The result is a lightweight and efficient E4B parameter language model designed for stable inference, fast deployment, and modern ecosystem integration.

[!IMPORTANT]
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.

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.

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.

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

Quick Start with Transformers

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

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)

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.

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.