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prithivMLmods/
gemma-4-E2B-it-Uncensored-MAX

Name: gemma-4-E2B-it-Uncensored-MAX
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gemma-4-E2B-it-Uncensored-MAX

gemma-4-E2B-it-Uncensored-MAX is an optimized release built on top of huihui-ai/Huihui-gemma-4-E2B-it-abliterated. This version focuses on updated shard sizing, repository optimization, and compatibility improvements for the latest Transformers releases, while preserving the efficiency and instruction-following capabilities of the original Gemma E2B architecture. The result is a lightweight E2B parameter language model designed for fast inference, stable 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.

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.
E2B Architecture Built on gemma-4-E2B-it, offering efficient reasoning with low compute requirements.
Improved Deployment Stability Designed for smoother inference across diverse 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-E2B-it-abliterated

Quick Start with Transformers

bash

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

python

from transformers import Gemma4ForConditionalGeneration, AutoProcessor
import torch

model = Gemma4ForConditionalGeneration.from_pretrained(
    "prithivMLmods/gemma-4-E2B-it-Uncensored-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/gemma-4-E2B-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 efficiency-focused transformer behavior and inference dynamics.
Red-Teaming & Evaluation Testing robustness across edge-case and adversarial prompts.
Lightweight Deployment Running small-scale models on CPU or limited GPU 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 configuration and prompt structure.
Resource Requirements While lightweight, GPU acceleration is recommended for best performance.
Deployment Constraints Performance depends on runtime optimization and hardware setup.
General Model Limitations May produce incorrect, incomplete, or inconsistent outputs in complex scenarios.