base_model: google/gemma-4-e2b-it tags:

• litert
• litertlm
• gemma
• gemma4
• abliterated
• uncensored
• multimodal
• text-generation
• image-text-to-text
• on-device license: gemma

Gemma 4 E2B — Abliterated LiteRT

An abliterated (uncensored) version of Google's Gemma 4 E2B Instruct, converted to LiteRT format (.litertlm) for on-device inference.

Capabilities

Inherits all capabilities of the base Gemma 4 E2B model:

• Text generation — instruction-tuned chat
• Vision — image understanding
• Audio — audio input processing
• Thinking — extended reasoning
• Tool use — function/tool calling

Refusal behaviours have been removed via abliteration (see below). The model will respond to requests that the base model would decline.

Files

The package contains INT8 weight-quantized TFLite subgraphs (4x compression from original BF16 weights):

Built with --cache-length 1024 and --prefill-lengths 256.

Usage

LiteRT CLI

pip install litert-cli-nightly[lm]
litert lm run model.litertlm

LiteRT-LM SDK (Android / iOS)

Drop model.litertlm into your app and load it with the LiteRT-LM LlmInference API:

// Android (Kotlin)
val options = LlmInference.LlmInferenceOptions.builder()
    .setModelPath("/path/to/model.litertlm")
    .setMaxTokens(1024)
    .build()
val inference = LlmInference.createFromOptions(context, options)
val result = inference.generateResponse("Hello!")

See the LiteRT-LM documentation for full SDK usage.

How This Was Made

1. Abliteration

The refusal direction was identified and removed from the model weights using the FailSpy abliterator methodology:

1. Forward passes were run on 15 harmful and 15 harmless prompts through the base model
2. The mean difference in hidden states at layer 17 (the probe layer, mid-model) was computed and normalised to produce the refusal direction
3. That direction was projected out of the o_proj and down_proj weight matrices in layers 11–22 (the middle third of the model's 35 layers) using:

W_new = W - outer(r, r @ W)

where r is the unit refusal direction. This removes the model's ability to activate refusal behaviour without degrading general capability.

2. LiteRT Conversion

The abliterated HuggingFace checkpoint was converted using litert-cli-nightly:

litert convert ./gemma-4-e2b-abliterated \
  --output ./gemma-4-e2b-abliterated-litert \
  --quantize weight_only_wi8_afp32 \
  --cache-length 1024

This uses litert-torch-nightly under the hood, which applies the Gemma4-specific export pipeline (image_text_to_text task, vision encoder export, externalized embedder).

Limitations

• Cache length: Built with --cache-length 1024. Context beyond ~1024 tokens may degrade or not be supported depending on the runtime.
• Prefill length: Fixed at 256 tokens. Prompts longer than this will be chunked at runtime.
• No RLHF safety: This model has no content filters. Use responsibly.
• Quantization: INT8 weight-only quantization introduces minor quality loss vs the BF16 original.

Base Model

google/gemma-4-e2b-it — subject to the Gemma Terms of Use. Usage of this model is also subject to those terms.