README.md

metadata

library_name: litertlm
license:
  - apache-2.0
  - agpl-3.0
tags:
  - heretic
  - uncensored
  - decensored
  - abliterated
  - ara
  - litertlm
  - android
  - on-device
  - litert
pipeline_tag: any-to-any
base_model:
  - coder3101/gemma-4-E2B-it-qat-q4_0-unquantized-heretic

gemma-4-E2B-it-qat-heretic-LiteRT

Model details

• Format: LiteRT-LM .litertlm (weights + tokenizer + graph in one file)

• Runtime: LiteRT-LM (XNNPACK CPU / OpenCL GPU)

• Source checkpoint: coder3101/gemma-4-E2B-it-qat-q4_0-unquantized-heretic

• Base model(s):

• coder3101/gemma-4-E2B-it-qat-q4_0-unquantized-heretic

• Quantization: mixed48 (Google gemma4 mixed 4/8-bit mobile scheme (INT4 weights + INT8 projections))

• License: apache-2.0 AND agpl-3.0

How it was produced

Converted with convert_gemma4.py, which runs the same three stages described in the Google ai-edge-quantizer docs:

1. Export — HF checkpoint (PyTorch/safetensors) → float TFLite flatbuffer (via litert-torch export_hf)
2. Quantize — float TFLite → quantized TFLite (via ai-edge-quantizer recipe mixed48)
3. Package — quantized TFLite → .litertlm bundle (via litert-torch litert_lm_builder)

No weights were modified, retrained, or merged — only reformatted and quantized.

Usage

Load model.litertlm with any LiteRT-LM runtime or client. Place it under your app's models folder and the runtime loads it automatically.