| --- |
| 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: |
| - google/gemma-4-E2B-it-qat-q4_0-unquantized |
| license_link: https://ai.google.dev/gemma/docs/gemma_4_license |
| --- |
| |
| # gemma-4-E2B-it-qat-heretic-LiteRT |
|
|
| > [!NOTE] |
| > This is a **quantized / format-converted derivative** of an upstream |
| > checkpoint, repackaged into the LiteRT-LM (`.litertlm`) format for |
| > on-device inference. It is **not** a retrained or fine-tuned model — the |
| > weights are the upstream checkpoint's, reformatted for LiteRT-LM. |
|
|
| ## Model details |
|
|
| - **Format:** LiteRT-LM `.litertlm` (weights + tokenizer + graph in one file) |
| - **Runtime:** [LiteRT-LM](https://github.com/google-ai-edge/litert-lm) (XNNPACK CPU / OpenCL GPU) |
| - **Source checkpoint:** `coder3101/gemma-4-E2B-it-qat-q4_0-unquantized-heretic` |
| - **Base model(s):** |
|
|
| - `google/gemma-4-E2B-it-qat-q4_0-unquantized` |
|
|
| - **Quantization:** `mixed48` (Google gemma4 mixed 4/8-bit mobile scheme (INT4 weights + INT8 projections)) |
| - **License:** `apache-2.0 AND agpl-3.0` (weights also subject to the [Gemma Terms of Use](https://ai.google.dev/gemma/docs/gemma_4_license)) |
|
|
| ## How it was produced |
|
|
| Converted with `convert_gemma4.py`, which runs the same three stages described in the [Google ai-edge-quantizer docs](https://github.com/google-ai-edge/ai-edge-quantizer/tree/main): |
|
|
| 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. |
|
|
