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README.md

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+# Apochat-tuned Gemma 4 E2B → LiteRT export
+
+This folder contains a script that converts the public Apochat-tuned MLX model into a
+`.litertlm` package that the iOS/macOS app can run with the LiteRT backend.
+
+**Do not run this on the 16 GB local Mac** — the conversion peaks at more than 16 GB of
+memory. Run it on a machine with at least:
+
+- 32 GB of CPU RAM, or
+- A GPU with 24 GB+ VRAM.
+
+## Quick start on Hugging Face
+
+1. Create a **GPU Space / Notebook** (or any cloud VM) with Python 3.10+.
+2. Clone this conversion repo:
+   ```bash
+   git clone https://huggingface.co/apoapps/apochat-gemma4-e2b-litert-conversion
+   cd apochat-gemma4-e2b-litert-conversion
+   ```
+3. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+4. Run the export:
+   ```bash
+   python export_apochat_litert.py \
+       --mlx-repo apoapps/apochat-gemma4-e2b-apochat-tuned-v1 \
+       --upload-repo apoapps/apochat-gemma4-e2b-apochat-tuned-v1-litert
+   ```
+
+The script will:
+- download the MLX-q4 fused snapshot,
+- dequantize it to bfloat16 PyTorch safetensors,
+- run `litert convert` with weight-only int4 quantization,
+- upload the resulting `.litertlm` to the `--upload-repo`.
+
+## Output
+
+When it finishes, the artifact will be available at:
+
+```
+https://huggingface.co/apoapps/apochat-gemma4-e2b-apochat-tuned-v1-litert
+```
+
+Use that URL/revision/SHA to add the entry to `Apochat/Sources/LocalAI/Catalog/BundledCatalog.swift`.