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

Create a GPU Space / Notebook (or any cloud VM) with Python 3.10+.

Clone this conversion repo:

git clone https://huggingface.co/apoapps/apochat-gemma4-e2b-litert-conversion
cd apochat-gemma4-e2b-litert-conversion

Install dependencies:
```
pip install -r requirements.txt
```

Run the export:

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.