This is a phone-scale, non-distilled derivative of poolside/Laguna-XS.2. It is a checkpoint-surgery experiment: the original Laguna XS.2 weights are statically pruned from 256 routed experts to 16 retained routed experts per sparse layer, context metadata is reduced, and the result is packaged as a Google LiteRT-LM artifact for Android experiments.

This is not a smoke model, LoRA, adapter, or distilled student. It is a stripped Laguna XS.2 checkpoint.

Upload and use:

Local source before publishing:

Artifact details:

litert-lm-peek verified that the package contains LLM metadata, HF tokenizer, prefill/decode TFLite, and embedder TFLite sections.

The packaged chat template removes Hugging Face's {% generation %} / {% endgeneration %} assistant-mask markers because Google Edge Gallery's LiteRT template parser rejects that extension during template creation.

The LiteRT prefill/decode graph uses dynamic int8 weight quantization for supported ops. BATCH_MATMUL is intentionally left unquantized in the current recipe because AI Edge Quantizer fails Laguna's high-rank expert batch-matmul with an axis-remapping error.

That makes this a real but still large high-memory-phone candidate. The next compression step is to make the expert batch-matmul path quantizable or repeat the same flow for a K8 variant.

This project folder is the reproducibility and publishing bundle:

The generated model payloads remain under ../models/ locally and are uploaded by publish_hf.ps1.

Log in to Hugging Face first:

On this workstation, publish_hf.ps1 will automatically use the WSL LiteRT Python at /root/.cache/lucebox-litert-venv/bin/python if it exists. You can also set HF_TOKEN in PowerShell before publishing.

Dry-run the upload:

Create or update the model repo and upload model.litertlm:

Use -IncludeTfliteParts if you also want the unpacked TFLite components in the repo. Use -IncludePrunedGguf only if you want to publish the intermediate K16 GGUF as well.

Copy model.litertlm to app-private storage or bundle it in a test build, then load it with a LiteRT-LM text-generation session. This artifact is for LiteRT-LM, not Google AICore; AICore is for Google-managed Gemini Nano weights rather than custom Laguna weights.

This checkpoint has not yet been quality-evaluated after expert pruning on the target phone. It should be treated as an experimental derived model for local testing, not as a quality-preserving release of Laguna XS.2.