gemma-4-31B-it-litert-lm

<div align="center"> <img src="gemma.webp" width="180px" style="border-radius: 10px; margin-bottom: 20px;"> <h1>google/gemma-4-31B-it <br>_{LiteRT-LM Optimized}</h1> <p><i>Deterministic Projection Memory (DPM) Artifact for Security Telemetry</i></p> </div>

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🟢 Overview

This repository contains a specialized LiteRT-LM conversion of google/gemma-4-31B-it. It is engineered for local-first DPM//BENCH experiments, specifically targeting long-horizon incident narratives and red-team traces.

Objective: Package the base instruction model into a runtime format for deterministic projection memory experiments, ensuring that append-only event logs map to a consistent structured memory surface.

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🛠️ Conversion Architecture

The conversion utilizes the latest LiteRT-LM stack, requiring specific flags to handle the Gemma 4 per-layer embedding structure.

<details> <summary><b>View Conversion Script</b></summary>

python -m litert_torch.generative.export_hf \
    --model /path/to/google/gemma-4-31B-it \
    --output_dir /path/to/out/gemma-4-31B-it-litert-lm \
    --externalize_embedder True \
    --single_token_embedder True \
    --experimental_lightweight_conversion True \
    --bundle_litert_lm True \
    --task text_generation

</details>

Critical Flags for Compatibility:

• --externalize_embedder True: Essential for per-layer embedding paths.
• --experimental_lightweight_conversion True: Prevents runtime artifact corruption.
• --bundle_litert_lm True: Packages tokenizer and templates into the .litertlm artifact.

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💻 Infrastructure Requirements

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🔍 Validation Protocol

For a successful DPM//BENCH run, the artifact must maintain byte-stability. Ensure the following conditions are met:

1. Integrity: LiteRT-LM binary successfully parses the .litertlm bundle.
2. Determinism: At temp 0 and a fixed seed, repeated projection calls must yield identical memory-surface bytes.
3. Format: JSON-only prompts must satisfy schema constraints under high-compression DPM tests.

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⚠️ Implementation Boundaries

• Intended Use: Security incident summarization, telemetry trace compression, and blue-team event reasoning.
• Non-Intended Use: This is not a standalone decision-making system. It is a projection tool. All outputs require human review and replay-validation in high-stakes environments.

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<div align="right"> <p><i>Base Model: google/gemma-4-31B-it</i></p> </div>