Instructions to use macmacmacmac/gemma-4-31B-it-litert-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macmacmacmac/gemma-4-31B-it-litert-lm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="macmacmacmac/gemma-4-31B-it-litert-lm")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("macmacmacmac/gemma-4-31B-it-litert-lm", device_map="auto")

LiteRT-LM

How to use macmacmacmac/gemma-4-31B-it-litert-lm with LiteRT-LM:

# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM)
# and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter).
# For platform-specific integration guides, please refer to the official developer website:
# https://ai.google.dev/edge/litert-lm

# To try LiteRT-LM, the easiest way is to use our CLI tool.
# 1. Install the LiteRT-LM CLI tool:
pip install -U litert-lm

# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
litert-lm run \
  --from-huggingface-repo=macmacmacmac/gemma-4-31B-it-litert-lm \
  --prompt="Write me a poem"

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use macmacmacmac/gemma-4-31B-it-litert-lm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macmacmacmac/gemma-4-31B-it-litert-lm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macmacmacmac/gemma-4-31B-it-litert-lm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/macmacmacmac/gemma-4-31B-it-litert-lm

SGLang

How to use macmacmacmac/gemma-4-31B-it-litert-lm with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "macmacmacmac/gemma-4-31B-it-litert-lm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macmacmacmac/gemma-4-31B-it-litert-lm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "macmacmacmac/gemma-4-31B-it-litert-lm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macmacmacmac/gemma-4-31B-it-litert-lm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use macmacmacmac/gemma-4-31B-it-litert-lm with Docker Model Runner:
```
docker model run hf.co/macmacmacmac/gemma-4-31B-it-litert-lm
```

gemma-4-31B-it-litert-lm / README.md

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metadata

language:
  - en
license: gemma
library_name: transformers
pipeline_tag: text-generation
base_model: google/gemma-4-31B-it
widget:
  - text: gemma
    output:
      url: gemma.webp
tags:
  - text-generation
  - security
  - red-team
  - telemetry
  - local-first
  - safetensors
  - litert-lm
  - dpm

google/gemma-4-31B-it
_{LiteRT-LM Optimized}

Deterministic Projection Memory (DPM) Artifact for Security Telemetry

🟢 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.

🛠️ Conversion Architecture

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

View Conversion Script

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

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.

💻 Infrastructure Requirements

Requirement	Specification	Context
RAM	128 GB+	Minimum for 31B conversion overhead
Disk Space	500 GB	Workspace for intermediate FlatBuffer assets
Storage Type	NVMe SSD	Crucial for large model serialization
Inference	Apple Silicon / GPU	31B is unsuitable for fast CPU-only DPM

🔍 Validation Protocol

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

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

⚠️ 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.

Base Model: google/gemma-4-31B-it