gemma-3-1b-base-onnx-genai-int4-emb-int8

Gemma-3-1B-PT optimized for Arm-based mobile CPUs with SME2

A decoder-only pretrained base language model for text generation, provided in ONNX for the ONNX Runtime GenAI runtime and quantized to INT4 groupwise asymmetric weights with INT8 per-token dynamic activations and an INT8 block-quantized embedding table tied to the INT8 LM head, targeting Arm-based mobile CPUs with SME2.

Summary

This repository contains an Arm-optimized version of google/gemma-3-1b-pt for text generation. The model is provided in ONNX for the ONNX Runtime GenAI runtime, targeting Mobile CPU systems. The transformer weights are quantized with GPTQ to INT4, asymmetric per-group with group size 32, activations are INT8 per-token dynamic, and the embedding table is INT8 block-quantized with block size 32 and tied to the INT8 LM head.

This is the base pretrained checkpoint, not the instruction-tuned variant, so it has no chat template and is prompted directly with raw text.

This version is intended to demonstrate efficient inference on Arm-based platforms while preserving the original model's intended behavior. Arm has evaluated this model on HellaSwag and measured performance on a representative evaluation target.

Key results

Original model

Model files

Performance

Performance was measured on the reference configuration below. Results are intended to make the optimization reproducible but do not guarantee identical performance on every Arm-based system.

Reference configuration

Measurement conditions. Each measured run consumes a 124-token prompt and generates 128 tokens, using 4 CPU threads, 5 warm-up runs and 20 measured runs. Each run starts only once Android reports thermal status 0 (NONE), after a 30 s settle. The device is set to fixed performance mode, which is the official recommendation.

Performance results

Accuracy

Accuracy was evaluated using the same preprocessing, input resolution, and evaluation protocol described below. Where possible, the optimized model is compared against the original model under the same evaluation conditions.

Evaluation setup

Accuracy results

Accuracy was measured using the evaluation setup described above. Users should re-evaluate the model on their own data before production use.

Arm optimization approach

Arm optimized this model for efficient inference on Arm-based platforms using a hardware-aware conversion and validation flow.

For this release, Arm used:

The goal of this process is to improve deployment characteristics such as latency, memory use, model size, and runtime compatibility while preserving the model's intended behavior.

Using this model

Note: The Python/uv example runs on AWS Graviton (Ubuntu arm64) to confirm runtime compatibility only, and is intended as a guideline for building an equivalent run script on Mobile CPU systems. Every performance and accuracy figure on this page was measured on the vivo X300; none of them was re-measured on the proxy.

Install dependencies

Dependencies are declared in pyproject.toml, which ships with this repository. Resolve and install them into a local virtual environment with uv:

uv python install
uv sync --frozen

Run the example

uv run example.py

The example writes predictions.json next to example.py.

Expected input

Expected output

Intended use

This model is intended for developers evaluating text generation workloads on Arm-based platforms. It is suitable as a reference implementation for benchmarking, prototyping, and integration exploration.

Limitations

• Performance depends on the target device, runtime version, backend/delegate support, memory configuration, and system load.
• Accuracy was evaluated on HellaSwag and may not generalize to all domains. It does not measure code, math, multi-turn instruction following, or long-context behavior.
• This release preserves the original model's intended task and behavior, but users should validate it for their own application, data, and deployment environment.
• This repository is not a replacement for the original model documentation.
• This is the base pretrained checkpoint, not an instruction-tuned one. It continues text rather than following chat-style instructions, and it is best prompted with in-context examples. For chat and question-answering use cases, start from the instruction-tuned variant instead.
• Greedy decoding is deterministic for a given machine and runtime build, but not portable between them. At this model size and weight precision the top candidate tokens are sometimes separated by a very small logit margin, so a different CPU or a different ONNX Runtime build can select another candidate and diverge for the rest of the continuation. Treat the committed generation as one known-good sample rather than a bit-exact conformance target.

Additional notes

• The ONNX Runtime GenAI bundle requires the literal file names model.onnx and model.onnx.data, because genai_config.json hard-references them.
• The base model architecture supports a 32768-token context, which is what config.yaml records. The bundled genai_config.json sets the ONNX Runtime GenAI context_length to 4096, so 4096 is the limit the runtime enforces as shipped. Raise it there if a longer context is needed.
• The embedding table and the LM head share a single INT8 block-quantized weight tensor, with block size 32, and its scales and zero points.
• Weight quantization was calibrated with GPTQ Hessians computed on 512 WikiText-2 raw samples. Activations are quantized dynamically at runtime and need no offline calibration.
• Accuracy evaluation scope is HellaSwag zero-shot acc_norm only.

About this version

Original Model: google/gemma-3-1b-pt by Google - Repository

Optimization/conversion: Arm-Optimized version for execution on Arm-based platforms.

Converted/optimized by: Arm

License: The Original Model and the Optimized Model are subject to Gemma Terms of Use.

This repository contains a converted or optimized version of the Original Model (the “Optimized Model”). The Original Model has been converted or optimized as described above for execution on Arm-based platforms.

No retraining or fine-tuning of the Original Model was performed as part of the conversion or optimization. The conversion or optimization was not intended to change the Original Model’s behavior or intended use.

Original Model and Documentation

For information about the Original Model, including its development, training data, intended uses, limitations and other relevant information, please refer to the Original Model repository. Information in that repository was provided by the original developer or other third parties and, unless expressly stated otherwise, has not been independently verified by Arm.

Licenses and Third-Party Terms

Use of the Original Model and the Optimized Model is subject to the applicable licenses, usage restrictions and other terms identified above and in the relevant repositories. Publication of the Optimized Model does not grant any rights beyond those provided under the applicable license terms.

You are responsible for reviewing those terms and ensuring that your use of the Original Model and the Optimized Model is permitted.

Purpose of this Release

The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.

Arm’s publication of the Optimized Model does not constitute an endorsement or certification of the Original Model or a representation that the Optimized Model is suitable for production use or any particular purpose.

To the fullest extent permitted by applicable law (i) the Optimized Model is provided “as is.” Arm makes no representations or warranties that the Original Model, the Optimized Model or their outputs are accurate, safe, secure, non-infringing, legally compliant, suitable for production use or fit for any particular purpose; and (ii) Arm will not be liable for any loss or damage arising from or in connection with the Optimized Model, its use or its outputs.

You are responsible for independently evaluating the Optimized Model, its outputs and its suitability for your intended use, including compliance with applicable legal, regulatory, safety and security requirements.

Arm does not commit to provide ongoing support, maintenance or updates for the Optimized Model. Any use of or reliance on the Optimized Model or its outputs is at your own risk.