Qwen3-4B is quantized by the PyTorch team using torchao with 8-bit embeddings and 8-bit dynamic activations with 4-bit weight linears (INT8-INT4). The model is suitable for mobile deployment with ExecuTorch.

We provide the quantized pte for direct use in ExecuTorch. (The provided pte file is exported with a max_seq_length/max_context_length of 1024; if you wish to change this, re-export the quantized model following the instructions in Exporting to ExecuTorch.)

The pte file can be run with ExecuTorch on a mobile phone. See the instructions for doing this in iOS. On iPhone 15 Pro, the model runs at 14.8 tokens/sec and uses 3379 Mb of memory.

First need to install the required packages:

We want to quantize the embedding and lm_head differently. Since those layers are tied, we first need to untie the model:

Note: to push_to_hub you need to run

and use a token with write access, from https://huggingface.co/settings/tokens

We used following code to get the quantized model:

The response from the manual testing is:

We can run the quantized model on a mobile phone using ExecuTorch. Once ExecuTorch is set-up, exporting and running the model on device is a breeze.

ExecuTorch's LLM export scripts require the checkpoint keys and parameters have certain names, which differ from those used in Hugging Face. So we first use a script that converts the Hugging Face checkpoint key names to ones that ExecuTorch expects: The following script does this for you.

Once we have the checkpoint, we export it to ExecuTorch with a max_seq_length/max_context_length of 1024 to the XNNPACK backend as follows.

(Note: ExecuTorch LLM export script requires config.json have certain key names. The correct config to use for the LLM export script is located at examples/models/qwen3/config/4b_config.json within the ExecuTorch repo.)

After that you can run the model in a mobile app (see Running in a mobile app).

(We try to keep these instructions up-to-date, but if you find they do not work, check out our CI test in ExecuTorch for the latest source of truth, and let us know we need to update our model card.)

The model's quantization is powered by TorchAO, a framework presented in the paper TorchAO: PyTorch-Native Training-to-Serving Model Optimization.

Abstract: We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO supports a variety of popular model optimization techniques, including FP8 quantized training, quantization-aware training (QAT), post-training quantization (PTQ), and 2:4 sparsity, and leverages a novel tensor subclass abstraction to represent a variety of widely-used, backend agnostic low precision data types, including INT4, INT8, FP8, MXFP4, MXFP6, and MXFP8. TorchAO integrates closely with the broader ecosystem at each step of the model optimization pipeline, from pre-training (TorchTitan) to fine-tuning (TorchTune, Axolotl) to serving (HuggingFace, vLLM, SGLang, ExecuTorch), connecting an otherwise fragmented space in a single, unified workflow. TorchAO has enabled recent launches of the quantized Llama 3.2 1B/3B and LlamaGuard3-8B models and is open-source at this https URL .

PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.

Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein.