README.md

---
library_name: transformers
tags:
- torchao
- qwen
- qwen3
- nlp
- code
- math
- chat
- conversational
license: mit
language:
- multilingual
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
---


[Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) is quantized by the PyTorch team using [torchao](https://huggingface.co/docs/transformers/main/en/quantization/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](https://github.com/pytorch/executorch).

We provide the [quantized pte](https://huggingface.co/pytorch/Qwen3-4B-INT8-INT4/blob/main/model.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](#exporting-to-executorch).)  

# Running in a mobile app
The [pte file](https://huggingface.co/pytorch/Qwen3-4B-INT8-INT4/blob/main/model.pte) can be run with ExecuTorch on a mobile phone.  See the [instructions](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) for doing this in iOS.
On iPhone 15 Pro, the model runs at 14.8 tokens/sec and uses 3379 Mb of memory.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66049fc71116cebd1d3bdcf4/eVHB7fVllmwVauKJvGu0d.png)


# Quantization Recipe

First need to install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install --pre torchao torch --index-url https://download.pytorch.org/whl/nightly/cu126
```

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

```Py
from transformers import (
  AutoModelForCausalLM,
  AutoProcessor,
  AutoTokenizer,
)
import torch

model_id = "Qwen/Qwen3-4B"
untied_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

print(untied_model)
from transformers.modeling_utils import find_tied_parameters
print("tied weights:", find_tied_parameters(untied_model))
if getattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings"):
    setattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings", False)

untied_model._tied_weights_keys = []
untied_model.lm_head.weight = torch.nn.Parameter(untied_model.lm_head.weight.clone())

print("tied weights:", find_tied_parameters(untied_model))

USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-untied-weights"

untied_model.push_to_hub(save_to)
tokenizer.push_to_hub(save_to)

# or save locally
save_to_local_path = f"{MODEL_NAME}-untied-weights"
untied_model.save_pretrained(save_to_local_path)
tokenizer.save_pretrained(save_to)
```

Note: to `push_to_hub` you need to run
```Shell
pip install -U "huggingface_hub[cli]"
huggingface-cli login
```
and use a token with write access, from https://huggingface.co/settings/tokens

## Quantization

We used following code to get the quantized model:

```Py
from transformers import (
  AutoModelForCausalLM,
  AutoProcessor,
  AutoTokenizer,
  TorchAoConfig,
)
from torchao.quantization.quant_api import (
    IntxWeightOnlyConfig,
    Int8DynamicActivationIntxWeightConfig,
    ModuleFqnToConfig,
    quantize_,
)
from torchao.quantization.granularity import PerGroup, PerAxis
import torch

# we start from the model with untied weights
model_id = "Qwen/Qwen3-4B"
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
untied_model_id = f"{USER_ID}/{MODEL_NAME}-untied-weights"
untied_model_local_path = f"{MODEL_NAME}-untied-weights"

embedding_config = IntxWeightOnlyConfig(
    weight_dtype=torch.int8,
    granularity=PerAxis(0),
)
linear_config = Int8DynamicActivationIntxWeightConfig(
    weight_dtype=torch.int4,
    weight_granularity=PerGroup(32),
    weight_scale_dtype=torch.bfloat16,
)
quant_config = ModuleFqnToConfig({"_default": linear_config, "model.embed_tokens": embedding_config})
quantization_config = TorchAoConfig(quant_type=quant_config, include_input_output_embeddings=True, modules_to_not_convert=[])

# either use `untied_model_id` or `untied_model_local_path`
quantized_model = AutoModelForCausalLM.from_pretrained(untied_model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Push to hub
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-INT8-INT4"
quantized_model.push_to_hub(save_to, safe_serialization=False)
tokenizer.push_to_hub(save_to)

# Manual testing
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
    {
        "role": "system",
        "content": "",
    },
    {"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
    templated_prompt,
    return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
```

The response from the manual testing is:

```
Hello! I'm Qwen, a large language model developed by Alibaba Cloud. While I don't have consciousness or personal experiences, I can engage in conversations with you and help answer questions. I can talk to you, share thoughts, and even have fun! What's on your mind?
```

# Model Quality

| Benchmark                        |                |                           |
|----------------------------------|----------------|---------------------------|
|                                  | Qwen3-4B       | Qwen3-4B-INT8-INT4            | 
| **Popular aggregated benchmark** |                |                           |
| mmlu                             | 68.38          | 66.74                     |
| mmlu_pro                         | 49.71          | 46.73                     |
| bbh                              | 74.86          | 67.47                     |
| **Reasoning**                    |                |                           |
| gpqa_main_zeroshot               | 33.93          | 31.03                     |
| **Multilingual**                 |                |                           |
| m_mmlu                           | 50.41          | 47.13                     |
| mgsm_en_cot_en                   | 30.40          | 29.20                     |   
| **Math**                         |                |                           |
| gsm8k                            | 84.76          | 82.87                     |
| leaderboard_math_hard (v3)       | 48.19          | 44.94                     |
| **Overall**                      | 55.08          | 52.01                     |

<details>
<summary> Reproduce Model Quality Results </summary>

We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.

Need to install lm-eval from source: https://github.com/EleutherAI/lm-evaluation-harness#install

## baseline
```Shell
lm_eval --model hf --model_args pretrained=Qwen3/Qwen3-4B --tasks mmlu --device cuda:0 --batch_size auto
```

## int8 dynamic activation and int4 weight quantization (INT8-INT4)
```Shell
lm_eval --model hf --model_args pretrained=pytorch/Qwen3-4B-INT8-INT4 --tasks mmlu --device cuda:0 --batch_size auto
```
</details>

# Exporting to ExecuTorch

We can run the quantized model on a mobile phone using [ExecuTorch](https://github.com/pytorch/executorch).
Once ExecuTorch is [set-up](https://pytorch.org/executorch/main/getting-started.html), 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.
```Shell
python -m executorch.examples.models.qwen3.convert_weights $(hf download pytorch/Qwen3-4B-INT8-INT4) pytorch_model_converted.bin
```

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.)

```Shell
python -m executorch.examples.models.llama.export_llama \
  --model "qwen3_4b" \
  --checkpoint pytorch_model_converted.bin \
  --params examples/models/qwen3/config/4b_config.json \
  --output_name model.pte \
  -kv \
  --use_sdpa_with_kv_cache \
  -X \
  --xnnpack-extended-ops \
  --max_context_length 1024 \
  --max_seq_length 1024 \
  --dtype fp32 \
  --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}'
```

After that you can run the model in a mobile app (see [Running in a mobile app](#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](https://github.com/pytorch/executorch/blob/main/.ci/scripts/test_torchao_huggingface_checkpoints.sh) for the latest source of truth, and let us know we need to update our model card.)

# Paper: TorchAO: PyTorch-Native Training-to-Serving Model Optimization
The model's quantization is powered by **TorchAO**, a framework presented in the paper [TorchAO: PyTorch-Native Training-to-Serving Model Optimization](https://huggingface.co/papers/2507.16099).

**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 .

# Resources
*   **Official TorchAO GitHub Repository:** [https://github.com/pytorch/ao](https://github.com/pytorch/ao)
*   **TorchAO Documentation:** [https://docs.pytorch.org/ao/stable/index.html](https://docs.pytorch.org/ao/stable/index.html)

# Disclaimer
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.