---
title: Qwen3-0.6B-ONNX
canonical_url: "https://www.modelscope.cn/models/onnx-community/Qwen3-0.6B-ONNX"
md_url: "https://www.modelscope.cn/models/onnx-community/Qwen3-0.6B-ONNX.md"
repository: onnx-community/Qwen3-0.6B-ONNX
last_updated: 2026-10-12
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen3
architectures:
  - Qwen3ForCausalLM
base_model:
  - Qwen/Qwen3-0.6B
base_model_relation: finetune
library_name:
  - onnx
  - pytorch
frameworks:
  - pytorch
downloads: 861
stars: 0
---

# Qwen3-0.6B-ONNX

> Qwen3-0.6B-ONNX - onnx-community 在 ModelScope 开源的模型。https://huggingface.co/Qwen/Qwen3-0.6B with ONNX weights to be compatible with Transformers.js.

onnx-community/Qwen3-0.6B-ONNX 是 ModelScope 魔搭社区上的text-generation模型，基于 Qwen/Qwen3-0.6B 构建。

- **Repository**: onnx-community/Qwen3-0.6B-ONNX
- **Tasks**: text-generation
- **Base model**: Qwen/Qwen3-0.6B
- **Downloads**: 861
- **Stars**: 0
- **Last updated**: 2026-10-12

Source: https://www.modelscope.cn/models/onnx-community/Qwen3-0.6B-ONNX

---

https://huggingface.co/Qwen/Qwen3-0.6B with ONNX weights to be compatible with Transformers.js.


### Transformers.js

If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```

You can then use the model like this:
```js
import { pipeline, TextStreamer } from "@huggingface/transformers";

// Create a text generation pipeline
const generator = await pipeline(
  "text-generation",
  "onnx-community/Qwen3-0.6B-ONNX",
  { device: "webgpu", dtype: "q4f16" },
);

// Define the list of messages
const messages = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Write me a poem about Machine Learning." },
];

// Generate a response
const output = await generator(messages, {
  max_new_tokens: 512,
  do_sample: false,
  streamer: new TextStreamer(generator.tokenizer, { skip_prompt: true, skip_special_tokens: true }),
});
console.log(output[0].generated_text.at(-1).content);
```


Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).