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---
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language:
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- en
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- zh
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
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license: mit
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen3-0.6B-Base
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---
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# Qwen3-0.6B
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<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
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<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
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</a>
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# GLM-4.7-Flash
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## Qwen3 Highlights
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<div align="center">
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<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
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</div>
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<p align="center">
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👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
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<br>
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📖 Check out the GLM-4.7 <a href="https://z.ai/blog/glm-4.7" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report(GLM-4.5)</a>.
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<br>
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📍 Use GLM-4.7-Flash API services on <a href="https://docs.z.ai/guides/llm/glm-4.7">Z.ai API Platform. </a>
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<br>
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👉 One click to <a href="https://chat.z.ai">GLM-4.7</a>.
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</p>
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Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
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## Introduction
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- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
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- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
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- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
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- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
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- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
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GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.
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## Model Overview
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**Qwen3-0.6B** has the following features:
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- Type: Causal Language Models
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- Training Stage: Pretraining & Post-training
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- Number of Parameters: 0.6B
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- Number of Paramaters (Non-Embedding): 0.44B
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- Number of Layers: 28
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- Number of Attention Heads (GQA): 16 for Q and 8 for KV
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- Context Length: 32,768
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### Performances on Benchmarks
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For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
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> [!TIP]
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> If you encounter significant endless repetitions, please refer to the [Best Practices](#best-practices) section for optimal sampling parameters, and set the ``presence_penalty`` to 1.5.
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| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B-Thinking-2507 | GPT-OSS-20B |
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|--------------------|---------------|-----------------------------|-------------|
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| AIME 25 | 91.6 | 85.0 | 91.7 |
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| GPQA | 75.2 | 73.4 | 71.5 |
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| LCB v6 | 64.0 | 66.0 | 61.0 |
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| HLE | 14.4 | 9.8 | 10.9 |
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| SWE-bench Verified | 59.2 | 22.0 | 34.0 |
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| τ²-Bench | 79.5 | 49.0 | 47.7 |
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| BrowseComp | 42.8 | 2.29 | 28.3 |
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## Quickstart
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The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
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### Evaluation Parameters
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With `transformers<4.51.0`, you will encounter the following error:
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```
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KeyError: 'qwen3'
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**Default Settings (Most Tasks)**
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* temperature: `1.0`
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* top-p: `0.95`
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* max new tokens: `131072`
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For multi-turn agentic tasks (τ²-Bench and Terminal Bench 2), please turn on [Preserved Thinking mode](https://docs.z.ai/guides/capabilities/thinking-mode).
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**Terminal Bench, SWE Bench Verified**
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* temperature: `0.7`
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* top-p: `1.0`
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* max new tokens: `16384`
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**τ^2-Bench**
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* Temperature: `0`
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* Max new tokens: `16384`
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For τ^2-Bench evaluation, we added an additional prompt to the Retail and Telecom user interaction to avoid failure modes caused by users ending the interaction incorrectly. For the Airline domain, we applied the domain fixes as proposed in the [Claude Opus 4.5](https://assets.anthropic.com/m/64823ba7485345a7/Claude-Opus-4-5-System-Card.pdf) release report.
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## Serve GLM-4.7-Flash Locally
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For local deployment, GLM-4.7-Flash supports inference frameworks including vLLM and SGLang. Comprehensive deployment
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instructions are available in the official [Github](https://github.com/zai-org/GLM-4.5) repository.
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vLLM and SGLang only support GLM-4.7-Flash on their main branches.
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### vLLM
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+ using pip (must use pypi.org as the index url):
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```shell
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pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
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pip install git+https://github.com/huggingface/transformers.git
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```
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The following contains a code snippet illustrating how to use the model generate content based on given inputs.
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### SGLang
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+ Install the supported versions of SGLang and Transformers (using `uv` is recommended):
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```shell
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uv pip install sglang==0.3.2.dev9039+pr-17247.g90c446848 --extra-index-url https://sgl-project.github.io/whl/pr/
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uv pip install git+https://github.com/huggingface/transformers.git@76732b4e7120808ff989edbd16401f61fa6a0afa
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```
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### transformers
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using with transformers as
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```shell
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pip install git+https://github.com/huggingface/transformers.git
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```
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and then run:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Qwen/Qwen3-0.6B"
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# load the tokenizer and the model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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MODEL_PATH = "zai-org/GLM-4.7-Flash"
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messages = [{"role": "user", "content": "hello"}]
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_dict=True,
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return_tensors="pt",
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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pretrained_model_name_or_path=MODEL_PATH,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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# prepare the model input
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prompt = "Give me a short introduction to large language model."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# conduct text completion
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=32768
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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# parsing thinking content
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try:
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# rindex finding 151668 (</think>)
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index = len(output_ids) - output_ids[::-1].index(151668)
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except ValueError:
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index = 0
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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print("thinking content:", thinking_content)
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print("content:", content)
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inputs = inputs.to(model.device)
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generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1]:])
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print(output_text)
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```
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For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
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- SGLang:
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### vLLM
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```shell
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python -m sglang.launch_server --model-path Qwen/Qwen3-0.6B --reasoning-parser qwen3
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vllm serve zai-org/GLM-4.7-Flash \
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--tensor-parallel-size 4 \
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--speculative-config.method mtp \
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--speculative-config.num_speculative_tokens 1 \
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--tool-call-parser glm47 \
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--reasoning-parser glm45 \
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--enable-auto-tool-choice \
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--served-model-name glm-4.7-flash
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```
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- vLLM:
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### SGLang
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```shell
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vllm serve Qwen/Qwen3-0.6B --enable-reasoning --reasoning-parser deepseek_r1
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python3 -m sglang.launch_server \
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--model-path zai-org/GLM-4.7-Flash \
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--tp-size 4 \
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--tool-call-parser glm47 \
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--reasoning-parser glm45 \
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--speculative-algorithm EAGLE \
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--speculative-num-steps 3 \
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--speculative-eagle-topk 1 \
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--speculative-num-draft-tokens 4 \
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--mem-fraction-static 0.8 \
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--served-model-name glm-4.7-flash \
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--host 0.0.0.0 \
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--port 8000
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```
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+ For Blackwell GPUs, include `--attention-backend triton --speculative-draft-attention-backend triton` in your SGLang launch command.
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For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
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## Citation
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## Switching Between Thinking and Non-Thinking Mode
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If you find our work useful in your research, please consider citing the following paper:
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> [!TIP]
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> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
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> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
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### `enable_thinking=True`
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By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
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```python
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True # True is the default value for enable_thinking
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)
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```
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In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
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> [!NOTE]
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> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
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### `enable_thinking=False`
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We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
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```python
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False # Setting enable_thinking=False disables thinking mode
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)
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```
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In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
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> [!NOTE]
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> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
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### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
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We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
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Here is an example of a multi-turn conversation:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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class QwenChatbot:
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def __init__(self, model_name="Qwen/Qwen3-0.6B"):
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self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.model = AutoModelForCausalLM.from_pretrained(model_name)
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self.history = []
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def generate_response(self, user_input):
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messages = self.history + [{"role": "user", "content": user_input}]
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text = self.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = self.tokenizer(text, return_tensors="pt")
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response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
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response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
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# Update history
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self.history.append({"role": "user", "content": user_input})
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self.history.append({"role": "assistant", "content": response})
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return response
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# Example Usage
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if __name__ == "__main__":
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chatbot = QwenChatbot()
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# First input (without /think or /no_think tags, thinking mode is enabled by default)
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user_input_1 = "How many r's in strawberries?"
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print(f"User: {user_input_1}")
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response_1 = chatbot.generate_response(user_input_1)
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print(f"Bot: {response_1}")
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print("----------------------")
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# Second input with /no_think
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user_input_2 = "Then, how many r's in blueberries? /no_think"
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print(f"User: {user_input_2}")
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response_2 = chatbot.generate_response(user_input_2)
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print(f"Bot: {response_2}")
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print("----------------------")
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# Third input with /think
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user_input_3 = "Really? /think"
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print(f"User: {user_input_3}")
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response_3 = chatbot.generate_response(user_input_3)
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print(f"Bot: {response_3}")
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```
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> [!NOTE]
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> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
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> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
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## Agentic Use
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Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
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To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
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```python
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from qwen_agent.agents import Assistant
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# Define LLM
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llm_cfg = {
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'model': 'Qwen3-0.6B',
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# Use the endpoint provided by Alibaba Model Studio:
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# 'model_type': 'qwen_dashscope',
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# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
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# Use a custom endpoint compatible with OpenAI API:
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'model_server': 'http://localhost:8000/v1', # api_base
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'api_key': 'EMPTY',
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# Other parameters:
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# 'generate_cfg': {
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# # Add: When the response content is `<think>this is the thought</think>this is the answer;
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# # Do not add: When the response has been separated by reasoning_content and content.
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# 'thought_in_content': True,
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# },
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}
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# Define Tools
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tools = [
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{'mcpServers': { # You can specify the MCP configuration file
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'time': {
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'command': 'uvx',
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'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
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},
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"fetch": {
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"command": "uvx",
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"args": ["mcp-server-fetch"]
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}
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}
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},
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'code_interpreter', # Built-in tools
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]
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# Define Agent
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bot = Assistant(llm=llm_cfg, function_list=tools)
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# Streaming generation
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messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
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for responses in bot.run(messages=messages):
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pass
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print(responses)
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```
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## Best Practices
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To achieve optimal performance, we recommend the following settings:
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1. **Sampling Parameters**:
|
||||
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
|
||||
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
|
||||
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
|
||||
|
||||
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
|
||||
|
||||
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
|
||||
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
|
||||
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
|
||||
|
||||
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
|
||||
|
||||
### Citation
|
||||
|
||||
If you find our work helpful, feel free to give us a cite.
|
||||
|
||||
```
|
||||
@misc{qwen3technicalreport,
|
||||
title={Qwen3 Technical Report},
|
||||
author={Qwen Team},
|
||||
```bibtex
|
||||
@misc{5team2025glm45agenticreasoningcoding,
|
||||
title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models},
|
||||
author={GLM Team and Aohan Zeng and Xin Lv and Qinkai Zheng and Zhenyu Hou and Bin Chen and Chengxing Xie and Cunxiang Wang and Da Yin and Hao Zeng and Jiajie Zhang and Kedong Wang and Lucen Zhong and Mingdao Liu and Rui Lu and Shulin Cao and Xiaohan Zhang and Xuancheng Huang and Yao Wei and Yean Cheng and Yifan An and Yilin Niu and Yuanhao Wen and Yushi Bai and Zhengxiao Du and Zihan Wang and Zilin Zhu and Bohan Zhang and Bosi Wen and Bowen Wu and Bowen Xu and Can Huang and Casey Zhao and Changpeng Cai and Chao Yu and Chen Li and Chendi Ge and Chenghua Huang and Chenhui Zhang and Chenxi Xu and Chenzheng Zhu and Chuang Li and Congfeng Yin and Daoyan Lin and Dayong Yang and Dazhi Jiang and Ding Ai and Erle Zhu and Fei Wang and Gengzheng Pan and Guo Wang and Hailong Sun and Haitao Li and Haiyang Li and Haiyi Hu and Hanyu Zhang and Hao Peng and Hao Tai and Haoke Zhang and Haoran Wang and Haoyu Yang and He Liu and He Zhao and Hongwei Liu and Hongxi Yan and Huan Liu and Huilong Chen and Ji Li and Jiajing Zhao and Jiamin Ren and Jian Jiao and Jiani Zhao and Jianyang Yan and Jiaqi Wang and Jiayi Gui and Jiayue Zhao and Jie Liu and Jijie Li and Jing Li and Jing Lu and Jingsen Wang and Jingwei Yuan and Jingxuan Li and Jingzhao Du and Jinhua Du and Jinxin Liu and Junkai Zhi and Junli Gao and Ke Wang and Lekang Yang and Liang Xu and Lin Fan and Lindong Wu and Lintao Ding and Lu Wang and Man Zhang and Minghao Li and Minghuan Xu and Mingming Zhao and Mingshu Zhai and Pengfan Du and Qian Dong and Shangde Lei and Shangqing Tu and Shangtong Yang and Shaoyou Lu and Shijie Li and Shuang Li and Shuang-Li and Shuxun Yang and Sibo Yi and Tianshu Yu and Wei Tian and Weihan Wang and Wenbo Yu and Weng Lam Tam and Wenjie Liang and Wentao Liu and Xiao Wang and Xiaohan Jia and Xiaotao Gu and Xiaoying Ling and Xin Wang and Xing Fan and Xingru Pan and Xinyuan Zhang and Xinze Zhang and Xiuqing Fu and Xunkai Zhang and Yabo Xu and Yandong Wu and Yida Lu and Yidong Wang and Yilin Zhou and Yiming Pan and Ying Zhang and Yingli Wang and Yingru Li and Yinpei Su and Yipeng Geng and Yitong Zhu and Yongkun Yang and Yuhang Li and Yuhao Wu and Yujiang Li and Yunan Liu and Yunqing Wang and Yuntao Li and Yuxuan Zhang and Zezhen Liu and Zhen Yang and Zhengda Zhou and Zhongpei Qiao and Zhuoer Feng and Zhuorui Liu and Zichen Zhang and Zihan Wang and Zijun Yao and Zikang Wang and Ziqiang Liu and Ziwei Chai and Zixuan Li and Zuodong Zhao and Wenguang Chen and Jidong Zhai and Bin Xu and Minlie Huang and Hongning Wang and Juanzi Li and Yuxiao Dong and Jie Tang},
|
||||
year={2025},
|
||||
eprint={2505.09388},
|
||||
eprint={2508.06471},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CL},
|
||||
url={https://arxiv.org/abs/2505.09388},
|
||||
url={https://arxiv.org/abs/2508.06471},
|
||||
}
|
||||
```
|
||||
Loading…
Reference in New Issue
Block a user