0 Qwen3
今天，通义千问Qwen团队正式开源推出 Qwen3，这是 Qwen 系列大型语言模型的最新成员。最新的Qwen3系列模型具备双模推理能力（深入思考/快速响应）、支持119种语言及方言，并强化了Agent功能与代码执行能力，全面满足复杂问题处理与全球化应用需求。
Github： https://github.com/QwenLM/Qwen3
Blog：https://qwenlm.github.io/zh/blog/qwen3/
模型合集：https://www.modelscope.cn/collections/Qwen3-bdc6b48
b站视频：https://www.bilibili.com/video/BV1spG1zEEYR
使用GPU平台: https://www.autodl.com/home
PyTorch / 2.3.0 / 3.12(ubuntu22.04) / 12.1
安装transformers、accelerate
source /etc/network_turbo pip install transformers pip install accelerateQwen3 模型广场：https://bailian.console.aliyun.com/?tab=model#/model-market
通过魔塔社区下载模型：https://www.modelscope.cn/collections/Qwen3-bdc6b48
选择一个模型Qwen3-0.6B：https://www.modelscope.cn/models/Qwen/Qwen3-0.6B/files
使用SDK下载下载：
开始前安装
source /etc/network_turbo pip install modelscope # source /etc/network_turbo from modelscope import snapshot_download # 指定模型的下载路径 cache_dir = '/root/autodl-tmp' # 调用 snapshot_download 函数下载模型 model_dir = snapshot_download('Qwen/Qwen3-0.6B', cache_dir=cache_dir) # model_dir = snapshot_download('Qwen/Qwen3-8B', cache_dir=cache_dir) # model_dir = snapshot_download('Qwen/Qwen3-14B', cache_dir=cache_dir) print(f"模型已下载到: {model_dir}")或者：
modelscope download --model Qwen/Qwen3-0.6B mv /root/.cache/modelscope/hub/models/Qwen/ /root/autodl-tmp/Qwenfrom transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-0.6B" # model_name = "Qwen/Qwen3-8B" # model_name = "Qwen/Qwen3-14B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language models." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # the result will begin with thinking content in <think></think> tags, followed by the actual response print(tokenizer.decode(output_ids, skip_special_tokens=True))enable_thinking=True的结果
enable_thinking=False的结果
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