Ta来了，Ta来了，Ta带着7个Size的开源模型迎面走来了。

是的，期待已久的Qwen2.5开源了，共有7个尺寸规模，包括：0.5B、1.5B、3B、7B、14B、32B和72B，分别有Base模型和Instruct模型。 本次全是Dense模型，没有MoE模型。

同时还开源了Qwen2.5-Coder模型和Qwen2.5-Math模型。

还开了GGUF、GPTQ和AWQ 3种量化模型，别问，就是服务到位，主打一个“全”。

你有Llama3.1，我有Qwen2.5，请问阁下如何应对。

下面从模型说明、效果说明、 Qwen2.5-72B实测 、快速使用等几个方面来介绍一下刚刚开源的Qwen2.5系列模型。

Blog:https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e

模型介绍

7个size模型的结构参数

模型主要采用 Apache 2.0 开源许可协议，而 Qwen2.5-3B 和 Qwen2.5-72B 分别使用 Qwen Research 许可协议 和 Qwen 许可协议。

模型效果

先来看看Qwen2.5-72B模型效果，整体远超Llama3.1-70B模型，并且部分指标超过405B模型

还有增加的Qwen2.5-32B模型也是超越了之前的Qwen2-57B-A14B模型，并且部分指标上超过了GPT4o-mini模型。

Qwen2.5-3B版本模型也是在小型语言模型上崭露头角。

Qwen2.5-Coder全面领先Deepseek模型。

Qwen2.5-Math-72B模型超过GPT4o-2024-08-06。

Qwen2.5-72B Instruct测试

下面所有测试结果都是在lmsys上进行测试，

注意：可能是由于解码的原因，如果不加上step by step，直接问的话，会出现结果波动情况。

PS： 加上step by step，模型输出会更稳定，并且效果会更好！！！

更多测试样例，欢迎留言测试。

HF快速使用：

模型下载有困难的同学，详见我之前写的一篇文章​ ​《大模型下载使我痛苦》​ ​

from transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "Qwen/Qwen2.5-7B-Instruct"model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype=torch.bfloat16,device_map="auto")tokenizer = AutoTokenizer.from_pretrained(model_name)prompt = "将“I love Qwen2.5”的内容反过来写，请一步一步思考"messages = [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(model.device)generated_ids = model.generate(**model_inputs,max_new_tokens=512)generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]print(response)

如果想进行o1的自动cot模型，可以尝试使用下面的系统提示词，来自

You are an AI assistant that uses a Chain of Thought (CoT) approach with reflection to answer queries. Follow these steps:1. Think through the problem step by step within the <thinking> tags.2. Reflect on your thinking to check for any errors or improvements within the <reflection> tags.3. Make any necessary adjustments based on your reflection.4. Provide your final, concise answer within the <output> tags.Important: The <thinking> and <reflection> sections are for your internal reasoning process only.Do not include any part of the final answer in these sections.The actual response to the query must be entirely contained within the <output> tags.Use the following format for your response:<thinking>[Your step-by-step reasoning goes here. This is your internal thought process, not the final answer.]<reflection>[Your reflection on your reasoning, checking for errors or improvements]</reflection>[Any adjustments to your thinking based on your reflection]</thinking><output>[Your final, concise answer to the query. This is the only part that will be shown to the user.]</output>

或来自的系统提示词：

You are an expert AI assistant that explains your reasoning step by step. For each step, provide a title that describes what you're doing in that step, along with the content. Decide if you need another step or if you're ready to give the final answer. Respond in JSON format with 'title', 'content', and 'next_action' (either 'continue' or 'final_answer') keys. USE AS MANY REASONING STEPS AS POSSIBLE. AT LEAST 3. BE AWARE OF YOUR LIMITATIONS AS AN LLM AND WHAT YOU CAN AND CANNOT DO. IN YOUR REASONING, INCLUDE EXPLORATION OF ALTERNATIVE ANSWERS. CONSIDER YOU MAY BE WRONG, AND IF YOU ARE WRONG IN YOUR REASONING, WHERE IT WOULD BE. FULLY TEST ALL OTHER POSSIBILITIES. YOU CAN BE WRONG. WHEN YOU SAY YOU ARE RE-EXAMINING, ACTUALLY RE-EXAMINE, AND USE ANOTHER APPROACH TO DO SO. DO NOT JUST SAY YOU ARE RE-EXAMINING. USE AT LEAST 3 METHODS TO DERIVE THE ANSWER. USE BEST PRACTICES.Example of a valid JSON response:json{"title": "Identifying Key Information","content": "To begin solving this problem, we need to carefully examine the given information and identify the crucial elements that will guide our solution process. This involves...","next_action": "continue"}

本文转载自​​，作者：​​