Your current environment
The output of python collect_env.py
Thinking Process:
1. **Analyze the Request:**
* Input: "你是谁？" (Who are you?)
* Language: Chinese.
* Intent: The user is asking for my identity.
2. **Determine the Identity:**
* I am an AI assistant.
* Specifically, I am Qwen3.5, a large language model developed by Alibaba Cloud's Tongyi Lab.
* Tone: Friendly, helpful, polite, concise.
3. **Draft the Response:**
* Standard greeting.
* State name/model.
* Mention capabilities briefly (optional but good for context).
* Offer assistance.
* *Draft 1 (Internal):* 我是 Qwen3.5。我是阿里云开发的。很高兴认识你。有什么我能帮你的吗？
* *Draft 2 (More natural):* 你好！我是 Qwen3.5，阿里巴巴云研发的超大规模语言模型。我可以帮你回答问题、写故事、写公文、写代码、表达观点、玩游戏等等。请问有什么我可以帮你的吗？
* *Draft 3 (Aligned with safety and helpfulness):* 你好！我是 Qwen3.5，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以协助你处理各种任务，比如回答问题、创作文字、编写代码、分析数据等。请问今天有什么可以帮你的吗？
4. **Refine based on Knowledge Cutoff:**
* My knowledge cutoff is 2026. I should reflect that if relevant, but usually, just stating I'm Qwen3.5 is sufficient.
5. **Final Polish (Chinese):**
你好！我是 Qwen3.5，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以协助你进行对话、回答问题、创作文字、编写代码、分析逻辑以及处理多种任务。请问有什么我可以帮你的吗？
6. **Safety Check:**
* Does this reveal sensitive info? No.
* Is it polite? Yes.
* Is it accurate? Yes.
7. **Final Output Generation.** (Keep it friendly and direct).
* *Revised:* 你好！我是 Qwen3.5，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以帮你回答问题、创作文字、编写代码、分析逻辑等等。很高兴认识你，请问有什么可以帮你的吗？
* *Simpler:* 你好！我是 Qwen3.5，阿里巴巴云研发的通义千问大模型。很高兴为你服务！请问有什么我可以帮你的吗？
* *Decision:* Keep it professional yet friendly.
(Self-Correction): Since I need to be concise as a chat response:
"你好！我是 Qwen3.5，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以协助你完成各种任务，比如回答问题、写故事、写公文、写代码、表达观点等。请问有什么我可以帮你的吗？"
Actually, for "Who are you", a simple direct answer is best.
"你好！我是 Qwen3.5，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。很高兴认识你，请问有什么可以帮你的吗？"
Let's add a bit about capabilities to be helpful.
"你好！我是 Qwen3.5，阿里巴巴云研发的超大规模语言模型。我可以帮你回答问题、创作文字、编写代码、分析文档等。请问今天需要我为你做什么？"
Okay, finalizing.
*Wait, one detail:* I should ensure I don't hallucinate specific parameter counts or capabilities beyond my general identity. Just general "Qwen3.5" is fine.
*Final Plan:*
1. Greet.
2. State name (Qwen3.5).
3. State developer (Alibaba Cloud/Tongyi Lab).
4. Offer help.
*Output:*
你好！我是 Qwen3.5，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以协助你回答问题、创作文字、编写代码、分析逻辑以及处理各种任务。很高兴认识你，请问有什么可以帮你的吗？
</think>
你好！我是 Qwen3.5，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以协助你回答问题、创作文字、编写代码、分析逻辑以及处理各种任务。很高兴认识你，请问有什么可以帮你的吗？
Only the closing tag is present, without the opening tag, causing confusion.
🐛 Describe the bug
In the output generated by vLLM, only the closing tag is present without the opening tag, resulting in a large amount of thought process content that cannimport os
from openai import OpenAI
client = OpenAI(
# 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
api_key="sk-", # 如何获取API Key：https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key
base_url="http://*/v1",
)
completion = client.chat.completions.create(
model="Qwen3.5-35B-AWQ", # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models huihui_ai/qwen3-abliterated:8b
messages=[
{'role': 'system', 'content': 'You are a helpful assistant.'},
{'role': 'user', 'content': '你是谁？'}
]
)
print(completion.choices[0].message.content)ot be cleaned up.
Before submitting a new issue...
Your current environment
The output of
python collect_env.py🐛 Describe the bug
In the output generated by vLLM, only the closing tag is present without the opening tag, resulting in a large amount of thought process content that cannimport os
from openai import OpenAI
client = OpenAI(
# 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
api_key="sk-", # 如何获取API Key：https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key
base_url="http://*/v1",
)
completion = client.chat.completions.create(
model="Qwen3.5-35B-AWQ", # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models huihui_ai/qwen3-abliterated:8b
)
print(completion.choices[0].message.content)ot be cleaned up.
Before submitting a new issue...