Source URL: https://simonwillison.net/2024/Nov/27/qwq/#atom-everything
Source: Simon Willison’s Weblog
Title: QwQ: Reflect Deeply on the Boundaries of the Unknown
Feedly Summary: QwQ: Reflect Deeply on the Boundaries of the Unknown
Brand openly licensed model from Alibaba Cloud’s Qwen team, this time clearly inspired by OpenAI’s work on reasoning in o1.
I love how the introduce the new model:
Through deep exploration and countless trials, we discovered something profound: when given time to ponder, to question, and to reflect, the model’s understanding of mathematics and programming blossoms like a flower opening to the sun. Just as a student grows wiser by carefully examining their work and learning from mistakes, our model achieves deeper insight through patient, thoughtful analysis.
It’s already available through Ollama as a 20GB download. I initially ran it like this:
ollama run qwq
This downloaded the model and started an interactive chat session. I tried the classic “how many rs in strawberry?" and got this lengthy but correct answer, which concluded:
Wait, but maybe I miscounted. Let’s list them: 1. s 2. t 3. r 4. a 5. w 6. b 7. e 8. r 9. r 10. y Yes, definitely three "r"s. So, the word "strawberry" contains three "r"s.
Then I switched to using LLM and the llm-ollama plugin. I tried prompting it for Python that imports CSV into SQLite:
Write a Python function import_csv(conn, url, table_name) which acceopts a connection to a SQLite databse and a URL to a CSV file and the name of a table – it then creates that table with the right columns and imports the CSV data from that URL
It thought through the different steps in detail and produced some decent looking code.
Finally, I tried this:
llm -m qwq ‘Generate an SVG of a pelican riding a bicycle’
For some reason it answered in Simplified Chinese. It opened with this:
生成一个SVG图像，内容是一只鹈鹕骑着一辆自行车。这听起来挺有趣的！我需要先了解一下什么是SVG，以及如何创建这样的图像。
Which translates (using Google Translate) to:
Generate an SVG image of a pelican riding a bicycle. This sounds interesting! I need to first understand what SVG is and how to create an image like this.
It then produced a full essay discussing the many aspects that go into constructing a pelican on a bicycle – full transcript here. After a full 227 seconds of constant output it produced this as the final result.
I think that’s pretty good!
Tags: llm, ollama, generative-ai, ai, qwen, llms
AI Summary and Description: Yes
Summary: The text discusses the capabilities of a newly released model from Alibaba Cloud’s Qwen team, reflecting on its reasoning skills and its ability to generate thoughtful outputs in both English and Simplified Chinese. This highlights advancements in generative AI, particularly regarding large language models (LLMs) and their application in programming tasks and creative generation.
Detailed Description: The text showcases a brand openly licensed model named QwQ from Alibaba Cloud, emphasizing its novel approach to reasoning and generative capabilities inspired by OpenAI’s advancements. It reflects a growing trend in AI that focuses on deep exploration and analysis, resulting in enhanced understanding and output quality.
Key points highlighted in the text include:
* **Model Inspiration**: The QwQ model is inspired by OpenAI’s methodologies, emphasizing a reflective learning approach analogous to human learning through error analysis.
* **Interactive Chat Feature**: The model is available for download and can be operated via an interactive chat session, showcasing seamless integration into user workflows.
* **Problem-Solving Abilities**: The author tested the model’s capabilities with various prompts, including basic mathematical queries and more complex programming tasks, noting its ability to provide correct and thoughtful answers.
* **Language Versatility**: The ability of the model to respond in different languages, such as Simplified Chinese when generating SVG graphics, illustrates its multilingual capabilities and potential for creative applications.
* **Performance Metrics**: A specific interaction resulted in a lengthy, detailed output, demonstrating the model’s capacity to sustain extended generative efforts.
Practical Implications for Security and Compliance Professionals:
– **Exploration of Generative AI**: Insights into how generative AI can be employed in programming and creative tasks could lead to better understanding and security protocols for applications that utilize such models.
– **Multilingual capabilities**: This raises considerations for data handling, particularly in environments with diverse language users, necessitating compliance with various privacy laws across different jurisdictions.
– **Reflective Learning and Security Models**: The approach taken by QwQ could inspire security professionals to develop adaptive learning systems that refine their security measures through ongoing analysis and feedback loops.
Overall, the text underlines a significant development in LLM technology, offering insights into the evolving landscape of generative AI and its implications for various professional fields, particularly in AI security and application development.