Simon Willison’s Weblog: Qwen3-8B

May 2, 2025

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Source URL: https://simonwillison.net/2025/May/2/qwen3-8b/#atom-everything
Source: Simon Willison’s Weblog
Title: Qwen3-8B

Feedly Summary: Having tried a few of the Qwen 3 models now my favorite is a bit of a surprise to me: I’m really enjoying Qwen3-8B.
I’ve been running prompts through the MLX 4bit quantized version, mlx-community/Qwen3-8B-4bit. I’m using llm-mlx like this:
llm install llm-mlx
llm download-models mlx-community/Qwen3-8B-4bit

This pulls 4.3GB of data and saves it to ~/.cache/huggingface/hub/models–mlx-community–Qwen3-8B-4bit.
I assigned it a default alias:
llm aliases set q3 mlx-community/Qwen3-8B-4bit

And now I can run prompts:
llm -m q3 ‘brainstorm questions I can ask my friend who I think is secretly from Atlantis that will not tip her off to my suspicions’

Qwen3 is a “reasoning" model, so it starts each prompt with a block containing its chain of thought. Reading these is always really fun. Here’s the full response I got for the above question.
I’m finding Qwen3-8B to be surprisingly capable for useful things too. It can summarize short articles. It can write simple SQL queries given a question and a schema. It can figure out what a simple web app does by reading the HTML and JavaScript. It can write Python code to meet a paragraph long spec – for that one it "reasoned" for an unreasonably long time but it did eventually get to a useful answer.
All this while consuming between 4 and 5GB of memory, depending on the length of the prompt.
I think it’s pretty extraordinary that a few GBs of floating point numbers can usefully achieve these various tasks, especially using so little memory that it’s not an imposition on the rest of the things I want to run on my laptop at the same time.
Tags: llm, models, qwen, mlx, generative-ai, ai, local-llms, llm-reasoning

AI Summary and Description: Yes

Summary: The text discusses the author’s experience with the Qwen3-8B language model, particularly focusing on its capabilities, such as reasoning, summarization, and code generation, while highlighting its efficient resource consumption. This is relevant to AI, generative AI, and LLM security due to the implications for resource management, deployment, and potential vulnerabilities.

Detailed Description: The author shares insights and experiences related to the Qwen3-8B model, emphasizing its effectiveness in various applications and its efficient use of system resources. Key points include:

– **Model Usage**: The author has been utilizing the MLX 4bit quantized version of the Qwen3-8B model, demonstrating how to install and manage it through command-line instructions.
– **Functional Capabilities**:
– The model notably handles prompt-based tasks such as brainstorming questions, summarizing articles, writing SQL queries, and understanding web app functionalities through HTML and JavaScript analysis.
– It also generates Python code based on specified requirements, showcasing its reasoning process and eventual capability to provide useful answers.
– **Resource Efficiency**: The model operates efficiently, consuming between 4 to 5GB of memory, indicating that it can run alongside other applications without significant performance impacts, which is appealing for users with limited resources.
– **Interesting Features**: The notation of a “think” block in its responses points to a structured approach in reasoning, which adds an engaging element to user interactions.

The text is significant for professionals in AI and infrastructure security as it illustrates practical applications of LLMs and discusses resource management strategies while hinting at the importance of monitoring resource use in production environments. Understanding such models’ behavior and performance can help in assessing their security implications.

– **Practical Implications**:
– Insights into model efficiency can guide cloud resource allocation and optimization strategies.
– Awareness of the reasoning chain may have implications for safeguarding user data and preventing unauthorized inferences or outputs.
– Leveraging tools so efficiently can assist in compliance with resource utilization policies in regulated environments.

Overall, this discussion touches on various components relevant to security, efficiency, and the deployment of generative AI in professional settings.

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