#qwen · Cascadity

CVCommunity Views #Alibaba #community #local-ai #open-weights #qwen #reddit

r/LocalLLaMA cheers a Qwen statement that future Qwen 4 and Qwen 5 models will stay open source

A window into how much the open-weights crowd depends on Alibaba's Qwen, and how far it trusts a corporate 'forever' promise.

The post answers rumors from the previous few days that Qwen4 would be the last open-weight Qwen series and that later models would be available only through an API. It shares an image of a statement it attributes to Qwen head Liu Dayiheng, saying future Qwen 4.0 and Qwen 5.0 models will continue to be open source, that AI's value should not belong to only a few people, and that the series has passed 3 billion downloads, 460 open-source models and 300,000 derivative models. We could not find a separate report of the Qwen 4/5 wording; 36Kr reported very similar remarks, and the same figures, from Liu's first appearance as Qwen's leader at Alibaba's conference in Hangzhou on September 22.

It was the top thread of our sweep relative to community size: about 4,800 upvotes and 500 comments in a 329,000-member community. The most-upvoted replies celebrate the pledge; others praise how capable Qwen's models are for their size. Skeptics point out that a company's 'forever' promise lasts only as long as its current management and business conditions, and a few add that it holds only as long as China's government allows it.

Read it at the source

Reddit (r/LocalLLaMA)Official Statement: Qwen Models Will Stick to Open Source to the End! Independent ↗ 36KrQwen New Top Leader Debut: Can Liu Dayiheng Withstand the Pressure? Independent ↗

Rabbit Holes 2

Qwen

After exploring Official Statement: Qwen Models Will Stick to Open Source to the End!, the model family's history is laid out in Qwen.

Wikipedia describes Qwen as a family of mostly open-weights large and small language models developed by Alibaba Cloud.

Wikipedia · Wade · 5 min

0
Open-source artificial intelligence

After exploring Official Statement: Qwen Models Will Stick to Open Source to the End!, for why 'open weights' and 'open source' are not quite the same thing, see Open-source artificial intelligence.

Wikipedia explains the Open Source Initiative's definition: an AI system that is freely available to use, study, modify and share, including its training data, code and model parameters.

Wikipedia · Wade · 5 min

0

MVMaker Views #gpt-6-astra #Hacker-News #interpretability #machine-learning #mit-csail #openai #qwen #research #research-debate #weight-space-programming

MIT researchers say GPT-6 Astra can read and edit neural network weights directly; a critic says the headline result needed code

The original post has the experiments and full logs; Matthew Honnibal's rebuttal tables which ones really worked from raw weights alone, and Simon Willison tried one on a laptop model.

In a November 2 blog post, Rikiya Takehi, Jayden Teoh, Alex Zhang and Omar Khattab of MIT CSAIL report that OpenAI's GPT-6 Astra can "natively" read and write neural network weights, which they call Weight-Space Programming. Given only the raw numbers of small networks, Astra named which of four grid games each was trained on and what shape each of three diffusion models draws. It also wrote small game-playing networks from scratch. Asked to make the Qwen3.5-0.8B language model better at math without training, it found a cause of the model's reasoning loops and edited 0.016% of its weights; the authors say MATH-500 rose from 7.9% to 48.1% and IFEval from 21.0% to 62.3%. A similar attempt on Gemma 3 270M did not help. The logs are public on GitHub.

spaCy author Matthew Honnibal called the post misleading: by his count only three experiments, on networks of 17,000 to 172,000 weights, worked from raw weights with no tools, while the Qwen result used Python and up to 50 test runs of the model, much as a human ML engineer would. Simon Willison found that Qwen 3.8 27B on his laptop also named three of the four games from the published prompts, and wrote that some wider commentary on the paper did not feel justified.

Read it at the source

Rikiya Takehi (MIT CSAIL)Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights ↗ Hacker NewsDiscussion: Astra can read and write weights to improve Qwen Independent ↗ honnibal.devThe 'Astra can read and write weights to improve Qwen' post is misleading · honnibal.dev Independent ↗ Hacker NewsDiscussion: The 'Astra can read and write weights to improve Qwen' post is misleading Independent ↗ Simon Willison's WeblogQwen 3.8 27B on my laptop can interpret model weights, too Independent ↗

Cascadic Analysis 2

UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

The striking numbers, MATH-500 from 7.9% to 48.1% after editing 0.016% of Qwen's weights, come from the authors' own experiments, and the same approach did not help Gemma 3 270M. Matthew Honnibal counts only three experiments that worked from raw weights without tools, while the Qwen result used Python and up to 50 test runs of the model. Simon Willison found a 27B model on his laptop named three of the four games from the published prompts, so the gap between "reads weights natively" and what the logs show is worth checking before relying on it. The logs being public is what makes that check possible.

UndertowWe don't completely understand why frontier models behave as they do

What hidden risk could pull against this, even if the news is good?

If the Qwen result holds, a model found a cause of another model's reasoning loops and changed a tiny fraction of its weights, a fix no person wrote and that the same method could not repeat on Gemma. Weights are not source code you can read line by line, so an edit that lifts test scores is not the same as an edit anyone can explain. Better scores show the change helped on those tests; they don't show what else in the model it touched.

Rabbit Holes 1

Mechanistic interpretability

After exploring Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights, for the field this work builds on, read about Mechanistic interpretability.

The research field that tries to understand what neural networks compute by analyzing their internal structures and circuits, much like reverse engineering conventional software.

Wikipedia · Wade · 5 min

0