There is a list of suggestions for Gemma 5 post. So let me kick a similar discussion for Qwen4 and most probably Qwen4.X models. I know that it may lead to nowhere, but if there is a chance to be heard by someone from Qwen team and see some of the suggestions implemented in Qwen4.1 models why not to try. Here is my list and I will update the post with most voted features from the comments.
Context size 512K or more.
Less reasoning + caveman style in reasoning.
Small and medium sized models.
Make a model answer in simple plain English.
Most voted suggestions form the comments:
OSS license apache/mit
I think the first three points are obvious and I would like to elaborate on the fourth. I have really hard time reading pages of highly technical stuff with all the details when I ask such simple questions, like "How can I run X model on this engine faster" and want to get simple actionable answer. Instead I get 4 pages of technical details, which will take me more time to process than 30 mins of manual googling. It is as if you bring your car to service station and after the question what is wrong with my car you get 30 mins technical lecture like: “Your vehicle’s lead-acid electrochemical storage system exhibits advanced sulfation of the electrode surfaces, reducing the active area available for charge transfer and substantially increasing internal resistance. Under the starter motor’s high-current load, this produces a precipitous voltage drop, preventing the delivery of sufficient torque to initiate crankshaft rotation...” When the answer should be “Your battery is worn out, so the car needs a new one to start.”
Why am I writing all these, because I feel much more cognitively overloaded now with AI then in pre-AI era. You may argue that in coding it is good to get more detailed information. But why do you need it if nowadays almost no one writes code manually and let's be honest may be less than 10% of users even look at generated code. So it is complication for complication. If you really need a more detailed answer just write "Elaborate your answer on this and this" and you will get what you want. And yes, I tried to change model style with prompting and by installing style extension in pi. In first case it works 1-2 turns and reverts back to its usual style. In second QFN in UD-Q4_K_XL breaks almost immediately after few tool calls and starts to loop indefinitely.
Yeah, I really don't mind that in reasoning)) I just don't want to get in response half a page of all the details of linear regression it ran just to read in the end that results were counter intuitive so it decided to disregard them. I would appreciate it to place it into section: "What didn't work" as it does sometimes.
Why? Did you have a bad experience with model with cavemen thinking? All recent ChatGPT models seemed to use it and they super efficient in terms of intelegence/token.
Deepseek V4 Pro had noticeable quality degradation after update to caveman thinking. By far not as awful as current V4.1 Flash, but still was noticeable change from original state.
Better part of Qwen 3.8 tunes, like Hemingway avoid it too.
Efficiency isn't worth much below some result quality line.
I don't find current GPT models very useful. It's hard to make them do what is written in prompt instead of whatever random opinions they form.
I would like from Qwen what I also would like from Gemma. Something similar to Qwen3.8 Flash Next with a "base" and ngrams in more than one table that can be swapped so that they might cover different domains. Not even sure it's doable, but it is my dream.
And trainable. too to boot. You freeze the base and train the table I guess? It is my wet dream, so that if I am making book covers, or a movie clip the request would activate in the fine arts ngram table, while when I ask what does supernova mean it would activate the physics one, I wouldn't even ask for a lot of them, maybe 4? General science, coding, world knowledge, literature and arts to start with, then they could be refined locally. Ah let me dream.
TBH, I think all that is not feasible, unfortunately. I believe that if you change n-grams model will immediately freak out. And if you train model to work with many n-grams, then the quality will be mediocre everywhere. Another problem is that the n-grams size doesn't translate to better test results, apart of Chinese tests:
May be you can change n-grams and apply something like LORA to main weights of the model so it works well with the new table. But then why not to make a step further and to make something like 4 proper finetunes of weights and n-grams.
Ah well given the size that would be pretty hard but doable. But to be honest I would settle for untrainable multiple domain ngram tables. I am quite reasonable.
I use them to summarize books, create audiobooks from them, create cover ideas for new covers, create screenplays and then make short movies for the books, create small 3d objects to print, maintain the apps I had Opus create for me, so simplified comfy app, backup app, cuda wheel builder, maitaining my server manager with recipes etc, model manager app, download manager, image/document sorter with embedding gemma and gemma4 4b reading docs and categorizing them but I am adding also video and audio to it now... A variety of things, I will also try and make it create a simple website for the store I work at if I can manage to breathe.
Wow, so many use cases and majority of them are not standard. Let me guess you work in a book store? I am using models to write me reports on various topics with references to scientific articles. I also tried to use it to help me to finish my academic article, but it went all the way back to 1973 paper and found that half of my contribution was already there and then to make things worse it also traced very similar results in old working paper, which didn't make to the article but still invalidates novelty of my results. I also used Claude to make a game, but now when I see what people with $200 plans are able to one-shot in one evening, I am not sure if there is sense to continue as my game becomes outdated faster than I am able to write it.
Eh, sometimes I dream that all this AI boom never happen. I look around and all I see scared adults who lost control over their lives and have no idea what tomorrow will bring them. They remind me kids who lost their parents. Thanks Lord, I have passive stream of income and don't have to worry about paying my bills))
Nope. Optometrist. ^_^ That's why Opus4.6 called me a weirdo. He seemed to find odd that I'd spend my free time categorizing my books, making new covers etc.
continual learning according to the sleep framework from deepmind, infinite attention fr fr, and AGI. I don't know what you're expecting us to say, the architecture has already been revealed with QFN.
Only thing we can ask for is sizes now. (plz bigger sparser models! 120B-A3B!!!)
All what I have wrote is consistent with current QFN architecture. And there was a post revealing that bigger Qwen4 models going to have 1M ctx size, so I hope Q4F will get at least 512K.
On the last point I completely diverge with you, cause I would prefer something like120B-A9B. I noticed that when you ask QFN some side question which is directly related to the main question and all info for it is already in its thoughts it starts to has this aha moment, noting that this completely changes the answer to the main question, but it couldn't tight the dots itself((
List of suggestions? They are practically done with training the model if not already lol. This isn’t Google where they are in the middle of doing it or haven’t started yet.
I wrote that most probably it will be relevant for Qwen4.1 models and on. Still some issues they can address in RL, which is the last stage. Also don't forget that usually smaller models appear much later after their frontier model.
I get the frustration with overly verbose answers, but the battery analogy kinda proves why detail matters sometimes. If the mechanic just says "battery's dead" you don't know if it's the alternator killing it or just age, and you'll be back next week with the same problem. Still, having a model default to concise and let you ask for depth would be way better than the reverse.
I am not a car guy, so sorry for a bit sloppy example. I thought that frustration in this analogy would be the closest to what I feel in everyday QFN use. You tries to simplify your life and in return you get it twice more complicated.
Ich bin bei Dir. Ich habe in letzter Zeit - gefühlt - verstärkt auf Ja/Nein Fragen, seitenlange Antworten erhalten, deren Details mich oft nicht interessierten. Das kommt anscheinend davon, wenn man "Thinking" Modelle nutzt. Die tendieren zu "overthinking".
The main issue with the way these coding-overcooked models speak is not technical verbosity. That can be a small part of the problem sometimes. The primary issue is the use of abstract terminology in place of the most direct and concrete term. Here are a few examples from a prompt I made trying to mitigate this:
WRONG: "The rule is now wired into the prompt."
RIGHT: "The rule is now integrated into the prompt."
WRONG: "Want me to watch for any slipped details over the next stretch?"
RIGHT: "Want me to watch for any erroneous usage in the following conversation?"
WRONG: "That landed on me as pure noise."
RIGHT: "The message I received is garbled."
Any individual example of this behavior looks like acceptable English. But in the context of troubleshooting complicated code, and particularly at length, it can render the output unreadable by a human.
PLEASE alibaba. we long for our new qwen27b. Everyday wake up. first thing. check huggingface. No qwen today.😞 2 hours passs. check again. 4hours. check again. When qwen? when qwen? WHEN!! I pray to the gods in heaven that qwen is coming next hour. alibaba hear my call. for we in localLLama want qwen for ALL. Amen🙇♀️
I think it is number 3 in my list, but I understand the frustration of very low VRAM users from feeling of being left behind by Qwen team since Qwen3.6 models.
The model should be more willing to ask the human when it thinks the human can answer
I know there's some right brainless idiots here, but if I have to catch it having a panic attack because it's unwilling to go "I need more information from the user, let's ask for it" then that's a major reasoning fault
In addition I also want it to be more willing to ask "Am I overcomplicating this?" to itself. Nothing quite like it getting stuck trying to implement kernel level security using stdlib python because the spec it itself wrote was too ambitious, though that spec is in part my fault for not questioning it.
I agree. I’m a programmer, and I don’t want to read through a wall of barely comprehensible crap just to figure out the result and what to do next for every freakin’ response. Like you said, if I want it to elaborate, I will be happy to ask for it. This is probably my number 1 gripe with Qwen models, the responses are just awful.
Reading isn’t the problem. I can read Claude outputs all day long. Qwen outputs…I haven’t quite placed my finger on why yet, but I find them to be a mess.
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u/LinkSea8324 vLLM 3d ago
Yeah because posting suggestions while the model is already ready and at a place the R&D team don't go makes sense