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Something is afoot in the land of Qwen
- raffael_de 8mo ago> me stepping down. bye my beloved qwen. the qwen is dead, long live the qwen.
- airstrike 8mo agoI'm hopeful they will pick up their work elsewhere and continue on this great fight for competitive open weight models. To be honest, it's sort of what I expected governments to be funding right now, but I suppose Chinese companies are a close second.
- zoba 8mo agoI tried the new qwen model in Codex CLI and in Roo Code and I found it to be pretty bad. For instance I told it I wanted a new vite app and it just started writing all the files from scratch (which didn’t work) rather than using the vite CLI tool. Is there a better agentic coding harness people are using for these models? Based on my experience I can definitely believe the claims that these models are overfit to Evals and not broadly capable.
- sosodev 8mo agoI've noticed that open weight models tend to hesitate to use tools or commands unless they appeared often in the training or you tell them very explicitly to do so in your AGENTS.md or prompt. They also struggle at translating very broad requirements to a set of steps that I find acceptable. Planning helps a lot. Regarding the harness, I have no idea how much they differ but I seem to have more luck with https://pi.dev https://pi.dev than OpenCode. I think the minimalism of Pi meshes better with the limited capabilities of open models.
- malwrar 8mo ago+1 to this, anecdotally I’ve found in my own evaluations that if your system prompt doesn’t explicitly declare how to invoke a tool and e.g. describe what each tool does, most models I’ve tried fail to call tools or will try to call them but not necessarily use the right format. With the right prompt meanwhile, even weak models shoot up in eval accuracy.
- mongrelion 8mo ago> [...] _but not necessarily use the right format._ This has also been my experience. But isn't the harness sending the instructions on how to invoke a tool? Maybe it is missing the formatting part. What do you think?
- ihsw 8mo ago[dead]
- vardalab 8mo agoHave frontier lab do the plan which is the most time consuming part anyways and then local llm do the implementation. Frontier model can orchestrate your tickets, write a plan for them and dispatch local llm agents to implement at about 180 tokens/s, vllm can probably ,manage something like 25 concurrent sessions on RTX 6000 Do it all in a worktrees and then have frontier model do the review and merge. I am just a retired hobbyist but that's my approach, I run everything through gitea issues, each issue gets launched by orchestrator in a new tmux window and two main agents (implementer and reviewer get their own panes so I can see what's going on). I think claude code now has this aspect also somewhat streamlined but I have seen no need to change up my approach yet since I am just a retired hobbyist tinkering on my personal projects. Also right now I just use claude code subagents but have been thinking of trying to replace them with some of these Qwen 3.5 models because they do seem cpable and I have the hardware to run them.
- lreeves 8mo agoIn my experience Qwen3.5/Qwen3-Coder-Next perform best in their own harness, Qwen-Code. You can also crib the system prompt and tool definitions from there though. Though caveat, despite the Qwen models being the state of the art for local models they are like a year behind anything you can pay for commercially so asking for it to build a new app from scratch might be a bit much.
- Tepix 8mo agoWhat is "the new qwen model"? There are a dozen and you can get them in a dozen different quantizations (or more) which are of different quality each.
- ChrisArchitect 8mo agoMore discussion: https://news.ycombinator.com/item?id=47246746 https://news.ycombinator.com/item?id=47246746
- sosodev 8mo agoI really hope this doesn't hinder development too much. As Simon says, Qwen3.5 is very impressive. I've been testing Qwen3.5-35B-A3B over the past couple of days and it's a very impressive model. It's the most capable agentic coding model I've tested at that size by far. I've had it writing Rust and Elixir via the Pi harness and found that it's very capable of handling well defined tasks with minimal steering from me. I tell it to write tests and it writes sane ones ensuring they pass without cheating. It handles the loop of responding to test and compiler errors while pushing towards its goal very well.
- paoliniluis 8mo agowhat's your take between Qwen3.5-35B-A3B and Qwen3-Coder-Next?
- sosodev 8mo agoIn my experience Qwen3.5 is better even at smaller distillations. From what I understand the Qwen3-next series of models was just a test/preview of the architectural changes underpinning Qwen3.5. So Qwen3.5 is a more complete and well trained version of those models.
- kamranjon 8mo agoIn my experience qwen 3 coder next is better. I ran quite a few tests yesterday and it was much better at utilizing tool calls properly and understanding complex code. For its size though 3.5 35B was very impressive. coder next is an 80b model so i think its just a size thing - also for whatever reason coder next is faster on my machine. Only model that is competitive in speed is GLM 4.7 flash
- skeeter2020 8mo agoGetting a bit of whiplash goin from AI is replacing people, to AI is dead without (these specific) people. Surely we're far enough ahead that AI can take it from here? Wild times!
- vidarh 8mo agoWho is suggesting "AI is dead without (these specific) people"? People are wondering what it means specifically for the Qwen model family.
- mhitza 8mo agoWe've gone from AGI goals to short-term thinking via Ads. That puts things better in perspective, I think.
- dude250711 8mo agoClaude is incapable of producing a native application for itself, and is bad enough with web ones to justify Anthropic acquiring Bun.
- janalsncm 8mo agoAnthropic has one nine of uptime right now. One. https://status.claude.com/ https://status.claude.com/ If AI could effectively replace people, you wouldn’t need CEOs to keep trying to convince people.
- OsrsNeedsf2P 8mo agoThat's 99% is two nines?
- softwaredoug 8mo agoI wonder how a US lab hasn't dumped truckloads of cash into various laps to ensure these researchers have a place at their lab
- mft_ 8mo agoIndeed; or, Europe badly needs a competitive model to hedge against US political nonsense.
- ivan_gammel 8mo agoOffering „You are welcome“ relocation package to Anthropic might be a good idea.
- Imustaskforhelp 8mo agoGiven how American govt. has treated Anthropic, I think you might be right. EU truly has a remarkable opportunity to make Anthropic/Claude European.
- petcat 8mo agoThis US administration (or any admin) would almost certainly impose export controls on US AI technology before it would allow one of the frontier model providers to be acquired/relocate outside the US. It did the same thing when ASML wanted to acquire Cymer (California company that provides the EUV light source technology). The acquisition was only allowed under strict technology sharing/export agreements with the Dutch government. Europe really just needs to rally behind Mistral. That's where they should dump their cash.
- ivan_gammel 8mo agoHaving one „champion“ is flawed European approach. We need local competition and headhunting to make it fly.
- vonneumannstan 8mo agoWere they kneecapped by Anthropic blocking their distillation attempts?
- zozbot234 8mo agoWhat Anthropic was complaining about is training on mass-elicited chat logs. It is very much a ToS violation (you aren't allowed to exploit the service for the purpose of building a competitor) so the complaint is well-founded but (1) it's not "distillation" properly understood; it can only feasibly extract the same kind of narrow knowledge you'd read out from chat logs, perhaps including primitive "let's think step by step" output (which are not true fine-tuned reasoning tokens); because you have no access to the actual weights; and (2) it's something Western AI firms are very much believed to do to one another and to Chinese models all the time anyway. Hence the brouhaha about Western models claiming to be DeepSeek when they answer in Chinese.
- red2awn 8mo agoThe "distillation attacks" are mostly using Claude as LLM-as-a-judge. They are not training on the reasoning chains in a SFT fashion.
- zozbot234 8mo agoSo they're paying expensive input tokens to extract at best a tiny amount of information ("judgment") per request? That's even less like "distillation" than the other claim of them trying to figure out reasoning by asking the model to think step by step.
- red2awn 8mo agoLLM-as-a-judge is quite effective method to RL a model, similar to RLHF but more objective and scalable. But yes, anthropic is making it more serious than it is. Plus DeepSeek only did it for 125k requests, significantly less than the other labs, but Anthropic still listed them first to create FUD.
- butILoveLife 8mo ago[flagged]
- kamranjon 8mo agoI use Qwen 3 Coder Next daily on my mac as my main coding agent. It is incredibly capable and its strange how you are painting this picture as if its a fringe use case, there are whole communities that have popped up around running local models.
- butILoveLife 8mo agoCan I doubt your claim? I have had such terrible luck with AI coding on <400B models. Not to mention, I imagine your codebase is tiny. Or you are working for some company that isnt keeping track of your productivity. I am trying super hard to use cheap models, and outside SOTA models, they have been more trouble than they are worth.
- arcanemachiner 8mo agoYesterday, I got Qwen-Coder-Next to build a python script that reads a Postman collection, pulls the data from it to build a request to one of the endpoints, download a specific group of files whose URLs were buried in the JSON payload in that endpoint, then transform then all to a specific size of PNG, all without breaking a sweat. I didn't even have to tell it to use Pillow, but it did everything to a T. Use case means everything. I doubt this model would fare well on a large codebase, but this thing is incredible.
- kamranjon 8mo agoAbsolutely. So my codebase is huge, it's a monolith. But my work is in very specific parts of the codebase, I don't pull the entire code base into context (and I don't think that is common practice even with claude) - I start at a specific point with a specific task and work with the agent to achieve something clearly defined, for example writing tests, extracting things into separate files, refactoring or even scaffolding a new feature. You have to periodically start new threads, because you'll start hitting the limits of the context, but I max it out at over 200k because I have the memory overhead on my 128gb mbp to do that, so I can get quite a lot done. I really recommend trying the Qwen models - 3 coder next is really incredible. GLM 4.7 flash is also incredibly performant on modest hardware. Important things to consider is setting the temperature and top_p and top_k values etc based on what is recommended by the provider of the model - a thing as simple as that could result in a huge difference in performance. The other big leap for me was switching to Zed editor and getting its agent stuff just seamlessly integrated. If you run LM Studio on your local machine it's super easy and even setting it up on a remote machine and calling out to LM Studio is dead simple.
- multisport 8mo ago[flagged]
- hwers 8mo agoMy conspiracy theory hat is that somehow investors with a stake in openai as well is sabotaging, like they did when kicking emad out of stabilityai
- storus 8mo agoMore likely some high ranking party member's nepobaby from Gemini sniffed success with Qwen and the original folks just walked away as their reward disappeared.
- liuliu 8mo agoapples v.s. oranges. The later is true, Emad did get sabotaged (for not being able to raise money in time, about 8-month before he's leaving). Junyang didn't have that long arc of incidents.
- jongjong 8mo agoInteresting reading this. It reminds me of my time in cryptocurrency sector. I suspected that some team members were paid by Ethereum folks to sabotage our project. Why do I suspect Ethereum? Because our project founders ended up switching to the Ethereum ecosystem and ignored/suppressed better solutions from their own ecosystem. I think there's something about tech hype which attracts these kinds of people who like to play dirty.
- ilaksh 8mo agoDoes anyone know when the small Qwen 3.5 models are going to be on OpenRouter?
- armanj 8mo agothey're already there ?? https://openrouter.ai/qwen/qwen3.5-27b https://openrouter.ai/qwen/qwen3.5-27b
- ilaksh 8mo agoLike 4B, 2B, 9B. Supposedly they are surprisingly smart.
- Sakthimm 8mo agoYep. The 9B has excellent image recognition. I showed it a PCB photo and it correctly identified all components and the board type from part numbers and shape. OCR quality was solid. Tool calling with opencode worked without issues, but general coding ability is still far from sonnet-tier. Asked it to add a feature to an existing react app, it couldn't produce an error-free build and fell into a delete-redo loop. Even when I fixed the errors, the UI looked really bad. A more explicit prompt probably would have helped. Opus one-shotted it, same prompt, the component looked exactly as expected. But I'll be running this locally for note summarization, code review, and OCR. Very coherent for its size.
- BoredomIsFun 8mo ago> Very coherent for its size. I found them to be less than stellar at writing coherent prose. Qwen 3.5 9b was worse in my tests than Gemma 3 4b.
- yorwba 8mo agoThere are smaller ones on HuggingFace https://huggingface.co/models?other=qwen3_5&sort=least_params&search=Qwen%2FQwen3.5 https://huggingface.co/models?other=qwen3_5&sort=least_param... with 0.8B, 2B, 4B and 9B parameters.
- quantum_state 8mo agoI would second that Qwen3.5 is exceptionally good. In a calibration, it (35b variant) was running locally with Ada NextGen 24GB to do the same things with easy-llm-cli in comparison with gemini-cli + Gemini 3 Pro, they were at par … really impressive it ran pretty fast …
- vardalab 8mo agoq4 quant gives you 175 tg and 7K pp, beats most cloud providers
- deleted 8mo ago[deleted]
- hintymad 8mo agoThere has been tension between Qwen's research team and Alibaba's product team, say the Qwen App. And recently, Alibaba tried to impose DAU as a KPI. It's understandable that a company like Alibaba would force a change of product strategy for any number of reasons. What puzzled me is why they would push out the key members of their research team. Didn't the industry have a shortage of model researchers and builders?
- cmrdporcupine 8mo agoPerhaps they wanted future Qwen models to be closed and proprietary, and the authors couldn't abide by that.
- gdiamos 8mo agoResults as good as Qwen has been posting would seem to trigger a power struggle. I think companies that don’t navigate these correctly eventually lose.
- nurettin 8mo agoI am singularly impressed by 35B/A3, hope that is not the reason he had to leave.
- kartika848484 8mo agowhat the hell, their models were promising tho
- aplomb1026 8mo ago[flagged]
- lacoolj 8mo agoI wonder if an american company poached one/all of them. They've been pretty much bleeding edge of open models and would not surprise me if Amazon or Google snatched them up
- ferfumarma 8mo agoIt would surprise me if they're willing to come to the US in the setting of the current DHS and ICE situation.
- lzaborowski 8mo agoOne thing I’ve noticed with local models is that people tolerate a lot more trial and error behavior. When a hosted model wastes tokens it feels expensive, but when a local model loops a bit it just feels like it’s “thinking.” If models like Qwen can get good enough for coding tasks locally, the real shift might be economic rather than purely capability.
- trvz 8mo agoWasted tokens are preferred for local models, I need the GPU mainframe in my bedroom to heat it as I live in a third world country with unreliable heating (Switzerland).
- MarsIronPI 8mo agohttps://xkcd.com/1172 https://xkcd.com/1172
- w10-1 8mo agoIt sounds like the lead was demoted to attract new talent, quit as a result, and the rest of the team also resigned to force management to change their minds. If so, I'm happy that the team held together, and I hope that endogenous tech leads get to control their own career and tech destiny after hard work leads to great products. (It's almost as inspiring as tank man, and the tank commanders who tried to avoid harming him...) (ducking the downvote for challenging the primacy of equity...)
- MarsIronPI 8mo agoWherever they end up next, I hope they can stick together as a team and that they keep insisting on publishing their models.
- vicchenai 8mo ago[dead]
- mmis1000 8mo ago> Only complaint is it sometimes decides to ignore half your prompt when instructions get long This sounds like your context is too big and getting cut off.
- ramgine 8mo agoRunning 32b on what hardware?
- nurettin 8mo agoI'm running it on a pure cpu 2020 model ryzen server with 2x128 GB RAM with llama.cpp, it seems as intelligent as gpt4. I optimized a little bit by forcing it to run on a single RAM stick and tuning llama.cpp build parameters, going from 3-5 tok/s to a more acceptable 5-8 tok/s. It can call tools and reason adequately enough to use them when appropriate.
- deleted 8mo ago[deleted]
- xyzsparetimexyz 8mo agoForget it Jake, its China(town)
- qwenverifier 8mo agoAs a mathematician, lately I experimented a lot with Qwen, to produce as good as possible professional summaries and relations between articles, and in one case even a verification of misattributions claims which was used in an arXiv article. All is collected in https://imar.ro/~mbuliga/ai-talks.html https://imar.ro/~mbuliga/ai-talks.html
- speedping 8mo agoopen blogpost → ⌘ + F "pelican" → 0 results ???
- RandyOrion 8mo agoFirst, thank you Junyang and Qwen team for your incredible work. You deserve better. This is sad for local LLM community. First we lost wizardLM, Yi and others, then we lost Llama and others, now we lost Qwen...
- umairnadeem123 8mo ago[dead]
- nopurpose 8mo agoHow do those companies make money? Qwen, GLM, Kimi, etc all released for free. I have no experience in the field, but from reading HN alone my impression was training is exceptionally costly and inference can be barely made profitable. How/why do they fund ongoing development of those models? I'd understand if they release some of their less capable models for street cred, but they release all their work for free.
- indrora 8mo agoOstensibly, a mix of VC funding and that they host an endpoint that lets them run the big (200+GB) models on their infrastructure rather than having to build machines with hundreds of gigs of llm-dedicated memory.
- wongarsu 8mo agoBut on inference they have to compete with other inference provider that just has a homepage, a bunch of GPUs running vllm and none of the training cost. Their only real advantage are the performance optimizations that they might have implemented in their inference clusters and not made public
- MarsIronPI 8mo agoQwen, at least, IIRC has some proprietary models, specifically the Max series. IIRC these have larger context windows.
- rwmj 8mo agoThe small spend may be worth it to destroy US proprietary AI companies.
- theshrike79 8mo agoChinese companies don't always operate on purely capitalistic principles, there is sometimes government direction in the background. For China, the country, it's a good thing if American AI companies have to scramble to compete with Chinese open models. It might not be massively profitable for the companies producing said models, but that's only a part of the equation