8 ms·
Will be interesting to see how Qwen3.8 27B compares against this once it releases this week. Seems like dense 30B is back in fashion? EDIT: An open weight vers
by scrlk 1mo ago
Will be interesting to see how Qwen3.8 27B compares against this once it releases this week. Seems like dense 30B is back in fashion?
EDIT: An open weight version of Muse Spark 1.2 is going to be released as well:
https://x.com/alexandr_wang/status/2086756152034066792 https://x.com/alexandr_wang/status/2086756152034066792
https://xcancel.com/alexandr_wang/status/2086756152034066792 https://xcancel.com/alexandr_wang/status/2086756152034066792
- spwa4 1mo agoWell, it has to, since even the MoE models can't really hold a conversation.
- wronglebowski 1mo agoIt’s really interesting timing, Qwen over thinking is what kills it for me. I’m just glad we have more options in this size class now.
- seanmcdirmid 1mo agoDisable thinking? I think many harnesses disable thinking on Qwen anyways because it interferes with tool calling.
- dannyw 1mo agoQwen thinking is really good in Mandarin; and probably natively trained the most there. Try a system prompt requiring it to think in Mandarin, while still delivering the response in the user’s language.
- kadoban 1mo agoIs the quality of the thinking better or it's just shorter since Mandarin is more compact?
- yiyu_earth 1mo agoThis is most likely because the vast majority of the information the model absorbed during training was in Chinese. As a native Mandarin speaker, I frequently need to convert the prompt into English and output it in English in order to avoid that the model falls back into Chinese reasoning logic. PS: Switching the thinking process from Chinese to English can also significantly circumvent certain self-censorship mechanisms built into the model.
- ComputerGuru 1mo agoJust to play devil’s advocate: you can’t compare Qwen to a (proprietary/closed source) hosted model and deduce that Qwen is overthinking, as Qwen gives you the full reasoning/thinking trace while all the proprietary models now give you only a summary “to prevent distillation”, making it hard to properly compare apples to apples here.
- naasking 1mo agoPeople say Qwen overthinks because they analyzed the thinking traces, and Qwen finds the answer relatively quickly but then second guesses itself multiple times for another 20,000+ tokens. Regardless of what other models do, that's clearly overthinking.
- seanmcdirmid 1mo agoYou can compare Qwen with thinking to Qwen with no thinking though. I find my results are better without thinking because of overthinking.
- Aurornis 1mo agoYou can tell how long the cloud models spend thinking based on the delay. The Qwen models have a habit of going into thought loops where they go in circles for a while.
- timmmmmmay 1mo agoNo, but you can compare it to the similarly-sized Gemma4 model and see the difference, it's not subtle
- jermaustin1 1mo agoI've been using Qwen3.6 35B A3B, and with reasoning turned on, I'd say 2/3 (give or take) of the tokens for a response are thinking tokens. Which at 70+ tps locally, that isn't that awful. I run an 80k context across 4-10 "agents" for my solo TTRPG, where Qwen is the GM, each NPC at a location, the director, and the narrator. Each turn is about 45-60 seconds to generate all of the various responses. The GM and director have reasoning on, and the NPCs/Location/Narrator do not. It's a fairly good "engine" for that. I'm not sure how a denser Qwen would do here regarding speed.
- jakswa 1mo agoI like the tabletop RPG use case, and wanted to say: If your hardware likes it you should check out Gemma 4 for creative DMing use case. I found it to be much better at holding the plotlines and being creative on gaming turns. My experimental case was an audio-only Zork and Gemma 12B and even E4B were pretty good!
- toyg 1mo agoIs there some sort of dedicated tool for this type of setup, or did you hand-craft it ?
- seanmcdirmid 1mo agoNot parent, but I use Goose for my non-handcrafted Qwen use cases, I’m also working on handcrafting as well. Goose was the only harness that didnt bloat context too much with system prompts (like openclaw) and I could get reasonable web search working with Qwen.
- jermaustin1 1mo agoSomewhat hand rolled, somewhat claude coded. Back in 2023 I started my own C# LLM library for doing tool calls and structured output, and over the years it has morphed bigger and bigger, and that is the backbone of almost all of my LLM-based projects. I've never released it, but its easy to understand, and simple to add your own tools: [AIDescription("Get current weather for a location")] static string GetWeather( [AIDescription("The city name")] string city, [AIDescription("The country name")] string country, [AIDescription("Temperature unit", ["C", "F"])] string unit = "C") { // make some API call to a weather API and return a string to the LLM return $"The weather in {city}, {country} is 22°{unit} and sunny"; } var chat = client.StartConversation("You are a helpful assistant with access to weather data."); var response = await chat.SendAsync<string>("What's the weather in London?", GetWeather); I'm sure plenty of better libraries exist for this now, but in 2023, I don't think any existed in the dotnet ecosystem. I've never released it though, because I've never "finished" it.
- cyanydeez 1mo agoLlamscpp provides reasoning budget and message. You can use the message to redirect it. Once you get the agent and message consistent,itll keep moving.
- ElectricalUnion 1mo agoYou can use any message you want, but the model was tested to react reasonably well to the specific token sequence of "\nConsidering the limited time by the user, I have to give the solution based on the thinking directly now.\n</think>.\n\n" (from a Alibaba paper, struggling to find it now) Edit: arXiv:2505.09388 Qwen3 Technical Report
- cyanydeez 1mo agoSince i have tools to prune context and run subagents, i just tell it to do either since both require summarization which is usually what it needs to avoid the long if...then chains
- Gecko4072 1mo agoMakes me feel hopeful. Things felt more positive around the llama 3 era. Now it’s like a dark, dreadful race.
- laybak 1mo agoI feel you. not sure if the "Glimmer" (of hope) branding is intentional to capture this vibe
- lostmsu 1mo agoIt seems worse than 3.6, but a bit smaller. UPD. was wrong on smaller, it's actually much larger
- IsTom 1mo agoHow is 30B smaller than 27B?
- LeBit 1mo agoIt uses fractal compression
- lostmsu 1mo agoThey say it is trained with quantization awareness, so it should only be 15GB or so. Qwen was only trained in FP8 with QAT. UPD, NVM, got misled by comments here. It is actually almost 60 GB so much larger
- xienze 1mo agoYou're mixing up sizes of different quants. The 60GB is unquantized, and Qwen's unquantized size is around 54GB. Their sizes as like quantization levels are similar.
- lostmsu 1mo agoFrom my perspective it doesn't make sense to talk about the number of parameters. What matters is model size in bytes and its performance at that certain size. Meta actually relesed official 4 bit quants in 17GB, but I haven't seen any indication that training was quant-aware, so the quants are not going to have same performance. 3.6 27B has official FP8 quant that AFAIR was trained with quantization awareness. The best example is last year's gpt-oss which was released prequantized in mxfp4 so 20B parameter model was under 14GB and 120B was under 70GB right away.
- petu 1mo ago
- karimf 1mo agoYes, and also waiting for the next iteration of Gemma. Muse or Qwen are optimized for coding, while IMO Gemma is still better for non-coding tasks. https://x.com/osanseviero/status/2086107547535122767 https://x.com/osanseviero/status/2086107547535122767
- dannyw 1mo agoYou can partially tell by the tokeniser; which gives you some hint into the training corpus mix. </div> is four Gemma4 tokens, but one Qwen3.6 token.
- stymaar 1mo agoLooks like we have a /r/localllama dweller here.
- venusenvy47 1mo agoWhere do you find this information for each model?
- ComputerGuru 1mo agoThe tokenizers are included in the open s̶o̶u̶r̶c̶e̶ weights releases; you wouldn’t be able to use the weights without the corresponding encoder/decoder, in fact.
- adrian_b 1mo agoWhen you look on HuggingFace.co at the files of a model, for each model you will see a file "tokenizer.json". In that file you can see all tokens and their corresponding numeric codes.
- deleted 1mo ago[deleted]
- malshe 1mo agoI am working on a project where we have to classify customer calls into more than 10 categories. As the client wants everything locally I tried a few local LLMs. Gemma turned out to be the best model for this task. The classification accuracy is impressive, and the client is happy that I am using an American model.
- pu_pe 1mo agoBased on the benchmarks, it seems that Muse Glimmer barely edges out against Qwen3.6 27B, except for tool-calling skills (MCP, etc.). I wouldn't be surprised if they released it now because they are afraid they wouldn't beat Qwen3.8 27B.
- mycall 1mo agoDo AI companies make release plans based on upcoming other models like this? I would think all the processes that go into the repository and weight infrastructure pre-training, checkpointing, knowledge distillation, model compression, post training pipeline, ecosystem integrations, inference API, benchmarking, human eval/safety/alignment, docs, etc... all that dictates the release schedule.
- echelon 1mo agoThere has been a long history of AI model releases made shortly before or after a major planned release by another company. Almost always to upstage or steal thunder. Just recently, Minimax H3 released as open weights on the eve of Seedance 2.5 global availability. It's not as good, but it's good enough and it's completely open. Flux 3, which is nowhere near as good as either, suddenly announced their release once news of these other two became public. They knew if they waited they'd be ignored. It didn't really help them much, unfortunately. The LLM releases are even more rivalrous. And don't forget all of the competing launches planned before Google IO or major release events. Companies like to eat into the news and press cycle of their rivals.
- Sabinus 1mo agoI've seen it here on HN (it's particularly noticeable via the /active page) multiple times. If Google, OpenAI or Anthropic release something significant, odds are good you'll see a headline from one of the others.
- Forgeties79 1mo ago>long history Seems a bit premature of a statement lol
- ignoramous 1mo ago> Seems like dense 30B is back in fashion? Surprising that Meta don't host this model, even as rate-limited free-tier. > open weight version of Muse Spark 1.2 Wait. Is this "version" different from what Meta serves?
- imilev 1mo agoyes i think everyone is waiting to see that ;d, i've been on qwen for the last year and a half now.
- aruggirello 1mo ago> Seems like dense 30B is back in fashion? Huh, well... no? Gemma A4B and Qwen A3B are quite popular in fact. I'm sure 3.8 35B A3B will outperform 3.6 27B by all metrics
- dannyw 1mo agoI'd be skeptical w.r.t. "by all metrics". Qwen3.6 is a definitive, significant downgrade from Qwen3.5 for creative writing and prose for example. Yes, it's better at agentic and coding, but it regresses in many non-coding areas compared to Qwen3.5. Of course, I do expect the 3.8 ones to perform better for agentic coding.
- dofm 1mo agoOne thing I would caution is staying out of the prediction market like this. Tech tends to get boring when you judge current products against the hypothetical capabilities of unannounced products that may never ship. It's like comparing Nikon cameras against Canon camera rumours, or comparing iPhones against unannounced and therefore largely imaginary Samsungs. - If they do a Qwen 3.8 35B A3B (and I hope they do because I love the 3.6 version) - and if it beats 3.6 27B by all metrics … then the local open weights world will be a better place. But they have said nothing about it and they dropped several weight classes for 3.6, so who is to say they won't drop the 35B? And even if they don't, this is a tall order; why would the MoE tradeoffs no longer be apparent? (Again, I really like both the Qwen and Gemma MoEs) FWIW I am enjoying testing Muse Glimmer — it's really quite impressive on chat, has nice terse and even amusing thinking traces, a bit of brass to it, and I'm hoping it will be good on agentic stuff.
- overfeed 1mo ago> Will be interesting to see how Qwen3.8 27B compares against this once it releases this week Considering that Meta distills Qwen[1] (and should!), it'd be hilarious if Muse loses the head-to-head; the "distillation attack!!1!" people claimed distillation on release n-1 is enough to match the intelligence of the latest version. 1. They wrote a paper about it
- christkv 1mo agoIs qwen confirmed to release?