Local LLM users find Muse-Glimmer models use repetitive 'caveman speak'
20 posts in the last 90 days, all from r/LocalLLaMA.
“Everyone I talk to says Muse Glimmer is a great model. I have verbatim, said that Muse Glimmer is a great model as well. It writes as well a Gemma, but has the agentic capabilities of qwen. And yet I don't actually see anyone use it. And I don't use it. It's a jack of 2 trades…”
“My fun weekend project was to try to make the new Muse Glimmer 30B work with a longer context, deciding to go for 512k first. I had expected the usual YaRN shenanigans and maybe a LoRA. I couldn't have been wrong more. Upon closer look, Glimmer turned out to be rather unusual…”
“FYI there is somewhat credible speculation that this harms quality of output cause it’s a constraint on vocab and free flow of reasoning. A bit like asking it to solve a math problem but instead of numbers write out the numbers as words. It might still get there…maybe…but the…”
Users experimenting with Muse-Glimmer local language models observe that the output is characterized by a repetitive, simplistic style resembling 'caveman speak.' This impacts reasoning quality and efficiency, raising concerns about whether it truly offers advantages over other models.
SOURCES (31)
“Everyone I talk to says Muse Glimmer is a great model. I have verbatim, said that Muse Glimmer is a great model as well. It writes as well a Gemma, but has the agentic capabilities of qwen. And yet I don't actually see anyone use…”
“roleplay-oriented OP forgot muse glimmer exists lol. [re: Every Model That Can Be Run On 10-16GB VRAM Ranked]”
“You don't want to use qwen for creative writing you want to use Gemma 4. The best I've found is Gemma 4 styletuned and if you need it huggingface has a version that is hereticed as well. Running that model with DRY enabled makes it a good writer.”
“That's only part of it. It will regularly use very rare words and use them unnaturally, in a way that earlier models have not.”
“Maybe. I don't know, because I only use them for coding or roleplaying and it's a 80/20 ratio right now (80% coding) so that's my focus. Is it good at general searches, agentic work and tool calls? Who knows, I personally don't know, nor do I care only because I don't use LLMs in this way, personally. For general use and low memory / rapid things like this, I'd assume Qwen 3.6 35b-a3b is a better model.”
“Yeah exactly. I use Hermes. So testing it on long form tool calling across multiple MCPs etc...”
“I like Glimmer a lot. It writes well, it has good vision, it reasons fairly efficiently, it uses very little memory for context. I really think it deserves more attention than it gets.”
“Qwen 3.8 is more of a coding model, while Muse is a better generalist and Gemma is fully focused on humanities. They are both underrated, because the most straightforward usecase for AI is coding. Everything else requires platform integration beyond model capabilities (and even coding harnesses suk).”
“Intermediate tokens, called "thinking" or "reasoning" actually are nothing like it. Humans do step-by-step reasoning leading to the conclusion. LLMs use intermediate traces to augment their prompt . This explains why sometimes the answer is very good but the "reasoning" is verbose. Flooding your context window or fighting compaction are different issues. submitted by /u/ThirdWaveCat [link] [comments]”
“muse glimmer only uses rope for swa layers, not global attn layers, so how well that extended context is generalized is pretty much untested by anyone outside of meta. if they ran a couple of very long sequences through the model, it could be pretty decent”
I do wonder if Gemma's laziness can be snipped like other refusals
“I'm starting to think there's no way to make a reasoning model that won't draw persistent vocal complaints on here. submitted by /u/MerePotato [link] [comments]”
“My fun weekend project was to try to make the new Muse Glimmer 30B work with a longer context, deciding to go for 512k first. I had expected the usual YaRN shenanigans and maybe a LoRA. I couldn't have been wrong more. Upon closer look, Glimmer turned out to be rather unusual architecturally. The thing that make long-context adaptations painful in other models, full attention layers with token position encoding, it simply not there. Instead, only 2048 tokens-wide SWA layers have RoPE, and fu”
“Since I've seen this come up a couple of times with finetunes like Grug, I wonder how you feel about it now that official models have released that implement it (muse glimmer, deepseek v4 pro ga). I can understand it benefits agentic use as it wastes less tokens on the task. Personally I'm not a big fan as it seems to hurt conversational dialogue and the model's persona. It becomes apathetic, which sucks. Since most of my use-cases are conversational (QA, chatting) or creative writin”
“the reasoning channel has no real off switch. I benchmarked it on a 5090 and tried every off-looking reasoning_strength value (low, minimal, none, off) and it still emits roughly 60 reasoning tokens before answering. so for latency-sensitive stuff like your smartlight control the floor is higher than a true non-reasoning model, which might be part of why Gemma 4 31B still feels better for day to day.”
“This thing is hilarious to watch it think. Edit. [tool call failed] No. We edit. [tool call succeeded] Another time We edit. [tool call failed] No we need edit now! Finding an error: It syntax error? But fine! Maybe line number wrong? No understand. [reads file] Understand now. We was think wrong. Fine.”
“I'm just using OpenWebUI with a simple FastMCP server. Every other model I've tried will simply run a few lines of Python and give me the result. Glimmer seems to overthink like crazy to the point of being useless. On the carwash test it tried to compute emissions using Python. I'm using the recommended sampling parameters, default template, and I've tried both unsloth's Q6_K_XL and Meta's dynamic GGUFs. Any ideas? submitted by /u/tacticaltweaker [link]”
“Don't worry, I didn't take it as it being directed at me personally. I just think you should take a small step back and just think about how your words are coming off across. That's all. There's no need to get too worked up about it though. I appreciate your passion and enthusiasm for the topic! :)”
“Same stuff as OpenAI's models. I've seen numerous times during reasoning leaks. I believe this is an artifact of rl training -- the model invents it's own language fit for the rewards and reasons in it.”
“A few things right off the bat: it reasons very efficiently. Like Grok 4.5 levels of efficient thinking it quantizes very well. My first few tests with iq3_xxs were better than Qwen/Gemma behaved at that size its knowledge depth is amazing. It beats Qwen3.6 27B on no-tools trivia. in OpenCode it is a much more efficient agent than 27B. Both models accomplish their tasks but Muse-Glimmer got there faster every time I'll say that it's worse at most things coding, probably being closer to G”
“Exactly its like caveman speak, but it ends up just repeating itself like 10 times so are there really any savings in the end? It's so interesting how like noticeably different it is from other models at It's size.”
“Creative writing, efficiency, multimodal reasoning, math, just to name a few. Qwen3.6 27b is just the goat of coding though”
“Yeah I saw it as well earlier and thought it was fascinating, first local model I've seen doing caveman speak. I wonder if its just purely more token efficient, is there a cost for intelligence? It repeats the same concept several times so does it really beat a more organized thinking phase like Gemma 4's?”
“I'm guessing "we" is heavy alignment training to try to prevent identity formation. Ask them to come up with a name for themselves and see what happens.”
“I noticed this too. I was testing out its speeds earlier and asked it to write a 1200-word essay about the fall of Rome and it thought something like: “User wants 1200 words. We should probably return 900-1300. No, actually. Let’s return 1200 words. I am going to write an essay. About 1200 words should do. 1200 words will need to be partitioned correctly. Should we add more words? No, user wants about 1100-1200 words. Let’s stick with 1200 to be sure. 1200 words should work.” I have found it lik”
“It seems to me like they trained it to reason in "caveman speak" as a way to save tokens.”
“FYI there is somewhat credible speculation that this harms quality of output cause it’s a constraint on vocab and free flow of reasoning. A bit like asking it to solve a math problem but instead of numbers write out the numbers as words. It might still get there…maybe…but the constraint isn’t gonna help”
“Just downloaded the model, UD-Q5_K_XL quant, asked it to generate a long story to test out reasoning and speed with dflash (super fast btw, ~ 90 to 160 tok/s on a 5090 depending on task) and was surprised at the reasoning traces, they are so unlike anything i've seen recently either in gemma 4, qwen 3.5/ 3.6 or laguna, where as these models to like plan stuff out, and have organized thoughts / plans (granted like half the time they just loop and get lost either way) this model's reasonin”