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Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

research.meta.ai

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Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#141

Post by dannyw »

Grombobulous wrote: post #122
larodi wrote: post #109

Earlier quoted context omitted.

Meta and its products, as a whole, is a threat to your kids, your mental health, your community's health and the planet as a whole. It is just sad and very repulsive everyone fell so easily addicted to their social drug. Yes - it is a drug, and it is hard to get off from. Nothing redeems them at this point of time, they are doing exactly ZERO to redeem. Tossing open weight models (not opensource!!) is not a basis for…

I think it’s also worth pointing out that that there are numerous less evil options to choose from. Perhaps none of the AI companies are shining examples of high ethics, but basically all of them have ethical high ground over Meta. At least Anthropic isn’t sending private videos from pervert glasses to contract workers in Africa. It’s a low bar but it’s a bar nonetheless.

I personally don’t like, and wouldn’t work for Meta; but it’s an Apache 2.0 model.

I’m liking it, and I don’t see a personal moral contradiction here. Do you use React for frontend for example?

I also wish this HN post is a bit more focused on the release, and less noise around Meta.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#142

Post by [deleted] »

skinfaxi wrote: post #120
avaer wrote: post #101

I lament the comments saying this in any way redeems Meta (the company). The researchers releasing this stuff have almost nothing to do with Meta other than being bankrolled by the slaughterhouse. You aren't the customer, you are the pawn in big tech's game of thrones. Your good will is a commodity to be traded, almost literally. It will be used against you the moment it's convenient. This is open weights because Met…

How is this non-sequitor the top comment?

[deleted]

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#143

Post by skohan »

formerly_proven wrote: post #60
sajithdilshan wrote: post #10

Still needs 32-64GB memory to run it locally. 64GB Macbook pro with an M5 chip costs more than 4k Euros in Germany. A more practical model would be a language specific (e.g Python or JVM language) and excellent at tool calling and reasoning. Maybe that way they can shrink it even more.

4K bucks buys you around 180 months of with zero upfront cost.

If you don't mind exfiltrating all your IP to the API provider

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#144

Post by armchairhacker »

avaer wrote: post #101

I lament the comments saying this in any way redeems Meta (the company). The researchers releasing this stuff have almost nothing to do with Meta other than being bankrolled by the slaughterhouse. You aren't the customer, you are the pawn in big tech's game of thrones. Your good will is a commodity to be traded, almost literally. It will be used against you the moment it's convenient. This is open weights because Met…

You can say the same about planet Earth.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#145

Post by mrloopex »

simianwords wrote: post #140
skinfaxi wrote: post #120

Earlier quoted context omitted.

How is this non-sequitor the top comment?

[flagged]

Think real hard about that. What does it mean if the only hacker chat group on the planet despises meta this much? Think.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#146

Post by pu_pe »

mycall wrote: post #64
pu_pe wrote: post #46

Earlier quoted context omitted.

Based 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.

Do 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.

Yeah but you can probably have everything ready and then accelerate as necessary. Meta itself did this when releasing Llama 4, it was a really botched release right when they were feeling the heat from DeepSeek and others.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#148

Post by dannyw »

stogot wrote: post #86
mycall wrote: post #64

Earlier quoted context omitted.

Do 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.

the last few items there (benchmarking, human evaluation, docs) can be rushed or skipped by leadership if they want to beat comp. they probably spend a few weeks on those things normally

One window that can be shortened is working with software ecosystem and upstream partners; think day 0 on together, fireworks, Unsloth, etc. That obviously happens from partners getting embargoed weights early.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#149

Post by LorenDB »

monster_truck wrote: post #119
avaer wrote: post #101

I lament the comments saying this in any way redeems Meta (the company). The researchers releasing this stuff have almost nothing to do with Meta other than being bankrolled by the slaughterhouse. You aren't the customer, you are the pawn in big tech's game of thrones. Your good will is a commodity to be traded, almost literally. It will be used against you the moment it's convenient. This is open weights because Met…

Meta can never be redeemed, but it's still valid to admit that FB at one point had a very badass engineering culture. They're one of 2 companies I would absolutely never work for (weapons etc aside). FB's recruiters hounded me so often I requested that they blackball me. The day they became Meta, I learned this by checking my email to see that they started trying to reach out again. I once again requested that they b…

What is the other company that you would never work for?

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#150

Post by dannyw »

karimf wrote: post #44
scrlk wrote: post #5

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://xcancel.com/alexandr_wang/status/2086756152034066792

Yes, 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

You can partially tell by the tokeniser; which gives you some hint into the training corpus mix.

is four Gemma4 tokens, but one Qwen3.6 token.

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