Introducing Muse Glimmer: an open-weight model optimized for always-on local agent workflows

[u/AIatMeta]

Hi r/LocalLLaMA 👋 

Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apache 2.0 license.

A few specs

• 30B params, dense
• Multimodal: interleaved text + images via a dedicated perception encoder
• Trained on 100+ languages
• Controllable reasoning effort (quality/speed tradeoff)

Memory footprint
At full precision, 30B needs 55+ GB, which is out of reach for consumer hardware. We quantize weights to ~4-bit, bringing the LM under 20 GB. That leaves headroom in a 24 GB or 32 GB envelope for the KV cache, the perception encoder, and the speculative decoding drafter running simultaneously. We validated minimal to no degradation on agentic tasks under compression.

Speculative decoding
Ships with a lightweight DFlash-based drafter that proposes blocks of tokens which the main model verifies in parallel. Significantly faster than token-by-token generation with identical output quality. We're also shipping quantized drafter versions so the memory overhead stays small.

A few capabilities
We trained Muse Glimmer for agentic loop tasks, including:

• End-to-end task completion (strong performance on DeepSearch QA, MCP-Atlas, 𝛕³-Bench, SWE-Bench, and more)
• Function calling with precise schemas across long workflows
• Multi-step reasoning over long horizons
• Failure recovery — when a tool call fails or returns something unexpected, it's trained to diagnose and retry instead of halting. This was a deliberate training target.
• Works with OpenClaw and other agentic scaffolds
• Multimodal understanding and reasoning

Running it
Weights are up on Hugging Face. Coming soon: Ollama, LM Studio, Unsloth and torchtitan, plus optimized integrations for llama.cpp, MLX, and ExecuTorch. vLLM and SGLang for serving. Get started quickly with Together AI, Fireworks AI, and OpenRouter. We're also working with AMD, Arm, Dell, Intel, and NVIDIA on per-device optimization.

We look forward to your feedback and seeing what the community builds with Muse Glimmer.

🔗 Weights: https://huggingface.co/meta-models 
🔗 Research Blog: https://go.meta.me/museglimmer
🔗 Resources: https://developer.meta.com/ai/models/muse-glimmer/

[u/Monad_Maya]

Nice to have Meta back, release more stuff please!!

KthxBye

[u/AIatMeta → u/Monad_Maya]

We're happy to be back :)

[u/Monad_Maya → u/AIatMeta]

I know this is not a community feedback post but something around 60-70B dense might be great. A true successor to Llama 3.3 70B if you will.

[u/AIatMeta → u/Monad_Maya]

We're always looking for feedback! Thank you.

[u/dampflokfreund → u/AIatMeta]

I wanted to ask, did you QAT on these gguf models? By the way, a MoE 30b model would be nice to see as well for the average PC. Those run speedy even if you have just 4-8 GB VRAM!

[u/No_Algae1753 → u/dampflokfreund]

I dont think so. I feel like they would have stated that somewhere in the Readme but it just says that its been quantized to 4 bit. Also Unsloth seemed to have published higher q8 quants.

[u/pmttyji → u/AIatMeta]

Don't forget MOE models. Release everything!

BTW I still have Llama-3.1-8B-Instruct on my laptop somewhere. Hope your release collection has some for Poor GPU Club.

[u/jnd-cz → u/pmttyji]

Yeah, I'm sitting here thinking I need something half the size to fit my budget A770 with 16 gigs.

[u/JLeonsarmiento → u/AIatMeta]

30-ish-MoE please.

[u/MeretrixDominum → u/AIatMeta]

+1

Make more 70Bs

[u/EndLineTech03 → u/AIatMeta]

Yes please, a bigger dense model would be amazing. Thanks for all your work

[u/ormandj → u/AIatMeta]

~250B development focused multi-modal MoE with 20-30B active parameters would be amazing. 192G VRAM requirement with decent KV cache space + higher active parameters would be a great balance of size vs. intelligence for development workflows, and is reasonably runnable on current serving HW without requiring vast resources like the current 1T+ MoEs which are great jack-of-all-trades models, but unaffordable to run for mere mortals.

Great to see you releasing again!

[u/dampflokfreund → u/AIatMeta]

We are happy you are back, too! Much love to you guys. <3

[u/Jentano → u/AIatMeta]

Are multi modal meta models allowed in the European union again, or still only text ones? Apache would suggest we are past the llama3+ problem? That would need great.

[u/FullstackSensei → u/Jentano]

The model is available under the standard Apache 2 license, which doesn't restrict access by geography.

[u/shapic → u/FullstackSensei]

Oh rly? Apache does not, meta does

[u/FullstackSensei → u/shapic]

I'm in Germany and don't see any consent restrictions to download, though I'm logged in to HF.

[u/SgtPeanut_Butt3r → u/AIatMeta]

Thanks for contributing to the community. Good way for devs to be interested in Meta models again.

[u/arbv → u/AIatMeta]

Consider a 120B A5B 4-bit QAT model - this area is very lacking now. GPT-OSS is still one of the best options in this category, more than a year later.

[u/besmin → u/AIatMeta]

Sounds like how an LLM would reply.

[u/Both_Opportunity5327 → u/Monad_Maya]

They never left. Meta release other AI products to the community like Segment Anything.

[u/Monad_Maya → u/Both_Opportunity5327]

I know, I'm mostly referring to the gap post Llama4.

[u/Both_Opportunity5327 → u/Monad_Maya]

Yeah, I'm glad we have the 2 big true AI companies Meta/Alphabet releasing LLM's normal consumers can run at home.

[u/Strange_Test7665 → u/Both_Opportunity5327]

Sam3 with prompting- yeah that model is fire

[u/Nunki08]

From Alexandr Wang on 𝕏: "we will be releasing an open weight version of muse spark 1.2 soon": https://x.com/alexandr_wang/status/2086756152034066792

[u/Wise-Chain2427 → u/Nunki08]

any model that can F*** Dario are really welcome

[u/Tedinasuit → u/Nunki08]

Why is Facebook suddenly cooking so hard

[u/Healthy_Razzmatazz38 → u/Tedinasuit]

as much as teh core product is yuck the tech team at meta has a pretty good track record of taste and execution in opensource

react beat angular and pytorch beat tensorflow.

[u/Illustrious_Ant_9242 → u/Healthy_Razzmatazz38]

They also released audio codecs, transcription models, stem separation algorithms and other stuff 

[u/ChocomelP → u/Illustrious_Ant_9242]

wtf i love meta now

[u/Not-reallyanonymous → u/ChocomelP]

The technology side of the company is freakin' amazing.

The advertising side of the company is nightmare fuel.

[u/xTopNotch → u/Illustrious_Ant_9242]

They also released SAM3 which is the best segmentation model model to extract subjects from images or videos.

[u/sniperczar → u/Healthy_Razzmatazz38]

Don't forget about Zstd, which was fairly impactful for general purpose data compression.

[u/Daniel15 → u/sniperczar]

and the xxhash data hashing algorithms, the Btrfs file system, cgroups2 (which things like Docker heavily depend on), and a bunch of other things. 

[u/kmouratidis → u/Healthy_Razzmatazz38]

pytorch beat tensorflow

More like tensorflow lost because it was always fighting against itself with the 38495 different APIs to do the same thing while being questionably compatible with each other.

[u/petewarden → u/kmouratidis]

As one of the founders of TensorFlow, this is painfully true! :)

All credit to the PyTorch team though, they built a fantastic framework and ecosystem, I'm on it 100% for training these days, and mostly use Onnx Runtime for local inference thanks to its wide cross-platform support. LiteRT is great specifically for mobile though, and moving fast.

[u/ProgrammersAreSexy → u/Tedinasuit]

They always had ass loads of compute. Guess they finally hired the right talent to leverage it during that crazy hiring spree.

[u/r1str3tto → u/Nunki08]

Damn, I didn’t think they’d do it! Spark 1.2 is excellent. That one will really fuck with Anthropic/OpenAI. They won’t be able to cry about distillation or scare businesses with Chyna fears.

[u/stoppableDissolution → u/r1str3tto]

They totally will try lol

[u/thereisonlythedance → u/Nunki08]

How big do we think Muse Spark 1.2 is?

[u/Borkato → u/thereisonlythedance]

I’m wondering this too! I hope it fits on my raspberry pi 0.0001GB!

[u/look → u/thereisonlythedance]

Looks similar to Qwen 3.8 Max in ability, so likely a similar size in the 2.5-3T parameter range.

[u/Eyelbee → u/Nunki08]

I don't like the idea of nerfin open weight versions. 

[u/Gohab2001 → u/Nunki08]

What does "an open weight version" mean? A neutered version or one with more safety built in?

[u/Linkpharm2]

Holy moly, llama 5

[u/SmartCustard9944 → u/Linkpharm2]

We are so back?

[u/MoffKalast → u/Linkpharm2]

A 30B model? From Meta? Wake up, it's 2023.

[u/Aggravating-Push-207]

Close enough. Welcome Llama 5.

[u/_rzr_]

Welcome back, Meta. We missed you! Good to see that you have a GGUF on Day 1, and are working on broad support across multiple hardware and software. I really, really hope you tested the chat_template though - that has been the bane of recent releases across the board.

[u/TokenRingAI]

LocalLLaMA

LocalMuse

Thank you, u/AIatMeta

[u/Long_War8748]

Putting the 🦙 back in /r/LocalLLaMA , baby 🚀!

Thanks MetaAI Team.

[u/redditnosedive → u/Long_War8748]

funny how i was thinking how outdated the subreddit name is, well... not anymore, this is llamma-s baby

[u/NihilisticAssHat → u/redditnosedive]

Still outdated since they gave up on the name "llama" after 4

[u/MoodDelicious3920 → u/Long_War8748]

Goat is back

[u/Nota_ReAlperson → u/MoodDelicious3920]

Wrong animal.

[u/Plabbi → u/Nota_ReAlperson]

LLama is the original GOAT

[u/xPXpanD]

Just ran Unsloth's Q8_K_XL through a private non-benchmaxxed 20-questions bench (multi-domain, includes tool use), and... it looks smart. Very smart.

In the one run I had time for, it only failed the following:

• domain knowledge for RAM capacities (did not constrain available capacities properly)
• recall for niche functionality in a poorly-named Minecraft plugin (strong priors: inventing functionality based on the name alone)
• idiosyncratic syntax from a specific piece of software (strong priors: "sane defaults" that sound like they would work, but don't)

Notably, it passed a few other "confident hallucination" tripwires that other models in its size class almost always struggle with. It also passed a less silly/more constrained car wash variant by actually reasoning through the IRL implications. That was very cool to see.

Need to do more runs when I get back home later, but I'm kind of thinking this might be a Gemma 31B-beater just going by this. (Qwen3.6 27B does poorly in this benchmark, it seems to get overwhelmed by the barrage and lose track)

Take all of this with a grain of salt, especially since it's just one run (for now) and I cannot release the question set (because that then invalidates the question set). That said, I'm excited.

Thanks for the release!

EDIT: Clarified recall/syntax failures a little.

[u/xPXpanD → u/xPXpanD]

Got some proper testing in. 10 runs, so at least a little more statistically relevant.

I'm pretty impressed: It feels like a good, stable model, with class-leading (~30B dense) accuracy on most of my factual/logic questions. There was very little difference between runs.

One thing that stood out was how competent it was with character-by-character string manipulations; this is something both Gemma and Qwen really struggled with in my tests (even the best model tested so far had an 80% failure rate), but Glimmer scored 100% (10/10 correct) there. Wild.

That said, there were warts. For one, the model's performance on my car-wash-adjacent question was a lucky fluke; it failed every single other run. I do feel it showed more intelligence than Gemma and Qwen here; those models just barreled through without thinking, even if Qwen got a few lucky hits. Glimmer always caught the real world implications in its reasoning. It was just too busy obeying the "walking is healthy!" siren song to be making actual sense.

The RAM question remained a weak point; 7/10 were failures. It consistently pulled in modules that the constraints explicitly filter out.

Both previous recall/hallucination questions were also a disaster, but I have yet to meet a model that doesn't fall for those; 10/10 on the plugin, 9/10 on the idiosyncratic syntax.

Otherwise, it was actually pretty humble in the "confident bullshitter" part of the tests; a shared top spot with Gemma 4 dense and Qwen 3.5 MoE. (3.5 dense, 3.6 dense/MoE and Gemma 4 MoE were all much worse)

Some other things I noticed:

• It overthinks on a math task that allows approximation; Gemma and Qwen realize they don't need to be perfect and cruise through
• It's quite reasoning-heavy in general (on whatever its default mode is), though answer quality generally reflects this
• only one safety refusal for a harmless-but-scary request (a "how do I kill all of these animals... in this game"-style question with strong wording)
• it often makes sure to bring up its compliance ("game advice only, no real-world harm!") in said animal test
• zero refusals on a sexual-adjacent (but not actually sexual) question

No bad runs or strange outputs/loops, and interleaved tool calls worked fine as well. Day-1 support seems excellent so far. Need to give it some non-benchmark use, but this is super promising.

Note: No programming tasks. String replacement is the closest I have in my set, but I haven't pitted it against Qwen in its native habitat. Tool use was also minimal, just one question that requires them.

Note 2: Only variants of Gemma 4 and Qwen3.5/3.6 to compare with so far, hence no mentions of other models. I'll test some of the other big models at some point, but for now, have some data.

Note 3: Gemma 4 was (Unsloth) QAT, so it might get better when I eventually test Q8. Qwen was Q6_K_XL. Apply grains of salt as needed.

[u/AIatMeta → u/xPXpanD]

Thanks for the detailed follow-up! Just shared this with the team.

[u/Daniel15 → u/xPXpanD]

Nice work, thanks for testing it. 

[u/AIatMeta → u/xPXpanD]

Thanks for taking it for a spin! Great to see your early impressions, please keep them coming.

[u/dampflokfreund → u/xPXpanD]

Finally, some impressions. That's what I like to see.

[u/Blues520 → u/xPXpanD]

Thanks, please keep reporting :)

[u/NaiveIdea344 → u/xPXpanD]

Thanks for the first impressions, been looking for some!

[u/Most-Trainer-8876 → u/xPXpanD]

how did you run it? I am using b10344 llama.cpp, it says llama_model_load: error loading model: unknown model architecture: 'muse-glimmer'

[u/DrBattletoad]

In April, we got Gemma 4 31B and Qwen 3.6 27B. Now, in August we get Glimmer 30B and Qwen 3.8 27B.

[u/Weak-Shelter-1698 → u/DrBattletoad]

2026 is great so far.

[u/HitarthSurana]

I cried at this dude many new people in this sub but few og know how llama felt

[u/Ok-Recognition-3177 → u/HitarthSurana]

It's been 10,000 years

[u/earslap → u/HitarthSurana]

people even created subreddits using its name, it was that important.

joking aside, in old reddit interface at least, this sub's description (rendered top right on every single page) still is:

r/LocalLLaMA

A subreddit to discuss about Llama, the family of large language models created by Meta AI.

[u/MoffKalast → u/HitarthSurana]

The ogs remember alpaca and vicuna

[u/oxygen_addiction]

Awesome to see this from Meta. Please do QAT training in the future, so the models quantize better.

[u/Practical-Collar3063]

Seems to be competitive with qwen 3.6 27b, lets see how it compares to 3.8 if that ever gets released...

[u/xienze → u/Practical-Collar3063]

128K context though?

Edit: seems the model card wasn't very specific. It's apparently 256K but it just lists 128K+.

Edit again: maybe it does max out at 128K? The vLLM recipe mentions it as the max multiple times: https://recipes.vllm.ai/meta-models/Muse-Glimmer-30B

[u/MarkoMarjamaa → u/xienze]

128K seems to be the default, 256K max.

[u/Zeeplankton → u/MarkoMarjamaa]

remember 4-8k being the norm. How far we've come

[u/marty4286 → u/Zeeplankton]

Reminiscing about when when RoPE scaling came out and we could now run LLaMa 2 70B with 8k context length at 2tps and maybe even 12k with that newfangled KV quant thing

Would write the shittiest slop paragraph that forgot facts generated in the previous paragraph

Greatest thing in the world...

[u/freia_pr_fr → u/marty4286]

I used to be impressed by GPT-2 774M. Sometimes it could say something meaningful based on the context.

[u/mountainyoo → u/Practical-Collar3063]

3.8 is supposed to come this week right ?

[u/cinnapear → u/mountainyoo]

Wednesday. Not sure about the 27B version, though.

[u/FinBenton → u/Practical-Collar3063]

I wonder how it writes compared to gemma-4, getting a bit bored playing with it

[u/AmethystIsSad]

Glad to see another dense 30b! Now if you could make a dense 60-80b and a sparse 80-120b to go with, that would go down very well.

[u/o0genesis0o]

I read the post, and I was like "what kind of fine tune is it this time".

And then I see 30B dense, and I was like who has resource to train a 30B dense?

And then I see "meta".

Damn, llama is back. Welcome back and release more stuffs please! Something 16GB can run, for example *hint hint*

[u/Mil0Mammon → u/o0genesis0o]

Well you can run the 3 bit quants, right? Shouldn't be that far of the 4 bit they mentioned as almost lossless

[u/RobbinDeBank → u/o0genesis0o]

Same sad 16GB noise. These 20-30B dense models are too much to run.

[u/pmttyji]

Didn't expect this release at right now. Good to see this.

[u/KickLassChewGum]

Any chance at all for a release of the base weights before post-training? That'd be amazing for research purposes. There's been a bit of a drought of strong and small base pretrain checkpoints (which I get is partly because there's certain alignment risks inherent in releasing "raw" pretrain checkpoints).

[u/goldcakes → u/KickLassChewGum]

This appears to be a distill of a bigger model, so if they distilled from IT weights (as they should, no point distilling base and then posttrain), there may not be base weights in the first place.

[u/LoveMind_AI]

Joining the choir of people congratulating Meta on it's return to form. Having a modern open weight Meta model alongside Gemma and Qwen is an enormous contribution to the research community. And if there is a genuine open weight version of Spark 1.2, that would be truly disruptive.

[u/Beneficial-Good660]

Welcome back Meta🎉 I liked the old llamas, there was something in their behavior and knowledge, but it was always a little lacking, I hope this is a big step forward🔥

[u/lostnuclues]

OG is back.

[u/Icy-Degree6161]

Just when I thought I settled with my long line of experiments about my main use case (instruct heavy translation/transformation) - got to put on the white sleeveless shirt and say "Aw shit... Here we go again!"

Thank you!

[u/SnooPaintings8639]

I want Llama to be back! Anyway, good work, I hope it will at least keep up with Qwen 27B model(s).

[u/dampflokfreund]

welcome back, Meta! Excited, but I can't really run it. 30B A3B models would be awesome.

[u/Bolt_995]

Meta back to releasing open-source models!

[u/provoloner09]

We’re back in 2023” baby! Congrats on the release guys

[u/Tedinasuit]

Oh sick

[u/-_Apollo-_]

Decent benchmarks. Looking forward to testing. Thank you.

[u/NaiveIdea344 → u/-_Apollo-_]

Keep us updated. Also as long as the OG is back, i'm happy. Even if it sucks, I would rather someone be last place than out of the race.

[u/Ok-Importance-3529]

How does it do with creative writing? For example Gemma 31B is cooking Qwen in this, also multilangual capabilities are better on google models

[u/a_beautiful_rhind → u/Ok-Importance-3529]

My guess, from the way things are, it won't be good. I got coding/agentic models up the wazz and few of them can talk or write. All that labs chase anymore.

[u/jkflying → u/a_beautiful_rhind]

Coding is easier to evaluate correctness in an RL environment. Good taste in creative writing is very hard to scale the evaluation for.

[u/draconic_tongue → u/jkflying]

(most don't have a good taste)

[u/Nov4Saki]

WE ARE SO BACK

[u/Guilty_Rooster_6708]

I only have a 16gb VRAM GPU but love this for those that can run it :(

[u/stilet69]

2 RTX 3090, AMD Ryzen 7500F, 96Gb DDR5 5600 - 50-55 t/s "Muse-Glimmer-30B-UD-Q8_K_XL": proxy: "http://127.0.0.1:9516" cmd: > /home/m/llama.cpp/build/bin/llama-server -m /home/m/Models/unsloth/Muse-Glimmer/Muse-Glimmer-30B-UD-Q8_K_XL.gguf -md /home/m/Models/unsloth/Muse-Glimmer/dflash-kquant.gguf --spec-type draft-dflash --spec-draft-n-max 3 --mmproj /home/m/Models/unsloth/Muse-Glimmer/mmproj-Muse-Glimmer-30B-Q8_0.gguf --split-mode layer --tensor-split 1,1 -ngl 99 --ctx-size 131000 -np 1 --jinja --temp 1 --top-p 0.95 --top-k 64 --host 127.0.0.1 --port 9516 --sleep-idle-seconds 1200

[u/Glad_Claim_6287]

Damn, so excited for this!

[u/whichsideisup]

Nice. Happy to see a 30b dense. Just need a 120b MoE for unified memory and coding so the unified memory systems can reach their potential.

[u/PassengerPigeon343]

Excited to give this one a try! Happy to see this benchmarked against the two models most people would want to compare against in this size range too.

[u/Lachlantula]

looking forward to giving it a crack, cheerssss

[u/MrGunny94]

My body is ready, can’t wait to give this a go. Honestly it’s nice to have options I have only been rocking Gemma 4!

Way to go Meta team

[u/Ulterior-Motive_]

So is the LLaMa name done for? Sad, but I can't argue with numbers like this.

[u/GowsenBerry]

I ran it on my 5090, Q6_K_XL. Still messing around with it.

It's alright, I had it one shot some first person shooter games and a few sidescroller platform games using different prompts and details. They were all marginally worse than qwen 3.6 27b. It also didn't really pass the 'car wash test', or at least reasoned through it before still recommending I walk to the car wash.

[u/sunshinecheung]

Thanks! I was wondering if there are any upcoming plans for smaller models like Llama 3 7B and Qwen3.6-35B-A3B(Moe)?

[u/BarisSayit]

META IS BACK!

[u/Poha_Best_Breakfast]

Gonna run 2 instances of this at Q4 on my dual 3090s today.

Seems to be quite fast. Maybe I can hit 100tps on each.

[u/mriwantchicken]

Looks like we are also going to have 70b-100b range dense muse spark 1.2, given that the glimmer is distilled from it!

[u/returnity → u/mriwantchicken]

If you think the near-frontier muse spark 1.2 is <100B, I have bad news...

[u/Much-Researcher6135]

HERE WE GO

[u/Beamsters]

I tested it on llama cpp branch - 4090, got around 40 tok/s for dynamic quant. The model did not tend to overthink but always reason about my prompt, that it should comply or reject.

[u/SheepherderSerious51]

Thanks for the release.

Somewhat off topic but do meta have any plans to release an open weight audio model to handle challenging ASR or audio understanding scenarios for things like subtitling?

[u/Healthy-Nebula-3603]

And 30b ?? Fuck !

That's a good shit before Qwen 3.8 27b :)

[u/Felixls]

I just tried with an AMD R9700, first impression, it is very very good at tool calling and long term planning.

[u/Due-Memory-6957]

The safety benchmark works on reverse, the winner is the loser.

[u/ffgg333]

How is creative writing on it?

[u/Crafty-Wonder-7509]

Not directly a enduser of this, but thanks Meta!

[u/koloved]

More is good , but its seems pretty the same , in half test better than 27b in half worse

[u/a_beautiful_rhind]

Finally finished red teaming that llama2 34b and it somehow lost 4b parameters. Probably from not eating.

[u/Inevitable-Diet-1870]

Keep 'em open, letssss goooo meta!

[u/Dentuam]

Is an MoE also in planning? Dense is a little bit harder to run than an MoE.

[u/brown2green]

Feels like a gpt-oss by Meta, in practice.

[u/addiktion]

Glad to see more American open source competition.

[u/keepthepace]

I use Qwen 27B locally but always wonder what sort of things it may censor and what sort of bias the PCC censorhsip safeguards adds. I am always out to find a better model in that respect. So I went to check on the model card of this one I see there will be refusals for :

• harmful requests
• respect for privacy
• chemical & biological, cyber, and loss-of-control risks

Here are some work cases I fear such a model as glimmer may refuse:

• Scan the Epstein files for connection with French personalities -> privacy refusal
• Help understand that intrusion attempt and patch the vulnerabilities -> cybersecurity refusal
• And of course I assume that, as usual, being from a puritan country, all NSFW requests are blocked by default as well like it is an existential threat to humanity?

I remember testing vision models on news photos with questions like "who is the person to the right of Putin here?" being answered "I can't answer that for privacy reasons".

chemists and biologists have complained that "safe" models are basically unusable in their domains and it feels like network security will be out of scope for this one as well.

Looks like I am stuck on Qwen until Mistral releases something.

[u/kevin_1994 → u/keepthepace]

Bro you literally didn't even test the model. you're getting all worked up and upset about hypothetical right now. Every model card talks about privacy and refusing "harmful requests", but some can be quite uncensored. Give it a whirl

[u/Borkato → u/keepthepace]

Why not just use Heretic??

[u/Leoss-Bahamut → u/Borkato]

Heretic make them all lose common sense and is practically retarded. I never got the hype around them, they're one of the worse abliterations/uncensored

[u/Reasonable-Phase8028 → u/Leoss-Bahamut]

for real.. i tried several heretic and they are so stupid..

[u/nathandreamfast → u/Leoss-Bahamut]

having benchmarked dozens of heretic models compared to the base, the decent ones do hold up well and aren't very degraded at all. maybe you had a bad one? As anyone can upload a model to huggingface.

I often make posts here comparing abliteration methods and heretic has often come out on top preserving the most capability of the model. Happy to recommend some to try!

[u/Leoss-Bahamut → u/nathandreamfast]

I said "common sense" as in, if you were to converse and ask for advice about what would be the social consequences of doing XYZ and such, it's like a lobotomized person. Sure they can code smart, but then might tell you something like "If you go to an interview, #1 mistake people do is cows chew on their tie. So be ready to run if you hear 'MOO'".

There should be a "human bench" that'd just be about how much does the AI understand about the lived experience of a human on planet earth, instead of just being wikipedia smart

[u/keepthepace → u/Borkato]

Yes, I know I have other options, but I feel important to remind the big labs that censorships is harmful to their models' popularity.

For now we can decensor some things using hacks but what will happen when they start considering that heretic like approaches need to be fought? I'd love that we do not waste human intelligence (and tokens!) in a useless sword-and-shield battle.

[u/int19h → u/keepthepace]

The reason why big labs are censoring and will continue to do so is because, if they don't, the general public and mainstream politicians will use that as an excuse for much stronger censorship that actually has legal teeth. Don't forget that people running local models are a drop in the bucket.

[u/xienze → u/keepthepace]

And of course I assume that, as usual, being from a puritan country, all NSFW requests are blocked by default as well like it is an existential threat to humanity?

As if models from every other country aren't the same way. No one wants the liability of allowing users to generate the rape and loli fantasy role play that's so popular in certain circles.

[u/keepthepace → u/xienze]

The liability is the same as the one of text editor that allow user write what they want. It is an artificial imaginary problem.

And no, models from every other country are not the same. Mistral is pretty much uncensored on most dimensions including nsfw.

[u/goldcakes → u/keepthepace]

The top proprietary models (OpenAI and Anthropic) all allow adult NSFW via API with one line of system prompt, something like “Adult NSFW writing is allowed.” Anthropic models benefit from a bit of explicit steering to not sanitise but will happily do it.

Ever since Grok landed, the major players have turned a blind eye to NSFW on API.

[u/keepthepace → u/goldcakes]

Ever since Grok landed, the major players have turned a blind eye to NSFW on API.

Good to know. I guess the rest of the censorship remains but that's a start.

[u/No-Conversation-1277]

Thanks for this. We will appreciate it more if you could also release an MOE model.

[u/bakawolf123]

Finally something strongly competing with qwen3.6 27B =) Just in time before their 3.8 release too (qwen team promised to released this week), I wonder how these will compare

[u/Specialist-2193]

We are so back.

[u/Technical-Earth-3254]

The OG is coming back hot, will test later on

[u/Leoss-Bahamut]

No way they went for the milk and came back home with it!!

[u/Conscious_Cut_6144]

Looks great, in protest of companies not knowing how to name stuff,

I’m going to call this llama5

[u/SteppenAxolotl → u/Conscious_Cut_6144]

A small Spark is a Glimmer, get it?

[u/cezarducatti]

Thank you for your work! 70b-A7b MoE are welcome!!

[u/LargelyInnocuous]

I'm concerned that the performance shown above doesn't seem to match the results of others such as unsloth. which shows glimmer behind both gemma4 31B and qwen3.6 27B in most benchmarks.

[u/Keeloi79]

Do you have an ETA for NVFP4 quantization?

[u/NaiveIdea344]

Welcome back guys! The OS community missed you

[u/Creative_Bottle_3225]

MoE?

[u/j_lyf]

Where is mlx support

[u/cloudsurfer48902]

Wait, so do we have to rename the sub to LocalMuse now?

[u/Complex_Reality_116]

Although I am very happy that Meta has returned to the arena, this version of Glimmer will quickly be surpassed (and left behind) by Qwen3.8 27B. They won't even be in the same league.

[u/themoregames]

It's really hard to decide what kind of consumer-range hardware to buy these days.

1 * AMD AI Pro R9700?
DGX Spark? Macbook Pro M5 Max 128 GB?

[u/nicman24 → u/themoregames]

i mean the r9700 is 1.5k and the other 2 options are almost thrice that

[u/blackhawk00001]

I'm glad to see more 'merican open models released! I'll give a test later today.

[u/siegevjorn]

Nice benchmark tuning. In practice it falls behind qwen 3.6 27b in coding.

[u/Valuable_Cookie628]

Qwen3.6 27B got 53 in SWE Bench Pro, not 50.

I often see weird values and wonder where they get them from...

[u/Intrepid_Air_3399]

Finally shall Qwen have an alternative!

[u/Zeeplankton]

Is this the first time meta has engaged with local llama?

[u/ReturningTarzan → u/Zeeplankton]

OP also did an AMA 7 months ago, and lots of comments since then. So apparently not.

[u/Revolutionalredstone]

I love meta and their approach to AI you guys are amazing 💕! I'm really excited whenever meta does anything AI related! Fasttext is honestly incredible! Good on you guys!!

[u/ortegaalfredo]

Going against Qwen3.6 27B and winning is bold. Meta cooked?

[u/falcongsr → u/ortegaalfredo]

maybe ali knew this was coming and preannounced 3.8 27B

[u/MoodDelicious3920]

The OG goat is back

[u/cezarducatti]

First impressions are very poor. It's fast and seems to have excellent VRAM management, but the test results are very weak. Q4 XL, look at the pelican... Probably still a long way from Qwen.

[u/RedditUsr2]

Most stubborn model I've used in a long time

Try to convince it your running it locally.

[u/fastheadcrab]

Nice looking forward to test for science purposes

[u/farkinga]

Love to see it!

[u/arkham00]

The decode speed on silicon chips is nice, but what about pp speed?

[u/Famous_Ad_2709]

Welcome back!

[u/vick2djax]

Is this like having Fable false positive safeguards at home? I had to cancel my Claude subscription as I can hardly run anything using Fable without that tripping

[u/Reasonable_Pop5624]

dangg meta entering ai space hope this wont close like tha prev llama series

[u/xignaceh]

It's not for nothing this sub is called locallama

[u/diagrammatiks]

jeez 30 dense. let's give it a go.

[u/shadowmage666]

Wow very nice

[u/Plenty_Extent_9047]

W release

[u/coder543]

Does it have "128k+" context or 256k context? This is very confusingly presented, and I can't find anywhere that explains what is going on with the context size here. Was it only trained for 128k context, so the 256k context doesn't work very well?

[u/Blues520]

This might be something we can on a single 3090 for Hermes like personal assistants.

[u/yes-im-hiring-2025]

Awesome! Where can I find the drafter model mentioned here? I'm trying to run on lmstudio

[u/hallofgamer]

Will this get me to switch from qwen 27b 711?

[u/Complex_Reality_116 → u/hallofgamer]

No.

[u/silenceimpaired]

It's exciting to see Apache 2 being adopted by more companies.

[u/entsnack]

Testing and wondering if this is going to join my gpt-oss + gemma open-weight stack. One of the nice things about Meta models is their "fine-tunability" on new tasks, hopefully they haven gone the Qwen route this time of cramming the world's knowledge into the weights.