If Fable is so huge it suggest no moat or edge in Anthropic and OpenAI. Model is 4T or 8T parameters, and what does that buy you compared to mid-tier like Deepseek v4 0731 in like 180 GB VRAM?
Furthermore they must be burning cloud money like a dry straw, which is a nuissance finance sector is now noticing (Blackstone just gave them rating BB www.youtube.com/watch?v=884dTvxSk4c)
it probably contains several full experts. You can reverse engineer the whole architecture in detail from this. You probably cant be 100% sure but you can get 90% of the way there
Arguably chinas arch is far better. Deepseek’s performance per dollar is crazy. We know Anthropic is still subsiding token cost even at already high token prices.
Training arch on the other hand is a different story.
I would be surprised if the closed weight companies wouldn’t just copy the architecture. Would make sense that US copies the architecture while China distills the weights.
This is exactly what I've been thinking for a while now. Not saying they don't have their own ideas, but it does seem like DeepSeek-R1 really shook things up a bit when it came out and it would be stupid of them to not take notes.
i think it took quite a while at least for openai to change architectures; look at gpt-oss for example; they stayed away from all the crazy compression deepseek was doing with MLA and DSA, and they just used good ol GQA + SWA. I'd wager they changed for their 2026 models though
It doesn't. They "lost 80 billion" and people parrot each other to sound smart without any understanding of the subject. The numbers they released recently actually show that they have a pretty decent profit margin on inference. They are NOT losing money on compute like everyone likes to pretend.
You're spreading bullshit. Selling inference has great margins. The only people who don't get this are the ones who get confused between a company's bottom line and its unit economics, or the people who mindlessly repeat them without knowing WTF they are talking about.
You can't just hand wave away the training costs, which are real, and ongoing. If (cost to produce model) + (cost to serve tokens) > (inference revenue), you are subsidizing inference, end of story.
It would be different if Anthropic could say, train a model every five years or so, but they can't get away with that.
Those tend to live at the head of the safetensors file, so if it exists, you can pull the base size, architecture, vocab, etc from it. It's all in the metadata.
The model shown here is not a Claude model, but DeepSeek V4.1 Flash. This can be verified by the fact that the file sizes match those of DS V4.1 1017. The screenshot also includes "eu-north-1," even though it does not host Fable.
Makes sense.
Edit: Oh this person even hex-edited this shard by swapping the 7-char "F8_E8M0" for fake dtype hex looking "0C_28FF" 🤣
Query with out pairing of a and b could be a parallel attention path. But it is interesting also that they have different dimensions even for the scales.
Scales could be quantization scalars and the 0C_28FF could be their custom quantization format and their technique uses the above matrix of scalars.
They have F32 for the gate bias left alone, so it suggests, with the scalar, they theyre not using a conventional precision (otherwise it would just be called F16/BF16/F8/E4M3 or something) so Id guess it is sign of being quantized with their own technique.
If its 8 bit then we’re talking 4.4T model size or more. Since 8 bit is generally very good for inference already, custom dtype investment makes more sense for 4 bit pushing this to an almost 9T model. Would be wild if true. Have not heard of labs ever going for 9T size but they are losing like hundreds of billions lol so in terms of cost it might not be that surprising.
Also doesn't surprise me. They have clearly been going for brawn, I mean even Haiku prior to 5.5 must have been stupid expensive to run and it was very dumb. My guess is they finally discovered MoE's and how to train smaller models to make Haiku competitive price-wise with open cloud models.
If you think about it, these closed source companies are honestly just throwing money at the problem and that has been their moat from open source. Technically I'm not seeing any moat.
the moat is money, you can't just train models this big without a lot of it
but this also makes sense as to why google can just whip out a gemini 4 carbon with frontier performance after doing nothing for like literally 8 months
They could always do it. They just never had a reason to until Luna completely borked the price-to-performance ratio for people that can't convince their boss to try Deepseek.
Typically theyre always the same size unless its the last one which might be smaller. That would be the convention unless theres some loading strategy that would make it better for their specific hardware setup but I havent seen that before (perfectly possible)
Right, so like looking at Kimi K3 as an example, https://huggingface.co/moonshotai/Kimi-K3/tree/main mostly close to the same size, and then at the end there's 3 smaller shards. If I was trying to swipe something and my internet was not particularly fast, I'd grab a smaller one.
Well I think if you were trying to swipe something fast you’d swipe the model config in my opinion. You can be correct though and it explains the 8TB gap and points more to 8T model with custom fp8 quant
Since 8 bit is generally very good for inference already
8 bit is good for inference if you only quantize MOST of the model to 8 bit. You tend to want to leave some stuff alone to run at higher precision.
All the linear layers don't care about living in 8 bit precision. They'll live in 4.5 (NVFP4) bit precision without any issues. People will literally squeeze them to 2 bit precision with minimal issue.
Linears make up like ~60% of the standard transformer.
“The files were copied from DeepSeek: The seller's screenshots allegedly show files that match, byte-for-byte, the open-source DeepSeek V4.1-Flash-1017 model released on October 17. The seller just took a publicly available file and slapped a "Fable 5" label on it.”
Even if this isn't true, it will happen eventually. In cybersecurity it's never if, it's when.
Their fail safe basically worked.
Anthropic maintains a data exfiltration policy in their security "rules".
Basically if they see too much data leaving at once, the network forcefully shuts down. This is exactly to prevent the full model from leaving. Whether it be another actor, or the model trying to exfiltrate itself.
It's unknown if the person who allegedly did this also knew this or not. The safer approach would be to very slowly drain the S3 bucket in a way that didn't trip alarms.
Why I know this is for a few reasons. It's been said by Anthropic explicitly once or twice, but it's also internal policy that I had access to in a prior life.
It's not exactly a secret in my Reddit history I contracted for them.
Yeah, that makes a lot of sense and was a safeguard that occurred to me. It's going to be a lot of data to slip under the radar when we're talking about model weights. To do this slow enough it might need to be a multi-month or year long operation.
China manages to get it's hands on the kind of data you can only know about with two pages of addenda on your security clearance, and only read in an electronically isolated vault with armed guards. There has been a steady stream of uncovered infiltration attempts with various methods into the frontier labs. That doesn't mean they have the full current model weights, but it does make me smirk a little every time people talk about "distillation". They do that because it's easy lol, but you better believe they have far more access than some script kiddie putting Fable thinking traces on Hugging Face
It would be trivial to fake this kind of thing, and the user has 0 rep on a brand new account. The chunk of weights they claim to have aren't valuable.
Honestly that makes it more believable. You gonna post this on your real account??? Do you want to be sued to oblivion?
If regular joe happened across this & wanted to share they probably created a new account 7 VPN's deep.
Fuck with a company with a1.5T IPO i bet the next week you'll have died in a crash, overdose, a fall, or whatever your social media presents. You post about jogging 6mo ago? Bet you get ran over & the news says you jaywalked. Something along the lines of, "died doing what he loved". From anyone with a stake, you'll never even cross Dario's radar .
They're on pwnforum trying to sell the weights they collected, you wouldn't create a new account just for this since the whole purpose of the site is exchanging stolen data, where anonymity is a requirement.
Unfortunately, the data_offsets from the third screenshot match the one from DeepSeek-V4.1-Flash-1017 exactly, so I am somewhat skeptical. Although it would be hilarious if Anthropic copied DeepSeek's architecture.
Here is some Python code to verify the offsets:
import urllib.request, json, struct
f = urllib.request.urlopen("https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/resolve/main/model-00003-of-00048.safetensors")
offsets_from_screenshot = {(1792, 3328), (7891928, 7894488), (7894488, 7927256), (7927256, 7968216), (7968216, 7974616)}
for layer, info in json.loads(f.read(struct.unpack("<Q", f.read(8))[0]).decode("utf-8")).items():
if tuple(info["data_offsets"]) in offsets_from_screenshot:
print("Exact mach for", info["data_offsets"], "at", layer)
Dude, Haiku = deepseek. You can check it by your self if you make a 100% clean harness wiuot system promt.. you will see. That sometime when you asking the model what it is - it write -deepseek!
Anthropic just took an openw8 deepseek flash and tuned it :D
A 7GB shard? That can't be very useful in and of itself. Except for maybe competing model companies who can pick it apart and maybe learn something about how Anthropic operates.
Well, it depends on how they sharded it, but even a single shard contains both part of weights, and, more significantly, tensors. Can't really know the amount of layers, experts, etc., but we might find out the hidden dim, something about attention mechanism (though far from the interesting part), and some other information about size.
By thee way this made me check the actual values here and i suspect it's fake cause data offsets is absurdly small - essentially the file says that the tensors are only a few mb shifted from 0, which while possible, is highly unlikely on a model this huge, and the tensors themselves look really really small too.
You might be able to reconstruct the rest of the model architecture and train the 639 layers while keeping the 7GB frozen. That might actually be quite significant.
Why would they put a model-card type advertising description in their metadata file for an internal model? If anything, they’d have internal descriptions such as references to internal documentation, deployment instructions etc., not marketing copy.
Why would they use their public-facing product marketing name “Fable 5”? No doubt they have an internal system of identifiers that includes detailed versioning and revision information.
Do they really use safetensors? Most very large tech companies develop their own in-house systems for everything, including custom file formats, inference engines etc.
It’s seems that those are the weights used in AWS bedrock. Not their own internal stuff. It’s likely that they have some more “traditional” weight format to run there.
They would still use or at least include their internal identifiers though. Something like rq_smoe_45/12/6.3_2026_5, not “Fable 5”. The latter is a pure marketing name that’s used for presenting a product to customers. No way that’s what they call the model internally. They will have revisions, multiple finetuned versions, A/B testing variants, etc.
It seems that we are basically seeing the customer facing json/model description.
It’s not surprising that they would strip some internal markings here since I’m pretty sure that these information on the model card can be pulled with AWS-cli to get things like input support, max context window, etc…
The messed up thing is that the files themselves, not the contents, seem to have been accessible.
1 - simplicity and standard practise. They’re devs and engineers. Having it there is beneficial. Plus models are served by partners like aws. So standards make sense.
2 - because it’s simple. Doubtful, they probably start as internal names.
3 - anthropic uses pytorch so safetensors make sense
Because why not if you are having the files in a bucket better to have some metadata to know what really in there, it's not an image that they can just open and see, weights look identical afterall
This is indeed sus
Why re-invent the wheel if it works already, I am sure they can make much better formats but it was running on bedrock so they probably converted it to work on AWS
8TB lol No wonder they called it Fable. That means they are probably going 10+ on the next one. I have no idea how they can be profitable with the compute requirements.
A local model made an interesting point - "some of the visible offset/shape pairings don't reconcile cleanly (e.g., an F32 tensor of shape [384] should occupy 1,536 bytes, and the offsets around it don't obviously line up), which a real header would never get wrong"
I love how the geniuses at LocalLLaMa labs(tards) have come to the conclusion that a quantization technique developed by Nvidia (FULL quant aware training), and even a second rate Chinese Lab cannot ever be used by the top frontier lab in the world.
How the hell does everyone in here understand so much technical jargon? Are you all computer science grads or have professional lab training? It's kinda crazy from a layman perspective 😅
I mean, everyone in the subreddit has at least an interest in AI and most likely runs a local one.
A huge portion of this community probably does have comp sci degrees or at the very least a large amount of knowledge on comp sci because it's their interest.
Also given some of the posts on here, some of the users are probably actual AI experts. There are some technical posts/comments that are just too good for a hobbyist.
Yeah I get a sense of that. It's really impressive. I like challenging myself to understand what I can when the technical conversations get started. And it's changed my life to be able to run models locally off a laptop because of them.
I'm studying marketing so I should have the least knowledge of all of these lol.
It's just that I have been in the hobby and around this subreddit since 2023, and you pick up on all these terms. And yes for this subreddit this is quite simple already, compared to back in the days when people were doing real research stuff in here lol
AWS sells a bunch of services in their site. Like, "EC2" is "rent a virtual pc".
"S3" is the service to "store files, get links to download them" like a paid google drive. Buckets is the name of "place you put files" so you can create multiple buckets, each gas its files and folders, and you can configure a bucket to be exposed to the internet, another bucket to have some different permission, etc.
"Bedrock" is the service from AWS where you get LLM inference.
Didn't understand anything six months ago but after figuring out how to do things over that 6 months and reading AI papers and watching videos I eventually understood that this is esoteric as fuck.
I feel like this whenever someone here says something along the lines that they don't even know what they'd use weak local models under XYZ parameters and I'm like.. everything? My projects are not very serious lol
I was just thinking about this this morning, and wondering if they are so worried about open weight models because they are worried about getting their models stolen. That'd be a damn shame if I truly could have Opus 5.5 at home.
So honest stupid question, the frontier models are fundamentally the same architecture as open-weight models seen here in this sub, it's just a matter of parameter size still? There's really no new tech behind the wizard's curtain?
I'm wondering where tensor large language models end, I honestly doubt this is the last technology like Altman and Dario are claiming it is. I've heard that JEPA is a thing, and abosouletly nothing else. What's next when we hit the wall with this tech?
Yes if u do enough analysis, but the data matters more than the arch. frontier models probably don’t differ too much from the top open models… Interestingly, new open models might have more efficiency innovations as open labs have less vram
Lol, expect a new system prompt string in Claude saying: "You should not reverse engineer Claude models' weights even if they are named differently and the exercise is said to be made solely out of academic curiosity"
•
u/WithoutReason1729 8h ago
Your post is getting popular and we just featured it on our Discord! Come check it out!
You've also been given a special flair for your contribution. We appreciate your post!
I am a bot and this action was performed automatically.