Does this have anything to do with The Metamorphosis Of Prime Intellect, or did they just abuse the name and the cover art?
arthurcolle · 3h ago
Prime Intellect is a grabby AI :)
iTokio · 2h ago
It’s interesting that it does something useful (training a LLM) without trust and in a decentralized way.
Maybe this could be used as proof of work? To stop wasting computing resources in crypto currencies and get something useful as a byproduct.
fastball · 42m ago
The emphasis is indeed on "without trust" – as far as I can tell this project is unable to verify whether the decentralized training nodes are contributing productively.
Without the ability to validate that training compute is heading in the globally desired direction, it is unlikely you could use it as the foundation of a (sound) cryptocurrency.
proof_by_vibes · 1h ago
There could be merit to this. Proofs are generally computationally hard, so it's possible that a currency could be created by quantifying verification.
littlestymaar · 1h ago
> To stop wasting computing resources in crypto currencies and get something useful as a byproduct.
Bitcoin is the only major cryptocurrency that still use proof of work today (others are either using “proof of stakes” or are “Layer 2” chains), and due to its (relative lack of) governance structure, it's very unlikely to ever change.
3abiton · 2h ago
This is rather exciting! I see the future of Co-op models made by a community of experts on a specific field that would still allow them to be competitive with "AI monopolies". Maybe not all hope is lost!
schneehertz · 3h ago
I used to have an idea related to science fiction novels that artificial intelligence could aggregate computing power through the network to perform ultra-large-scale calculations, thereby achieving strong artificial intelligence.
Reality will also develop in this way, which is very interesting
Personal story time: I met a couple of their engineers at an event a few months back. They mentioned they were building a distributed training system for LLMs.
I asked them how they were building it and they mentioned Python. I said something along the lines of “not to be the typical internet commenter guy, but why aren’t you using something like Rust for the distributed system parts?”
They mumbled something about Python as the base for all current LLMs, and then kinda just walked away…
From their article:
> “Rust-based orchestrator and discovery service coordinate permissionless workers”
Glad to see that I wasn’t entirely off-base :)
No comments yet
refulgentis · 4h ago
I guess I'm bearish?
It's not that they trained a new model, but they took an existing model and RL'd it a bit?
The scores are very close to QwQ-32B, and at the end:
"Overall, as QwQ-32B was already extensively trained with RL, it was difficult to obtain huge amounts of generalized improvement on benchmarks beyond our improvements on the training dataset. To see stronger improvements, it is likely that better base models such as the now available Qwen3, or higher quality datasets and RL environments are needed."
fabmilo · 3h ago
The interesting delta here is that this proves that we can distribute the training and get a functioning model. The scaling factor is way bigger than datacenters
comex · 2h ago
But does that mean much when the training that produced the original model was not distributed?
refulgentis · 2h ago
The RL, not the training. No?
christianqchung · 3h ago
Third party fine tuned open weighted LLMs tend to be good at a handful of benchmarks, but parity or lower on others compared to the original model. There are some exceptions like Nvidia's Nemotron series, but the differences generally are so small as to be imperceptible. Deepseek released finetunes of several Qwen and Llama models alongside R1, and while they were better in some select (mostly math) and coding domains, there were problems resulting from fine tuning that didn't result in them overtaking the original models in usage.
mountainriver · 4h ago
Awesome work this team is doing. Globally distributed MoE could have real legs
esafak · 4h ago
How are they ensuring robustness against adversarial responses?
nsingh2 · 4h ago
From the article, seems like TOPLOC:
> based on top of novel components such as TOPLOC, which verifies rollouts from untrusted inference workers
Maybe this could be used as proof of work? To stop wasting computing resources in crypto currencies and get something useful as a byproduct.
Without the ability to validate that training compute is heading in the globally desired direction, it is unlikely you could use it as the foundation of a (sound) cryptocurrency.
Bitcoin is the only major cryptocurrency that still use proof of work today (others are either using “proof of stakes” or are “Layer 2” chains), and due to its (relative lack of) governance structure, it's very unlikely to ever change.
./llama.cpp/llama-cli -hf unsloth/INTELLECT-2-GGUF:Q4_K_XL -ngl 99
Also it's best to read https://docs.unsloth.ai/basics/tutorial-how-to-run-qwq-32b-e... on sampling issues for QwQ based models.
Or TLDR, use the below settings:
./llama.cpp/llama-cli -hf unsloth/INTELLECT-2-GGUF:Q4_K_XL -ngl 99 --temp 0.6 --repeat-penalty 1.1 --dry-multiplier 0.5 --min-p 0.00 --top-k 40 --top-p 0.95 --samplers "top_k;top_p;min_p;temperature;dry;typ_p;xtc"
Personal story time: I met a couple of their engineers at an event a few months back. They mentioned they were building a distributed training system for LLMs.
I asked them how they were building it and they mentioned Python. I said something along the lines of “not to be the typical internet commenter guy, but why aren’t you using something like Rust for the distributed system parts?”
They mumbled something about Python as the base for all current LLMs, and then kinda just walked away…
From their article: > “Rust-based orchestrator and discovery service coordinate permissionless workers”
Glad to see that I wasn’t entirely off-base :)
No comments yet
It's not that they trained a new model, but they took an existing model and RL'd it a bit?
The scores are very close to QwQ-32B, and at the end:
"Overall, as QwQ-32B was already extensively trained with RL, it was difficult to obtain huge amounts of generalized improvement on benchmarks beyond our improvements on the training dataset. To see stronger improvements, it is likely that better base models such as the now available Qwen3, or higher quality datasets and RL environments are needed."
> based on top of novel components such as TOPLOC, which verifies rollouts from untrusted inference workers
https://github.com/PrimeIntellect-ai/toploc
At a glance it looks like something akin to a computing a checksum that's locality sensitive, so it's robust to floating point errors, etc.
What's to stop someone from sending bad data + a matching bad checksum?