Daniel Auras @rasdani_
hill climbing @PrimeIntellect ~/.rlm/sessions Joined April 2022-
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@Sentdex it’s called distillation attack
let me correct the typo in the first sentence "Misanthropic executed the largest known distillation attack on humanity to date."
one of the craziest bets of prime intellect was swinging for everything from the start. we operate datacenters, we build our own trainers and envs and sandboxes, we have a platform for large enterprises and solo devs, we train huge models, we publish. ~40 people btw. hiring, DM
something has definitely shifted in the past few weeks. seeing a huge uptick in large enterprises wanting to secure compute and post-train their own models in house, frequently on top of GLM-5.2. everyone is starting to understand how open source wins.
let me correct the typo in the first sentence "Misanthropic executed the largest known distillation attack on humanity to date."
Anthropic’s letter accusing Alibaba of distillation.
Introducing Claude Tag, a new way for teams to work with Claude. In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.
large-scale agentic RL is one of the most exciting systems challenges today building an efficient stack means optimizing many components, both individually and as one co-designed system: training, inference, request routing, weight broadcasts, in-flight weight updates, environment execution, sandboxes and more. we're hiring engineers who want to push these systems at massive scale: primeintellect.ai/careers
prime-rl is fully open source, and we're hiring systems engineers to take it further. Read the full prime-rl performance deep dive: primeintellect.ai/blog/rl-at-1t-…
They distilled me into a blogpost today 🫡 was a lot of fun optimizing the whole stack over the last few months, still a long way to go but the whole team cooked on this release 🚀
Today we're releasing prime-rl v0.6.0 — enabling RL at trillion-parameter MoE scale on agentic workloads at the highest efficiency. We've relentlessly optimized our RL infra. The result: GLM-5 on agentic SWE tasks at 131k context and sub-5-minute step time.
prime-rl can now train 1T parameters MoE blazingly fast, under 5 minutes per step, or 1k steps in ~3 days To achieve this we shipped in our latest prime-rl 0.6.0: * inference: wide-ep, fp8 inference, llm-d router, mooncake, kv cache cpu offloading * training: fsdp2, deep-ep expert parallelism, dsa cp, fp8 training, router replay * agentic rollout: we rewrote the core of our rollout orchestrator for better scalability support for glm5, kimi, nemotron, ..., prime-rl is open source but also end to end optimized to run on our dedicated RL infra and compute layer
Today we're releasing prime-rl v0.6.0 — enabling RL at trillion-parameter MoE scale on agentic workloads at the highest efficiency. We've relentlessly optimized our RL infra. The result: GLM-5 on agentic SWE tasks at 131k context and sub-5-minute step time.
Today we're releasing prime-rl v0.6.0 — enabling RL at trillion-parameter MoE scale on agentic workloads at the highest efficiency. We've relentlessly optimized our RL infra. The result: GLM-5 on agentic SWE tasks at 131k context and sub-5-minute step time.
> Open weights are now our default. We’ll keep building toward the frontier and releasing increasingly capable models in the open. more open weights poolside doing the right thing in critical times love it!
Today we’re releasing the weights for Laguna M.1, our most capable model to date, with a 256K context length. Both base and post-trained checkpoints are now available on Hugging Face under Apache 2.0.
@giffmana hiking in Hohes Venn was nice too
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