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Rick Holmberg

juiceb0xc0de
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https://github.com/JuiceB0xC0de

AI & ML interests

Mechanistic interpretation SAE weight training Novel architecture exploration Brain atlas builds

Recent Activity

updated a dataset 1 day ago
juiceb0xc0de/lfm2-5-2-6b-atlas
published a dataset 1 day ago
juiceb0xc0de/lfm2-5-2-6b-atlas
reacted to sergiopaniego's post with 😎 1 day ago
LFM2.5-2.6B just dropped! and the @liquidai blog comes with some nice details about the training procedure, so let's analyze it. basically, a full agent training pipeline but compressed into 2.6B base model → SFT → specialized teachers per domain (SFT + RLVR) → on-policy distillation back into one student → agentic RL the two most interesting stages → MOPD: the student generates, each prompt routes to its domain teacher for token-level feedback. teachers branch from the same SFT checkpoint, so their signal stays close to the student's distribution → agentic RL: multi-turn GRPO inside real harnesses (OpenClaw, Hermes Agent), one sandbox per rollout, a proxy captures token-level trajectories while the harness stays a black box this makes a 2.6B that beats much larger models on instruction following and tool use SFT, distillation, RL, RL envs: exactly what we're covering in our Training Agents livestream series (next one coming soon!) → model: https://huggingface.co/LiquidAI/LFM2.5-2.6B → blog: https://www.liquid.ai/blog/lfm2-5-2-6b → live series: https://www.youtube.com/playlist?list=PLo2EIpI_JMQvQZm-kVlz4wY1vWF0LBcf5
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Organizations

Blog-explorers's profile picture ML intern explorers's profile picture Humanity's Last Hackathon's profile picture Build Small Hackathon's profile picture Smilyai Laboratories BETA TESTERS COMMUNITY's profile picture