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Arcee Trinity Mini: US-Trained Moe Model
- bitwize 1y agoA moe model you say? How kawaii is it? uwu
- htrp 1y agoTrinity Nano Preview: 6B parameter MoE (1B active, ~800M non-embedding), 56 layers, 128 experts with 8 active per token Trinity Mini: 26B parameter MoE (3B active), fully post-trained reasoning model They did pretraining on their own and are still training the large version on 2048 B300 GPUs
- halJordan 1y agoLooks like a less good version of qwen 30b3a which makes sense bc it is slightly smaller. If they can keep that effiency going into the large one it'll be sick. Trinity Large [will be] a 420B parameter model with 13B active parameters. Just perfect for a large Ram pool @ q4.
- ksynwa 1y ago> Trinity Large is currently training on 2048 B300 GPUs and will arrive in January 2026. How long does the training take?
- arthurcolle 1y agoCouple days or weeks usually. No one is doing 9 month training runs
- davidsainez 1y agoExcited to put this through its paces. It seems most directly comparable to GPT-OSS-20B. Comparing their numbers on the Together API: Trinity Mini is slightly less expensive ($0.045/$0.15 v $0.05/$0.20) and seems to have better latency and throughput numbers.
- trvz 1y agoMoe ≠ MoE
- Balinares 1y agoInteresting. Always glad to see more open weight models. I do appreciate that they openly acknowledge the areas where they followed DeepSeek's research. I wouldn't consider that a given for a US company. Anyone tried these as a coding model yet?