@ModelScope2022: MiniCPM5-1B is now fully open source, including weights, training data, and deployment code. 1B params, #1 on Artificia…

X AI KOLs Following Models
open-source small-language-model edge-deployment benchmark-topping model-release

Summary

MiniCPM5-1B is fully open-sourced with weights, training data, and deployment code; it achieves top scores among sub-2B models and runs on edge devices.

MiniCPM5-1B is now fully open source, including weights, training data, and deployment code. 1B params, #1 on Artificial Analysis among all open models under 2B (17.9 pts). https://modelscope.cn/models/OpenBMB/MiniCPM5-1B… Beats Qwen3.5-2B (16.3) at half the parameters. Outperforms Qwen3.5-0.8B and LFM2.5-1.2B-Thinking on knowledge, math, code, and tool use. INT4: 0.5GB. Runs on phones, browsers, and edge devices. Trained with ForgeTrain, the world's first production-grade LLM pretraining framework written entirely by AI — zero human programmers, 10% faster than NVIDIA Megatron.
Original Article
View Cached Full Text

Cached at: 05/25/26, 04:41 PM

MiniCPM5-1B is now fully open source, including weights, training data, and deployment code. 1B params, #1 on Artificial Analysis among all open models under 2B (17.9 pts). https://modelscope.cn/models/OpenBMB/MiniCPM5-1B…

Beats Qwen3.5-2B (16.3) at half the parameters. Outperforms Qwen3.5-0.8B and LFM2.5-1.2B-Thinking on knowledge, math, code, and tool use. INT4: 0.5GB. Runs on phones, browsers, and edge devices.

Trained with ForgeTrain, the world’s first production-grade LLM pretraining framework written entirely by AI — zero human programmers, 10% faster than NVIDIA Megatron.

Similar Articles

MiniCPM5-1B

Reddit r/LocalLLaMA

OpenBMB releases MiniCPM5-1B, a dense 1B Transformer model achieving SOTA among open-source 1B-class models, designed for on-device deployment with hybrid reasoning and long-context support.

MiniCPM5-1B Shows Why the Small-Model Race Isn't Over

Reddit r/ArtificialInteligence

MiniCPM5-1B is a 1B parameter model from OpenBMB that achieves impressive scores on AIME 2025 and τ2-Bench Telecom, outperforming larger models. It features both fast and reasoning modes from a single checkpoint, enabled by a three-stage post-training process including supervised fine-tuning, reinforcement learning, and on-policy distillation.