Deep Principle Open-Sources Hydrogen-1.0, a 753B Science Model Post-Trained From GLM-5.3 on Lab Records, MIT Licensed
Hangzhou-based AI-for-science company Deep Principle released Hydrogen-1.0 on November 2, its first general-purpose scientific model. It post-trains Zhipu's GLM-5.3 with SFT and RL on chemistry, materials and life-science data plus its own lab records and discovery trajectories, and ships about 753B parameters of BF16 weights on Hugging Face under the MIT license. By the vendor's own evaluation it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra; none of the results have been independently reproduced yet.
Key Takeaways
- Scale: Hugging Face lists about 753B total parameters (BF16, roughly 1.5 TB of weights), glm_moe_dsa architecture, 1M-token context and 128K max output, MIT licensed (download requires accepting HF terms).
- Benchmarks (vendor-reported): 38.3% on FrontierScience-Research vs 33.3% for GPT-6 Astra; 93.0% on GPQA Diamond, which the company says roughly matches Kimi K3.
- Internal scenarios (vendor-reported): leads max-thinking GPT-6 Astra by 5.13 points on scientific computing and 4.76 points on forward reaction prediction, but trails it by 3.33 points on NMR structure elucidation.
- Materials discovery: after five rounds of transition-metal-complex search, the mean best HOMO-LUMO gap reached 4.318 eV vs 3.689 eV for GPT-6 Astra and 3.353 eV for Kimi K3 (official blog).
- Access: weights on HF and ModelScope, plus NVFP4 (B200/B300) and MXFP4 (MI350X/MI355X) quantized repos; API via NeuroSpark and in-app use in the Mira AI Scientist; tested with SGLang on 8 GPUs.
Linked Models & Agents:
