Deep Principle有发布 Hydrogen-1.0，是化学，材料科学和生命科学的开量科学模型。 它由后训练Z.ai的GLM-5.3所建造，内容是关于域数据，实验室记录和agent发现轨迹。 对于开发者来说，它提供报告一个Frontier Science-Research胜过GPT-6-Astra的开放权重。 麻省理工学院执照下的重量船
TL; DR 数据
- 大小：753B参数(MoE, ~40B活用于GLM-5.3碱基), 1M-token上下文，128K最大输出。
- 运行时间：8个GPU带SGLang(测试);NVFP4为NVIDIA B200/B300;MXFP4为AMD MI350X/MI355X.
- 性能：优于GPT-6-Astra关于前沿科学-研究；大致与GPQA-Diamond上的Kimi-K3相匹配。
- 最佳：边境科学研究38.3%,GPT-6-Astra33.3%(自报)。
- 最糟糕的：在NMR结构澄清上，用3.33分的分数计算出GPT-6-Astra。
什么是氢-1.0?
Hydrogen-1.0是一款出自"深原则"的科研助理模型，是一家以杭州为基地的AI-换材料公司。 模型卡将其描述为用于科学解决问题的GLM-5.3衍生。 它的目标是问题框架、实验规划、结果分析和无限制的发现。 Deep Principle团队表示，专有模型产生了最近的AI科学成果，但其发现能力并不开放。 氢-1.0是它尝试一种可再生，可检验的替代品。
氢-1.0是如何训练的？
管道将受监督的微调(SFT)与强化学习(RL)相结合。 数据来自4个来源：
- 领域知识：化学、材料科学和生命科学数据。
- 研究设想：" 深度原则 " 科学家针对具体任务的反馈。
- 实验室记录：通过米拉采集的全周期电子实验室笔记本记录。
- 发现轨迹：选择和反馈的顺序，包括失败的尝试。
基础设施以威震天和SGLang为基础，跨越了NVIDIA CUDA和AMD ROCm. 团队保持动态稀少的注意力因果跨上下文平行的硬。 Muon优化器状态被移动到CPU,GPU和磁盘每个后端之间。 RL使用可回收的推出，而每个令牌都保留了原有的行为日志概率来进行重要校正。 一个布局适配器将训练检查站转换为BF16和128×128以区块为单位的FP8推断分量。 完整的数据集和食谱不是发布。
基准测试上氢-1.0的性能如何？.
关于公开的基准测试，"深度原则"报告Hydrogen-1.0的成绩超过GPT-6-Astra关于前沿科学-研究。 在GPQA-Diamond上，这在GLM-5.3上有所改进
内部研究任务 evals 将其与 GPT-6-Astra 在最大思考：
- 科学计算：氢-1.0领先5.13个百分点。
- 前向反应预测：氢-1.0引出4.76个百分点。
- NMR结构说明：GPT-6-Astra领先3.33分。
在过渡金属复合设计中，模型在5发子弹中追逐了更大的HOMO-LUMO缺口。 到第5轮时，氢-1.0达到了平均最佳相差4.318个eV. GPT-6-Astra达到了3.689个eV,Kimi-K3达到了3.353个eV. 在117个实验规划问题上，超过Kimi-K3. Hydrogen-1.0得分，GPT-6-Astra和Opus-5.5在拒绝超过50%的请求后被排除在外。
在真正的实验室工作流程中能做什么？
技术帖显示4演示。 Hydrogen-1.0写作Python来构建聚乙烯熔融并运行GROMACS. 它检测出一个地貌错误，进行修复，并分析被修正的40ns轨迹。 它通过30个可信的反应，形成由27个结构组成的爱尔兰-克莱森反应网络。 它从1H NMR的痕量中将2-溴甲苯的7个候选单成型产物排出。 它还为12个子村铃木接合研究撰写了JSON协议。 虚拟实验室编译步骤在处决前捕捉到没有新鲜提示的欲望。
如何部署氢-1.0?
重量在Hugging Face和ModelScope上。 Deep Principal team在8个GPU上测试了与SGLang一同服役的BF16，采用了8. 的拉伸平行性。 NVFP4的目标是NVIDIA B200和B300. MXFP4的目标是AMD Instinct MI350X和MI355X. 服务器将OpenAI兼容的API与推理-努力设置曝光。
氢-1.0 vs GLM-5.3, Kimi K3和GPT-6-Astra
关键外卖
- Hydrogen-1.0是一种麻省理工学院颁发的许可证，753B GLM-5.3型后列车用于科学。
它报告38.3%的前沿科学研究，高于GPT-6-Astra的33.3%。
- 训练新增实验室记录和发现轨迹，而不仅仅是论文。
- 它仍然跟踪GPT-6-Astra 在NMR结构澄清。
- NVFP4和MXFP4建立了目标布莱克威尔和MI350级GPU.
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本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Deep Principle has released Hydrogen-1.0, an open-weight scientific model for chemistry, materials science and life sciences. It is built by post-training Z.ai’s GLM-5.3 on domain data, laboratory records and agent discovery trajectories. For developers, it offers open weights that report a FrontierScience-Research win over GPT-6-Astra. The weights ship under the MIT License.
TL;DR
- Size: 753B parameters (MoE, ~40B active per the GLM-5.3 base), 1M-token context, 128K max output.
- Runs on: 8 GPUs with SGLang (tested); NVFP4 for NVIDIA B200/B300, MXFP4 for AMD MI350X/MI355X.
- Performance: Beats GPT-6-Astra on FrontierScience-Research; roughly matches Kimi-K3 on GPQA-Diamond.
- Best: 38.3% on FrontierScience-Research vs 33.3% for GPT-6-Astra (self-reported).
- Worst: Trails GPT-6-Astra (max thinking) by 3.33 points on NMR structure elucidation.
What is Hydrogen-1.0?
Hydrogen-1.0 is a scientific research assistant model from Deep Principle, a Hangzhou-based AI-for-materials company. The model card describes it as a GLM-5.3 derivative for scientific problem solving. It targets question framing, experiment planning, results analysis and open-ended discovery. Deep Principle team states that proprietary models produced recent AI science results, but their discovery capabilities are not open. Hydrogen-1.0 is its attempt at a reproducible, inspectable alternative.
How was Hydrogen-1.0 trained?
The pipeline combines supervised fine-tuning (SFT) with reinforcement learning (RL). Data comes from 4 sources:
- Domain knowledge: chemistry, materials science and life sciences data.
- Research scenarios: task-specific feedback from Deep Principle scientists.
- Laboratory records: full-cycle electronic lab notebook records captured through Mira.
- Discovery trajectories: sequences of choices and feedback, including failed attempts.
Infrastructure builds on Megatron and SGLang across NVIDIA CUDA and AMD ROCm. The team keeps dynamic sparse attention causal across context-parallel shards. Muon optimizer state is moved between CPU, GPU and disk per backend. RL uses resumable rollouts, and each token keeps its original behavior log-probability for importance correction. A layout adapter converts training checkpoints into BF16 and 128×128 blockwise FP8 inference weights. Full datasets and recipes are not released.
How does Hydrogen-1.0 perform on benchmarks?
On public benchmarks, Deep Principle reports Hydrogen-1.0 outperforms GPT-6-Astra on FrontierScience-Research. The tweet thread from one of the founders’ report 93.0% on GPQA-Diamond. That improves on GLM-5.3 and roughly matches Kimi-K3, per the blog.
Internal research-task evals compare it with GPT-6-Astra at maximum thinking:
- Scientific computing: Hydrogen-1.0 leads by 5.13 percentage points.
- Forward reaction prediction: Hydrogen-1.0 leads by 4.76 percentage points.
- NMR structure elucidation: GPT-6-Astra leads by 3.33 points.
In transition metal complex design, models chased a larger HOMO-LUMO gap over 5 rounds. By round 5, Hydrogen-1.0 reached a mean best-so-far gap of 4.318 eV. GPT-6-Astra reached 3.689 eV and Kimi-K3 reached 3.353 eV. On 117 experiment-planning questions, Hydrogen-1.0 scored above Kimi-K3. GPT-6-Astra and Opus-5.5 were excluded after refusing more than 50% of requests.
What can it do in a real lab workflow?
The technical post shows 4 demos. Hydrogen-1.0 writes Python to build a polyethylene melt and runs GROMACS. It detects a topology error, repairs it, and analyzes the corrected 40 ns trajectory. It grows an Ireland-Claisen reaction network of 27 structures through 30 plausible reactions. It ranks 7 candidate monoformylation products of 2-bromonaphthalene from a 1H NMR trace. It also writes a JSON protocol for a 12-vial Suzuki coupling study. A virtual lab compilation step catches an aspiration without a fresh tip before execution.
How do you deploy Hydrogen-1.0?
Weights are on Hugging Face and ModelScope. Deep Principle team tested BF16 serving with SGLang on 8 GPUs using tensor parallelism of 8. NVFP4 targets NVIDIA B200 and B300. MXFP4 targets AMD Instinct MI350X and MI355X. The server exposes an OpenAI-compatible API with a reasoning_effort setting.
Hydrogen-1.0 vs GLM-5.3, Kimi K3 and GPT-6-Astra
Key Takeaways
- Hydrogen-1.0 is an MIT-licensed, 753B GLM-5.3 post-train for science.
- It reports 38.3% on FrontierScience-Research, above GPT-6-Astra’s 33.3%.
- Training adds lab records and discovery trajectories, not just papers.
- It still trails GPT-6-Astra on NMR structure elucidation.
- NVFP4 and MXFP4 builds target Blackwell and MI350-class GPUs.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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