MODEL PROFILE · 模型档案

GLM-5.3-Flash-0.1B-A0.1B

inference-optimization · GLM-5.3-Flash-0.1B-A0.1B

This tiny model preserves the full architecture of the base model: - Hybrid attention: **KDA linear attention** (`linear_attention`) layers **and** **DeepSeek sparse attention / MLA** (`deepseek_sparse_attention`) layers with the token indexer. - Mixed FFN schedule: **dense** MLP layers (first 3) **and** **sparse MoE** layers (routed experts + shared expert). - Manifold-Constrained **Hyper-Connections** (mHC) at every attention/FFN site. - The **vision tower** (`Glm5NextVisionModel`) and multimodal projector. The model is a bf16 dense checkpoint (the base model's fp8 `quantization_config` was removed so the tiny model can be randomly initialized and fine-tuned). Per-layer schedules were regenerated for the reduced depth: - `layer_types`: `[linear, linear, linear, deepseek_sparse_attention, linear]` - `mlp_layer_types`: `[dense, dense, dense, sparse, sparse]` - `indexer_types`: `[full, full, full, full, full]`

译文排队中,当前展示来源原文。

视觉问答license:mit
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/10/31 11:05:59
OVERVIEW

模型介绍

This tiny model preserves the full architecture of the base model: - Hybrid attention: **KDA linear attention** (`linear_attention`) layers **and** **DeepSeek sparse attention / MLA** (`deepseek_sparse_attention`) layers with the token indexer. - Mixed FFN schedule: **dense** MLP layers (first 3) **and** **sparse MoE** layers (routed experts + shared expert). - Manifold-Constrained **Hyper-Connections** (mHC) at every attention/FFN site. - The **vision tower** (`Glm5NextVisionModel`) and multimodal projector. The model is a bf16 dense checkpoint (the base model's fp8 `quantization_config` was removed so the tiny model can be randomly initialized and fine-tuned). Per-layer schedules were regenerated for the reduced depth: - `layer_types`: `[linear, linear, linear, deepseek_sparse_attention, linear]` - `mlp_layer_types`: `[dense, dense, dense, sparse, sparse]` - `indexer_types`: `[full, full, full, full, full]`

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。