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]`
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]`
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