GLM-5.3-FP8-NVFP4
GLM-5.3 with NVFP4 routed experts and Z.ai's original FP8 weights for everything NVIDIA left in BF16.
How it was made
- Base:
nvidia/GLM-5.3-NVFP4@e3b8f9f2fad29727dfa95e766c5cac18ea1414bf. Every tensor not listed below is copied byte-identical, including the NVFP4 experts (packed U8 weights, F8_E4M3 group-16 scales, F32 global scales and input scales) and NVIDIA's per-tensor FP8 dense MLP of layers 0-2. - Replaced: 1435 tensors that NVIDIA stores as BF16 and
zai-org/GLM-5.3@ba32f8c19c8738e2c57c71798c601d477a7bf844stores as FP8 e4m3 with 128x128 block scales. Each is replaced by Z.ai'sF8_E4M3weight (same name and shape) plus its<name>_scale_invF32 tensor of shape ceil(rows/128) x ceil(cols/128), copied byte for byte.- self_attn
q_a_proj,q_b_proj,kv_a_proj_with_mqa,kv_b_proj,o_proj(79 layers), indexerwq_bandwk(22 layers) shared_expertsgate/up/down (layers 3-78)- MTP layer 78 routed experts, 256 x gate/up/down (NVIDIA left them BF16)
- self_attn
- Kept BF16 (BF16 in both): embeddings, lm_head, norms, router
gate.weight,eh_proj, indexerweights_projandk_norm.e_score_correction_biasstays F32. - Tensor files keep NVIDIA's names (
model-000NN-of-00050.safetensors);model.safetensors.index.jsonis regenerated. Tokenizer, chat template and generation config are NVIDIA's, unchanged.
Formats
- Routed experts, layers 3-77: NVFP4 as declared in
quantization_config(modelopt, group size 16). - Dense MLP, layers 0-2: FP8 per-tensor with scalar
weight_scale/input_scale(NVIDIA's). - Attention projections, shared experts, MTP experts: FP8 e4m3 128x128 block weights with
weight_scale_inv(Z.ai's).quantization_config.fp8_block_quantizationinconfig.jsonlists these modules. - NVIDIA's
hf_quant_config.jsonis omitted because it would not describe the FP8-block modules.
Size
438.4 GB total in 50 safetensors shards (438,379,016,352 bytes), versus 464.1 GB for nvidia/GLM-5.3-NVFP4.
License
Governed by the GLM-5.3 License (LICENSE, verbatim from Z.ai) and, for NVIDIA's quantization, the NVIDIA Open Model Agreement; see NOTICE.
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