Downloads · 30 days
145
22% of all-time downloads
thebriangao/GLM-5.3-Flash-Uncensored-NVFP4
GLM-5.3-Flash-Uncensored-NVFP4 is a image-text-to-text model from thebriangao. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Native packed NVFP4 conversion of orcarouter/GLM-5.3-Flash-Uncensored-FP8 at revision 55c78761fb78d0f765e2bd01687900d2fd91b5d0. This checkpoint preserves that specific OrcaRouter abliteration; it is not a quantization…
Downloads · 30 days
145
22% of all-time downloads
All-time downloads
652
Public
Parameters
321B
205 GB on disk
Likes
3
Public
Click a slice to open those files.
.safetensors205 GB · 100%
How the weights are stored.
U8304B · 95%
From the Hugging Face model README
Native packed NVFP4 conversion of orcarouter/GLM-5.3-Flash-Uncensored-FP8 at revision
55c78761fb78d0f765e2bd01687900d2fd91b5d0. This checkpoint preserves that specific OrcaRouter
abliteration; it is not a quantization of the unmodified Z.ai base model.
lm_head, and the complete layer-45 MTP block.The routed experts use per-16 E2M1 weights, E4M3 block scales, and an FP32
per-tensor scale in standard nvfp4-pack-quantized compressed-tensors format.
For every tensor, the converter compares classic NVFP4 scaling with NVIDIA's
Four-Over-Six M=6/M=4 block-scale search and stores the lower-error result.
The exact OrcaRouter fine-tune is published only as a block-FP8 checkpoint. Its weights are dequantized from their own 128x128 FP8 scale planes before the NVFP4 pass. This preserves the linked fine-tune, but it is necessarily a second lossy quantization step relative to an unavailable BF16 version of that exact fine-tune. Non-expert FP8 weights are materialized as BF16 in this checkpoint.
See conversion-manifest.json and MANIFEST.sha256 for pinned provenance,
tool versions, tensor counts, aggregate error metrics, and file hashes.