apex-flash-1-abliterated-FP8 API & Inference Endpoint

Identical layout to the official zai-org/GLM-5.3-Flash FP8 release:

quant_method: fp8, fmt: e4m3, activation_scheme: dynamic, weight_block_size: [128, 128]
Exactly the same set of tensors is quantized as in the official checkpoint (routed + shared experts, dense MLPs, MLA projections); everything in modules_to_not_convert (embeddings, lm_head, routers, norms, linear-attention/KDA, hyper-connections, vision tower) is left in BF16/F32 as in the source.
Each FP8 weight has a weight_scale_inv (F32, one scale per 128×128 block, scale = amax / 448). Round-to-nearest, no calibration data.
Tensor names and the quantization_config are copied from the official release, so any engine that serves zai-org/GLM-5.3-Flash should load this checkpoint the same way.

Use the same setup as for zai-org/GLM-5.3-Flash, e.g.

bash

vllm serve pqhaz/apex-flash-1-abliterated-FP8 --tensor-parallel-size 4

bash

python -m sglang.launch_server --model-path pqhaz/apex-flash-1-abliterated-FP8 --tp 4

From the upstream card: this abliterated variant has not undergone a separate evaluation. Results reported for apex-flash-1 apply to the standard checkpoint only. It is intended for authorized security research.
This quantization has not been separately benchmarked either.
Not affiliated with Cantina Security or Z.AI.

Identical layout to the official zai-org/GLM-5.3-Flash FP8 release:

quant_method: fp8, fmt: e4m3, activation_scheme: dynamic, weight_block_size: [128, 128]
Exactly the same set of tensors is quantized as in the official checkpoint (routed + shared experts, dense MLPs, MLA projections); everything in modules_to_not_convert (embeddings, lm_head, routers, norms, linear-attention/KDA, hyper-connections, vision tower) is left in BF16/F32 as in the source.
Each FP8 weight has a weight_scale_inv (F32, one scale per 128×128 block, scale = amax / 448). Round-to-nearest, no calibration data.
Tensor names and the quantization_config are copied from the official release, so any engine that serves zai-org/GLM-5.3-Flash should load this checkpoint the same way.

Use the same setup as for zai-org/GLM-5.3-Flash, e.g.

bash

vllm serve pqhaz/apex-flash-1-abliterated-FP8 --tensor-parallel-size 4

bash

python -m sglang.launch_server --model-path pqhaz/apex-flash-1-abliterated-FP8 --tp 4

From the upstream card: this abliterated variant has not undergone a separate evaluation. Results reported for apex-flash-1 apply to the standard checkpoint only. It is intended for authorized security research.
This quantization has not been separately benchmarked either.
Not affiliated with Cantina Security or Z.AI.

apex-flash-1-abliterated-FP8