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Model Details
Model Provider
intel-ai
Model Tree
zai-org/GLM-5.3-Flash
Input Modalities
Output Modalities
Supported Functionality
This model is an MXFP8 (W8A8, group_size 32) model of zai-org/GLM-5.3-Flash generated by intel/auto-round using RTN. Please follow the license of the original model.
bash
uv pip install --extra-index-url https://wheels.vllm.ai/nightly/cu130 "vllm>=0.29.1rc1.dev0"bash
vllm serve intel-ai/GLM-5.3-Flash-MXFP8-CT-AutoRound \ --block-size 128 \ --dtype bfloat16 \ --language-model-only \ --reasoning-parser glm45| Benchmark | BF16 | MXFP8 | Recovery |
|---|---|---|---|
| PIQA | 82.92 | 83.13 | 100.25% |
| HellaSwag | 69.03 | 69.19 | 100.23% |
| MMLU | 86.66 | 86.68 | 100.02% |
| GSM8K | 97.35 | 97.42 | 100.07% |
| Average | 83.99 | 84.11 | 100.14% |
bash
auto-round \ --model_name zai-org/GLM-5.3-Flash-BF16 \ --model_free \ --scheme MXFP8 \ --ignore_layers lm_head,embed_tokens,visual,hc_,indexer,mlp.gate.,eh_proj,enorm,hnorm,shared_head,norm,A_log,dt_bias,self_attn.b_proj,f_a_proj,f_b_proj,g_a_proj,g_b_proj,conv1d,self_attn.q_proj,self_attn.k_proj,self_attn.v_proj,self_attn.kv_b_proj \ --format llm_compressor \ --output_dir GLM-5.3-Flash-MXFP8-CT-AutoRoundThe model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software:
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
text
@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023}}Signed-off-by: Lv, Kaokao kaokao.lv@intel.com
uv pip install --extra-index-url https://wheels.vllm.ai/nightly/cu130 "vllm>=0.29.1rc1.dev0"vllm serve intel-ai/GLM-5.3-Flash-MXFP8-CT-AutoRound \ --block-size 128 \ --dtype bfloat16 \ --language-model-only \ --reasoning-parser glm45auto-round \ --model_name zai-org/GLM-5.3-Flash-BF16 \ --model_free \ --scheme MXFP8 \ --ignore_layers lm_head,embed_tokens,visual,hc_,indexer,mlp.gate.,eh_proj,enorm,hnorm,shared_head,norm,A_log,dt_bias,self_attn.b_proj,f_a_proj,f_b_proj,g_a_proj,g_b_proj,conv1d,self_attn.q_proj,self_attn.k_proj,self_attn.v_proj,self_attn.kv_b_proj \ --format llm_compressor \ --output_dir GLM-5.3-Flash-MXFP8-CT-AutoRound@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023}}