GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
eyes-ml
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
eyes-ml
Model Tree
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.3-33B-Base', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.3-33B-Base', torch_dtype='auto', device_map='auto', trust_remote_code=True) prompt = "The capital of China is"input_ids = tokenizer(prompt, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to(model.device))resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)bash
MODEL_PATH=/path/to/your/Nanbeige4.3-33B-Basepython -m sglang.launch_server \ --model-path ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tp-size 4 \ --mem-fraction-static 0.8bash
MODEL_PATH=/path/to/your/Nanbeige4.3-33B-Basevllm serve ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tensor-parallel-size 4 \ --gpu-memory-utilization 0.8If you have any questions, please open an issue in this repository or contact us at nanbeige@kanzhun.com.
We also welcome collaborations to build domain-specific and industry-focused agents, including joint work on data and training.
If you find our model useful or would like to use it in your own work, please cite this Hugging Face project:
bibtex
@misc{nanbeige43base2026, title = {Nanbeige4.3-33B-Base}, author = {Nanbeige Team}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base}}}from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.3-33B-Base', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.3-33B-Base', torch_dtype='auto', device_map='auto', trust_remote_code=True) prompt = "The capital of China is"input_ids = tokenizer(prompt, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to(model.device))resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)MODEL_PATH=/path/to/your/Nanbeige4.3-33B-Basepython -m sglang.launch_server \ --model-path ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tp-size 4 \ --mem-fraction-static 0.8@misc{nanbeige43base2026, title = {Nanbeige4.3-33B-Base}, author = {Nanbeige Team}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base}}}