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eyes-ml
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Model Details
Model Provider
eyes-ml
Model Tree
Nanbeige/Nanbeige4.3-33B-Base
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = 'Nanbeige/Nanbeige4.3-33B'tokenizer = AutoTokenizer.from_pretrained( model_id, use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype='auto', device_map='auto', trust_remote_code=True) messages = [{'role': 'user', 'content': 'Which number is bigger, 9.11 or 9.8?'}]inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors='pt').to(model.device)output_ids = model.generate(inputs, max_new_tokens=1024)response = tokenizer.decode(output_ids[0][inputs.shape[-1]:], skip_special_tokens=True)print(response)bash
MODEL_PATH=/path/to/your/Nanbeige4.3-33Bpython -m sglang.launch_server \ --model-path ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tp-size 4 \ --mem-fraction-static 0.8 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coderbash
MODEL_PATH=/path/to/your/Nanbeige4.3-33Bvllm serve ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tensor-parallel-size 4 \ --gpu-memory-utilization 0.8 \ --enable-auto-tool-choice \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coderIf 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{nanbeige43chat2026, title = {Nanbeige4.3-33B}, author = {Nanbeige Team}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B}}}from transformers import AutoModelForCausalLM, AutoTokenizer model_id = 'Nanbeige/Nanbeige4.3-33B'tokenizer = AutoTokenizer.from_pretrained( model_id, use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype='auto', device_map='auto', trust_remote_code=True) messages = [{'role': 'user', 'content': 'Which number is bigger, 9.11 or 9.8?'}]inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors='pt').to(model.device)output_ids = model.generate(inputs, max_new_tokens=1024)response = tokenizer.decode(output_ids[0][inputs.shape[-1]:], skip_special_tokens=True)print(response)MODEL_PATH=/path/to/your/Nanbeige4.3-33Bpython -m sglang.launch_server \ --model-path ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tp-size 4 \ --mem-fraction-static 0.8 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coderMODEL_PATH=/path/to/your/Nanbeige4.3-33Bvllm serve ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tensor-parallel-size 4 \ --gpu-memory-utilization 0.8 \ --enable-auto-tool-choice \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder@misc{nanbeige43chat2026, title = {Nanbeige4.3-33B}, author = {Nanbeige Team}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B}}}