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DazzlingXeno
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Model Details
Model Provider
DazzlingXeno
Model Tree
Nanbeige/Nanbeige4-3B-Base
Input Modalities
Output Modalities
Supported Functionality
For inference hyperparameters, we recommend the following settings:
For the chat scenario:
python
from transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', torch_dtype='auto', device_map='auto', trust_remote_code=True)messages = [ {'role': 'user', 'content': 'Which number is bigger, 9.11 or 9.8?'}]prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to('cuda'), eos_token_id=166101)resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)For the tool use scenario:
python
from transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', torch_dtype='auto', device_map='auto', trust_remote_code=True)messages = [ {'role': 'user', 'content': 'Help me check the weather in Beijing now'}]tools = [{'type': 'function', 'function': {'name': 'SearchWeather', 'description': 'Find out the current weather in a place on a certain day.', 'parameters': {'type': 'dict', 'properties': {'location': {'type': 'string', 'description': 'A city in China.'}, 'required': ['location']}}}}]prompt = tokenizer.apply_chat_template( messages, tools, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to('cuda'), max_new_tokens=512, eos_token_id=166101)resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)For the deep-search scenario:
| Server | Description | Tools Provided |
|---|---|---|
| tool-python | Execution environment and file management (E2B sandbox) | create_sandbox, run_command, run_python_code, upload_file_from_local_to_sandbox, download_file_from_sandbox_to_local, download_file_from_internet_to_sandbox |
| search_and_scrape_webpage | Google search via Serper API | google_search |
| jina_scrape_llm_summary | Web scraping with LLM-based information extraction with Jina | scrape_and_extract_info |
While we place great emphasis on the safety of the model during the training process, striving to ensure that its outputs align with ethical and legal requirements, it may not completely avoid generating unexpected outputs due to the model's size and probabilistic nature. These outputs may include harmful content such as bias or discrimination. Please don't propagate such content. We do not assume any responsibility for the consequences resulting from the dissemination of inappropriate information.
If you find our model useful or want to use it in your projects, please cite as follows:
text
@misc{yang2026nanbeige413bsmallgeneralmodel, title={Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts}, author={Chen Yang and Guangyue Peng and Jiaying Zhu and Ran Le and Ruixiang Feng and Tao Zhang and Xiyun Xu and Yang Song and Yiming Jia and Yuntao Wen and Yunzhi Xu and Zekai Wang and Zhenwei An and Zhicong Sun and Zongchao Chen}, year={2026}, eprint={2602.13367}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2602.13367}, }If you have any questions, please raise an issue or contact us at nanbeige@kanzhun.com.
from transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', torch_dtype='auto', device_map='auto', trust_remote_code=True)messages = [ {'role': 'user', 'content': 'Which number is bigger, 9.11 or 9.8?'}]prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to('cuda'), eos_token_id=166101)resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)from transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', torch_dtype='auto', device_map='auto', trust_remote_code=True)messages = [ {'role': 'user', 'content': 'Help me check the weather in Beijing now'}]tools = [{'type': 'function', 'function': {'name': 'SearchWeather', 'description': 'Find out the current weather in a place on a certain day.', 'parameters': {'type': 'dict', 'properties': {'location': {'type': 'string', 'description': 'A city in China.'}, 'required': ['location']}}}}]prompt = tokenizer.apply_chat_template( messages, tools, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to('cuda'), max_new_tokens=512, eos_token_id=166101)resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)@misc{yang2026nanbeige413bsmallgeneralmodel, title={Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts}, author={Chen Yang and Guangyue Peng and Jiaying Zhu and Ran Le and Ruixiang Feng and Tao Zhang and Xiyun Xu and Yang Song and Yiming Jia and Yuntao Wen and Yunzhi Xu and Zekai Wang and Zhenwei An and Zhicong Sun and Zongchao Chen}, year={2026}, eprint={2602.13367}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2602.13367}, }