EllaPriest45

Nanbeige4.1

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README

License: apache-2.0

Quickstart

For inference hyperparameters, we recommend the following settings:

  • Temperature: 0.6
  • Top-p: 0.95
  • Repeat penalty: 1.0
  • Max New Tokens: 131072

For the chat scenario:

markdown

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = 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_ids
output_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:

markdown

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = 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_ids
output_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 deep-search scenario:

  • Inference Framework: miroflow-framework!
  • Switch tokenizer configuration to tokenizer_config_search.json
  • Tools Configuration:
Table
ServerDescriptionTools Provided
tool-pythonExecution 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_webpageGoogle search via Serper APIgoogle_search
jina_scrape_llm_summaryWeb scraping with LLM-based information extraction with Jinascrape_and_extract_info
  • Summary model: Qwen3-14B-thinking
  • Temperature: 1.0
  • Note, access to HuggingFace has been explicitly disabled in these tools.

Limitations

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.

Contact

If you have any questions, please raise an issue or contact us at nanbeige@kanzhun.com.

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