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cyankiwi
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Model Details
Model Provider
cyankiwi
Model Tree
Nanbeige/Nanbeige4.1-3B
Input Modalities
Output Modalities
Supported Functionality
For inference hyperparameters, we recommend the following settings:
For the chat scenario:
markdown
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:
markdown
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'), 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 have any questions, please raise an issue or contact us at nanbeige@kanzhun.com.