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humanailabs
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Model Details
Model Provider
humanailabs
Model Tree
unsloth/Phi-4-mini-instruct
Input Modalities
Output Modalities
Supported Functionality
Given a natural language request and context (owner vs visitor, current datetime, timezone), the model returns one of:
tool_call — fires create_event, modify_event, delete_event, or check_availabilityclarification — asks for missing required fields (one field at a time)unsupported — rejects requests outside calendar scopeOutput is always raw JSON. No markdown, no explanation.
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "humanailabs/calendar-agent"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "system", "content": "<system prompt here>"}, {"role": "user", "content": "[Context]\nis_owner: true\ncurrent_datetime: 2026-06-25T09:00:00-07:00\nuser_timezone: America/Los_Angeles\n\n[User Request]\nSchedule a team sync tomorrow at 3pm for 30 minutes."}] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)output = model.generate(inputs, max_new_tokens=128)print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))# {"type": "tool_call", "action": "create_event", "parameters": {"title": "Team sync", "start_time": "2026-06-26T15:00:00-07:00", "duration_minutes": 30}}from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "humanailabs/calendar-agent"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "system", "content": "<system prompt here>"}, {"role": "user", "content": "[Context]\nis_owner: true\ncurrent_datetime: 2026-06-25T09:00:00-07:00\nuser_timezone: America/Los_Angeles\n\n[User Request]\nSchedule a team sync tomorrow at 3pm for 30 minutes."}] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)output = model.generate(inputs, max_new_tokens=128)print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))# {"type": "tool_call", "action": "create_event", "parameters": {"title": "Team sync", "start_time": "2026-06-26T15:00:00-07:00", "duration_minutes": 30}}