gtyagikevadiya/transtuitive-passenger-agent-v_0.1

Transtuitive Passenger Agent — Fine-Tuning Dataset

A realistic tool-calling dataset for fine-tuning small LLMs to act as a transit passenger assistant. All data was generated using live MCP server responses from the Transtuitive transit platform.

Dataset Summary

Split	Examples
Train	64
Validation	8
Total	72

Tools Covered

Tool	# Calls	Description
get_upcoming_trips	20	Fetch passenger's scheduled trips
cancel_trip	7	Cancel a trip by ID (always preceded by get_upcoming_trips)
get_public_alerts	8	Check for active service disruptions
get_public_fare_plans	11	Browse available fare plans and pricing
get_wallet_info	10	Check travel wallet balance
get_general_services_timings	11	Get service hours and holiday closures
get_fixed_routes	9	Plan a fixed-route bus/rail trip

Data Format

Each example uses the OpenAI messages format (compatible with HuggingFace TRL SFTTrainer):

{
  "messages": [
    {"role": "system", "content": "...system prompt with tool definitions..."},
    {"role": "user", "content": "What are my upcoming trips?"},
    {
      "role": "assistant",
      "content": null,
      "tool_calls": [{
        "id": "c1",
        "type": "function",
        "function": {"name": "get_upcoming_trips", "arguments": "{}"}
      }]
    },
    {"role": "tool", "tool_call_id": "c1", "content": "{...real tool response...}"},
    {"role": "assistant", "content": "You have 3 upcoming trips: ..."}
  ]
}

Real Data Sources

All tool responses use real data fetched from the live Transtuitive MCP server:

• Fare Plans: 6 real plans (Month Pass, January Pass, Blue Plan, 4 Ticket Day Pass, New Plan, Usage Test)
• Service Hours: On-Demand Prime: Mon–Fri 8am–8pm; ParaTransit: Tue–Fri 8am–10pm
• Fixed Routes: Woodward Round / Woodward Near Office route, $5.00 fare
• Wallet: Real wallet schema with realistic balance values

Usage

Load with HuggingFace datasets

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/transtuitive-passenger-agent")
train_data = dataset["train"]
val_data = dataset["validation"]

Fine-tune with Unsloth + TRL

See the accompanying train.py script in the project repository for the complete QLoRA training setup using Unsloth.

Recommended Models

• unsloth/Qwen2.5-7B-Instruct (best tool-calling performance)
• unsloth/Meta-Llama-3.1-8B-Instruct
• unsloth/mistral-7b-instruct-v0.3