README.md

metadata

title: Tiny Dispatch Coach
emoji: 🚚
colorFrom: green
colorTo: yellow
sdk: gradio
sdk_version: 6.14.0
python_version: 3.12
app_file: app.py
pinned: false
license: mit
short_description: Small-model route coach
tags:
  - gradio
  - hackathon
  - small-models
  - minicpm
  - openbmb
  - operations-research
  - logistics
models:
  - openbmb/MiniCPM5-1B-GGUF
preload_from_hub:
  - openbmb/MiniCPM5-1B-GGUF MiniCPM5-1B-Q4_K_M.gguf

Tiny Dispatch Coach

Tiny Dispatch Coach is a Backyard AI project for small delivery teams. It uses OpenBMB MiniCPM5-1B-GGUF as the local small model for dispatch-note parsing, then hands the structured constraints to a deterministic route planner.

It converts a daily order sheet and messy dispatcher notes into:

• structured delivery constraints parsed from messy human notes,
• route plans with time-window and capacity checks,
• before/after metrics against a manual baseline,
• driver-ready route cards,
• a simple visual route map.

The app is designed for the Build Small Hackathon rule set: Gradio, Hugging Face Spaces, and models under 32B parameters.

Model

• Model repo: openbmb/MiniCPM5-1B-GGUF
• File: MiniCPM5-1B-Q4_K_M.gguf
• Parameter count: 1.08B
• Runtime target: local GGUF through llama-cpp-python
• Cloud LLM APIs: none

The Space preloads the Q4 MiniCPM5 GGUF file and installs the CPU llama.cpp wheel. The public CPU Basic demo defaults to fast deterministic parsing so judges can inspect the route planner immediately. The MiniCPM5 parser can be enabled from the checkbox in the UI; if a runtime cold start cannot load the model or the model returns invalid JSON, the app falls back to the deterministic parser and makes that visible in the parser trace.

The route optimizer never depends on hidden model output: every route, time window, lateness minute, and baseline delta is computed deterministically.

Current Scope

Included now:

• MiniCPM5 text constraint parsing with deterministic CPU fallback.
• Capacity-safe multi-trip route planning.
• Manual baseline comparison.
• Synthetic sample data only.
• Field notes and a shareable model/planner trace.

Planned after the core demo is stable:

• Optional image intake with MiniCPM-V 4.6 for order-sheet OCR.
• Optional deeper reporting with MiniCPM4.1-8B if Space resources allow it.

Not included:

• VoxCPM2 or voice/TTS features.