| # Field Notes: Building TutorDesk AI — An On-Laptop Copilot for Indian Tuition Teachers |
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| *Submitted for the Hugging Face × Gradio "Build Small" Hackathon · June 2026 · Backyard AI track* |
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| ## The Problem I Was Solving |
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| In India, roughly 70 million students attend private tuition classes outside school hours. |
| Their teachers — often solo practitioners working from home — spend 60–90 minutes every day |
| doing the same things: writing worksheets, marking homework, typing parent updates. |
| That is prep time that could be teaching time. |
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| I wanted to build something that could cut that 90 minutes to under 10, using only |
| open-weight models, no paid APIs, and — crucially — software that *works when the internet |
| doesn't*. |
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| ## What I Built |
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| **TutorDesk AI** is a five-feature Gradio app for Classes 6–10 Math and Science (CBSE/NCERT): |
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| | Feature | What it does | Model | |
| |---------|-------------|-------| |
| | Worksheet from Textbook | Photograph a chapter page → instant worksheet + quiz + answer key | MiniCPM-V 4.5 (OpenBMB, 8B) | |
| | Weekly Teaching Pack | One click → worksheet + homework + quiz + key + parent note (5-agent pipeline) | Fine-tuned Qwen3-4B | |
| | Regional Language | Translate any artifact into Hindi or Tamil | Tiny Aya (CohereLabs, 3.35B) | |
| | Illustrated Worksheets | Embed AI-generated science diagrams in the PDF | FLUX.1-schnell (Black Forest Labs) | |
| | Photo Auto-Grading | Photograph an answer sheet → marks awarded per step, CBSE-style | MiniCPM-V + fine-tuned Qwen3-4B | |
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| Total model stack: **≈27B parameters** (well under the 32B per-model cap). |
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| ## Key Technical Decisions |
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| ### 1. Self-hosting everything on Modal |
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| My dev laptop cannot hold an 8B vision model and a 12B diffusion model at the same time. |
| Rather than reaching for a paid API, I deployed every model as a **scale-to-zero GPU function |
| on Modal**. The Gradio Space is a thin client that calls these functions by name. Cold-start |
| latency (~20–40 s on first request) is acceptable for a teacher generating a weekly pack — |
| they are not running a real-time chat. |
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| This approach also lets me claim the Off-the-Grid path cleanly: in offline mode, only |
| llama.cpp + GGUF is needed locally. |
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| ### 2. Fine-tuning Qwen3-4B for Indian grading style |
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| The base Qwen3-4B is a competent text model, but it grades generously — full marks for |
| directionally correct answers, no attention to *step marks* or *partial credit*, which are |
| central to CBSE assessment culture. |
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| I built a grading dataset of ~600 `(marking_scheme, student_answer, expected_grade)` triples |
| synthesised from NCERT worked examples, then LoRA-fine-tuned for 3 epochs on a Modal A10G |
| (< 30 minutes). The result (`naazimsnh02/tutordesk-qwen3-4b`) reliably outputs structured |
| `MARKS: X/Y` tokens and penalises skipped steps. |
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| Training loss dropped from ~2.1 → 0.4. Not measured on a held-out set (hackathon scope), |
| but qualitative grading felt noticeably tighter. |
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| ### 3. Tiny Aya for South-Asian languages |
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| `CohereLabs/tiny-aya-fire` (3.35B) is specifically trained for South-Asian languages |
| including Hindi and Tamil — a much better fit than a general-purpose multilingual model. |
| I self-host it on a Modal L4 (cheaper than A10G, sufficient for 3B generation) and fall back |
| to English pass-through when offline. |
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| ### 4. The 5-agent architecture |
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| Each "agent" is a single function: system prompt + one LLM call + trace logging. |
| The Weekly Teaching Pack chains five of them: |
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| ``` |
| chapter text → [CurriculumAgent] → topics |
| → [QuestionGenAgent] → questions (×N) |
| → [DifficultyAgent] → labelled questions |
| → [AnswerAgent] → answer key |
| → [ReportAgent] → parent note |
| ``` |
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| The pipeline is sequential (each agent depends on the previous), but the structure makes |
| it easy to swap models per agent in future — e.g., a smaller model for the parent note, |
| a reasoning model for the answer key. |
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| ### 5. Off-the-Grid mode via llama.cpp |
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| For the Off-Grid + Llama Champion badges, the same fine-tuned model can run locally via |
| **llama-cpp-python** (GGUF quantisation). Set `TUTORDESK_OFFLINE=1` and point |
| `QWEN_GGUF_PATH` at a Q4_K_M quant (~2.5 GB), and the entire Weekly Teaching Pack, |
| Auto-Grading (text path), and Regional Language (English pass-through) work without |
| any internet connection. Vision features degrade gracefully to a text-input prompt. |
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| ## What Surprised Me |
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| **MiniCPM-V's OCR is remarkably good on printed text.** I expected to need extensive |
| preprocessing — contrast boost, deskew, upscale. In practice, a phone photo of a clear |
| NCERT page produces clean extraction on the first try. Handwriting is a different story |
| (as expected), which is why the demo uses printed/neat answer sheets. |
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| **The grading dataset bottleneck is prompt engineering, not data volume.** Getting Qwen to |
| output a consistent `MARKS: X/Y` token with a breakdown table required more iteration on |
| the system prompt than on the examples themselves. Once the prompt was tight, 600 examples |
| was enough. |
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| **Modal's scale-to-zero genuinely works for hackathon demos.** The fear is "cold start at |
| demo time." The reality: cache the model in the Modal volume, and the first warm-up is |
| ~20 s — slow, but not demo-breaking. |
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| ## What I Would Do Differently |
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| 1. **Add a GGUF export step to the fine-tuning pipeline.** Right now, Off-Grid mode points |
| to a separate Qwen3-4B base GGUF; the fine-tuned weights live only on HF Hub as a full |
| checkpoint. Merging LoRA → base → GGUF export would close that gap. |
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| 2. **Async the pipeline.** The 5-agent chain runs serially (~45 s end-to-end on A10G). Most |
| agents only need the curriculum output, so question_gen, difficulty, answer, and report |
| could run in parallel and cut latency by ~3×. |
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| 3. **Handwriting robustness.** The auto-grading is scoped to printed sheets. A real |
| deployment needs either a dedicated HTR model or a user-assisted transcription step. |
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| ## Links |
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| - **Space:** https://huggingface.co/spaces/build-small-hackathon/tutordesk-ai |
| - **Demo Video:** https://youtu.be/HBBLSwXUeaM |
| - **Fine-tuned model:** https://huggingface.co/naazimsnh02/tutordesk-qwen3-4b |
| - **Agent traces dataset:** https://huggingface.co/datasets/naazimsnh02/tutordesk-agent-traces |
| - **Source:** https://github.com/Naazimsnh02/tutordesk_ai |
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| *Built by Naazim · June 2026 · TutorDesk AI* |
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