docs/plans/01_research_verified_stack.md

BugLens Plan 01 - Verified Research And Tech Stack

Research date: 2026-06-09

Purpose: lock the correct hackathon rules, prize strategy, model choice, and current library versions before any code is written.

Executive Decision

Build BugLens as a Hugging Face Gradio Space for the Backyard AI track. Use openbmb/MiniCPM-V-4.6 as the primary model because it is a sponsor model, tiny enough for the Tiny Titan award, strong at UI/OCR screenshots, and aligned with the "honest small model" story.

The app should convert a screenshot plus a short tester note into four structured outputs:

1. Jira-ready bug report.
2. Missing-info checklist.
3. Regression test cases.
4. Edge cases and risk list.

The killer differentiator is the missing-info card. BugLens should never invent browser, OS, device, user role, environment, or backend state from an image. It should say what it cannot know.

Correct Prize Map

Verified from the official Build Small Hackathon page:

Source: https://huggingface.co/build-small-hackathon

Core requirements:

• Every model must be <= 32B parameters.
• App must be built on Gradio.
• App must be hosted as a Hugging Face Space.
• Submission needs a short demo video.
• Submission needs a social-media post.
• Hack window: June 5-15, 2026.
• Submission close date: June 15, 2026.

Main target:

• Backyard AI track.
• Judged on specific real problem, real user use, honest fit with small-model constraint, and polish of the Gradio app.

Sponsor and special targets:

Target	Why BugLens fits	Priority
Backyard AI placement	Real QA/product pain, direct practical use	Must-have
OpenBMB Awards	Core model is MiniCPM-V 4.6	Must-have
Tiny Titan	MiniCPM-V 4.6 is built from SigLIP2-400M + Qwen3.5-0.8B, effectively around 1.2B-1.3B	Must-have
Off-Brand	Custom UI past default Gradio, ideally with gradio.Server or strong HTML/CSS cards	High
Best Demo	Strong demo video and social post	High
Field Notes	Blog/report about what was built and learned	High
Best Use of Codex	Develop through OpenAI Codex and preserve attributed commits	High
Modal Awards	Modal GPU endpoint or Modal-powered inference path	Medium
Bonus Quest Champion	Stack as many valid badges/awards as possible	Medium

Do not falsely claim:

• Best Agent, unless you truly add agentic planning/tool execution. BugLens is a pipeline by default.
• Off the Grid if the final app depends on Modal, hosted APIs, or cloud inference.
• Llama Champion unless the final model runtime actually uses llama.cpp.
• Well-Tuned unless you publish and use a fine-tuned model on Hugging Face.
• Sharing is Caring unless you publish a useful trace/dataset on the Hub.

Badge Strategy

Verified merit badges on the official page:

• Off the Grid: no cloud APIs; whole thing runs on the model in front of you.
• Well-Tuned: app uses a fine-tuned model published on Hugging Face.
• Off-Brand: custom frontend beyond default Gradio; official hint says gr.Server.
• Llama Champion: model runs through llama.cpp.
• Sharing is Caring: shared agent trace on the Hub.
• Field Notes: blog post or report about what was built and learned.

Recommended claim set for the realistic plan:

• Off-Brand.
• Field Notes.
• Possibly Sharing is Caring if you publish anonymized BugLens prompt/outputs as a dataset.

Recommended special-award claim set:

• Tiny Titan.
• Best Demo.
• OpenBMB.
• Best Use of Codex.
• Modal only if it is stable by the Thursday decision gate.

Model Selection

Primary model:

• Model: openbmb/MiniCPM-V-4.6
• URL: https://huggingface.co/openbmb/MiniCPM-V-4.6
• License: Apache-2.0
• Task: image-text-to-text
• Official install note: pip install "transformers[torch]>=5.7.0" torchvision torchcodec
• Architecture noted by Hugging Face docs: SigLIP vision encoder plus Qwen3.5 language model backbone.
• Model card states it is based on SigLIP2-400M and Qwen3.5-0.8B.
• Model card says it supports image, multi-image, video understanding, OCR-like UI reading, vLLM, SGLang, llama.cpp, Ollama, BNB, AWQ, GPTQ, and GGUF variants.

Why this is the right model:

• It directly targets OpenBMB sponsor awards.
• It is small enough for Tiny Titan.
• It is image-native, so screenshots are a natural task.
• The small size strengthens the story: "BugLens does one narrow useful thing, and admits uncertainty."
• It avoids the temptation to use a larger 7B+ VLM for a task that mostly needs OCR, UI perception, and schema discipline.

Optional model/runtime variants:

Use case	Option	When to use
Hosted GPU backend	Transformers on Modal	Best for speed and Modal award
HF Space fallback	ZeroGPU with @spaces.GPU	If Modal blocks progress
Local-first demo	GGUF / llama.cpp variant	If chasing Off the Grid and Llama Champion
Harder reasoning	MiniCPM-V-4.6-Thinking	Only if screenshots need more reasoning and latency is acceptable

Current Library Versions

Pin exact versions before final submission. Current verified versions from PyPI and official docs:

Package	Current verified version / constraint	Source
gradio	6.17.3, uploaded Jun 7, 2026	https://pypi.org/project/gradio/
transformers	5.10.2, uploaded Jun 4, 2026	https://pypi.org/project/transformers/
modal	1.4.3, released May 18, 2026	https://pypi.org/project/modal/
pillow	12.2.0, released Apr 1, 2026	https://pypi.org/project/pillow/
requests	2.34.2, released May 14, 2026	https://pypi.org/project/requests/
pydantic	stable: 2.13.4; latest visible is pre-release 2.14.0a1	https://pypi.org/project/pydantic/

Recommended pins for the final Space:

gradio==6.17.3
pydantic==2.13.4
pillow==12.2.0
requests==2.34.2

Recommended pins for the Modal backend:

modal==1.4.3
transformers[torch]==5.10.2
torchvision
torchcodec
pillow==12.2.0
accelerate

Important note from the MiniCPM model card:

• torchcodec can have CUDA compatibility issues.
• If this happens, replace torchcodec with av, or pin torch to the CUDA runtime used by the environment.

Gradio And ZeroGPU Facts

Sources:

• Gradio Server mode guide: https://www.gradio.app/guides/server-mode
• Gradio Server announcement: https://huggingface.co/blog/introducing-gradio-server
• ZeroGPU docs: https://huggingface.co/docs/hub/spaces-zerogpu

Key facts:

• gradio.Server allows a fully custom frontend while keeping Gradio backend features.
• Server mode keeps API, queuing, streaming, MCP support, ZeroGPU support, and Spaces hosting.
• ZeroGPU is Gradio-only.
• ZeroGPU uses @spaces.GPU to allocate and release GPU for decorated functions.
• ZeroGPU hosting requires PRO for personal accounts, or Team/Enterprise for organizations.
• ZeroGPU supports Gradio 4+ and PyTorch 2.8+ to latest.
• Current ZeroGPU backing hardware is NVIDIA RTX Pro 6000 Blackwell, with 48GB default large and 96GB xlarge.

Recommended UI path:

• Use gradio.Server if time allows. It is the strongest Off-Brand signal.
• If time is tight, use Gradio Blocks plus custom HTML/CSS cards. Still visibly avoid stock UI.

Modal Facts

Sources:

• Modal partner info on official hackathon page: https://huggingface.co/build-small-hackathon
• Gradio with Modal guide: https://www.gradio.app/guides/deploying-gradio-with-modal

Key facts:

• Modal is a hackathon sponsor.
• Official page lists $20,000 in Modal credits for top Modal-powered apps.
• The Gradio Modal guide suggests max_containers=1 for Gradio sticky-session needs if serving Gradio through Modal.
• The guide also explicitly says GPU compute can be deployed in a separate Modal function and called from the Gradio app.

Recommended Modal path:

• Keep Hugging Face Space as the public submission front door.
• Put MiniCPM inference in a Modal GPU web endpoint.
• Have the Space call Modal for inference.
• This avoids serving the whole Gradio session through Modal, reducing sticky-session risk.

Final Technical Decision

Primary build:

• HF Space: Gradio 6.17.3, Python 3.12 if available, CPU-basic frontend.
• Backend: Modal GPU endpoint running MiniCPM-V 4.6 through Transformers 5.10.2.
• App contract: Pydantic 2.13.4 schema.
• Model flow: screenshot + note -> factual observation -> structured JSON -> four cards and exports.

Fallback:

• If Modal is not stable by end of Thursday, switch to HF ZeroGPU or a hosted test path and drop the Modal award.

Optional stretch:

• Add a llama.cpp/GGUF local mode only if core app is already done. This is needed for Llama Champion/Off the Grid claims.