docs/SUBMIT_NOW.md

SignBridge — paste-ready lablab.ai submission

Submission deadline: 2026-05-11 03:00 Malaysia Time (= Sunday May 10 12:00 PM Pacific Time). Open https://lablab.ai/ai-hackathons/amd-developer → bottom of page → Submit Project. Each block below maps 1:1 to a form field. Paste verbatim.

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Project Title (form max: 50 chars, min 5)

SignBridge — fine-tuned Qwen3-VL on AMD MI300X

(47 characters; leads with Qwen + AMD for both the Qwen Special Reward and Track 3 narratives.)

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Short Description (form max: 255 chars, min 50)

Two people who couldn't communicate, now can. Real-time ASL → English speech, powered by Qwen3-VL we fine-tuned on AMD MI300X.

(126 characters — fits comfortably.)

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Long Description (form max: 2000 chars, min 600)

SignBridge is a real-time American Sign Language → English speech translator built for the AMD Developer Hackathon, Track 3 (Vision & Multimodal AI). We fine-tuned Qwen3-VL-8B on a single AMD Instinct MI300X and serve it natively through vLLM's video understanding API.

The user signs at the webcam — fingerspelled letters (Snapshot tab) or full motion words (Record sign tab) — and SignBridge replies in spoken English. Two people who couldn't communicate, now can.

Architecture: (1) MediaPipe Hand → trained MLP classifier handles static fingerspelling at 90% accuracy, ~50 ms on CPU. (2) For motion words the webcam clip is transcoded with ffmpeg and sent natively to a LoRA-fine-tuned Qwen3-VL-8B via vLLM's video_url block — Qwen3-VL processes the clip with its own temporal encoder, no manual frame sampling. The 54-minute LoRA on a single MI300X lifts ASL accuracy from 19% zero-shot to 92% in transformers eval. (3) Qwen3-8B composes recognised tokens into English; gTTS speaks it. Both LLMs run concurrently on the same MI300X via vLLM 0.17.1 on ROCm 7.2.

One MI300X did three jobs on one GPU: ran the LoRA fine-tune (54 min), hosts the merged Qwen3-VL-8B for inference, and hosts the 8B composer in parallel. 192 GB HBM3 means no swapping or sharding. The same workload on H100 (80 GB) needs a 3-GPU cluster.

Fine-tune artefacts (judge-verifiable): merged Qwen3-VL-8B-ASL at huggingface.co/LucasLooTan/signbridge-qwen3vl-8b-asl; MediaPipe-MLP classifier at huggingface.co/LucasLooTan/signbridge-asl-classifier. Both pulled at runtime via hf_hub_download.

Why it matters: ASL interpreters cost $50–200/hr and are scarce. Sorenson VRS books $4B+/yr filling this gap. SignBridge is MIT-licensed open source — any Deaf-led NGO, school, ministry can self-host on their own AMD compute. V1 is ASL-only by design; sign languages aren't interchangeable.

Built solo by Lucas Loo Tan Yu Heng, May 5–11, 2026.

(~1980 chars — fits the 2000 max with ~20 char buffer.)

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Technology & Category Tags

Pick from lablab dropdown:

Primary (must select):

• Qwen and/or Qwen3-VL
• AMD Developer Cloud
• AMD ROCm
• HuggingFace Spaces

Secondary (relevant):

• LLaMA (no — we replaced this with Qwen3-8B; skip)
• Gradio
• FastAPI
• Vision
• Multimodal
• Accessibility
• Open Source
• vLLM

Track: Track 3 — Vision & Multimodal AI (also satisfies Track 2 fine-tuning narrative if dual-track allowed)

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Pipeline at a glance (May 10 — current shipping)

Paste this block anywhere a one-screen architecture summary is needed (lablab form, slide notes, README):

- Static fingerspelling: MediaPipe Hand → trained MLP classifier (90% accuracy, ~50 ms on CPU)
- Motion signs: webcam recording → ffmpeg (480p, 8 fps, ≤4 s, H.264) → vLLM /v1/chat/completions
                 with a video_url block → fine-tuned Qwen3-VL-8B on AMD MI300X
- Sentence composer: Qwen3-8B on the same MI300X (vLLM, separate port)
- Speech synthesis: gTTS (Google's free TTS, fast, MP3 output)
- Live demo: HF Space (Gradio Docker SDK) — both tabs, end-to-end

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Cover Image

Upload assets/cover.png from the repo (1280×640 PNG, indigo→pink gradient with 🤟 + project name).

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Video Presentation

Paste the YouTube Unlisted URL of your demo video.

Reference shot list: docs/demo-video-script.md.

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Slide Presentation

Upload the deck PDF.

Build from docs/pitch-deck.md:

1. Open Google Slides → blank deck
2. Paste each slide's content into a blank slide
3. File → Download → PDF
4. Upload here

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Public GitHub Repository

https://github.com/seekerPrice/signbridge

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Demo Application Platform

Hugging Face Space

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Application URL

https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/signbridge

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Final pre-submit checklist

Before clicking Submit:

• Title pasted (70 chars)
• Short description pasted (132 chars)
• Long description pasted (~350 words)
• Tags selected (at minimum: Qwen, AMD Developer Cloud, AMD ROCm, HuggingFace Spaces)
• Cover image uploaded (assets/cover.png)
• Video URL pasted (YouTube unlisted)
• Pitch deck PDF uploaded
• GitHub URL pasted
• HF Space URL pasted
• Track selection: Track 3 — Vision & Multimodal AI
• Open Space in incognito → confirm it loads
• GitHub repo public + has clean README
• LICENSE file is MIT

When all boxes ticked → click Submit → wait for confirmation email → done.

Aim to submit by 2026-05-11 02:00 MYT (1-hour buffer before the 03:00 cutoff).