hf_space/README.md

metadata

title: ForgeSight
emoji: 🔍
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 5.29.1
app_file: app.py
pinned: true
license: mit
short_description: Multimodal QC Copilot on AMD MI300X + ROCm
tags:
  - amd
  - rocm
  - mi300x
  - qwen
  - vllm
  - quality-control
  - agents

🔍 ForgeSight — Multimodal QC Copilot on AMD Instinct™ MI300X

ForgeSight is a production-ready Agentic Quality Control (QC) Pipeline designed for high-throughput manufacturing environments. Built exclusively for the AMD + lablab.ai Developer Hackathon, it leverages the massive 192GB VRAM of the AMD Instinct MI300X to run a state-of-the-art multimodal multi-agent workflow.

🚀 Key Features

• Multimodal Reasoning: Uses Qwen2-VL-7B to "see" and understand complex assembly line defects in a single forward pass.
• 4-Agent Pipeline: Chained reasoning workflow:
  1. Inspector — Identifies surface defects, anomalies, and violations.
  2. Diagnostician — Performs industry-literate root-cause analysis.
  3. Action — Generates prioritized work orders and tool checklists.
  4. Reporter — Summarizes findings into human-readable executive reports.
• MI300X Optimized: Served via vLLM on ROCm, utilizing continuous batching and paged attention for near-instant inference.
• Audit-Ready: Generates downloadable PDF QC Audit Reports for every inspection.
• Persistent Data: Integrated with MongoDB Atlas for long-term defect tracking and telemetry history.

🏗️ Technical Architecture

graph TD
    A[React Dashboard] --> B[FastAPI Gateway]
    B --> C[Gradio Admin Console]
    B --> D[4-Agent Pipeline]
    D --> E[AMD MI300X Inference Server]
    E --> F[vLLM / ROCm]
    F --> G[Qwen2-VL-7B-Instruct]
    B --> H[MongoDB Atlas]
    B --> I[PDF Generator]

Stack

• Hardware: AMD Instinct MI300X (192GB HBM3)
• Software: ROCm 6.2, PyTorch 2.4, vLLM
• Frontend: React 18, Tailwind CSS, Recharts
• Backend: FastAPI, Gradio, Python 3.10

🛠️ Installation & Setup

1. Clone the Repo: git clone https://github.com/rasali535/hans.git
2. Install Deps: pip install -r requirements.txt
3. Configure Environment: Set AMD_INFERENCE_URL and AMD_INFERENCE_TOKEN in your .env.
4. Launch: python hf_space/app.py

📊 Performance on AMD

The MI300X's 5.3 TB/s bandwidth allows ForgeSight to maintain >2500 tokens/sec throughput, enabling real-time visual inspection of high-speed manufacturing lines without the latency typical of cloud-based VLM APIs.

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Built by Hans for the AMD Developer Hackathon.