import { useEffect, useState, useRef } from "react"; import mermaid from "mermaid"; import { forgesight } from "@/lib/api"; import { Cpu, HardDrive, Server, BookOpen, Bot, Rocket, ArrowRight, Terminal, Zap, ShieldCheck } from "lucide-react"; const LAYER_ICONS = { Hardware: Cpu, Runtime: HardDrive, Serving: Server, Model: BookOpen, Agents: Bot, Product: Rocket, }; export default function Blueprint() { const [data, setData] = useState(null); const mermaidRef = useRef(null); useEffect(() => { forgesight.getBlueprint().then((d) => setData(d)).catch(() => {}); mermaid.initialize({ theme: "dark", startOnLoad: true, securityLevel: "loose", themeVariables: { primaryColor: "#ED1C24", primaryTextColor: "#fff", primaryBorderColor: "#ED1C24", lineColor: "#333", secondaryColor: "#141416", tertiaryColor: "#0A0A0A", fontSize: "12px", fontFamily: "JetBrains Mono", }, }); }, []); useEffect(() => { if (data && mermaidRef.current) { mermaid.contentLoaded(); } }, [data]); const pipelineDiagram = ` graph TD subgraph "Data Acquisition" IMG[Image Feed] end subgraph "AMD MI300X Cluster" VLLM[vLLM Engine] QWEN[Qwen2-VL-7B] VLLM --- QWEN end subgraph "Agentic Pipeline" I[Inspector Agent] D[Diagnose Agent] A[Action Agent] R[Report Agent] I --> D --> A --> R end IMG --> I I -.-> VLLM D -.-> VLLM A -.-> VLLM R -.-> VLLM classDef device font-family:Inter,fill:#0d0d10,stroke:#333,color:#888 classDef compute fill:#ED1C24,stroke:#ED1C24,color:#fff,stroke-width:2px classDef agent fill:#141416,stroke:#ED1C24,color:#fff,padding:10px class IMG device class VLLM,QWEN compute class I,D,A,R agent `; return (
{/* HERO SECTION */}
System Architecture

Built for Pure Performance.

ForgeSight is architected to leverage the massive memory bandwidth of the AMD MI300X. A six-layer stack designed for zero-latency industrial inference.

{pipelineDiagram}
{/* STACK LAYERS */}
The Stack

Top-to-Bottom Integration

06 TOTAL LAYERS
{data?.stack?.map((layer, i) => { const Icon = LAYER_ICONS[layer.layer] || Cpu; return (
L{String(i + 1).padStart(2, "0")}
{layer.layer}

{layer.title}

{layer.why}

Tech Spec
{layer.detail}
); })}
{/* FINETUNE RECIPE */} {data?.finetune_recipe && (
Training Protocol

QLoRA Optimization

Maximum efficiency training recipe for Qwen2-VL-7B.

8× MI300X
BF16 MIXED
                # ForgeSight ROCm Optimized Fine-tune{"\n"}
                accelerate launch --mixed_precision bf16 train_qlora.py \{"\n"}
                {"  "}--base Qwen/Qwen2-VL-7B-Instruct \{"\n"}
                {"  "}--data forgesight/qc-industrial-v1 \{"\n"}
                {"  "}--lora_r 64 --lora_alpha 128 \{"\n"}
                {"  "}--epochs 3 --batch_size 4 --grad_accum 8{"\n\n"}
                # Production Inference{"\n"}
                vllm serve forgesight/qwen2-vl-mi300x \{"\n"}
                {"  "}--enforce-eager --no-enable-chunked-prefill \{"\n"}
                {"  "}--dtype bfloat16 --port 8000
              
)}
); } function Stat({ label, value }) { return (
{label}
{value}
); } function SpecItem({ icon: Icon, label, value }) { return (
{label}
{value}
); }