deployment kokonaan uusiksi

network-poc/build-binaries.sh

@@ -1,63 +0,0 @@
-#!/bin/bash
-# Käännä kipina-node binäärit kaikille alustoille
-set -e
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-OUT="$SCRIPT_DIR/frontend/public/download"
-HASH_FILE="$OUT/.build-hash"
-mkdir -p "$OUT"
-
-# Tarkistetaan onko native-node muuttunut edellisen buildin jälkeen
-CURRENT_HASH=$(git -C "$SCRIPT_DIR" log -1 --format=%H -- native-node/ Cargo.toml Cargo.lock)
-if [ -n "$(git -C "$SCRIPT_DIR" status --porcelain -- native-node/ Cargo.toml Cargo.lock)" ]; then
-    CURRENT_HASH="dirty-$(git -C "$SCRIPT_DIR" status --porcelain | md5sum | awk '{print $1}')"
-fi
-
-if [ -f "$HASH_FILE" ] && [ "$(cat "$HASH_FILE")" = "$CURRENT_HASH" ]; then
-    echo "=== Kipinä Node — ei muutoksia, ohitetaan build ==="
-    ls -lh "$OUT"/kipina-node-* 2>/dev/null || true
-    exit 0
-fi
-
-echo "=== Kipinä Node — Binary Build ==="
-
-# macOS ARM (natiivi)
-echo "[1/4] macOS ARM64..."
-cd "$SCRIPT_DIR"
-cargo build --release -p native-node --no-default-features 2>&1 | tail -1
-cp target/release/native-node "$OUT/kipina-node-macos-arm64"
-echo "      $(ls -lh "$OUT/kipina-node-macos-arm64" | awk '{print $5}')"
-
-# Linux x86_64 (Docker)
-echo "[2/4] Linux x86_64..."
-docker run --rm \
-    -v "$SCRIPT_DIR":/app -w /app \
-    --platform linux/amd64 \
-    rust:slim \
-    bash -c "apt-get update -qq && apt-get install -y -qq pkg-config libssl-dev >/dev/null 2>&1 && cargo build --release -p native-node --no-default-features --target-dir target/docker_linux_amd64 && cp target/docker_linux_amd64/release/native-node /app/frontend/public/download/kipina-node-linux-x86_64"
-echo "      $(ls -lh "$OUT/kipina-node-linux-x86_64" | awk '{print $5}')"
-
-# Linux ARM64 (Docker)
-echo "[3/4] Linux ARM64..."
-docker run --rm \
-    -v "$SCRIPT_DIR":/app -w /app \
-    --platform linux/arm64 \
-    rust:slim \
-    bash -c "apt-get update -qq && apt-get install -y -qq pkg-config libssl-dev >/dev/null 2>&1 && cargo build --release -p native-node --no-default-features --target-dir target/docker_linux_arm64 && cp target/docker_linux_arm64/release/native-node /app/frontend/public/download/kipina-node-linux-arm64"
-echo "      $(ls -lh "$OUT/kipina-node-linux-arm64" | awk '{print $5}')"
-
-# Windows x86_64 (Docker + mingw-w64)
-echo "[4/4] Windows x86_64..."
-docker run --rm \
-    -v "$SCRIPT_DIR":/app -w /app \
-    --platform linux/amd64 \
-    rust:slim \
-    bash -c "apt-get update -qq && apt-get install -y -qq gcc-mingw-w64-x86-64 pkg-config libssl-dev >/dev/null 2>&1 && rustup target add x86_64-pc-windows-gnu && cargo build --release -p native-node --no-default-features --target x86_64-pc-windows-gnu && cp target/x86_64-pc-windows-gnu/release/native-node.exe /app/frontend/public/download/kipina-node-windows-x86_64.exe"
-echo "      $(ls -lh "$OUT/kipina-node-windows-x86_64.exe" | awk '{print $5}')"
-
-# Tallennetaan onnistuneen buildin hash
-echo "$CURRENT_HASH" > "$HASH_FILE"
-
-echo ""
-echo "=== Binäärit valmiina ==="
-ls -lh "$OUT"/kipina-node-*

network-poc/deploy-binaries.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-set -e
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-SERVER="ubuntu@86.50.252.98"
-REMOTE_DIR="~/code/agentic-studio/network-poc"
-KEY="$HOME/.ssh/id_rsa"
-SSH_OPTS="-o StrictHostKeyChecking=no -i $KEY"
-
-if ! ssh-add -l 2>/dev/null | grep -q id_rsa; then
-    echo "SSH-avain ei ole agentissa. Lisätään..."
-    ssh-add "$KEY"
-fi
-
-echo "=== Kipinä Node - Vain Binäärien Päivitys ==="
-
-# 1. Käännetään binäärit (hyödyntää korjattua build-binaries.sh cache logiikkaa)
-"$SCRIPT_DIR/build-binaries.sh"
-
-# 2. Siirretään binäärit suoraan kohdekoneen hakemistoon ohittaen Docker-imagen täyden rakennuksen
-echo ""
-echo "[Vieminen uuteen kohteeseen...]"
-ssh $SSH_OPTS "$SERVER" "mkdir -p $REMOTE_DIR/frontend/dist/download"
-scp $SSH_OPTS "$SCRIPT_DIR/frontend/public/download"/kipina-node-* "$SERVER:$REMOTE_DIR/frontend/dist/download/"
-scp $SSH_OPTS "$SCRIPT_DIR/frontend/public"/kipina-node "$SERVER:$REMOTE_DIR/frontend/dist/"
-
-# 3. Luvat kuntoon
-ssh $SSH_OPTS "$SERVER" "chmod +x $REMOTE_DIR/frontend/dist/download/kipina-node-*"
-
-echo "=== Valmis! Binäärit ovat nyt asennettu livenä ja ladattavissa kipina.studiosta ==="

network-poc/deploy-fast.sh

@@ -1,28 +0,0 @@
-#!/bin/bash
-# Nopea deploy: päivittää vain frontendin (ei kontin uudelleenkäynnistystä)
-# Hub-binäärin päivitys: käytä deploy.sh tai deploy-light.sh
-set -e
-
-SERVER="ubuntu@86.50.252.98"
-REMOTE_DIR="~/code/agentic-studio/network-poc"
-SSH_OPTS="-o StrictHostKeyChecking=no"
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-
-echo "=== Kipinä Studio — Frontend Deploy ==="
-
-# 1. Buildaa frontend paikallisesti
-echo "[1/2] Rakennetaan frontend..."
-cd "$SCRIPT_DIR/frontend"
-[ -d node_modules ] || npm install --silent
-npm run build --silent 2>&1 | tail -1
-
-# 2. Synkataan dist/ palvelimelle (vain muuttuneet tiedostot)
-echo "[2/2] Synkataan dist/ → palvelin..."
-ssh $SSH_OPTS $SERVER "mkdir -p $REMOTE_DIR/frontend/dist"
-rsync -az --delete -e "ssh $SSH_OPTS" "$SCRIPT_DIR/frontend/dist/" "$SERVER:$REMOTE_DIR/frontend/dist/"
-
-echo ""
-echo "=== Valmis! Frontend päivitetty — ei uudelleenkäynnistystä ==="
-echo "    https://kipina.studio"
-echo ""
-echo "Huom: Jos Rust-koodi (hub/) muuttui, aja: ./deploy.sh"

network-poc/deploy-light.sh

@@ -1,33 +0,0 @@
-#!/bin/bash
-# Kevyt deploy: lähetetään vain koodi, palvelin buildaa itse
-set -e
-
-SERVER="ubuntu@86.50.252.98"
-REMOTE_DIR="~/code/agentic-studio/network-poc"
-SSH_OPTS="-o StrictHostKeyChecking=no"
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-
-echo "=== Kipinä Studio Deploy (remote build) ==="
-
-# 1. Synkataan koodi palvelimelle (vain muuttuneet tiedostot)
-echo "[1/3] Synkataan koodi..."
-rsync -az --delete \
-    --exclude 'target/' \
-    --exclude 'node_modules/' \
-    --exclude 'dist/' \
-    --exclude '.astro/' \
-    --exclude 'temp/' \
-    --exclude '*.db' \
-    --exclude '.git/' \
-    "$SCRIPT_DIR/" "$SERVER:$REMOTE_DIR/"
-
-# 2. Rakennetaan image palvelimella
-echo "[2/3] Rakennetaan image palvelimella..."
-ssh $SSH_OPTS $SERVER "cd $REMOTE_DIR && docker build -f Dockerfile.prod -t kipina-agentic:latest ."
-
-# 3. Käynnistetään
-echo "[3/3] Käynnistetään..."
-ssh $SSH_OPTS $SERVER "cd $REMOTE_DIR && docker compose -f docker-compose.prod.yml down && docker compose -f docker-compose.prod.yml up -d"
-
-echo "=== Valmis! https://kipina.studio ==="

network-poc/deploy-local.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# Kipinä Studio — paikallinen kehitysympäristö
+# Buildaa frontendin, käynnistää hubin ja native-noden (Ollama)
+# Käyttö: ./deploy-local.sh
+set -e
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$SCRIPT_DIR"
+
+cleanup() { echo ""; echo "Pysäytetään..."; kill $HUB_PID $NODE_PID 2>/dev/null; exit 0; }
+trap cleanup INT TERM
+
+# Portti vapaaksi
+lsof -ti:3000 | xargs kill -9 2>/dev/null || true
+
+# Frontend
+echo "[1/3] Frontend..."
+cd "$SCRIPT_DIR/frontend"
+[ -d node_modules ] || npm install --silent
+npm run build 2>&1 | tail -1
+cd "$SCRIPT_DIR"
+
+# Hub
+echo "[2/3] Hub..."
+STATIC_DIR="$SCRIPT_DIR/frontend/dist" cargo run -p hub 2>&1 &
+HUB_PID=$!
+until curl -sf http://localhost:3000 >/dev/null 2>&1; do sleep 1; done
+
+# Native-node
+NODE_PID=""
+if curl -sf http://localhost:11434/api/tags >/dev/null 2>&1; then
+    MODEL=$(curl -s http://localhost:11434/api/tags | python3 -c "
+import sys,json
+ms=json.load(sys.stdin).get('models',[])
+for m in ms:
+    n=m['name']
+    if '7b' in n and 'coder' in n: print(n); exit()
+for m in ms:
+    if 'coder' in m['name']: print(m['name']); exit()
+if ms: print(ms[0]['name'])
+" 2>/dev/null)
+    if [ -n "$MODEL" ]; then
+        echo "[3/3] Native-node ($MODEL)..."
+        HUB_URL=ws://localhost:3000/ws OLLAMA_MODEL="$MODEL" \
+            cargo run -p native-node --no-default-features 2>&1 &
+        NODE_PID=$!
+    else
+        echo "[3/3] Ollama: ei malleja (ollama pull qwen2.5-coder:7b)"
+    fi
+else
+    echo "[3/3] Ei Ollamaa — Wasm-fallback selaimessa"
+fi
+
+echo ""
+echo "=== http://localhost:3000 === Ctrl+C pysäyttää"
+open http://localhost:3000 2>/dev/null || xdg-open http://localhost:3000 2>/dev/null || true
+wait $HUB_PID

network-poc/deploy-remote.sh

@@ -0,0 +1,58 @@
+#!/bin/bash
+# Kipinä Studio — tuotanto-deploy kipina.studioon
+# Buildaa Docker-imagen (frontend + hub + wasm) ja vie palvelimelle
+# Käyttö: ./deploy-remote.sh
+set -e
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$SCRIPT_DIR"
+
+SERVER="ubuntu@86.50.252.98"
+REMOTE_DIR="~/code/agentic-studio/network-poc"
+KEY="$HOME/.ssh/id_rsa"
+SSH_OPTS="-o StrictHostKeyChecking=no -i $KEY"
+
+# SSH-avain
+if ! ssh-add -l 2>/dev/null | grep -q id_rsa; then
+    echo "SSH-avain puuttuu agentista..."
+    ssh-add "$KEY"
+fi
+
+# Auto-commit
+if ! git diff --quiet HEAD 2>/dev/null || \
+   [ -n "$(git ls-files --others --exclude-standard 2>/dev/null)" ]; then
+    echo "Uncommitted muutoksia — commitoidaan..."
+    read -rp "  Commit-viesti: " msg
+    [ -z "$msg" ] && msg="Deploy $(date +%Y-%m-%d\ %H:%M)"
+    git add -A && git commit -m "$msg"
+fi
+
+echo "=== Kipinä Studio Deploy → kipina.studio ==="
+
+# 1. Docker-image
+echo "[1/4] Docker build..."
+docker build --platform linux/amd64 -f Dockerfile.prod -t kipina-agentic:latest .
+
+# 2. Pakkaus
+echo "[2/4] Pakataan..."
+docker save kipina-agentic:latest | gzip > /tmp/kipina-agentic.tar.gz
+echo "  $(du -h /tmp/kipina-agentic.tar.gz | cut -f1)"
+
+# 3. Siirto
+echo "[3/4] Siirretään..."
+scp $SSH_OPTS /tmp/kipina-agentic.tar.gz "$SERVER:/tmp/"
+scp $SSH_OPTS docker-compose.prod.yml Caddyfile.prod "$SERVER:$REMOTE_DIR/"
+
+# 4. Käynnistys
+echo "[4/4] Käynnistetään..."
+ssh $SSH_OPTS "$SERVER" "gunzip -c /tmp/kipina-agentic.tar.gz | docker load && rm /tmp/kipina-agentic.tar.gz"
+ssh $SSH_OPTS "$SERVER" "cd $REMOTE_DIR && docker compose -f docker-compose.prod.yml down && docker compose -f docker-compose.prod.yml up -d"
+
+# Discord
+WEBHOOK="https://discord.com/api/webhooks/1489504066898755687/8U02d0wug-3MkVax0xMmRoj0s_-V1psnNLPWdSOjnGnKRBUpPjaU6XiX9Iu8DgJI69AP"
+HASH=$(git log -1 --pretty=format:"%h" 2>/dev/null || echo "?")
+MSG=$(git log -1 --pretty=format:"%s" 2>/dev/null || echo "?")
+PAYLOAD=$(python3 -c "import json,sys; print(json.dumps({'content':sys.argv[1]}))" \
+    "🚀 **Kipinä Studio julkaistu!** \`${HASH}\` ${MSG} https://kipina.studio")
+curl -sf -H "Content-Type: application/json" -d "$PAYLOAD" "$WEBHOOK" >/dev/null || true
+
+echo "=== Valmis! https://kipina.studio ==="

network-poc/deploy-with-native.sh

@@ -1,14 +0,0 @@
-#!/bin/bash
-# Deploy + native-node-binäärien käännös (jos muutoksia)
-set -e
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-cd "$SCRIPT_DIR"
-
-echo "=== Kipinä Studio Deploy (+ native binäärit) ==="
-
-# 1. Käännetään native-node-binäärit (ohittaa automaattisesti jos ei muutoksia)
-./build-binaries.sh
-
-# 2. Ajetaan normaali deploy
-exec ./deploy.sh

network-poc/deploy.sh

@@ -1,68 +0,0 @@
-#!/bin/bash
-set -e
-
-if [ "$1" == "local" ]; then
-    echo "=== Kipinä Studio Local Development ==="
-    echo "Käynnistetään kokonaisuus puhtaasti Docker-kontissa..."
-    docker compose up agentic-poc
-    exit 0
-fi
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-cd "$SCRIPT_DIR"
-
-SERVER="ubuntu@86.50.252.98"
-REMOTE_DIR="~/code/agentic-studio/network-poc"
-KEY="$HOME/.ssh/id_rsa"
-SSH_OPTS="-o StrictHostKeyChecking=no -i $KEY"
-
-if ! ssh-add -l 2>/dev/null | grep -q id_rsa; then
-    echo "SSH-avain ei ole agentissa. Lisätään..."
-    ssh-add "$KEY"
-fi
-
-echo "=== Kipinä Studio Deploy ==="
-
-# 0. Commitoidaan uncommitted muutokset ennen deployta
-if ! git diff --quiet HEAD 2>/dev/null || \
-   [ -n "$(git ls-files --others --exclude-standard 2>/dev/null)" ]; then
-    echo "[0] Uncommitted muutoksia havaittu — commitoidaan..."
-    read -rp "    Commit-viesti: " DEPLOY_MSG
-    if [ -z "$DEPLOY_MSG" ]; then
-        DEPLOY_MSG="Deploy $(date +%Y-%m-%d\ %H:%M)"
-    fi
-    git add -A
-    git commit -m "$DEPLOY_MSG"
-    echo "    Commitoitu: $DEPLOY_MSG"
-fi
-
-# 1. Docker-image (frontend + hub + wasm)
-echo "[1/4] Rakennetaan image..."
-docker build --platform linux/amd64 -f Dockerfile.prod -t kipina-agentic:latest .
-
-# 2. Pakataan
-echo "[2/4] Pakataan image..."
-docker save kipina-agentic:latest | gzip > /tmp/kipina-agentic.tar.gz
-echo "       Koko: $(du -h /tmp/kipina-agentic.tar.gz | cut -f1)"
-
-# 3. Siirretään
-echo "[3/4] Siirretään palvelimelle..."
-scp $SSH_OPTS /tmp/kipina-agentic.tar.gz $SERVER:/tmp/
-scp $SSH_OPTS docker-compose.prod.yml Caddyfile.prod $SERVER:$REMOTE_DIR/
-
-# 4. Käynnistetään
-echo "[4/4] Käynnistetään palvelimella..."
-ssh $SSH_OPTS $SERVER "gunzip -c /tmp/kipina-agentic.tar.gz | docker load && rm /tmp/kipina-agentic.tar.gz"
-ssh $SSH_OPTS $SERVER "cd $REMOTE_DIR && docker compose -f docker-compose.prod.yml down && docker compose -f docker-compose.prod.yml up -d"
-
-echo "=== Valmis! https://kipina.studio ==="
-
-# Discord-notifikaatio
-DISCORD_WEBHOOK="https://discord.com/api/webhooks/1489504066898755687/8U02d0wug-3MkVax0xMmRoj0s_-V1psnNLPWdSOjnGnKRBUpPjaU6XiX9Iu8DgJI69AP"
-COMMIT_HASH=$(git log -1 --pretty=format:"%h" 2>/dev/null || echo "?")
-COMMIT_MSG=$(git log -1 --pretty=format:"%s" 2>/dev/null || echo "?")
-PAYLOAD=$(python3 -c "import json,sys; print(json.dumps({'content': sys.argv[1]}))" \
-  "🚀 **Kipinä Studio julkaistu!**
-> \`${COMMIT_HASH}\` ${COMMIT_MSG}
-> https://kipina.studio")
-curl -s -H "Content-Type: application/json" -d "$PAYLOAD" "$DISCORD_WEBHOOK" > /dev/null

network-poc/frontend/src/pages/index.astro

@@ -613,7 +613,7 @@ OUTPUT FORMAT:
     // === Terminal commands ===
     const kpnCommands = {
         'kpn': ['help','run','project','pipeline','stop','load','status','models','clear'],
-        'kpn run': ['coder','coder-3b','manager','tester','qa','qwen-coder','smollm-135m'],
+        'kpn run': ['coder','coder-3b','manager','tester','qa','qwen-coder'],
         'kpn load': ['1','2'],
         'kpn project': ['"'],
         'kpn pipeline': ['"'],
@@ -703,9 +703,8 @@ OUTPUT FORMAT:
             if (btn && btn.textContent.includes('Valmis')) { termLog('  ✓ Malli jo ladattu', '#3fb950'); }
             else { btn?.click(); }
         } else if (sub === 'models') {
-            termLog('  <span style="color:var(--accent)">1</span>  qwen-coder     Qwen2.5-Coder:0.5B  <span style="color:#8b949e">~990 MB</span>');
-            termLog('  <span style="color:var(--accent)">2</span>  qwen-coder-3b  Qwen2.5-Coder:3B    <span style="color:#8b949e">~6.2 GB</span>');
-            termLog('  <span style="color:var(--accent)">3</span>  smollm-135m    SmolLM 135M         <span style="color:#8b949e">~270 MB</span>');
+            termLog('  <span style="color:var(--accent)">1</span>  qwen-coder     Qwen2.5-Coder:0.5B  <span style="color:#8b949e">~990 MB (selain)</span>');
+            termLog('  <span style="color:var(--accent)">2</span>  qwen-coder-3b  Qwen2.5-Coder:3B    <span style="color:#8b949e">~6.2 GB (Ollama)</span>');
         } else if (sub === 'status') {
             termLog(`  Hub: ${document.getElementById('hub-label').textContent} | Laskenta: ${document.getElementById('compute-label').textContent}`, '#a5d6ff');
         } else if (sub === 'run') {

network-poc/hub/src/main.rs

@@ -196,7 +196,7 @@ async function load() {
     ].map(s => `<div class="stat-card"><div class="val">${s.v}</div><div class="label">${s.l}</div></div>`).join('');
 
     // Sessions — lajittelu: 1) aktiiviset nodet (online + ei viewer), 2) katsojat (online + viewer), 3) offline
-    const taskNames = {'tokenize':'Tokenisaatio','smollm-135m':'SmolLM 135M','qwen-05b':'Qwen2.5 0.5B','phi3-mini':'Phi-3 Mini','qwen-coder-05b':'Coder 0.5B','qwen-coder-3b':'Coder 3B','viewer':'Katsoja','codelab-viewer':'Koodilabra'};
+    const taskNames = {'tokenize':'Tokenisaatio','qwen-05b':'Qwen2.5 0.5B','qwen-coder-05b':'Coder 0.5B','qwen-coder-3b':'Coder 3B','viewer':'Katsoja','codelab-viewer':'Koodilabra'};
     sessions.sort((a, b) => {
         const aOnline = !a.disconnected_at;
         const bOnline = !b.disconnected_at;
@@ -419,9 +419,7 @@ async fn main() {
                         // Vapaa node -> lähetetään oikea tehtävä
                         let msg = match task.as_str() {
                             "tokenize" => Some(serde_json::json!({ "type": "pair_task", "en": en, "fi": fi })),
-                            "smollm-135m" => Some(serde_json::json!({ "type": "llm_prompt", "prompt": llm_prompts[llm_idx], "model": "smollm-135m" })),
                             "qwen-05b" => Some(serde_json::json!({ "type": "llm_prompt", "prompt": llm_prompts[llm_idx], "model": "qwen-05b" })),
-                            "phi3-mini" => Some(serde_json::json!({ "type": "llm_prompt", "prompt": llm_prompts[llm_idx], "model": "phi3-mini" })),
                             _ => None, // Coder ja viewer ei saa auto-tehtäviä
                         };
 

network-poc/install.sh

@@ -1,59 +0,0 @@
-#!/bin/bash
-# Kipinä Agentic Studio — asennusskripti (Debian/Ubuntu)
-set -e
-
-echo "=== Kipinä Agentic Studio — Asennus ==="
-echo ""
-
-# Tarkistetaan käyttöjärjestelmä
-if [ ! -f /etc/debian_version ]; then
-    echo "⚠ Tämä skripti on suunniteltu Debian/Ubuntu-järjestelmille."
-    echo "  Muilla jakeluilla voit asentaa riippuvuudet manuaalisesti."
-    read -p "  Jatketaanko? (k/e) " -n 1 -r; echo
-    [[ $REPLY =~ ^[Kk]$ ]] || exit 1
-fi
-
-echo "[1/6] Päivitetään pakettilistaus..."
-sudo apt-get update -qq
-
-echo "[2/6] Asennetaan peruspaketteja..."
-sudo apt-get install -y -qq curl git build-essential pkg-config libssl-dev
-
-# Rust
-if command -v rustc &>/dev/null; then
-    echo "[3/6] Rust löytyi: $(rustc --version)"
-else
-    echo "[3/6] Asennetaan Rust..."
-    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
-    source "$HOME/.cargo/env"
-fi
-
-# Node.js (Astro-frontend vaatii)
-if command -v node &>/dev/null; then
-    echo "[4/6] Node.js löytyi: $(node --version)"
-else
-    echo "[4/6] Asennetaan Node.js 22..."
-    curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
-    sudo apt-get install -y -qq nodejs
-fi
-
-# Ollama
-if command -v ollama &>/dev/null; then
-    echo "[5/6] Ollama löytyi"
-else
-    echo "[5/6] Asennetaan Ollama..."
-    curl -fsSL https://ollama.ai/install.sh | sh
-fi
-
-# Malli
-echo "[6/6] Ladataan kielimalli (qwen2.5-coder:3b)..."
-ollama pull qwen2.5-coder:3b
-
-echo ""
-echo "=== Asennus valmis! ==="
-echo ""
-echo "Käynnistä:"
-echo "  cd $(pwd)"
-echo "  ./network-poc/local.sh"
-echo ""
-echo "Avaa selaimessa: http://localhost:3000"

network-poc/local.sh

@@ -1,68 +0,0 @@
-#!/bin/bash
-set -e
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-cd "$SCRIPT_DIR"
-
-echo "=== Kipinä Studio Local Development ==="
-
-# Tapetaan vanhat prosessit portissa 3000
-if lsof -ti:3000 >/dev/null 2>&1; then
-    echo "[0] Vapautetaan portti 3000..."
-    lsof -ti:3000 | xargs kill -9 2>/dev/null || true
-    sleep 1
-fi
-
-# Frontend
-echo "[1/3] Rakennetaan frontend..."
-cd "$SCRIPT_DIR/frontend"
-[ -d node_modules ] || npm install --silent
-npm run build 2>&1 | tail -1
-cd "$SCRIPT_DIR"
-
-# Hub
-echo "[2/3] Käynnistetään hub..."
-STATIC_DIR="$SCRIPT_DIR/frontend/dist" cargo run -p hub 2>&1 &
-HUB_PID=$!
-
-# Odotetaan että hub on pystyssä
-for i in $(seq 1 10); do
-    if curl -s -o /dev/null http://localhost:3000 2>/dev/null; then break; fi
-    sleep 1
-done
-
-# Native-node (jos Ollama on käynnissä)
-NODE_PID=""
-if curl -s http://localhost:11434/api/tags >/dev/null 2>&1; then
-    MODEL=$(curl -s http://localhost:11434/api/tags | python3 -c "
-import sys, json
-models = json.load(sys.stdin).get('models', [])
-# Priorisoi: 7b > 3b > mikä tahansa coder > mikä tahansa
-best = None
-for m in models:
-    name = m['name']
-    if '7b' in name and 'coder' in name: best = name; break
-    if 'coder' in name and not best: best = name
-if not best and models: best = models[0]['name']
-if best: print(best)
-" 2>/dev/null)
-
-    if [ -n "$MODEL" ]; then
-        echo "[3/3] Ollama: $MODEL — käynnistetään native-node..."
-        HUB_URL=ws://localhost:3000/ws OLLAMA_MODEL="$MODEL" cargo run -p native-node --no-default-features 2>&1 &
-        NODE_PID=$!
-    else
-        echo "[3/3] Ollama käynnissä mutta ei malleja — asenna: ollama pull qwen2.5-coder:7b"
-    fi
-else
-    echo "[3/3] Ollama ei käynnissä — käytetään selaimen Wasm-laskentaa"
-fi
-
-echo ""
-echo "=== http://localhost:3000 ==="
-echo "    Ctrl+C pysäyttää"
-
-open http://localhost:3000 2>/dev/null || xdg-open http://localhost:3000 2>/dev/null || true
-
-trap 'echo ""; echo "Pysäytetään..."; kill $HUB_PID $NODE_PID 2>/dev/null; exit 0' INT TERM
-wait $HUB_PID

network-poc/node/src/burn_smollm/attention.rs

@@ -1,118 +0,0 @@
-use burn::module::{Module, Param};
-use burn::tensor::{backend::Backend, Tensor};
-use super::rope::RoPE;
-use super::config::SmolLMConfig;
-
-#[derive(Clone, Debug)]
-pub struct KVCache<B: Backend> {
-    pub k: Tensor<B, 4>,
-    pub v: Tensor<B, 4>,
-}
-
-#[derive(Module, Debug)]
-pub struct Attention<B: Backend> {
-    pub q_proj: Param<Tensor<B, 2>>,  // [hidden, num_heads * head_dim]
-    pub k_proj: Param<Tensor<B, 2>>,  // [hidden, num_kv_heads * head_dim]
-    pub v_proj: Param<Tensor<B, 2>>,  // [hidden, num_kv_heads * head_dim]
-    pub o_proj: Param<Tensor<B, 2>>,  // [num_heads * head_dim, hidden]
-
-    num_heads: usize,
-    num_kv_heads: usize,
-    head_dim: usize,
-
-    rope: RoPE<B>,
-}
-
-impl<B: Backend> Attention<B> {
-    pub fn new(config: &SmolLMConfig, device: &B::Device) -> Self {
-        let head_dim = config.hidden_size / config.num_attention_heads;
-
-        Self {
-            q_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_attention_heads * head_dim], device)),
-            k_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_key_value_heads * head_dim], device)),
-            v_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_key_value_heads * head_dim], device)),
-            o_proj: Param::from_tensor(Tensor::zeros([config.num_attention_heads * head_dim, config.hidden_size], device)),
-
-            num_heads: config.num_attention_heads,
-            num_kv_heads: config.num_key_value_heads,
-            head_dim,
-
-            rope: RoPE::new(head_dim, config.max_position_embeddings, config.rope_theta, device),
-        }
-    }
-
-    pub fn forward(
-        &self,
-        x: Tensor<B, 3>,
-        offset: usize,
-        cache: Option<KVCache<B>>
-    ) -> (Tensor<B, 3>, KVCache<B>) {
-        let [batch, seq_len, hidden_dim] = x.dims();
-
-        // Project Q, K, V: x @ W -> [batch, seq, proj_dim]
-        let q = x.clone().matmul(self.q_proj.val().unsqueeze());
-        let k = x.clone().matmul(self.k_proj.val().unsqueeze());
-        let v = x.matmul(self.v_proj.val().unsqueeze());
-
-        // Reshape: [batch, seq, heads, head_dim] -> [batch, heads, seq, head_dim]
-        let q = q.reshape([batch, seq_len, self.num_heads, self.head_dim]).swap_dims(1, 2);
-        let k = k.reshape([batch, seq_len, self.num_kv_heads, self.head_dim]).swap_dims(1, 2);
-        let v = v.reshape([batch, seq_len, self.num_kv_heads, self.head_dim]).swap_dims(1, 2);
-
-        // Apply RoPE
-        let q = self.rope.forward(q, offset);
-        let k = self.rope.forward(k, offset);
-
-        // KV cache
-        let (k, v) = if let Some(c) = cache {
-            (Tensor::cat(vec![c.k, k], 2), Tensor::cat(vec![c.v, v], 2))
-        } else {
-            (k, v)
-        };
-
-        let new_cache = KVCache { k: k.clone(), v: v.clone() };
-        let kv_len = k.dims()[2];
-
-        // GQA: repeat K,V heads — [batch, kv_heads, kv_len, hd] -> [batch, num_heads, kv_len, hd]
-        let num_reps = self.num_heads / self.num_kv_heads;
-        let k = if num_reps > 1 {
-            let [b, kv_h, s, hd] = k.dims();
-            k.reshape([b, kv_h, 1, s, hd]).repeat_dim(2, num_reps).reshape([b, self.num_heads, s, hd])
-        } else { k };
-        let v = if num_reps > 1 {
-            let [b, kv_h, s, hd] = v.dims();
-            v.reshape([b, kv_h, 1, s, hd]).repeat_dim(2, num_reps).reshape([b, self.num_heads, s, hd])
-        } else { v };
-
-        // Attention: Q @ K^T / sqrt(d)
-        let scale = 1.0 / (self.head_dim as f64).sqrt();
-        let scores = q.matmul(k.swap_dims(2, 3)).mul_scalar(scale);
-        // scores: [batch, heads, seq_len, kv_len]
-
-        // Causal mask for prefill (seq_len > 1)
-        let scores = if seq_len > 1 {
-            let mask_data: Vec<f32> = (0..seq_len).flat_map(|i| {
-                (0..kv_len).map(move |j| {
-                    if j > offset + i { f32::NEG_INFINITY } else { 0.0 }
-                })
-            }).collect();
-            let mask = Tensor::<B, 2>::from_data(
-                burn::tensor::TensorData::new(mask_data, [seq_len, kv_len]),
-                &scores.device()
-            ).reshape([1, 1, seq_len, kv_len]);
-            scores + mask
-        } else {
-            scores
-        };
-
-        let attn_weights = burn::tensor::activation::softmax(scores, 3);
-
-        let context = attn_weights.matmul(v);
-        // [batch, heads, seq, hd] -> [batch, seq, heads*hd]
-        let context = context.swap_dims(1, 2).reshape([batch, seq_len, self.num_heads * self.head_dim]);
-
-        let output = context.matmul(self.o_proj.val().unsqueeze());
-
-        (output, new_cache)
-    }
-}

network-poc/node/src/burn_smollm/config.rs

@@ -1,28 +0,0 @@
-#[derive(Clone, Debug)]
-pub struct SmolLMConfig {
-    pub hidden_size: usize,
-    pub intermediate_size: usize,
-    pub vocab_size: usize,
-    pub num_hidden_layers: usize,
-    pub num_attention_heads: usize,
-    pub num_key_value_heads: usize,
-    pub rms_norm_eps: f64,
-    pub rope_theta: f32,
-    pub max_position_embeddings: usize,
-}
-
-impl Default for SmolLMConfig {
-    fn default() -> Self {
-        Self {
-            hidden_size: 576,
-            intermediate_size: 1536,
-            vocab_size: 49152,
-            num_hidden_layers: 30,
-            num_attention_heads: 9,
-            num_key_value_heads: 3,
-            rms_norm_eps: 1e-5,
-            rope_theta: 10000.0,
-            max_position_embeddings: 2048,
-        }
-    }
-}

network-poc/node/src/burn_smollm/loader.rs

@@ -1,90 +0,0 @@
-use burn::tensor::{backend::Backend, Tensor, TensorData};
-use candle_core::safetensors;
-use candle_core::Device as CandleDevice;
-use burn::module::Param;
-use super::model::LlamaModel;
-use super::config::SmolLMConfig;
-
-fn load_tensor_2d<B: Backend>(
-    tensors_map: &std::collections::HashMap<String, candle_core::Tensor>,
-    name: &str,
-    device: &B::Device,
-    shape_out_in: [usize; 2]
-) -> Result<Param<Tensor<B, 2>>, String> {
-    let t = tensors_map.get(name).ok_or_else(|| format!("Puuttuu: {}", name))?;
-    let t = t.to_dtype(candle_core::DType::F32).unwrap();
-    let vec = t.flatten_all().unwrap().to_vec1::<f32>().unwrap();
-    let t_burn = Tensor::<B, 2>::from_data(burn::tensor::TensorData::new(vec, shape_out_in), device);
-    // transpose from [out, in] to [in, out]
-    Ok(Param::from_tensor(t_burn.transpose()))
-}
-
-fn load_tensor_1d<B: Backend>(
-    tensors_map: &std::collections::HashMap<String, candle_core::Tensor>,
-    name: &str,
-    device: &B::Device,
-    _shape: [usize; 1]
-) -> Result<Param<Tensor<B, 1>>, String> {
-    let t = tensors_map.get(name).ok_or_else(|| format!("Puuttuu: {}", name))?;
-    let t = t.to_dtype(candle_core::DType::F32).unwrap();
-    let vec = t.flatten_all().unwrap().to_vec1::<f32>().unwrap();
-    Ok(Param::from_tensor(Tensor::<B, 1>::from_floats(vec.as_slice(), device)))
-}
-
-fn load_embed<B: Backend>(
-    tensors_map: &std::collections::HashMap<String, candle_core::Tensor>,
-    name: &str,
-    device: &B::Device,
-    shape: [usize; 2]
-) -> Result<Param<Tensor<B, 2>>, String> {
-    let t = tensors_map.get(name).ok_or_else(|| format!("Puuttuu: {}", name))?;
-    let t = t.to_dtype(candle_core::DType::F32).unwrap();
-    let vec = t.flatten_all().unwrap().to_vec1::<f32>().unwrap();
-    // Embed ei transponoi samalla tavalla, se pysyy [vocab, hidden]
-    Ok(Param::from_tensor(Tensor::<B, 2>::from_data(burn::tensor::TensorData::new(vec, shape), device)))
-}
-
-pub fn load_safetensors_to_model<B: Backend>(
-    buffer: &[u8], 
-    config: &SmolLMConfig, 
-    device: &B::Device
-) -> Result<LlamaModel<B>, String> {
-    
-    let mut model = LlamaModel::new(config, device);
-    let tensors_map = safetensors::load_buffer(buffer, &CandleDevice::Cpu)
-        .map_err(|e| format!("Virhe Safetensors luennassa: {}", e))?;
-    
-    // Embeddings
-    model.embed_tokens = load_embed(&tensors_map, "model.embed_tokens.weight", device, [config.vocab_size, config.hidden_size])?;
-    model.norm.weight = load_tensor_1d(&tensors_map, "model.norm.weight", device, [config.hidden_size])?;
-    model.lm_head = load_embed(&tensors_map, "lm_head.weight", device, [config.vocab_size, config.hidden_size]).or_else(|_| {
-        load_embed(&tensors_map, "model.embed_tokens.weight", device, [config.vocab_size, config.hidden_size])
-    })?;
-    
-    let head_dim = config.hidden_size / config.num_attention_heads;
-
-    for i in 0..config.num_hidden_layers {
-        let prefix = format!("model.layers.{}", i);
-        
-        let layer = &mut model.layers[i];
-        
-        // Norms
-        layer.input_layernorm.weight = load_tensor_1d(&tensors_map, &format!("{}.input_layernorm.weight", prefix), device, [config.hidden_size])?;
-        layer.post_attention_layernorm.weight = load_tensor_1d(&tensors_map, &format!("{}.post_attention_layernorm.weight", prefix), device, [config.hidden_size])?;
-        
-        // Attention
-        let num_heads = config.num_attention_heads;
-        let num_kv_heads = config.num_key_value_heads;
-        layer.self_attn.q_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.q_proj.weight", prefix), device, [num_heads * head_dim, config.hidden_size])?;
-        layer.self_attn.k_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.k_proj.weight", prefix), device, [num_kv_heads * head_dim, config.hidden_size])?;
-        layer.self_attn.v_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.v_proj.weight", prefix), device, [num_kv_heads * head_dim, config.hidden_size])?;
-        layer.self_attn.o_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.o_proj.weight", prefix), device, [config.hidden_size, num_heads * head_dim])?;
-        
-        // MLP
-        layer.mlp.gate_proj = load_tensor_2d(&tensors_map, &format!("{}.mlp.gate_proj.weight", prefix), device, [config.intermediate_size, config.hidden_size])?;
-        layer.mlp.up_proj = load_tensor_2d(&tensors_map, &format!("{}.mlp.up_proj.weight", prefix), device, [config.intermediate_size, config.hidden_size])?;
-        layer.mlp.down_proj = load_tensor_2d(&tensors_map, &format!("{}.mlp.down_proj.weight", prefix), device, [config.hidden_size, config.intermediate_size])?;
-    }
-    
-    Ok(model)
-}

network-poc/node/src/burn_smollm/mod.rs

@@ -1,6 +0,0 @@
-pub mod attention;
-pub mod config;
-pub mod loader;
-pub mod model;
-pub mod modules;
-pub mod rope;

network-poc/node/src/burn_smollm/model.rs

@@ -1,96 +0,0 @@
-use burn::module::{Module, Param};
-use burn::tensor::{backend::Backend, Tensor, Int};
-use super::modules::{RmsNorm, Mlp};
-use super::attention::{Attention, KVCache};
-use super::config::SmolLMConfig;
-
-#[derive(Module, Debug)]
-pub struct LlamaBlock<B: Backend> {
-    pub self_attn: Attention<B>,
-    pub mlp: Mlp<B>,
-    pub input_layernorm: RmsNorm<B>,
-    pub post_attention_layernorm: RmsNorm<B>,
-}
-
-impl<B: Backend> LlamaBlock<B> {
-    pub fn new(config: &SmolLMConfig, device: &B::Device) -> Self {
-        Self {
-            self_attn: Attention::new(config, device),
-            mlp: Mlp::new(config.hidden_size, config.intermediate_size, device),
-            input_layernorm: RmsNorm::new(config.hidden_size, config.rms_norm_eps, device),
-            post_attention_layernorm: RmsNorm::new(config.hidden_size, config.rms_norm_eps, device),
-        }
-    }
-
-    pub fn forward(
-        &self,
-        x: Tensor<B, 3>,
-        offset: usize,
-        cache: Option<KVCache<B>>
-    ) -> (Tensor<B, 3>, KVCache<B>) {
-        let residual = x.clone();
-        let x_norm = self.input_layernorm.forward(x);
-        
-        let (attn_out, new_cache) = self.self_attn.forward(x_norm, offset, cache);
-        
-        let x = residual + attn_out;
-        
-        let residual = x.clone();
-        let x_norm = self.post_attention_layernorm.forward(x);
-        let mlp_out = self.mlp.forward(x_norm);
-        
-        let x = residual + mlp_out;
-        (x, new_cache)
-    }
-}
-
-#[derive(Module, Debug)]
-pub struct LlamaModel<B: Backend> {
-    pub embed_tokens: Param<Tensor<B, 2>>,
-    pub layers: Vec<LlamaBlock<B>>,
-    pub norm: RmsNorm<B>,
-    pub lm_head: Param<Tensor<B, 2>>, // For tie_word_embeddings this can point to embed_tokens
-}
-
-impl<B: Backend> LlamaModel<B> {
-    pub fn new(config: &SmolLMConfig, device: &B::Device) -> Self {
-        let embed = Tensor::zeros([config.vocab_size, config.hidden_size], device);
-        let lm_head = Tensor::zeros([config.vocab_size, config.hidden_size], device);
-        
-        let mut layers = Vec::new();
-        for _ in 0..config.num_hidden_layers {
-            layers.push(LlamaBlock::new(config, device));
-        }
-
-        Self {
-            embed_tokens: Param::from_tensor(embed),
-            layers,
-            norm: RmsNorm::new(config.hidden_size, config.rms_norm_eps, device),
-            lm_head: Param::from_tensor(lm_head),
-        }
-    }
-
-    pub fn forward(
-        &self,
-        input_ids: Tensor<B, 2, Int>,
-        offset: usize,
-        caches: &mut Vec<Option<KVCache<B>>>
-    ) -> Tensor<B, 3> {
-        let [_batch, _seq_len] = input_ids.dims();
-        
-        let mut x = burn::tensor::module::embedding(self.embed_tokens.val(), input_ids);
-        
-        for (i, layer) in self.layers.iter().enumerate() {
-            let cache = caches[i].take();
-            let (out, new_cache) = layer.forward(x, offset, cache);
-            x = out;
-            caches[i] = Some(new_cache);
-        }
-        
-        x = self.norm.forward(x);
-        
-        // Matmul with lm_head (or embed_tokens if tied) to get logits
-        // Notice: lm_head is typically [vocab_size, hidden_size] in HF, so we swap dims
-        x.matmul(self.lm_head.val().swap_dims(0, 1).unsqueeze())
-    }
-}

network-poc/node/src/burn_smollm/modules.rs

@@ -1,59 +0,0 @@
-use burn::module::{Module, Param};
-use burn::tensor::{backend::Backend, Tensor};
-
-#[derive(Module, Debug)]
-pub struct RmsNorm<B: Backend> {
-    pub weight: Param<Tensor<B, 1>>,
-    epsilon: f64,
-}
-
-impl<B: Backend> RmsNorm<B> {
-    pub fn new(size: usize, epsilon: f64, device: &B::Device) -> Self {
-        let weight = Param::from_tensor(Tensor::ones([size], device));
-        Self { weight, epsilon }
-    }
-
-    pub fn forward(&self, x: Tensor<B, 3>) -> Tensor<B, 3> {
-        // x: [batch, seq_len, dim]
-        // RMSNorm: x * weight / sqrt(mean(x^2) + eps)
-        let x_sq = x.clone().powf_scalar(2.0);
-        // mean over last dim, keeping dims for broadcast
-        let [b, s, d] = x_sq.dims();
-        let variance = x_sq.sum_dim(2).div_scalar(d as f32);
-        let norm = x.div(variance.add_scalar(self.epsilon).sqrt());
-
-        let w = self.weight.val().unsqueeze::<2>().unsqueeze::<3>().reshape([1, 1, d]);
-        norm * w
-    }
-}
-
-#[derive(Module, Debug)]
-pub struct Mlp<B: Backend> {
-    pub gate_proj: Param<Tensor<B, 2>>, // [in, intermediate]
-    pub up_proj: Param<Tensor<B, 2>>,   // [in, intermediate]
-    pub down_proj: Param<Tensor<B, 2>>, // [intermediate, out]
-}
-
-impl<B: Backend> Mlp<B> {
-    pub fn new(hidden_size: usize, intermediate_size: usize, device: &B::Device) -> Self {
-        Self {
-            gate_proj: Param::from_tensor(Tensor::zeros([hidden_size, intermediate_size], device)),
-            up_proj: Param::from_tensor(Tensor::zeros([hidden_size, intermediate_size], device)),
-            down_proj: Param::from_tensor(Tensor::zeros([intermediate_size, hidden_size], device)),
-        }
-    }
-
-    pub fn forward(&self, x: Tensor<B, 3>) -> Tensor<B, 3> {
-        // x: [batch, seq, hidden]
-        // gate = x @ gate_proj  -> [batch, seq, intermediate]
-        let gate = x.clone().matmul(self.gate_proj.val().unsqueeze());
-        let up = x.matmul(self.up_proj.val().unsqueeze());
-
-        // SiLU(gate) * up
-        let silu = gate.clone() * burn::tensor::activation::sigmoid(gate);
-        let intermediate = silu * up;
-
-        // intermediate @ down_proj -> [batch, seq, hidden]
-        intermediate.matmul(self.down_proj.val().unsqueeze())
-    }
-}

network-poc/node/src/burn_smollm/rope.rs

@@ -1,59 +0,0 @@
-use burn::module::Module;
-use burn::tensor::{backend::Backend, Tensor};
-
-#[derive(Module, Debug)]
-pub struct RoPE<B: Backend> {
-    cos_cache: Tensor<B, 2>,
-    sin_cache: Tensor<B, 2>,
-}
-
-impl<B: Backend> RoPE<B> {
-    pub fn new(head_dim: usize, max_seq_len: usize, theta: f32, device: &B::Device) -> Self {
-        // (head_dim / 2) values
-        let half_dim = head_dim / 2;
-        let inv_freq: Vec<f32> = (0..half_dim)
-            .map(|i| 1.0 / theta.powf((2 * i) as f32 / head_dim as f32))
-            .collect();
-        
-        let inv_freq = Tensor::<B, 1>::from_floats(inv_freq.as_slice(), device).unsqueeze::<2>();
-        let t_floats: Vec<f32> = (0..max_seq_len).map(|v| v as f32).collect();
-        let t = Tensor::<B, 1>::from_floats(t_floats.as_slice(), device).unsqueeze::<2>().transpose();
-        // t shape: [max_seq_len, 1]
-        // inv_freq shape: [1, half_dim]
-        
-        // freqs shape: [max_seq_len, half_dim]
-        let freqs = t.matmul(inv_freq);
-        
-        let cos_cache = freqs.clone().cos();
-        let sin_cache = freqs.sin();
-
-        Self {
-            cos_cache,
-            sin_cache,
-        }
-    }
-
-    pub fn forward(&self, x: Tensor<B, 4>, offset: usize) -> Tensor<B, 4> {
-        let [batch, heads, seq_len, head_dim] = x.dims();
-        let half_dim = head_dim / 2;
-        
-        // x shape: [batch, heads, seq_len, head_dim]
-        // valitaan viipaleet (x1 ja x2) jotta saadaan pyöritettyä rotaatiot
-        let x1 = x.clone().slice([0..batch, 0..heads, 0..seq_len, 0..half_dim]);
-        let x2 = x.clone().slice([0..batch, 0..heads, 0..seq_len, half_dim..head_dim]);
-        
-        // haetaan vastaava seq offsetista alkaen
-        let cos = self.cos_cache.clone().slice([offset..offset+seq_len, 0..half_dim])
-            .unsqueeze::<4>() // [seq, half_dim, 1]
-            .reshape([1, 1, seq_len, half_dim]);
-        let sin = self.sin_cache.clone().slice([offset..offset+seq_len, 0..half_dim])
-            .reshape([1, 1, seq_len, half_dim]);
-
-        // x1 * cos - x2 * sin
-        let o1 = x1.clone().mul(cos.clone()) - x2.clone().mul(sin.clone());
-        // x2 * cos + x1 * sin
-        let o2 = x2.mul(cos) + x1.mul(sin);
-
-        Tensor::cat(vec![o1, o2], 3)
-    }
-}

network-poc/node/src/lib.rs

@@ -8,11 +8,8 @@ use burn::backend::{Wgpu, NdArray};
 
 pub mod storage;
 pub mod sampling;
-pub mod smollm;
 pub mod qwen;
 pub mod qwen_coder;
-pub mod phi3;
-pub mod burn_smollm;
 
 #[macro_export]
 macro_rules! console_log {
@@ -246,7 +243,7 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
     HAS_WEBGPU.store(has_webgpu, Ordering::SeqCst);
     SELECTED_TASK.store(task_id, Ordering::SeqCst);
     let backend_name = if has_webgpu { "WebGPU" } else { "CPU (NdArray)" };
-    let task_names = ["tokenize", "smollm-135m", "qwen-05b", "phi3-mini", "qwen-coder-05b", "qwen-coder-3b"];
+    let task_names = ["tokenize", "qwen-05b", "qwen-coder-05b", "qwen-coder-3b"];
     let task_name = task_names.get(task_id as usize).unwrap_or(&"tokenize");
     console_log!("Kipinä Agent Node käynnistyy — backend: {} | tehtävä: {}", backend_name, task_name);
 
@@ -303,22 +300,6 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
                     }
                 }
             } else if msg.contains("llm_prompt") && current_task == 1 && auto_on {
-                // Vain SmolLM-solmut, ja vain yksi inferenssi kerrallaan
-                if LLM_BUSY.load(Ordering::SeqCst) {
-                    // Ohitetaan — edellinen inferenssi vielä käynnissä
-                } else if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
-                    let prompt = task.get("prompt").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    let model = task.get("model").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    if !prompt.is_empty() && model == "smollm-135m" {
-                        LLM_BUSY.store(true, Ordering::SeqCst);
-                        let ws_for_async = ws_clone.clone();
-                        wasm_bindgen_futures::spawn_local(async move {
-                            smollm::run_smollm_inference(prompt, ws_for_async).await;
-                            LLM_BUSY.store(false, Ordering::SeqCst);
-                        });
-                    }
-                }
-            } else if msg.contains("llm_prompt") && current_task == 2 && auto_on {
                 // Qwen2.5-0.5B
                 if LLM_BUSY.load(Ordering::SeqCst) {
                 } else if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
@@ -333,21 +314,6 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
                         });
                     }
                 }
-            } else if msg.contains("llm_prompt") && current_task == 3 && auto_on {
-                // Phi-3 Mini
-                if LLM_BUSY.load(Ordering::SeqCst) {
-                } else if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
-                    let prompt = task.get("prompt").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    let model = task.get("model").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    if !prompt.is_empty() && model.starts_with("phi3-mini") {
-                        LLM_BUSY.store(true, Ordering::SeqCst);
-                        let ws_for_async = ws_clone.clone();
-                        wasm_bindgen_futures::spawn_local(async move {
-                            phi3::run_phi3_inference(prompt, ws_for_async).await;
-                            LLM_BUSY.store(false, Ordering::SeqCst);
-                        });
-                    }
-                }
             } else if msg.contains("llm_prompt") {
                 console_log!("[DEBUG] llm_prompt vastaanotettu! current_task={}, busy={}", current_task, LLM_BUSY.load(Ordering::SeqCst));
                 if current_task == 4 || current_task == 5 {

network-poc/node/src/phi3.rs

@@ -1,36 +0,0 @@
-use candle_core::{Device, Tensor, DType};
-use candle_nn::VarBuilder;
-use candle_transformers::models::phi3::{Config as Phi3Config, Model as Phi3Model};
-use wasm_bindgen::JsCast;
-use std::cell::RefCell;
-use std::rc::Rc;
-use web_sys::WebSocket;
-
-use crate::storage;
-
-macro_rules! console_log {
-    ($($t:tt)*) => (web_sys::console::log_1(&format_args!($($t)*).to_string().into()))
-}
-
-const MODEL_URL: &str = "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/model.safetensors.index.json";
-const TOKENIZER_URL: &str = "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/tokenizer.json";
-
-// Phi-3 Mini on iso (7.6 GB) — käytetään kvantisoidumpaa versiota myöhemmin
-// Tällä hetkellä: placeholder joka raportoi koon ja jättää inferenssin väliin
-pub async fn run_phi3_inference(prompt: String, ws: Rc<RefCell<WebSocket>>) {
-    console_log!("[Phi-3] Phi-3 Mini 3.8B on liian suuri selaimessa ajettavaksi (~7.6 GB).");
-    console_log!("[Phi-3] Käytä SmolLM 135M tai Qwen2.5 0.5B selaininferenssiin.");
-    console_log!("[Phi-3] Phi-3 tuetaan native-node:lla (Docker + GPU).");
-
-    let done = serde_json::json!({
-        "type": "llm_done",
-        "prompt": prompt,
-        "model": "Phi-3-Mini (ei tuettu selaimessa)",
-        "response": "Phi-3 Mini 3.8B on liian suuri selaimessa ajettavaksi. Käytä SmolLM 135M tai Qwen2.5 0.5B.",
-        "tokens_generated": 0,
-        "duration_ms": 0,
-        "tokens_per_sec": 0,
-        "load_time_ms": 0,
-    });
-    let _ = ws.borrow().send_with_str(&done.to_string());
-}

network-poc/node/src/smollm.rs

@@ -1,232 +0,0 @@
-use candle_core::{Device, Tensor, DType};
-use candle_nn::VarBuilder;
-use candle_transformers::models::llama::{Llama, LlamaConfig, LlamaEosToks, Cache};
-// LogitsProcessor poistettu — käytetään greedy samplingia (argmax) Wasm-yhteensopivuuden vuoksi
-use wasm_bindgen::JsCast;
-use std::cell::RefCell;
-use std::rc::Rc;
-use web_sys::WebSocket;
-
-use crate::storage;
-
-macro_rules! console_log {
-    ($($t:tt)*) => (web_sys::console::log_1(&format_args!($($t)*).to_string().into()))
-}
-
-const MODEL_URL: &str = "https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct/resolve/main/model.safetensors";
-const TOKENIZER_URL: &str = "https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct/resolve/main/tokenizer.json";
-
-/// Lataa tiedosto HuggingFacesta streaming-latauksella (progress-ilmoitukset) ja tallentaa IndexedDB:hen
-async fn ensure_cached(key: &str, url: &str, ws: &Rc<RefCell<WebSocket>>) -> Result<Vec<u8>, String> {
-    if let Ok(Some(bytes)) = storage::load_from_idb(key).await {
-        console_log!("[SmolLM] {} löytyi välimuistista ({} MB)", key, bytes.len() / 1024 / 1024);
-        send_progress(ws, key, 100, bytes.len(), bytes.len());
-        return Ok(bytes);
-    }
-
-    console_log!("[SmolLM] Ladataan {}...", key);
-    send_progress(ws, key, 0, 0, 0);
-
-    // Fetch API:lla saadaan Content-Length ja streaming-luku
-    let resp = crate::worker_fetch(url).await?;
-
-    if !resp.ok() {
-        return Err(format!("HTTP {}", resp.status()));
-    }
-
-    // Kokonaiskoko Content-Length-headerista
-    let total_size: usize = resp.headers()
-        .get("content-length").ok().flatten()
-        .and_then(|s| s.parse().ok())
-        .unwrap_or(0);
-
-    let body = resp.body().ok_or("Ei bodyä")?;
-    let reader = body.get_reader();
-    let reader: web_sys::ReadableStreamDefaultReader = reader.dyn_into().map_err(|_| "Ei ReadableStreamDefaultReader".to_string())?;
-
-    let mut data: Vec<u8> = Vec::with_capacity(total_size);
-    let mut last_pct: u32 = 0;
-
-    loop {
-        let chunk = wasm_bindgen_futures::JsFuture::from(reader.read())
-            .await.map_err(|e| format!("Luku epäonnistui: {:?}", e))?;
-
-        let done = js_sys::Reflect::get(&chunk, &"done".into())
-            .map_err(|_| "done-kenttä puuttuu".to_string())?
-            .as_bool().unwrap_or(true);
-
-        if done { break; }
-
-        let value = js_sys::Reflect::get(&chunk, &"value".into())
-            .map_err(|_| "value-kenttä puuttuu".to_string())?;
-        let array = js_sys::Uint8Array::new(&value);
-        let mut buf = vec![0u8; array.length() as usize];
-        array.copy_to(&mut buf);
-        data.extend_from_slice(&buf);
-
-        // Progress-päivitys (joka 5%)
-        if total_size > 0 {
-            let pct = ((data.len() as f64 / total_size as f64) * 100.0) as u32;
-            if pct >= last_pct + 5 || pct == 100 {
-                last_pct = pct;
-                console_log!("[SmolLM] {} lataus: {}% ({}/{} MB)", key, pct, data.len() / 1024 / 1024, total_size / 1024 / 1024);
-                send_progress(ws, key, pct, data.len(), total_size);
-            }
-        }
-    }
-
-    console_log!("[SmolLM] Tallennetaan {} ({} MB) IndexedDB:hen...", key, data.len() / 1024 / 1024);
-    let _ = storage::save_to_idb(key, &data).await;
-    console_log!("[SmolLM] {} tallennettu!", key);
-    send_progress(ws, key, 100, data.len(), data.len());
-
-    Ok(data)
-}
-
-fn send_progress(ws: &Rc<RefCell<WebSocket>>, file: &str, pct: u32, loaded: usize, total: usize) {
-    let msg = serde_json::json!({
-        "type": "download_progress",
-        "file": file,
-        "pct": pct,
-        "loaded_mb": loaded / 1024 / 1024,
-        "total_mb": total / 1024 / 1024,
-    });
-    let _ = ws.borrow().send_with_str(&msg.to_string());
-}
-
-/// Lataa malli ja tokenizer, suorita inferenssi ja streamaa tokenit hubille
-pub async fn run_smollm_inference(prompt: String, ws: Rc<RefCell<WebSocket>>) {
-    // performance via crate::perf_now()
-
-    // 1. Lataa tokenizer
-    let tok_bytes = match ensure_cached("smollm-tokenizer.json", TOKENIZER_URL, &ws).await {
-        Ok(b) => b,
-        Err(e) => { console_log!("[SmolLM] Tokenizer-virhe: {}", e); return; }
-    };
-
-    let tokenizer = match tokenizers::Tokenizer::from_bytes(&tok_bytes) {
-        Ok(t) => t,
-        Err(e) => { console_log!("[SmolLM] Tokenizer-parsinta epäonnistui: {}", e); return; }
-    };
-
-    // 2. Lataa mallin painot
-    let model_bytes = match ensure_cached("smollm-model.safetensors", MODEL_URL, &ws).await {
-        Ok(b) => b,
-        Err(e) => { console_log!("[SmolLM] Malli-virhe: {}", e); return; }
-    };
-
-    // Burn 0.14 wgpu ei yhteensopiva nykyisten selainten kanssa (maxInterStageShaderComponents)
-    // Burn 0.21-pre.2 cubecl-runtime ei käänny Wasmille (println! puuttuu)
-    // → NdArray kunnes Burn 0.21 stable + Wasm-tuki
-    console_log!("[SmolLM] Burn NdArray (CPU) inferenssi...");
-    run_burn_inference::<burn::backend::NdArray>(prompt, model_bytes, tokenizer, ws).await;
-}
-
-async fn run_burn_inference<B: burn::tensor::backend::Backend>(
-    prompt: String,
-    model_bytes: Vec<u8>,
-    tokenizer: tokenizers::Tokenizer,
-    ws: Rc<RefCell<WebSocket>>,
-) {
-    let start_load = crate::perf_now();
-
-    let device = Default::default();
-    let config = crate::burn_smollm::config::SmolLMConfig::default();
-    
-    console_log!("[SmolLM] Injektoidaan Safetensors -> Burn Params...");
-    let model = match crate::burn_smollm::loader::load_safetensors_to_model::<B>(&model_bytes, &config, &device) {
-        Ok(m) => m,
-        Err(e) => { console_log!("[SmolLM] Lataus epäonnistui: {}", e); return; }
-    };
-
-    let load_time = crate::perf_now() - start_load;
-    console_log!("[SmolLM] Burn-malli ladattu ({:.0}ms). Generoidaan...", load_time);
-
-    let formatted_prompt = format!("<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n", prompt);
-    let encoding = match tokenizer.encode(formatted_prompt.as_str(), true) {
-        Ok(e) => e,
-        Err(e) => { console_log!("[SmolLM] Tokenisointivirhe: {}", e); return; }
-    };
-
-    let mut input_ids: Vec<u32> = encoding.get_ids().to_vec();
-    let input_len = input_ids.len();
-    console_log!("[SmolLM] Syöte: {} tokenia", input_len);
-
-    let start_gen = crate::perf_now();
-    let max_new_tokens = 32;
-    let mut generated_text = String::new();
-    let mut tokens_generated: usize = 0;
-    
-    // KV-välimuistin taulukko kerroksittain
-    let mut caches: Vec<Option<crate::burn_smollm::attention::KVCache<B>>> = vec![None; config.num_hidden_layers];
-    let mut current_offset = 0;
-
-    // Prefill: yksitellen, vältetään future token leakage koska ei causal maskia
-    let input_ids_i32: Vec<i32> = input_ids.iter().map(|&x| x as i32).collect();
-    let mut last_logits = None;
-    
-    for &id in &input_ids_i32 {
-        let input_tensor = burn::tensor::Tensor::<B, 1, burn::tensor::Int>::from_data(
-            burn::tensor::TensorData::from([id]),
-            &device
-        ).unsqueeze::<2>(); // [1, 1]
-        
-        last_logits = Some(model.forward(input_tensor, current_offset, &mut caches));
-        current_offset += 1;
-    }
-
-    let mut logits = last_logits.unwrap();
-
-    // Argmax sämpläys
-    let next_token_tensor = logits.clone().argmax(2);
-    let mut next_token: u32 = next_token_tensor.into_scalar().to_string().parse().unwrap_or(2); // Yksinkertainen cast koska int scalar
-
-    if next_token != 2 {
-        if let Ok(text) = tokenizer.decode(&[next_token], true) {
-            generated_text.push_str(&text);
-            let chunk = serde_json::json!({ "type": "llm_chunk", "token": text, "prompt": prompt, "model": "SmolLM-135M (WebGPU)" });
-            let _ = ws.borrow().send_with_str(&chunk.to_string());
-        }
-        tokens_generated += 1;
-    }
-
-    // Autoregressiivinen luuppi
-    for _ in 1..max_new_tokens {
-        if next_token == 2 { break; }
-        
-        let mut input_tensor = burn::tensor::Tensor::<B, 1, burn::tensor::Int>::from_data(
-            burn::tensor::TensorData::from([next_token as i32]),
-            &device
-        ).unsqueeze::<2>();
-        
-        logits = model.forward(input_tensor, current_offset, &mut caches);
-        current_offset += 1;
-        
-        let next_token_tensor = logits.argmax(2);
-        next_token = next_token_tensor.into_scalar().to_string().parse().unwrap_or(2);
-
-        if next_token == 2 { break; }
-
-        if let Ok(text) = tokenizer.decode(&[next_token], true) {
-            generated_text.push_str(&text);
-            let chunk = serde_json::json!({ "type": "llm_chunk", "token": text, "prompt": prompt, "model": "SmolLM-135M (WebGPU)" });
-            let _ = ws.borrow().send_with_str(&chunk.to_string());
-        }
-        tokens_generated += 1;
-    }
-
-    let gen_time = crate::perf_now() - start_gen;
-    let tokens_per_sec = if gen_time > 0.0 { (tokens_generated as f64 / gen_time) * 1000.0 } else { 0.0 };
-
-    let done = serde_json::json!({
-        "type": "llm_done",
-        "prompt": prompt,
-        "model": "SmolLM-135M-Instruct (WebGPU)",
-        "response": generated_text,
-        "tokens_generated": tokens_generated,
-        "duration_ms": (gen_time * 100.0).round() / 100.0,
-        "tokens_per_sec": (tokens_per_sec * 10.0).round() / 10.0,
-        "load_time_ms": (load_time * 100.0).round() / 100.0,
-    });
-    let _ = ws.borrow().send_with_str(&done.to_string());
-}

network-poc/start-local.sh

@@ -1,33 +0,0 @@
-#!/usr/bin/env bash
-# Kipinä Agentic Network lokaali pikatesti
-# Tämä ohjelma käynnistää lokaalin Kipinä Hubin taustalle, ja heittää sen jälkeen näkyviin
-# visuaalisen Natiivisolmun Ratatui-ruudun yhdistäen sen automaattisesti siihen.
-# Kun poistut Ratatui-näytöstä (esim painamalla Q), niin skripti sammuttaa siististi
-# myös taustalla pyörivän lokaalin Hubin!
-
-cd "$(dirname "$0")"
-
-echo "========================================="
-echo " 🔥  Kipinä Agentic Network - Local Run  🔥"
-echo "========================================="
-echo "Varmistetaan portin 3000 vapaus..."
-lsof -ti :3000 | xargs kill -9 2>/dev/null || true
-
-echo ""
-echo "Käynnistetään Kipinä Hub taustalle..."
-# Ohjataan Hubin logit erilliseen tiedostoon jottei se sotke näkymää!
-env STATIC_DIR=frontend/dist cargo run -p hub > hub-local.log 2>&1 &
-HUB_PID=$!
-
-# Odotellaan, että Hub saa portit kuunteluun
-sleep 2
-
-echo "Käynnistetään Natiivisolmu ja Ratatui-dashboard..."
-# Käynnistetään TUI ja pakotetaan yhdistämään lokaaliin Hubiin.
-env -u OLLAMA_MODEL HUB_URL=ws://127.0.0.1:3000/ws cargo run -p native-node
-
-# Kun TUI ohjelmasta on poistuttu
-echo ""
-echo "Dashboard suljettu! Ajetaan lokaali Hub (#$HUB_PID) siististi alas..."
-kill $HUB_PID
-echo "Kaikki sammutettu. Kiitos!"