using LLM for sentiment analysis

download_test_model.sh

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+#!/bin/bash
+
+# Download a test model for AMD GPU runner
+echo "=== Downloading Test Model for AMD GPU ==="
+echo ""
+
+MODEL_DIR="models"
+MODEL_FILE="$MODEL_DIR/current_model.gguf"
+
+# Create directory if it doesn't exist
+mkdir -p "$MODEL_DIR"
+
+echo "Downloading SmolLM-135M (GGUF format)..."
+echo "This is a small, fast model perfect for testing AMD GPU acceleration"
+echo ""
+
+# Download SmolLM GGUF model
+wget -O "$MODEL_FILE" \
+  "https://huggingface.co/TheBloke/SmolLM-135M-GGUF/resolve/main/smollm-135m.Q4_K_M.gguf" \
+  --progress=bar
+
+if [[ $? -eq 0 ]]; then
+    echo ""
+    echo "✅ Model downloaded successfully!"
+    echo "📁 Location: $MODEL_FILE"
+    echo "📊 Size: $(du -h "$MODEL_FILE" | cut -f1)"
+    echo ""
+    echo "🚀 Ready to start AMD GPU runner:"
+    echo "docker-compose up -d amd-model-runner"
+    echo ""
+    echo "🧪 Test the API:"
+    echo "curl http://localhost:11434/completion \\"
+    echo "  -H 'Content-Type: application/json' \\"
+    echo "  -d '{\"prompt\": \"Hello, how are you?\", \"n_predict\": 50}'"
+else
+    echo ""
+    echo "❌ Download failed!"
+    echo "Try manually downloading a GGUF model from:"
+    echo "- https://huggingface.co/TheBloke"
+    echo "- https://huggingface.co/ggml-org/models"
+    echo ""
+    echo "Then place it at: $MODEL_FILE"
+fi