using LLM for sentiment analysis

docker-compose.yml

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+version: '3.8'
+
+services:
+  # Working AMD GPU Model Runner - Using Docker Model Runner (not llama.cpp)
+  model-runner:
+    image: docker/model-runner:latest
+    container_name: model-runner
+    privileged: true
+    user: "0:0"  # Run as root to fix permission issues
+    ports:
+      - "11434:11434"  # Main API port (Ollama-compatible)
+      - "8083:8080"    # Alternative API port
+    environment:
+      - HSA_OVERRIDE_GFX_VERSION=11.0.0  # AMD GPU version override
+      - GPU_LAYERS=35
+      - THREADS=8
+      - BATCH_SIZE=512
+      - CONTEXT_SIZE=4096
+      - DISPLAY=${DISPLAY}
+      - USER=${USER}
+    devices:
+      - /dev/kfd:/dev/kfd
+      - /dev/dri:/dev/dri
+    group_add:
+      - video
+    volumes:
+      - ./models:/models:rw
+      - ./data:/data:rw
+      - /home/${USER}:/home/${USER}:rslave
+    working_dir: /models
+    restart: unless-stopped
+    command: >
+      /app/model-runner serve
+      --port 11434
+      --host 0.0.0.0
+      --gpu-layers 35
+      --threads 8
+      --batch-size 512
+      --ctx-size 4096
+      --parallel
+      --cont-batching
+      --log-level info
+      --log-format json
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 40s
+    networks:
+      - model-runner-network
+
+volumes:
+  model_runner_data:
+    driver: local
+
+networks:
+  model-runner-network:
+    driver: bridge