demo_gemma4.sh

#!/bin/bash
# LiteRT CLI Gemma4 LLM Demo & Test Script
set -e


echo -e "${BLUE}${BOLD}==================================================================${NC}"
echo -e "${BLUE}${BOLD}>>> LiteRT CLI Gemma4 LLM Demo Script${NC}"
echo -e "${BLUE}${BOLD}==================================================================${NC}"

# --- Environment Setup ---
export SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
export LITERT_CLI_ROOT="/tmp/litert_cli_gemma4"

# Source shared utilities
source "$SCRIPT_DIR/demo_utils.sh"


# Clean up and create work directory
echo -e "\n${YELLOW}Setting up workspace at: $LITERT_CLI_ROOT...${NC}"
rm -rf "$LITERT_CLI_ROOT"
mkdir -p "$LITERT_CLI_ROOT"
cd "$LITERT_CLI_ROOT"

# Create output directories
export MODEL_DIR="$LITERT_CLI_ROOT/models"
mkdir -p "$MODEL_DIR"


# Create Python virtual environment
echo -e "${YELLOW}Creating Python virtual environment...${NC}"
python3 -m venv venv_gemma4
source venv_gemma4/bin/activate

# Install litert-cli from source
echo -e "${YELLOW}Installing litert-cli from source...${NC}"
pip install -e "$REPO_ROOT"




# --- 1. Convert HuggingFace Model google/gemma-4-E2B-it ---
# TODO: Bring this back when we add support for --externalize_embedder in CLI convert command.
# run_case "Convert: HuggingFace google/gemma-4-E2B-it" \
#     litert convert google/gemma-4-E2B-it --output "$MODEL_DIR/gemma4"


# --- 2. Run Gemma4 Generative LLM Model ---
run_case "Run Gemma4: Generative inference with custom prompt" \
    litert lm run gemma-4-E2B-it.litertlm --prompt "Explain machine learning in one sentence."


# --- 3. Benchmark Gemma4 LLM Model ---
run_case "Benchmark Gemma4: Local benchmark of LLM generation" \
    litert lm benchmark gemma-4-E2B-it.litertlm



# --- Summary Report ---
print_summary_report "Gemma4"