{"repo": "ahmedsaberellaithy/saber-code", "files": {".env.example": {"eb36650ebadb1f4d8d6849f4b619f15a2d3addaf": {"t": 1785746763.7267902, "n": 4648, "h": "82c243662d5e6b28", "c": "# Saber Code CLI Configuration Example\n# Copy this file to .env and customize as needed\n\n# ═══════════════════════════════════════════════════════════\n#  PRIMARY AI MODEL CONFIGURATION\n# ═══════════════════════════════════════════════════════════\n\n# Recommended Model (Best Performance)\n# See MODEL_COMPARISON.md for detailed benchmarks and comparisons\nSABER_CODE_MODEL=qwen2.5-coder:32b-instruct\n\n# Alternative Models:\n# \n# For smaller laptops (8GB+ RAM):\n# SABER_CODE_MODEL=deepseek-coder-v2:16b\n#\n# For budget/low-end systems (6GB+ RAM):\n# SABER_CODE_MODEL=qwen2.5-coder:7b-instruct\n#\n# Original default (may produce template responses):\n# SABER_CODE_MODEL=codellama:13b\n#\n# Good general + code (better than codellama):\n# SABER_CODE_MODEL=mistral\n\n\n# ═══════════════════════════════════════════════════════════\n#  OLLAMA SERVER CONFIGURATION\n# ═══════════════════════════════════════════════════════════\n\n# Ollama API Base URL (local server)\nSABER_CODE_BASE_URL=http://localhost:11434\n\n# API Timeout in milliseconds\n# Recommendations:\n#   - Chat/Quick tasks: 60000 (1 minute)\n#   - Plan creation: 120000 (2 minutes)\n#   - Complex analysis: 180000 (3 minutes)\nSABER_CODE_TIMEOUT=120000\n\n\n# ═══════════════════════════════════════════════════════════\n#  CONTEXT & TOKEN MANAGEMENT\n# ═══════════════════════════════════════════════════════════\n\n# Maximum tokens to send in context\n# Model limits:\n#   - qwen2.5-coder:32b-instruct: 32000 (128000 capable)\n#   - deepseek-coder-v2:16b: 128000\n#   - codellama:13b: 16000\nSABER_CODE_MAX_TOKENS=32000\n\n# Maximum files to keep in context\n# More files = better context but uses more tokens\nSABER_CODE_MAX_FILES=20\n\n# Maximum conversation messages to retain\n# Balance between context and token usage\nSABER_CODE_MAX_CONVERSATION=50\n\n\n# ═══════════════════════════════════════════════════════════\n#  DEBUGGING & LOGGING\n# ═══════════════════════════════════════════════════════════\n\n# Enable verbose debug output\n# Set to 1 to see:\n#   - API requests and responses\n#   - Token counts and context usage\n#   - Timing information\n#   - Prompt construction\n# Set to 0 for clean output (default)\nDEBUG=0\n\n\n# ═══════════════════════════════════════════════════════════\n#  PROJECT STORAGE (Advanced - Usually Don't Need to Change)\n# ═══════════════════════════════════════════════════════════\n\n# Directory for chat history and knowledge base\n# SABER_CODE_HISTORY_DIR=.saber_code_history\n\n# Directory for saved plans\n# SABER_CODE_PLANS_DIR=_saber_code_plans\n\n\n# ═══════════════════════════════════════════════════════════\n#  QUICK START GUIDE\n# ═══════════════════════════════════════════════════════════\n#\n# 1. Copy this file:\n#    $ cp .env.example .env\n#\n# 2. Install Ollama:\n#    $ brew install ollama  # macOS\n#    # or visit https://ollama.com/download\n#\n# 3. Pull recommended model:\n#    $ ollama pull qwen2.5-coder:32b-instruct\n#\n# 4. Start Ollama server:\n#    $ ollama serve\n#\n# 5. Test Saber Code:\n#    $ node cli.js models\n#    $ node cli.js chat\n#\n# 6. Read documentation:\n#    $ cat START_HERE.md\n#    $ cat MODEL_COMPARISON.md\n#\n# ═══════════════════════════════════════════════════════════\n\n\n# ═══════════════════════════════════════════════════════════\n#  MODEL SELECTION GUIDE\n# ═══════════════════════════════════════════════════════════\n#\n# Choose based on your hardware:\n#\n# 32GB+ RAM, M1/M2/M3 Mac or high-end PC:\n#   → qwen2.5-coder:32b-instruct (RECOMMENDED)\n#     Best quality, excellent instruction following\n#\n# 16GB RAM, modern laptop:\n#   → qwen2.5-coder:32b-instruct (works, might be slower)\n#   → deepseek-coder-v2:16b (faster alternative)\n#\n# 8-12GB RAM, mid-range laptop:\n#   → deepseek-coder-v2:16b (good balance)\n#   → qwen2.5-coder:7b-instruct (lighter option)\n#\n# 6-8GB RAM, older/budget laptop:\n#   → qwen2.5-coder:7b-instruct\n#   → codellama:13b (fallback, but lower quality)\n#\n# See MODEL_COMPARISON.md for detailed benchmarks!\n#\n# ═══════════════════════════════════════════════════════════\n\n\n# ═══════════════════════════════════════════════════════════\n#  TROUBLESHOOTING\n# ═══════════════════════════════════════════════════════════\n#\n# Issue: \"Ollama not responding\"\n# Fix: Ensure Ollama is running\n#   $ ollama serve\n#\n# Issue: \"Model not found\"\n# Fix: Pull the model first\n#   $ ollama pull qwen2.5-coder:32b-instruct\n#\n# Issue: \"Plan returns template/placeholders\"\n# Fix: Use qwen2.5-coder:32b-instruct (better than codellama)\n#\n# Issue: \"Timeout errors\"\n# Fix: Increase SABER_CODE_TIMEOUT to 180000 or higher\n#\n# Issue: \"Out of memory\"\n# Fix: Use smaller model (deepseek-coder-v2:16b or qwen2.5-coder:7b)\n#\n# ═══════════════════════════════════════════════════════════\n"}}}}