Add RL training configuration and tools

- Updated `.env.example` to include Tinker and WandB API keys for reinforcement learning training.
- Enhanced `model_tools.py` to clarify configuration options and streamline the RL training process.
- Expanded `README.md` with detailed instructions for setting up RL training using Tinker and WandB.
- Modified `hermes_cli` files to integrate RL training tools and ensure proper configuration checks.
- Improved `rl_training_tool.py` to reflect changes in training parameters and configuration management.

hermes_cli/status.py

@@ -74,6 +74,8 @@ def show_status(args):
         "Firecrawl": "FIRECRAWL_API_KEY",
         "Browserbase": "BROWSERBASE_API_KEY",
         "FAL": "FAL_KEY",
+        "Tinker": "TINKER_API_KEY",
+        "WandB": "WANDB_API_KEY",
     }
     
     for name, env_var in keys.items():