Create advanced_usage_examples.md

Created new structure for docs folder, and added advanced usage examples tutorial

docs/tutorials/advanced_usage_examples.md

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+# Advanced Usage Examples
+LLMeBench offers support for advanced benchmarking use cases. In this tutorial, we provide example commands for such cases starting from the following general command: 
+
+``` bash
+python -m llmebench --filter '*benchmarking_asset*' <benchmark-dir> <results-dir>
+```
+
+As can be seen in the previous command, the framework performs a wildcard search over the [benchmarking assets directory](https://github.com/qcri/LLMeBench/tree/main/assets) to identify the assets(s) to run as specified by `'*benchmarking_asset*'`. This is possible becuase we roughly maintain the following strcutrue and file naming scheme in the benchmarking assets directory.
+> language_code/task_category/task/Dataset_Model_LearningSetup.py
+
+
+### Running all assets for a specific language
+The framework currently uses a two letter language code. It is possible to run all assets implemented for a single language using the command:
+
+```bash
+python -m llmebench --filter '*language_code/*' <benchmark-dir> <results-dir>
+```
+- `language_code`: Example values: "ar"(--> Arabic), "en"(--> English), "fr"(--> French), etc.
+
+
+### Running all assets for a category of tasks
+We currently release assets under **eight** task categories as listed [here](https://github.com/qcri/LLMeBench/tree/main/assets/ar). Running assets for one category can be done as follows:
+```bash
+python -m llmebench --filter '*task_category/*' <benchmark-dir> <results-dir>
+```
+- `task_category`: Example values: "MT"(for Machine Translation), "semantics", "sentiment_emotion_others", etc.
+
+Running the above command will run assets from all _models_, _languages_, _subtasks_, and _learning setups_ for a `task_category`. 
+
+### Running all assets for a specific task
+As with task categories, we also maintain consistent task names across languages, learning setups, etc. To run assets for a sinlge task:
+
+```bash
+python -m llmebench --filter '*task/*' <benchmark-dir> <results-dir>
+```
+- `task`: Example values: "sentiment", "SNS", "NLI", "news_categorization", etc.
+
+
+### Running all assets for a specific model
+It is possible to benchmark a single model using the following command:
+```bash
+python -m llmebench --filter '*model*' <benchmark-dir> <results-dir>
+```
+- `model`: Example values: "GPT35", "GPT4", "BLOOMZ", etc.
+
+
+### Running all assets for a specific learning setup
+The framework currently supports both zero-shot and few-shot learning setups. To run all zero-shot assets:
+```bash
+python -m llmebench --filter '*ZeroShot*' <benchmark-dir> <results-dir>
+```
+To run all few shot assets:
+
+```bash
+python -m llmebench --filter '*FewShot*' --n_shots <n> <benchmark-dir> <results-dir>
+```