diff --git a/docs/blog/posts/distilation-part1.md b/docs/blog/posts/distilation-part1.md
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@@ -14,6 +14,8 @@ tags:
 
 Get ready to dive deep into the world of fine-tuning task specific language models with Python functions. We'll explore how the `instructor.instructions` streamlines this process, making the task you want to distil  more efficient and powerful while preserving its original functionality and backwards compatibility.
 
+If you want to see the full example checkout [examples/distillation](https://github.com/jxnl/instructor/tree/main/examples/distilations)
+
 ## Why You Need Instructor
 
 Imagine you're developing a backend service that uses a mix old and new school ML practises, it may involve pipelines with multiple function calls, validations, and data processing. Sounds cumbersome, right? That's where `Instructor` comes in. It simplifies complex procedures, making them more efficient and easier to manage by adding a decorator to your function that will automatically generate a dataset for fine-tuning and help you swap out the function implementation.
diff --git a/examples/distilations/readme.md b/examples/distilations/readme.md
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+# What to Expect
+This script demonstrates how to use the `Instructor` library for fine-tuning a Python function that performs three-digit multiplication. It uses Pydantic for type validation and logging features to generate a fine-tuning dataset.
+
+## How to Run
+
+### Prerequisites
+- Python 3.9
+- `Instructor` library
+
+### Steps
+1. **Install Dependencies**  
+   If you haven't already installed the required libraries, you can do so using pip:
+    ```
+    pip install instructor pydantic
+    ```
+
+2. **Set Up Logging**  
+   The script uses Python's built-in `logging` module to log the fine-tuning process. Ensure you have write permissions in the directory where the log file `math_finetunes.jsonl` will be saved.
+
+3. **Run the Script**  
+    Navigate to the directory containing `script.py` and run it:
+    ```
+    python three_digit_mul.py
+    ```
+
+    This will execute the script, running the function ten times with random three-digit numbers for multiplication. The function outputs and logs are saved in `math_finetunes.jsonl`.
+
+4. **Fine-Tuning**  
+    Once you have the log file, you can run a fine-tuning job using the following `Instructor` CLI command:
+    ```
+    instructor jobs create-from-file math_finetunes.jsonl
+    ```
+    Wait for the fine-tuning job to complete.
+
+### Output
+
+That's it! You've successfully run the script and can now proceed to fine-tune your model.
+
+### Dispatch 
+
+Once you have the model you can replace the model in `three_digit_mul_dispatch.py` with the model you just fine-tuned and run the script again. This time, the script will use the fine-tuned model to predict the output of the function.
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