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참고/instructor-main/examples/distilations/math_finetunes_val.jsonl

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+{"messages": [{"role": "system", "content": "Predict the results of this function:\n\ndef fn(a: int, b: int, c: str) -> __main__.Multiply\n\"\"\"\n_summary_\n\nArgs:\n    a (int): _description_\n    b (int): _description_\n    c (str): _description_\n\nReturns:\n    Response: _description_\n\"\"\""}, {"role": "user", "content": "Return `fn(540, b=677, c=\"hello\")`"}, {"role": "assistant", "function_call": {"name": "Multiply", "arguments": "{\n  \"a\": 540,\n  \"b\": 677,\n  \"result\": 1217\n}"}}], "functions": [{"name": "Multiply", "description": "Correctly extracted `Multiply` with all the required parameters with correct types", "parameters": {"properties": {"a": {"type": "integer"}, "b": {"type": "integer"}, "result": {"description": "The result of the multiplication", "type": "integer"}}, "required": ["a", "b", "result"], "type": "object"}}]}
+{"messages": [{"role": "system", "content": "Predict the results of this function:\n\ndef fn(a: int, b: int, c: str) -> __main__.Multiply\n\"\"\"\n_summary_\n\nArgs:\n    a (int): _description_\n    b (int): _description_\n    c (str): _description_\n\nReturns:\n    Response: _description_\n\"\"\""}, {"role": "user", "content": "Return `fn(798, b=534, c=\"hello\")`"}, {"role": "assistant", "function_call": {"name": "Multiply", "arguments": "{\n  \"a\": 798,\n  \"b\": 534,\n  \"result\": 1332\n}"}}], "functions": [{"name": "Multiply", "description": "Correctly extracted `Multiply` with all the required parameters with correct types", "parameters": {"properties": {"a": {"type": "integer"}, "b": {"type": "integer"}, "result": {"description": "The result of the multiplication", "type": "integer"}}, "required": ["a", "b", "result"], "type": "object"}}]}
+{"messages": [{"role": "system", "content": "Predict the results of this function:\n\ndef fn(a: int, b: int, c: str) -> __main__.Multiply\n\"\"\"\n_summary_\n\nArgs:\n    a (int): _description_\n    b (int): _description_\n    c (str): _description_\n\nReturns:\n    Response: _description_\n\"\"\""}, {"role": "user", "content": "Return `fn(608, b=669, c=\"hello\")`"}, {"role": "assistant", "function_call": {"name": "Multiply", "arguments": "{\n  \"a\": 608,\n  \"b\": 669,\n  \"result\": 1277\n}"}}], "functions": [{"name": "Multiply", "description": "Correctly extracted `Multiply` with all the required parameters with correct types", "parameters": {"properties": {"a": {"type": "integer"}, "b": {"type": "integer"}, "result": {"description": "The result of the multiplication", "type": "integer"}}, "required": ["a", "b", "result"], "type": "object"}}]}
+{"messages": [{"role": "system", "content": "Predict the results of this function:\n\ndef fn(a: int, b: int, c: str) -> __main__.Multiply\n\"\"\"\n_summary_\n\nArgs:\n    a (int): _description_\n    b (int): _description_\n    c (str): _description_\n\nReturns:\n    Response: _description_\n\"\"\""}, {"role": "user", "content": "Return `fn(982, b=768, c=\"hello\")`"}, {"role": "assistant", "function_call": {"name": "Multiply", "arguments": "{\n  \"a\": 982,\n  \"b\": 768,\n  \"result\": 1750\n}"}}], "functions": [{"name": "Multiply", "description": "Correctly extracted `Multiply` with all the required parameters with correct types", "parameters": {"properties": {"a": {"type": "integer"}, "b": {"type": "integer"}, "result": {"description": "The result of the multiplication", "type": "integer"}}, "required": ["a", "b", "result"], "type": "object"}}]}

참고/instructor-main/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.
+
+    If you have validation date you can run:
+
+    ```
+    instructor jobs create-from-file math_finetunes.jsonl --n-epochs 4 --validation-file math_finetunes_val.jsonl 
+    ```
+
+### 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.

참고/instructor-main/examples/distilations/three_digit_mul.py

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+import logging
+
+from pydantic import BaseModel, Field
+from instructor import Instructions
+
+logging.basicConfig(level=logging.INFO)
+
+# Usage
+instructions = Instructions(
+    name="three_digit_multiply",
+    finetune_format="messages",
+    log_handlers=[
+        logging.FileHandler("math_finetunes.jsonl"),
+    ],
+)
+
+
+class Multiply(BaseModel):
+    a: int
+    b: int
+    result: int = Field(..., description="The result of the multiplication")
+
+
+@instructions.distil
+def fn(a: int, b: int) -> Multiply:
+    """Return the result of multiplying a and b together"""
+    resp = a * b
+    return Multiply(a=a, b=b, result=resp)
+
+
+if __name__ == "__main__":
+    import random
+
+    log_lines = {
+        "messages": [
+            {
+                "role": "system",
+                "content": 'Predict the results of this function:\n\ndef fn(a: int, b: int) -> __main__.Multiply\n"""\nReturn the result of multiplying a and b together\n"""',
+            },
+            {"role": "user", "content": "Return `fn(169, b=166)`"},
+            {
+                "role": "assistant",
+                "function_call": {
+                    "name": "Multiply",
+                    "arguments": '{\n  "a": 169,\n  "b": 166,\n  "result": 28054\n}',
+                },
+            },
+        ],
+        "functions": [
+            {
+                "name": "Multiply",
+                "description": "Correctly extracted `Multiply` with all the required parameters with correct types",
+                "parameters": {
+                    "properties": {
+                        "a": {"title": "A", "type": "integer"},
+                        "b": {"title": "B", "type": "integer"},
+                        "result": {
+                            "description": "The result of the multiplication",
+                            "title": "Result",
+                            "type": "integer",
+                        },
+                    },
+                    "required": ["a", "b", "result"],
+                    "type": "object",
+                },
+            }
+        ],
+    }
+    for _ in range(10):
+        a = random.randint(100, 999)
+        b = random.randint(100, 999)
+        print("returning", fn(a, b=b))

참고/instructor-main/examples/distilations/three_digit_mul_dispatch.py

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+import logging
+
+from pydantic import BaseModel, Field
+from instructor import Instructions
+import instructor
+from openai import OpenAI
+
+client = instructor.from_openai(OpenAI())
+
+logging.basicConfig(level=logging.INFO)
+
+# Usage
+instructions = Instructions(
+    name="three_digit_multiply",
+    finetune_format="messages",
+    include_code_body=True,
+    log_handlers=[
+        logging.FileHandler("math_finetunes.jsonl"),
+    ],
+    openai_client=client,
+)
+
+
+class Multiply(BaseModel):
+    a: int
+    b: int
+    result: int = Field(..., description="The result of the multiplication")
+
+
+@instructions.distil(mode="dispatch", model="ft:gpt-3.5-turbo-0125:personal::9i1JeuxJ")
+def fn(a: int, b: int) -> Multiply:
+    """Return the result of the multiplication as an integer"""
+    resp = a * b
+    return Multiply(a=a, b=b, result=resp)
+
+
+if __name__ == "__main__":
+    import random
+
+    for _ in range(5):
+        a = random.randint(100, 999)
+        b = random.randint(100, 999)
+        result = fn(a, b)
+        print(f"{a} * {b} = {result.result}, expected {a * b}")
+    """
+    972 * 508 = 493056, expected 493776
+    145 * 369 = 53505, expected 53505
+    940 * 440 = 413600, expected 413600
+    114 * 213 = 24282, expected 24282
+    259 * 650 = 168350, expected 168350
+    """