Add standalone finetuning and evaluation scripts for chapter 7 (#234)

* add finetuning and eval scripts

* update link

* update links

* fix link

ch07/01_main-chapter-code/ollama_evaluate.py

@@ -0,0 +1,120 @@
+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+#
+# A minimal instruction finetuning file based on the code in chapter 7
+
+import json
+import psutil
+from tqdm import tqdm
+import urllib.request
+
+
+def query_model(prompt, model="llama3", url="http://localhost:11434/api/chat"):
+    # Create the data payload as a dictionary
+    data = {
+        "model": model,
+        "seed": 123,        # for deterministic responses
+        "temperature": 0,   # for deterministic responses
+        "messages": [
+            {"role": "user", "content": prompt}
+        ]
+    }
+
+    # Convert the dictionary to a JSON formatted string and encode it to bytes
+    payload = json.dumps(data).encode("utf-8")
+
+    # Create a request object, setting the method to POST and adding necessary headers
+    request = urllib.request.Request(url, data=payload, method="POST")
+    request.add_header("Content-Type", "application/json")
+
+    # Send the request and capture the response
+    response_data = ""
+    with urllib.request.urlopen(request) as response:
+        # Read and decode the response
+        while True:
+            line = response.readline().decode("utf-8")
+            if not line:
+                break
+            response_json = json.loads(line)
+            response_data += response_json["message"]["content"]
+
+    return response_data
+
+
+def check_if_running(process_name):
+    running = False
+    for proc in psutil.process_iter(["name"]):
+        if process_name in proc.info["name"]:
+            running = True
+            break
+    return running
+
+
+def format_input(entry):
+    instruction_text = (
+        f"Below is an instruction that describes a task. "
+        f"Write a response that appropriately completes the request."
+        f"\n\n### Instruction:\n{entry['instruction']}"
+    )
+
+    input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""
+
+    return instruction_text + input_text
+
+
+def main(file_path):
+    ollama_running = check_if_running("ollama")
+
+    if not ollama_running:
+        raise RuntimeError("Ollama not running. Launch ollama before proceeding.")
+    print("Ollama running:", check_if_running("ollama"))
+
+    with open(file_path, "r") as file:
+        test_data = json.load(file)
+
+    model = "llama3"
+    scores = generate_model_scores(test_data, "model_response", model)
+    print(f"Number of scores: {len(scores)} of {len(test_data)}")
+    print(f"Average score: {sum(scores)/len(scores):.2f}\n")
+
+
+def generate_model_scores(json_data, json_key, model="llama3"):
+    scores = []
+    for entry in tqdm(json_data, desc="Scoring entries"):
+        prompt = (
+            f"Given the input `{format_input(entry)}` "
+            f"and correct output `{entry['output']}`, "
+            f"score the model response `{entry[json_key]}`"
+            f" on a scale from 0 to 100, where 100 is the best score. "
+            f"Respond with the integer number only."
+        )
+        score = query_model(prompt, model)
+        try:
+            scores.append(int(score))
+        except ValueError:
+            print(f"Could not convert score: {score}")
+            continue
+
+    return scores
+
+
+if __name__ == "__main__":
+
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Instruction finetune a GPT model"
+    )
+    parser.add_argument(
+        "--file_path",
+        required=True,
+        help=(
+            "The path to the test dataset `.json` file with the"
+            " `'output'` and `'model_response'` keys"
+        )
+    )
+    args = parser.parse_args()
+
+    main(file_path=args.file_path)