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https://github.com/rasbt/LLMs-from-scratch.git
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127 lines
3.9 KiB
Python
127 lines
3.9 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
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# Source for "Build a Large Language Model From Scratch"
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# - https://www.manning.com/books/build-a-large-language-model-from-scratch
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# Code: https://github.com/rasbt/LLMs-from-scratch
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#
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# A minimal instruction finetuning file based on the code in chapter 7
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import json
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import psutil
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from tqdm import tqdm
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import urllib.request
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def query_model(prompt, model="llama3", url="http://localhost:11434/api/chat"):
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# Create the data payload as a dictionary
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data = {
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"model": model,
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"messages": [
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{"role": "user", "content": prompt}
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],
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"options": { # Settings below are required for deterministic responses
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"seed": 123,
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"temperature": 0,
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"num_ctx": 2048
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}
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}
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# Convert the dictionary to a JSON formatted string and encode it to bytes
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payload = json.dumps(data).encode("utf-8")
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# Create a request object, setting the method to POST and adding necessary headers
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request = urllib.request.Request(url, data=payload, method="POST")
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request.add_header("Content-Type", "application/json")
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# Send the request and capture the response
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response_data = ""
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with urllib.request.urlopen(request) as response:
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# Read and decode the response
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while True:
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line = response.readline().decode("utf-8")
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if not line:
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break
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response_json = json.loads(line)
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response_data += response_json["message"]["content"]
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return response_data
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def check_if_running(process_name):
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running = False
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for proc in psutil.process_iter(["name"]):
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if process_name in proc.info["name"]:
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running = True
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break
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return running
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def format_input(entry):
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instruction_text = (
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f"Below is an instruction that describes a task. "
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f"Write a response that appropriately completes the request."
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f"\n\n### Instruction:\n{entry['instruction']}"
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)
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input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""
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return instruction_text + input_text
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def main(file_path):
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ollama_running = check_if_running("ollama")
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if not ollama_running:
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raise RuntimeError("Ollama not running. Launch ollama before proceeding.")
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print("Ollama running:", check_if_running("ollama"))
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with open(file_path, "r") as file:
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test_data = json.load(file)
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model = "llama3"
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scores = generate_model_scores(test_data, "model_response", model)
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print(f"Number of scores: {len(scores)} of {len(test_data)}")
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print(f"Average score: {sum(scores)/len(scores):.2f}\n")
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def generate_model_scores(json_data, json_key, model="llama3"):
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scores = []
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for entry in tqdm(json_data, desc="Scoring entries"):
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if entry[json_key] == "":
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scores.append(0)
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else:
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prompt = (
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f"Given the input `{format_input(entry)}` "
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f"and correct output `{entry['output']}`, "
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f"score the model response `{entry[json_key]}`"
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f" on a scale from 0 to 100, where 100 is the best score. "
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f"Respond with the integer number only."
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)
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score = query_model(prompt, model)
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try:
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scores.append(int(score))
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except ValueError:
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print(f"Could not convert score: {score}")
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continue
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return scores
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(
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description="Evaluate model responses with ollama"
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)
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parser.add_argument(
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"--file_path",
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required=True,
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help=(
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"The path to the test dataset `.json` file with the"
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" `'output'` and `'model_response'` keys"
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)
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)
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args = parser.parse_args()
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main(file_path=args.file_path)
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