To complete the analogy of passing the baton, let’s explore how to upload the prepared JSONL files to OpenAI using their Files API, enabling us to move closer to fine-tuning the model.
Step-by-Step Guide to Uploading Files
Prerequisites
pip install openai
_ Upload Files to OpenAI_
from openai import OpenAI client = OpenAI() # File paths for training and testing datasets file_paths = { "train": "train.jsonl", "test": "test.jsonl" } # Function to upload a file def upload_file(file_path, purpose="fine-tune"): try: response = client.files.create( file=open(file_path, "rb"), purpose=purpose ) print(f"File uploaded successfully: {file_path}") print(f"File ID: {response['id']}") return response["id"] except Exception as e: print(f"Failed to upload {file_path}: {e}") return None # Upload both training and test files file_ids = {split: upload_file(file_paths[split]) for split in file_paths} print("Uploaded file IDs:", file_ids)
Explanation of the Code
API Key Setup:
File Paths:
Uploading Files:
Error Handling:
File IDs:
Output Example
If the upload is successful, you’ll see something like this:
File uploaded successfully: dataset/train.jsonl File ID: file-abc123xyz456 File uploaded successfully: dataset/test.jsonl File ID: file-def789uvw012 Uploaded file IDs: {'train': 'file-abc123xyz456', 'test': 'file-def789uvw012'}
Why Is This Step Important?
Uploading the JSONL files is akin to the Six Triple Eight handing over their sorted mail to postal services for final delivery. Without this step, the fine-tuning process cannot proceed, as OpenAI’s infrastructure needs access to structured, validated data to train the model effectively.
Once uploaded, the baton has been passed to OpenAI, and you’re ready to move on to fine-tuning the model using these files.
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