Inference with Fine-Tuned Models: Delivering the Message

The women of the Six Triple Eight, during World War II, meticulously processed countless letters, ensuring each reached its intended recipient. Their work wasn't complete until delivery. In the AI realm, inference mirrors this crucial final step. After fine-tuning a model, inference validates its performance on real-world data, confirming its ability to accurately execute its trained tasks – be it classification, summarization, or other specialized functions.

The following Python example demonstrates inference with a fine-tuned OpenAI model. Here, we classify articles into categories: business, tech, entertainment, politics, or sports.

<code class="language-python">fine_tuned_model = "ft:gpt-3.5-turbo:xxxxxxxxxxxx"

system_prompt = "Classify this article into one of these categories: business, tech, entertainment, politics, sport"

user_input = "A new mobile phone is launched"

try:
    response = openai.ChatCompletion.create(
        model=fine_tuned_model,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_input}
        ]
    )
    print("Model Response:")
    print(response.choices[0].message.content)  # Access the content directly
except Exception as e:
    print("Error during inference:", e)</code>

A Historical Analogy

Like the Six Triple Eight verifying each letter's destination, inference verifies our fine-tuned model's accuracy in classifying new data. It's the final quality control step, ensuring our "mail" (user queries) arrives at the correct "destination" (accurate classification).

This final stage confirms the model's reliability in handling real-world applications, justifying the time and effort invested in the earlier fine-tuning process.

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