How Much Does It Cost to Fine-Tune GPT3.5?

Fine-Tuning Improves Your Model’s Performance

Improving LLM performance requires fine-tuning, but it is important to first understand the costs associated with fine-tuning GPT-3.5. This understanding is crucial for optimizing efficiency and budgeting. This guide provides insights into token pricing and inference rates, offering valuable information on the financial considerations of using fine-tuned models.

Key Takeaways

• Fine-tuning GPT-3.5 involves costs that impact budgeting and efficiency.
• Token count plays a vital role in determining pricing for fine-tuning processes.
• The token pricing for fine-tuning GPT-3.5 is $0.0080 per 1K tokens.
• Inference costs for input with fine-tuned models are $0.0030 per 1K tokens.
• The inference cost for output with fine-tuned models is $0.0060 per 1K tokens.

Understanding GPT-3.5 Fine-Tuning Costs

Cost of Fine-Tuning GPT-3.5

When considering the cost of fine-tuning GPT-3.5, it’s crucial to recognize that the expense extends beyond the training process. After fine-tuning the model, the matter of inference becomes significant. Developers frequently inquire about any additional charges for using the fine-tuned model. The answer is yes. While you don’t pay a fixed price for hosting the model (as OpenAI covers that), the cost increases based on the number of tokens you use.

Here’s a quick breakdown of the pricing structure for fine-tuning and inference using the OpenAI platform:

• Training cost: $0.0080 / 1K tokens
• Input token usage: $0.0030 / 1K tokens
• Output token usage: $0.0060 / 1K tokens

Token Pricing Explained

When discussing token pricing, we are delving into the details of the cost associated with interacting with the GPT-3.5 API. Unlike the regular ChatGPT subscription, the pricing structure is not a subscription. Instead, it is based on the number of tokens used.

When you’re running a fine-tuned GPT-3.5 model, every input you provide has a cost associated with it. The inference cost is the cost of calling a large language model (LLM) to generate a response. Each time you prompt the model, you’re essentially ‘renting’ its capabilities for that brief moment. This cost is calculated based on the number of tokens used in your input and the generated output.

A token refers to a unit of text processed by the model. In the case of GPT3.5, a general guideline is to consider a token as equivalent to 3/4 of a word. For example, if you generate an SEO article that is 1000 words long, you can estimate the token cost to be around 750 tokens.

As an example, let’s imagine that your prompt is the following:

The system and user prompts are considered input tokens. According to the OpenAI token calculator, the total number of tokens is approximately 25. Similarly, the 1000-word article is approximately 750 tokens. The total tokens cost can be calculated as:

Keep in mind that these prices are subject to change, and it’s always a good idea to check the latest pricing updates on the OpenAI website for the most accurate information.

Token Pricing for Fine-Tuning

To fine-tune GPT3.5, you will need a fine-tuning dataset that includes numerous example conversations to guide the model. You should have a minimum of 10 examples to begin with. Keep in mind that depending on the size of your dataset, you will be using a substantial number of tokens right from the beginning.

Imagine you have 10 input-output examples, similar to the one above, with an average total token length of 775 tokens. In total, these examples would consist of 7750 tokens.

The second important variable to consider is the number of epochs you will be using to fine-tune the model. The number of epochs determines the number of times the model will be trained on the dataset. The more epochs you use, the more tokens you will consume. OpenAI defaults to 4 epochs, but you can adjust this number based on your specific requirements.

The total fine-tuning cost can be calculated as:

Can Fine-Tuning Actually Save You Money?

The short answer is a resounding yes.

One notable advantage of fine-tuning is the ability to skip providing in-context learning examples, which results in lower token usage on each prompt. Although the price per token is higher with a fine-tuned model, in most cases, you will be using a lower token amount.

In addition, the selection of the underlying model can have a substantial impact on costs. Although newer models such as GPT-4 may appear appealing, choosing to fine-tune with a smaller model like GPT-3.5 can deliver comparable or even better results at a lower cost. The cost-effectiveness of fine-tuning on a well-established model can be especially appealing for businesses seeking to balance performance and budget considerations.

Fine-tuning also improves performance, reducing the need for retries. In situations where accurate and timely outputs are important, decreasing the number of retries can result in significant cost savings. The fine-tuning process refines the model’s understanding of specific tasks or domains, reducing errors and improving overall efficiency.

In addition to reducing retry rates, fine-tuning can also improve application performance by delivering faster outputs. A fine-tuned model can significantly decrease response times, resulting in a smoother and more efficient user experience. The faster outputs not only lead to enhanced customer satisfaction but also enable better resource utilization, which can potentially reduce infrastructure costs associated with prolonged processing times.

Frequently Asked Questions

How much does it cost to fine-tune GPT-3.5?

The cost of fine-tuning GPT-3.5 depends on the number of tokens used in the process. Here is a breakdown of the pricing structure:

• Training cost: $0.0080 per 1K tokens
• Input token usage: $0.0030 per 1K tokens
• Output token usage: $0.0060 per 1K tokens

What is a token?

A token is a unit of measurement used to quantify the input and output of fine-tuned models in GPT-3.5.

How many tokens make up one word?

A useful guideline is that one token typically corresponds to around 4 characters of text in common English text. This means that it roughly equates to 3/4 of a word, so 1000 tokens are approximately equal to 750 words.

Can fine-tuning reduce costs?

The answer is yes. Fine-tuning has advantages like using fewer tokens and lower costs. Choosing a smaller model like GPT-3.5 can give comparable or better results at lower cost than newer models like GPT-4. Fine-tuning improves performance and reduces retries, saving costs. It also delivers faster outputs, improving user experience and resource utilization.

FinetuneDB: Bringing It All Together

In conclusion, it is essential for anyone looking to fine-tuning GPT-3.5 to understand the associated costs and the pricing of tokens. FinetuneDB offers an integrated solution to capture, analyze, and enhance your LLM’s interactions, making the process of fine-tuning easy and accessible without the need to write a single line of code.