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- Slash Your AI Spend: Insider Secrets to Budget-Friendly AI Models and Strategies
Slash Your AI Spend: Insider Secrets to Budget-Friendly AI Models and Strategies
AI Budget Hacks Every Business Needs + Fine-Tuning Strategies for Peak Performance. Plus our new AI Intelligence Platform for Entrepreneurs.
Candyd Mendoza & Benson Sung
December 01, 2023
In today’s Future Friday…
Why GPT-4 Costs You More Than You Think!
From $80K to $15K: How OpenPipe Use Fine-Tuning to Slash LLM Costs
Budget-Friendly AI Models: Your Ultimate Guide to Saving Big’
How Microsoft took fine-tuning to the next level
Amazon Q is ready to Reimagine the Future of Work
AI costs breaking the bank? Here's how to save smart and earn big.
Read Online and listen to the audio version.
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TOPIC OF THE WEEK
Techniques for building LLM applications with budget constraints
Running large AI models comes with big $$$.
For example, for every dollar spent on developing an AI algorithm, there might be about $100 spent on its deployment.
When companies launch AI projects, they often need to switch gears, focusing more on support and maintenance than innovation.
But here's some good news: OpenAI updated its pricing model early this month, slashing prices across the board to pass on savings to developers. Depending on the model you use, you’d get between 2x and 3x price reductions.
ℹ️ Why This Matters Today
Despite the price reduction, it’s safe to say GPT-4 is still pricier than many other AI models.
Like we’ve said in our previous OpenAI DevDay piece, these costs, although made cheaper by OpenAI, could still pile up into tens of thousands of dollars per day depending on your demands.
The hefty price tag of GPT-4 comes from various factors, including:
Hardware
Energy use
Research
Legal fees
Training
Maintenance
Computing power
But fear not, we're here to offer solutions, not just highlight problems.
For those on a budget, open-source alternatives like Mistral AI (we’ll dive deeper into that in a bit) offer similar capabilities at a fraction of the cost, about 187x less cheaper than GPT-4.
Then, there’s the cherry on top, the AI technique you can easily implement with your team.
The Power of Fine-tuning
Fine-tuning LLMs lets you tweak these massive models for specific tasks, transforming them into specialized tools. This process involves training with data relevant to your needs.
The aim is to transform these general-purpose models into specialized tools that cater precisely to a business's unique needs.
There are various ways to fine-tune LLMs, such as:
Transfer Learning: Use a pre-trained model from one task as a starting point for another, leveraging its prior knowledge for better outcomes.
Sequential Fine-Tuning: Gradually adjust a general-trained language model for more specific tasks, enhancing adaptability across domains.
Task-Specific Fine-Tuning: Tailor a pre-trained model for a particular task, such as sentiment analysis, to boost accuracy and performance.
Multi-Task Learning: Train a model on multiple tasks simultaneously, especially beneficial for similar tasks to enhance overall performance.
Adaptive Fine-Tuning: Dynamically adjust the learning rate during fine-tuning to improve performance and prevent overfitting.
Behavioral Fine-Tuning: Incorporate user interaction data, like chatbot conversations, to fine-tune specific skills such as conversational abilities.
Parameter-Efficient Fine-Tuning: Reduce the model size during fine-tuning for greater efficiency and resource management, while preserving performance.
Text-Text Fine-Tuning: Fine-tune using input-output text pairs, ideal for tasks like language translation.
💰 Impact On Your Business
Fine-tuning LLMs can offer several benefits, such as:
Enhancing the model's performance for specific tasks or domains.
Allowing businesses to control the data the model is exposed to, ensuring data privacy and security.
Addressing rare but crucial scenarios specific to a business or application.
💡 Ideas to Marinate
However, fine-tuning LLMs may not always be necessary or cost-effective, especially if the model already fits the task.
Carefully consider when to use fine-tuning to maximize efficiency and performance.
How OpenPipe Cut Costs From $80,000 to $15,000
Remember our AI Hallucinations piece? We mentiontioned a solution that can reduce the cost of running an LLM by up to 50x. Recently we had a very interesting conversation with David Corbitt, one of OpenPipe’s cofounders.
He explained to us how they capture your existing prompts and completions and then use them to fine-tune a model specific to your use case, achieving a fine-tuned replacement that’s faster, cheaper and often more accurate than your original prompt.
🏆 Winning Case Study
Fine-tuning LLMs can be a powerful strategy for businesses.
In our latest podcast jam, OpenPipe co-founder David Corbitt said they have a client who was spending $80,000 monthly on GPT-3.5 for data extraction from call transcripts, but the results were only about 50% accurate.
Even though it's expensive, OpenPipe suggested using GPT-4 initially to gather high-quality training data.
With just a few thousand rows of this data, they could fine-tune a smaller, more cost-effective model.
When you're fine-tuning these models, it depends on the complexity of the task, but a few hundred to a few thousand rows is enough to get good results. We had them collect a few thousand rows, and all that was necessary then was to train.
Using OpenPipe and switching to a 7 billion parameter Llama model and later to Mistral, tailored to GPT-4's output, reduced their costs to $15,000 monthly.
This shift not only saved them $65,000 per month but also increased efficiency, giving them three times the tokens at a lower price. The accuracy improved too, with 70% to 80% fewer errors than GPT-3.5.
This example showcases the effectiveness of fine-tuning in achieving high performance at a significantly lower cost, especially for complex tasks like data extraction.
⚒️ Actionable Steps
Looking to harness LLMs without burning through cash? Here’s a strategy for smaller businesses and startups:
Prepare Your Dataset: This dataset should include examples of the input and desired output that are relevant.
Split Your Dataset: Divide your dataset into training, validation, and test sets.
Start Fine-Tuning: Adjust the model's parameters with your specific data. You might need to experiment with the different fine-tuning techniques:
Implement Active Learning: Begin fine-tuning with a smaller dataset and add more data based on identified weaknesses.
Crowdsource Model Feedback: Use user insights to guide model enhancements.
Apply Transfer Learning from Unrelated Fields: Use cross-domain insights for potential improvements.
Integrate AI Optimization Tools: Employ AI-driven tools for optimal parameter settings.
Leverage Decentralized AI Resources: Explore cost-effective computational options in decentralized AI.
Conduct A/B Testing with Competing Models: Test different models simultaneously to find the most effective.
Engage in Open-Source AI Projects: Collaborate in open-source for shared knowledge and resources.
Experiment with Model Pruning: Streamline the model by removing non-critical components to reduce load.
Test and deploy: Once satisfied with the model's performance on the validation set, test it on the unseen test dataset to evaluate its real-world applicability, then deploy.
Monitor Costs and Performance: Adjust your strategy as needed to balance performance with cost-effectiveness.
Do you have to do it all by yourself?
Of course not!
You can try this strategy with your team or outsource this process to fine-tuning service providers.
That’s why companies like David’s and Kyle’s exist. And that’s why we invite them to chat with us!
So stay tuned to our podcast, because many more eye-opening episodes are coming 👀
Would you like to get the in-depth how-to of how to apply the fine-tuning strategies from this episode?
Get our premium membership today for this and much more incredible features.
CAVEMINDS’ CURATION
Open-Source Alternatives to GPT-4 That are Cheaper to Train
If we talk about Open-Source LLMs, Hugging Face Transformers is the place to go.
Open-source models, while not exactly on par with GPT-4 in terms of scale and performance, offer a more affordable solution for those looking to leverage large language models.
And some, even better performance as you’ll see in the Needle Movers section.
Other fine-tuning tools that topped our curated list
Synthflow - an AI tool that offers cutting-edge fine-tuning technology to enhance NLP models, resulting in better results with less data in less time.
OtterTune - an AI-powered database tuning tool designed to improve performance and reduce costs for PostgreSQL and MySQL databases.
Terracota - an easy-to-use platform that allows users to experiment with Large Language Models (LLMs) quickly and efficiently. Users can manage multiple fine-tuned models in one place, enabling them to iterate and improve their models rapidly.
NEEDLE MOVERS
Microsoft Research has introduced Orca 2, a smaller language model with 7 billion or 13 billion parameters.
The model is comparable to or better than models 5-10 times larger, in complex tasks that test advanced reasoning abilities.
Orca 2's performance surpasses models of similar size and showcases the potential of equipping smaller models with better reasoning capabilities.
Read the full paper here.
Just one week after rumors surfaced about OpenAI's Q* project, which Sam Altman described as 'pushing the veil of ignorance' due to its breakthrough nature, Amazon officially announced Amazon Q.
But is it the same, or does it at least bear some similarities?
It doesn't appear so, but it's poised to compete in the big leagues.
You can check out their official press release here.
That’s all for today, folks!
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