GPT-3 Fine Tuning Service
We are excited to announce that Tensor Labs now offer a fine tuning for GPT-3, a model that changed dynamics of AI/ML industry and contributed a lot in Machine Learning and Natural language Processing Field.
Why GPT-3 needs Fine Tuning ?
As, we already know, GPT-3 is a language model, developed by OpenAI. It uses Deep Learning to produce human-like texts after given an initial prompt. It also saves context of conversations and answer the questions accordingly. However, it can sometimes struggle to perform specific tasks with less efficiency.
This is where Fine Tuning plays the role.
Fine tuning lets you customize the model according to your specific needs to achieve higher quality results, lower latency requests, higher efficiency rate and save costs on Natural Language Processing (NLP) tasks. For, Fine tuning the model, we use the basic technique Few Shot Learning (FSL) .
What is Few Shot Learning (FSL) ?
“Few Shot Learning (FSL)” is a process that is majorly followed to perform an accurate fine tuning on GPT-3 Model in which a training datasets contains limited information. It is important because it will allow model to learn like humans, learn from rare cases and also reduced data collection effort and computational costs.
there are 3 simple steps to perform high level fine tuning.
1. Prepare and Upload Training Data
2. Train a Fine-Tuned model
3. Use your Fine-Tuned model
Use Cases for the Fine Tuned GPT-3 Model?
1. Customer Service
We can train our model on a data set of inquiries and responses by customers and can Fine Tune GPT-3 to generate accurate responses for complex customer’s queries.
2. Market Analysis
We can train our model on a data set that contains variety of audiences and their interests and Fine Tune GPT-3 to identify target audiences, ideas to reach them and design our market campaigns.
3. Financial Analysis
We can train our model on a data set that contains financial reports and articles that describes the financial situation and trends of given market or industry or organization. By performing GPT-3 Fine Tuning we can analyze market trends and generate accurate predictions withing the given domain.
4. Law Case Analysis
We can train our model on legal documents and outcomes of different scenarios datasets. Fine Tuning this model can get us predictions of different cases and help us analyze different aspects of cases.
5. Software Development
Gpt-3 Fine Tuning can also helps us detecting bugs, unfixed issues and better implementations with in code. Proper Fine Tuning also allows us to complete and generate code for assistance.
These are just general scenarios in which implementation of Fine Tuning of a model helps and provide assistance in any way. There are other vast set of use cases in which we can implement a Fine Tuning on GPT-3 model to help you achieve the desired results within ant domain in which it is applicable.
We are proud of the success of our team providing this successfully to our clients. It is a testament to our expertise in developing and deploying machine learning products, as well as our ability to provide complete end-to-end solutions for clients. If you have an idea and want to bring it to fruition, we would love to hear from you. You can visit our LinkedIn or reach out to us at info@tensorlabs.io to learn more about how we can help you turn your ideas into reality.
What’s next?
In the next articles we will keep introducing more of our clients which in collaboration with us have turned their ideas into reality. Or maybe the new services that we are offering. Stay tuned for more updates 😃.
