Use the standard GPT-J-6B model by EleutherAI or fine-tune on your dataset.
GPT-Neo
Fine-tune the GPT-Neo 120M, 1.3B, or 2.7B model by EleutherAI on your dataset.
GPT-NeoX
Use the standard GPT-NeoX model by EleutherAI or fine-tune on your dataset.
T5
Use the standard T5 model by Google or fine-tune on your dataset.
Blender Bot 2.0
Use the standard Blender Bot model by Facebook or fine-tune on your dataset.
Solutions
Use a pre-trained model for a specific task.
Question Answering
Answer natural language questions from any amount of text.
Blog Generator
Generate a full-length blog from a topic.
Rewriter
Paraphrase any amount of text.
Dialogue Summarizer
Summarize dialogue and transcripts.
Blog Outliner
Generate a blog outline from a topic.
Paragraph Generator
Generate a paragraph from a topic and bullet point.
Text Summarizer
Summarize open-ended text like articles and books.
Emotion Classifier
Classify text across 27 types of emotion.
get started with solutions
Pre-built NLP solutions that work
Multiple models, each with different capabilities and price points. GPT-J is the fastest model, while GPT-NeoX is the most powerful—and more are on the way.
Use these models for classification, entityextraction, code generation, chatbots, content generation, summarization, paraphrasing, sentiment analysis, and much more.
Forefront Solutions are built to help teams quickly integrate powerful NLP solutions into their products and workflows. Each solution is a single API endpoint that can be applied to solve many different use cases for each task.
These solutions allow you to focus on building your product without needing to fine-tune custom models, write prompts, experiment with parameters, or preprocess data. See docs
Multiple models, each with different capabilities and price points. GPT-J is the fastest model, while GPT-NeoX is the most powerful—and more are on the way.
Use these models for classification, entityextraction, code generation, chatbots, content generation, summarization, paraphrasing, sentiment analysis, and much more.
These models have been pre-trained on a vast amount of text from the open internet. Fine-tuning improves upon this for specific tasks by training on many more examples than can fit in a prompt, letting you achieve better results on a wide number of tasks.
Prepare a dataset with training examples for your task then upload it to the platform to start fine-tuning.
02
Set hyperparameters
Define how many epochs your model should train for and how many checkpoints, or model versions, should be saved throughout training.
03
Evaluate performance
Set test prompts and parameters to automatically test model performance, and add any checkpoint to get an HTTP endpoint for inference. See docs for more information
We manage all the infrastructure so you can just send API requests. Forget about deployments, autoscaling rules, and the cloud.
Scale with dedicated GPUs
Unlock better cost efficiency and latency by using dedicated GPUs, and set autoscaling rules to optimize inference at scale.
solve any nlp problem
Perform a variety of natural language tasks
Large language models can perform various tasks like question answering, content generation, text summarization, and code generation. These large language models need very few examples to understand tasks, and when fine-tuned on as few as 100 training examples, can outperform models 10x in size.
Choose a genre category for each movie 1. Interstellar, 2. The Departed 3. Airplane and make a list of the movie and its genre:
Lex: Let's start with an easy question about consciousness. In your view, is consciousness something that's unique to humans or is it something that permeates all matter? Almost like a fundamental force of physics? Elon: I don't think consciousness permeates all matter. Lex: Panpsychics believe that. There's a philosophical Elon: How would you tell? Lex: That's true? That's a good point. Elon: I believe in scientific method. Don't want to blow your mind or anything, but the scientific method is if you cannot test the hypothes is, then you cannot reach meaningful conclusion that it is true. Lex: Do you think consciousness, understanding consciousness is within the reach of science of the scientific method? Elon: We can dramatically improve our understanding of consciousness. I'd be hard pressed to say that we understand anything with complete accuracy, but can we dramatically improve our understanding of consciousness? I believe the answer is yes.
Company Name: Netflix Product Description: Netflix is a subscription-based streaming service that allows our members to watch TV shows and movies without commercials on an internet-connected device. You can also download TV shows and movies to your iOS, Android, or Windows 10 device and watch without an internet connection.
Blog Idea:
Extract the mailing address from this email:
Dear Jonathan,
It was great to connect at the seminar. I thought your presentation was amazing and I really appreciate offering to send a copy of the book!
My address is 4837 N Henderson Ave, Dallas, TX 75204.
Best, Sara Davenport
Name and address:
Human: Hello, who are you? AI: I am an AI created by Forefront. How can I help you today? Human: I'd like to cancel my subscription. AI:
Write a SQL query to list the names of employees with the department ID matching "1238123"
Apple announced new iPhones, iPads, and a new Apple Watch during their event on September 14, 2021. The new iPhone 13 ranges from $699 (iPhone 13 Mini 128GB) to $1599 (iPhone 13 Pro Max 1TB). The new iPads range from $329 (iPad 64GB) to $2199 (12.9-inch iPad Pro 2TB). The new Apple Watch ranges from $279 (Apple Watch SE) to $579 (Apple Watch Series 7 45mm GPS + Cellular).
Make a table summarizing the recent products from Apple's announcement:
Translate the following into German: What time is breakfast?
Relentlessly optimized.
2x
more cost efficient than any alternative
50%
better throughput and latency for any model or hardware
Built to scale.
18,000,000,000+
tokens processed per week
22,000,000+
API requests per week
global scale
Infrastructure for your language models
We provide key infrastructure for language models in a simple interface so your team doesn’t have to stitch together different systems or spend months to build training and hosting infrastructure.
10B+
tokens processed per week
50%
faster inference on any hardware
33%
better throughput on any hardware
20M+
requests processed per week
A playground for all your models
Instantly experiment with any of your models without having to use an API.
Science fiction books 1. Dune by Frank Herbert (1965)
case studies
Built with Forefront’s API
Our API processes billions of tokens per day with tasks ranging from extracting data in medical records to searching internal documents with natural language questions.
The NLP problems that the models on our platform can be used to solve are countless, and it’s just the beginning. See below on how some of our customers use them to build powerful products and solutions.
We give you access to open source models with full control and ownership.
Use open source language models without usage quotas, content filtering, or approval processes.
For use cases with privacy or security in mind, you can turn off request logs and dataset storage or export your fine-tuned models to host yourself or anywhere else.
Frequently asked questions
What is a prompt and completion?
How does fine-tuning work?
Which model should I use?
Is there an SLA for the various models?
Can I self-host a model in my VPC?
Ready to get started?
Start fine-tuning and deploying language models or explore Forefront Solutions.
Natural Language Processing (NLP) is a subfield of AI that aims to help machines make sense of how humans write and speak. This is made particularly difficult by the fact that language by nature can be messy and inconsistent. In an effort to standardize text a subfield of NLP called Named Entity Recognition (NER) sprang up to extract and classify elements of unstructured text into a structured dataset to better help categorize and handle free text.
For example, the sentence “Jeremy is going to New York City on Thursday to attend Stern's Gala on American Business.” contains at least six different entities within it. We have the subject’s name (Jeremy), a date (Thursday), and a geopolitical entity (American) to name a few. Entity extraction, the act of identifying and classifying these elements of text, has long been a vital part of NLP.
Customer support organizations use entity extraction to route customer queries to the proper support center. Financial firms extract entities from news articles and financial documents to quickly sort incoming news and information. The applications of entity extraction are virtually limitless but the technology has up until now been for the very technical minded to set up and use.
Forefront Extract
That’s why we created Forefront Extract, an easy to use entity extraction API. Extracting high-quality entities from text of any length is now only an API call away. Using the API is easy and only requires two inputs:
text - a piece of text that can be any length
entities - A list of entity types that you are looking for. The options are:
"locations" - references to geographical locations like New York, or Istanbul
"people" - references to people generally by their name
"events" - references to a planned occasion like a meeting or a Gala
"organizations" - references to groups of people like Apple or a local food bank
"dates" - references to times like Thursday or simply “tomorrow”
"geopolitics" - references to large political entities like Americans or a government
Let’s see the Extract API in action.
Example 1: Customer support routing
Forefront Extract can take a free text query from a customer and provide the necessary tags that a routing system can use to assign the proper agent to the customer.
We can see that with Forefront Extract, a routing system can automatically recognize a customer’s location and send them to the proper support center with structured information so now the new agent will know the customer’s name and where they live.
Example 2: Structuring financial news
The constant influx of financial news can be overwhelming but with Forefront Extract, we can extract the relevant entities from news articles to sort which news is the most urgent. Let’s use Forefront Extract to extract organizations from one news headline that is about Target and one that could have been confusing with just a keyword search.
We can see that the Extract API is smart enough to recognize that the first headline is about the organization “Target” whereas the second headline merely uses the capitalized word in context with the F.B.I.
Get started with Forefront Extract
It’s clear from our examples that Forefront Extract can intelligently extract entities from all kinds of text from multiple domains. The possibilities with what we can do with Forefront Extract are boundless. Documentation for the Extract API can be found here.
Ready to sign up and get started? Sign up for Forefront today!
If you want to get anything done, chances are you have to communicate with someone else. Transcripts are artifacts of our conversations with others and the ability to distill transcripts into short form summaries can unlock all kinds of previously unattainable analyses.
Customer support organizations can use summaries of conversations their customers have with their agents or chatbots to track evolving customer queries. They can even cluster these summaries to create visual representations of their customers’ needs. People can recap interviews with their favorite celebrities and role models to stay up to date with their lives. Economists can summarize interviews with key figures to keep up with rapidly shifting policy and news.
Forefront Summarize
Modern large language models excel at creating high-fidelity abstractive summaries that can freely paraphrase source transcripts into easy to read text snippets with varying degrees of compression. These models are powerful but can be hard to set up and get started with. Forefront Summarize is an easy to use abstractive text summarization API that can generate high-quality dialogue summaries of any length. Using the API to summarize dialogue is easy and only requires two inputs:
dialogue - a structured list of conversation turns.
compression_level - an integer from 1 to 5 where 1 means the summary will be longer and have more information about the transcript whereas 5 will mean the summary will be as short as possible. This defaults to 3 if not given.
Let’s see the Forefront Summarize API in action.
Example 1: Recapping celebrity interviews
Interviews are often people’s favorite portions of TV shows and podcasts but with so many out there it can be daunting to try and keep up. Forefront Summarize can reduce entire interviews down to the most important points to give us what we need. Let’s use Forefront Summarize to summarize a 60 minute interview with Trevor Noah.
With a compression level of 5 we see over 80% of the character count replaced with the key facts of the interview. Imagine reading the insights from 5 interviews in the same time it would take to read the entire transcript of 1 interview.
Example 2: Summing up the Fed
Forefront can tackle dialogue summaries from nearly any field. Let’s use Forefront Summarize to sum up a recent interview with current Fed Chairman Jerome Powell.
The interview saw a reduction in character count by nearly 90% and still conveys the key economic points that Chairman Powell is trying to get across.
Example 3: Summarizing Customer Support Transcripts
Forefront Summarize can transform a multi-turn dialogue between a customer and a customer support agent into a brief or longer-form summary by setting the desired compression level.
We can see that the lower compression level of 1 gives us more information about the customer’s sentiment where the highest compression level of 5 just gets straight to the point about what happened.
Dialogue summaries made easy
It’s clear from our examples that Forefront Summarize can summarize all kinds of dialogue from different domains. The possibilities with what we can do with Forefront Summarize are virtually limitless. Documentation for the Summarize API can be found here.
Summarizing long pieces of text and dialogue is an exciting field of AI with applications in nearly every field of research and business. Customer support organizations use summaries of conversations with customers to track evolving customer queries. Financial firms summarize news articles and financial documents to keep up with rapidly shifting markets. Medical institutions rely on summaries for medical notes and research articles to efficiently handle high volumes of patients while keeping up to date with the most recent clinical discoveries.
Types of Summarization
There are in general two kinds of AI-based text summarization: extractive summarization where the summaries must be quoted exactly from the source material and abstractive summarization where the AI is free to paraphrase information to be more concise. The latest large language models excel at creating high-fidelity abstractive summaries but can be hard to set up and use.
Forefront Summarize
That’s where Forefront Summarize, an easy to use abstractive text summarization API, comes in! Generating high-quality text or dialogue summaries of any length is now only an API call away. Using the API is easy and only requires two inputs:
Either text - a piece of text that can be any length or dialogue - a structured list of conversation utterances.
compression_level - an integer from 1 to 5 where 1 means the summary will be longer and have more information and 5 means the summary will be as short as possible. This defaults to 3 if not given.
Let’s see the API in action for the examples above.
Example 1: Summarizing Customer Support Transcripts
Forefront Summarize can turn a multi-turn dialogue into a brief or longer-form summary by setting the desired compression level.
We can see that the lower compression level of 1 gives us more information about the customer’s sentiment where the highest compression level of 5 just gets straight to the point about what happened.
Example 2: Summarizing News Articles
The constant firehose of news can be overwhelming but with Forefront Summarize, we can reduce entire news articles down to the most important points to give us the news we crave on the go. Let’s use Forefront Summarize to summarize a New York Times Article on gig-work.
With a compression level of 5 we see over 90% of the character count replaced with the key facts of the article. Now you can get the facts from 10 articles in the same time it would take to read 1!
Example 3: Summarizing Research Articles
Forefront can tackle summaries in nearly any domain. Let’s use Forefront to summarize a medical research article on pre-surgery beverage consumption.
It’s clear from our examples that Forefront Summarize can summarize all kinds of text and dialogue from many domains. The possibilities with what we can do with Forefront Summarize are virtually limitless. Documentation for the Summarize API can be found here.
Less than two weeks ago, EleutherAI announced their latest open source language model, GPT-NeoX-20B. Today, we’re excited to announce that Forefront is the first platform where you can fine-tune GPT-NeoX, enabling our customers to train the largest open source language model on any natural language processing or understanding task. Start fine-tuning GPT-NeoX for free
The same fine-tuning experience our customers have come to know with GPT-J will be offered for GPT-NeoX including free fine-tuning, JSON Lines and text file support, test prompts, Weights & Biases integration, and control over hyperparameters like epochs and checkpoints. We look forward to seeing all the ways our customers will fine-tune GPT-NeoX models to solve complex NLP problems at scale. Let’s take a closer look at fine-tuning.
What is fine-tuning?
Recent research in Natural Language Processing (NLP) has led to the release of multiple large transformer-based language models (LLMs) like OpenAI’s GPT-[2,3], EleutherAI’s GPT-[Neo, J], and most recently, GPT-NeoX-20B, a 20 billion parameter language model, by EleutherAI. One of the most impactful outcomes of this research has been the finding that the performance of LLMs scales predictably as a power-law with the number of parameters; the downside of scaling parameters being the increased cost to fine-tune and inference. For those not impressed by the leap of tunable parameters now in the tens of billions, the performance that these models can achieve on a variety of tasks after fine-tuning just a few epochs on as little as 100 training examples is where you start to see the value.
Fine-tuning refers to the practice of further training language models on a dataset to achieve better performance on a specific task. This practice can enable a model to outperform one 10x its size on virtually any task. As such, fine-tuned models are the majority of models deployed in production on the Forefront platform and where businesses get the most value.
Until now, one had to choose between GPT-J’s 6 billion parameters and GPT-3 Davinci’s 175 billion parameters. The former model small enough to fine-tune and inference cost efficiently, but not big enough to perform well on complex tasks. The latter model big enough to perform well on complex tasks, but incredibly expensive to fine-tune and inference. Enter GPT-NeoX-20B, and solving many more complex NLP tasks at scale starts to look doable. Let’s look at how GPT-NeoX fine-tuned on various tasks compares to vanilla GPT-NeoX and GPT-3 Davinci.
Text summarization
Summarize text into a few sentences.
Emotion classification
Classify text as an emotion.
Question answering
Answer natural language questions about provided text.
Chat summarization
Summarize dialogue and transcripts.
Content generation
Write a paragraph based on a topic and bullet point.
Question answering with context
Answer natural language questions based on the provided information and scenario.
Chatbot with personality
Imitate Elon Musk in a conversation.
Blog idea generation
Generate blog ideas based on a company name and product description.
Blog Outline
Provide a blog outline based on a topic
How to fine-tune GPT-NeoX on Forefront
The first (and most important) step to fine-tuning a model is to prepare a dataset. A fine-tuning dataset can be in one of two formats on Forefront: JSON Lines or plain text file (UTF-8 encoding). For the purpose of this example, we’ll format our dataset as JSON Lines where each example is a prompt-completion pair. Here are some example dataset formats for the emotion classification, text summarization, question answering, and chat summarization use cases above.
After uploading your dataset, you can set the number of epochs your model will train for. Epochs refer to the number of complete passes through a training dataset, or put another way, how many times a model will “see” each training example in your dataset. A range of 2-4 epochs is typically recommended depending on the size of your dataset.
Next, you’ll set a number of checkpoints. Checkpoints refer to how many model versions will be saved throughout training. Training a model for the optimal amount of time is incredibly important, and checkpoints lets you easily find the optimal time by comparing performance between models at different points during training. Performance is compared by setting test prompts.
Test prompts are a simple method to validate the performance of your model checkpoints. They work by adding prompts and parameters for each model checkpoint to provide completions. After training, you can review the completions from each checkpoint to find the best performing model.
Alternative ways to fine-tune GPT-NeoX
Alternatively, you could fine-tune GPT-NeoX on your own infrastructure. To do this, you'll need at least 8 NVIDIA A100s, A40s, or A6000s and use the NeoX Github repo to preprocess your dataset and run the training script. The script will need to be run with the many degrees of parallelism that EleutherAI's repo supports.
Helpful Tips
Prioritize quality training examples of the chosen task over a large dataset.
Use Weights & Biases. Enter your W&B API key in Settings → General and monitor training progress for your models.
Train for 3-4 epochs on smaller datasets (<1MB). Train for 2-3 epochs on larger datasets (>1MB). Recommendations are provided in-app.
Save 5-10 checkpoints. It’s important to find the optimal training time, and the more checkpoints you have with test prompts to compare performance, the more likely a checkpoint was trained for the optimal amount of time.
Set test prompts that are not included in your dataset and will likely be seen in production.
You can deploy multiple checkpoints and conduct further testing in your Playground.
For more detailed information on fine-tuning and preparing your dataset, refer to our docs.
These tips are meant as loose guidelines and experimentation is encouraged.
At Forefront, we believe building a simple, free experience for fine-tuning will lower the cost of experimentation with large language models enabling businesses to solve a variety of complex NLP problems. If you have any ideas on how we can further improve the fine-tuning experience, please get in touch with our team. Don't have access to the Forefront platform? Get access
GPT-NeoX-20B: An Introduction to the Largest Open Source GPT Model | Forefront
The Forefront Team
A few days ago, EleutherAI announced their latest open source language model, GPT-NeoX-20B. Today, we’re excited to announce that GPT-NeoX is live on the Forefront platform, and the model looks to outperform any previous open source language model on virtually any natural language processing or understanding task. Start using GPT-NeoX
We are bringing the same relentless focus to optimizing cost efficiency, throughput, and response speeds as we have with GPT-J. Today, you can host GPT-NeoX on our flat-rate dedicated GPUs at 2x better cost efficiency than any other platform.
The full model weights for GPT-NeoX will be downloadable for free from February 9, under a permissive Apache 2.0 license from The Eye. Until then, you can use the model on the Forefront platform. We look forward to seeing all the ways our customers use GPT-NeoX to build world-changing applications and solve difficult NLP problems.
Let's take a more technical look at the model.
What is GPT-NeoX?
GPT-NeoX-20B is a transformer model trained using EleutherAI’s fork of Microsoft’s Deepspeed which they have coined “Deeperspeed”. "GPT" is short for generative pre-trained transformer, "NeoX" distinguishes this model from its predecessors, GPT-Neo and GPT-J, and "20B" represents the 20 billion trainable parameters. The approach to train the 20B parameter model includes data, pipeline, and model parallelism (”3d parallelism”) to maximize performance and training speed from a fixed amount of hardware. Transformers have increasingly become the model of choice for NLP problems, replacing recurring neural network (RNN) models such as long short-term memory (LSTM), and GPT-NeoX is the newest and largest open source version of such language models.
The model consists of 44 layers with a model dimension of 6144, and a feedforward dimension of 24576. The model dimension is split into 64 heads, each with a size of 96. Rotary Position Embedding (RoPE) is applied to 24 dimensions of each head. The model is trained with a tokenization vocabulary of roughly 50,000. Unlike previous models, GPT-NeoX uses a tokenizer that was trained on the Pile along with added special tokens like multiple white spaces to make code more efficient.
Training data
GPT-NeoX was trained on the Pile, a large-scale curated dataset created by EleutherAI.
Training procedure
GPT-NeoX was trained as a causal, autoregressive language model for 3 months on 96 NVIDIA A100s interconnected by NVSwitch, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.
Intended Use
GPT-NeoX learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at generating text from a prompt due to the core functionality of GPT-NeoX being to take a string of text and predict the next token. When prompting GPT-NeoX it is important to remember that the statistically most likely next token is often the one that will be provided by the model.
Model comparisons
See how GPT-NeoX compares on task accuracy, factual accuracy, and real-world use cases.
Task accuracy
While Davinci still outperforms due to its 10x larger parameter size, GPT-NeoX holds up well in performance and outpaces other models on most standard NLP benchmarks.
Factual accuracy
The model excels at knowledge-based, factual tasks given the Pile contains a lot of code, scientific papers, and medical papers.
Completion comparisons
The following comparisons between GPT-J and GPT-NeoX use the same prompts and parameters. Completions are provided by the general model weights for each model. Keep in mind that fine-tuning will achieve significantly better performance.
Text to command
Translate text into programmatic commands.
Product description rewriting
Generate a new product description based on a given tone.
Named Entity Recognition
Locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations.
Content generation
Generate structured HTML blog content.
Summarization
Summarize complex text into a few words.
Product review generation
Generate a product review based on a product description.
Code generation
Create code based on text instructions.
Fine-tuning GPT-NeoX
A unique aspect to GPT-NeoX is that it fills a gap between GPT-3 Curie and Davinci, pushing the edge of how large a language model can be while still reasonable to fine-tune without incurring significant training or hosting costs. We’ve seen a majority of our customers get the most value out of fine-tuning GPT-J, and we expect GPT-NeoX to be no different. For this reason, we’re enabling customs to fine-tune GPT-NeoX models for free. Stay tuned for a blog post comparing fine-tuned GPT-NeoX models with the standard GPT-NeoX model.
What’s next
Our team is currently working to release GPT-NeoX fine-tuning within 24-48 hours. You can start using GPT-NeoX here. Please contact our team for specific questions or help related to your use case.
Fine-tuning is a powerful technique to create a new GPT-J model that is specific to your use case. When done correctly, fine-tuning GPT-J can achieve performance that exceeds significantly larger, general models like OpenAI's GPT-3 Davinci.
To fine-tune GPT-J on Forefront, all you need is a set of training examples formatted in a single text file with each example generally consisting of a single input example and its associated output. Fine-tuning can solve a variety of problems, and the optimal way to format your dataset will depend on your specific use case. Below, we'll list the most common use cases for fine-tuning GPT-J, corresponding guidelines, and example text files.
Before diving into the most common use cases, there are a few best practices that should be followed regardless of the specific use case:
Your data must be in a single text file.
You should provide at least one hundred high-quality examples, ideally vetted by a human knowledgeable in the given task.
You should use some kind of separator at the end of each prompt + completion in your dataset to make it clear to the model where each training example begins and ends. A simple separator which works well in almost all cases is " <|endoftext|> ".
Ensure that the prompt + completion doesn't exceed 2048 tokens, including the separator.
Classification
Classification is the process of categorizing text into a group of words. In classification problems, each input in the prompt should be classified into one of your predefined classes.
Choose classes that map to a single token. At inference time, specify the parameter, length=1, since you only need the first token for classification.
Let's say you'd like to organize your customer support messages by topics. You may want to fine-tune GPT-J to filter incoming support so they can be routed appropriately.
The dataset might look like the following:
In the example above, we provided instructions for the model followed by the input containing the customer support message and the output to classify the message to the corresponding category. As a separator we used " <|endoftext|> " which clearly separated the different examples. The advantage of using " <|endoftext|> " as the separator is that the model natively uses it to indicate the end of a completion. It does not need to be set as a stop sequence either because the model automatically stops a completion before outputting " <|endoftext|> ".
Now we can query our model by making a Completion request.
Sentiment Analysis
Sentiment Analysis is the act of identifying and extracting opinions within a given text across blogs, reviews, social media, forums, news, etc. Let's say you'd like to get a degree to which a particular product review is positive or negative.
The dataset might look the following:
Now we can query our model by making a Completion request.
Chatbot
The purpose of a chatbot is to simulate human-like conversations with users via text message or chat. You could fine-tune GPT-J to imitate a specific person or respond in certain ways provided the context of a given conversation to use in a customer support situation. First, let's look at getting GPT-J to imitate Elon Musk.
The dataset might look like the following:
Here we purposefully left out separators to divide specific examples. Instead, you can opt for compiling long-form conversations when attempting to imitate a specific person since we want to capture a wide variety of responses in an open-ended format.
You could query the model by making a Completion request.
Notice that we provide "User:" and "Elon Musk:" as a stop sequence. It's important to anticipate how the model may continue to provide completions beyond the desired output and use stop sequences to stop the model from continuing. Given the pattern of the dataset where a User says something followed by Elon Musk, it makes sense to use "User:" and "Elon Musk:" as the stop sequence.
A similar but different chatbot use case would be that of a customer support bot. Here, we'll go back to providing specific examples with separators so the model can identify how to respond in different situations. Depending on your customer support needs, this use case could require a few thousand examples, as it will likely deal with different types of requests and customer issues.
The dataset might look like the following:
An optional improvement that could be made to the above dataset would be to provide more context and exchanges leading up to the resolution for each example. However, this depends on the role you're hoping to fill with your customer support chatbot.
Now we can query our model by making a Completion request.
As with the previous example, we're using "Customer:" and "#####" as stop sequences so the model stops after providing the relevant completion.
Entity Extraction
The main purpose of entity extraction is to extract information from given text to understand the subject, theme, or other pieces of information like names, places, etc. Let's say, you'd like to extract names from provided text.
The dataset might look like the following:
Now we can query our model by making a Completion request.
Idea Generation
A common use case is to use GPT-J to generate ideas provided specific information. Whether it's copy for ads or websites, blog ideas, or products, generating ideas is a useful task for GPT-J. Let's look at the aforementioned use case of generating blog ideas.
The dataset might look like the following:
Now we can query our model by making a Completion request.
Following the above example on preparing a dataset for each use case should lead to well-performing fine-tuned GPT-J models when having a sufficient amount of examples (>100MB dataset). For datasets less than 100MB, it is recommended to also provide explicit instructions with each example like the following dataset for blog idea generation.
Generative Pre-trained Transformer (GPT) models, the likes of which GPT-J and GPT-3 belong to, have taken the NLP community by storm. These powerful language models excel at performing various NLP tasks like question-answering, entity extraction, categorization, and summarization without any supervised training. They require very few to no examples to understand a given task and outperform state-of-the-art models trained in a supervised fashion.
GPT-J is a 6-billion parameter transformer-based language model released by a group of AI researchers called EleutherAI in June 2021. The goal of the group since forming in July of 2020 is to open-source a family of models designed to replicate those developed by OpenAI. Their current focus is on the replication of the 175-billion parameter language model, GPT-3. But don’t let the difference in parameter size fool you. GPT-J outperforms GPT-3 in code generation tasks and, through fine-tuning, can outperform GPT-3 on a number of common natural language processing (NLP) tasks. The purpose of this article will be to outline an array of use cases that GPT-J can be applied to and excel at. For information on how to fine-tune GPT-J for any of the use cases below, check out our fine-tuning tutorial.
Use cases
Code generation
The most natural use case for GPT-J is generating code. GPT-J was trained on a dataset called the Pile, an 835GB collection of 22 smaller datasets—including academic sources (e.g., Arxiv, PubMed), communities (StackExchange, Wikipedia), code repositories (Github), and more. The addition of Github into the data has led to GPT-J outperforming GPT-3 on a variety of code generating tasks. While “vanilla” GPT-J is proficient at this task, it becomes even more capable when one fine-tunes the model on any given programming language.
To get started fine-tuning GPT-J for code generation, check out Hugging Face’s CodeSearchNet containing 2 million comment/code pairs from open-source libraries hosted on GitHub for Go, Java, Javascript, PHP, Python, and Ruby.
Input:
Output:
Chat bot
An increasingly common NLP use case is to build a chatbot. A chatbot is software that simulates human-like conversations with users via text message or chat. With its main commercial use case to help users by providing answers to their questions, chatbots are commonly used in a variety of customer support scenarios. However, chatbots can also be used to imitate specific people like Kanye West.
Regardless of your reason for using a chatbot, it is recommended to fine-tune GPT-J by providing transcripts of the specific task. For instance, let’s say you want a custom chatbot to assist with customer support requests. A simple method to curate a fine-tuning dataset would be to record transcripts of typical customer support exchanges between your team and customers. Somewhere in the order of one hundred or so examples would be enough for GPT-J to become proficient at your company’s specific customer support tasks.
Story writing
Story writing is simply a work of fiction that is written in easily understandable grammatical structure with a natural flow of speech.
Story writing with GPT-J becomes interesting as one could fine-tune to a particular author’s writing style or book series. Imagine having a Stephen King writing bot or a bot that could help generate books 6 and 7 to Game of Thrones because, let’s be honest, George R.R. Martin is dragging his feet at this point.
Here’s an example of the begin ning to a fictitious piece written by GPT-J-6B:
Entity extraction
The main purpose of entity extraction is to extract information from given text to understand the subject, theme, or other pieces of information like names, places, etc. Some interesting use cases for entity extraction include:
Financial market analysis: Extract key figures from financial news articles or documents to use as signals for trading algorithms or market intelligence
Email inbox optimization: Notify users of flight times, meeting locations, and credit card charges without having to open emails
Content recommendation: Extract information from articles and media to recommend content based on entity similarity and user preferences
GPT-J shines new light on entity extraction, providing a model that is adaptive to both general text and specialized documents through few-shot learning.
Summarization
Summarization is the process of summarizing information in given text for quicker consumption without losing its original meaning. GPT-J is quite proficient out-of-the-box at summarization. What follows is an example of taking a snippet of the Wikipedia article for Earth and tasking GPT-J to provide a short summary.
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Output:
Paraphrasing
Not to be confused with summarization, paraphrasing is the process of rewriting a passage without changing the meaning of the original text. Where summarization attempts to condense information, paraphrasing rewords the given information. While GPT-J is capable of summarization out-of-the-box, paraphrasing with GPT-J is best achieved through fine-tuning. Here is an example of paraphrasing a shorter snippet from the same Earth Wikipedia article in the previous summarization example after training on hand-written paraphrasing examples.
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Output:
Copywriting
A widely used commercial use case for GPT-J and other transformer-based language models is copywriting for websites, ads, and general marketing. Copywriting is a crucial marketing process to increase website, ad, and other conversion rates. Through fine-tuning GPT-J on a given company’s voice or previously successful ad campaigns, GPT-J can automatically provide effective copy at a fraction of the cost of hiring a copywriter.
Input:
Output:
Classification
Text classification is the process of categorizing text into organized groups. Unstructured text is everywhere, such as emails, text conversations, websites, and social media, and the first step in extracting value from this data is to categorize it into organized groups. This is another use case where fine-tuning GPT-J will lead to the best performance. By providing one hundred examples or more of your given classification task, GPT-J can perform as good or better than the largest language models available like OpenAI’s GPT-3 Davinci.
Sentiment Analysis
Sentiment analysis is the act of identifying and extracting opinions within given text like blogs, reviews, social media, forums, new, etc. Perhaps you’d like to automatically analyze thousands of reviews about your products to discover if customers are happy about your pricing plans or gauge brand sentiment on social media in real-time so you can detect disgruntled customers and immediately respond, the applications of sentiment analysis are endless and applicable to any type of business.
Given the infancy of large transformer-based language models, further experimentation will inevitably lead to more use cases that these models prove to be effective at. As you may have noticed, a number of the use cases are the result of fine-tuning GPT-J. At Forefront, we believe the discovery of more use cases will not only come from increased usage of these models, but by providing a simple experience to fine-tune that allows for easy experimentation and quick feedback loops. For a tutorial on easily fine-tuning GPT-J on Forefront, check out our recent tutorial.
Recent research in Natural Language Processing (NLP) has led to the release of multiple large transformer-based language models like OpenAI’s GPT-[2,3], EleutherAI’s GPT-[Neo, J], and Google’s T5. For those not impressed by the leap of tunable parameters in the billions, the ease with which these models could perform on a never before seen task without training a single epoch is something to behold. While it has become evident that the more parameters a model has the better it will generally perform, an exception to this rule applies when one explores fine-tuning. Fine-tuning refers to the practice of further training transformer-based language models on a dataset for a specific task. This practice has led to the 6 billion parameter GPT-J outperforming the 175 billion GPT-3 Davinci on a number of specific tasks. As such, fine-tuning will continue to be the modus operandi when using language models in practice, and, consequently, fine-tuning is the main focus of this post. Specifically, how to fine-tune the open-source GPT-J-6B.
Curate a dataset
The first step in fine-tuning GPT-J is to curate a dataset for your specific task. The specific task for this tutorial will be to imitate Elon Musk. To accomplish this, we compiled podcast transcripts of Elon’s appearances on the Joe Rogan Experience and Lex Fridman Podcast into a single text file. Here’s the text file for reference. Note that the size of the file is only 150kb. When curating a dataset for fine-tuning, the main focus should be to encapsulate an evenly-distributed sample of the given task instead of prioritizing raw size of the data. In our case, these podcast appearances of Elon were great as they encompass multiple hours of him speaking on a variety of different topics.
Believe it or not, once you have your dataset, the hard part is done since Forefront abstracts all of the actual fine-tuning complexity away. Let’s go over the remaining steps to train your fine-tuned model.
Create deployment
Once logged in, click “New deployment”.
Select Fine-tuned GPT-J
From here, we’ll add a name and optional description for the deployment then select "Fine-tuned GPT-J".
Upload dataset
Then, we’ll upload our dataset in the form of a single text file. Again, if the dataset is 100MB or greater, get in touch with our team.
Set training duration
A good rule of thumb for smaller datasets is to train 5-10 minutes every 100kb. For text files in the order of megabytes, you’ll want to train 45-60 minutes for every 10MB.
Set number of checkpoints
A checkpoint is a saved model version that you can deploy. You’ll want to set a number of checkpoints that evenly divides the training duration.
Add test prompts
Test prompts are prompts that every checkpoint will automatically provide completions for so you can compare the performance of the different models. Test prompts should be pieces of text that are not found in your training text file. This allows you to see how good the model is at understanding your topic and prevents the model from regurgitating information it has seen in your training set.
You can also customize model parameters for your specific task.
Once your test prompts are set, you can press 'Fine-tune' and your fine-tuned model will begin training. You may notice the estimated completion time is longer than your specified training time. This is because it takes time to load the base weights prior to training.
View test prompts
As checkpoints being to appear, you can press 'View test prompts' to start comparing performance between your different checkpoints.
Deploy to Playground and integrate in application
Now for the fun part: deploying your best-performing checkpoint(s) for further testing in the Playground or integration into your app.
Using Forefront isn’t the only way to fine-tune GPT-J. For a tutorial on fine-tuning GPT-J by yourself, check out Eleuther’s guide. However, it’s important to note that not only do you save time by fine-tuning on Forefront, but it’s absolutely free—saving you $8 per hour of training. Also, when you go to deploy your fine-tuned model you save up to 33% on inference costs with increased throughput by deploying on Forefront.
Helpful Tips
Prioritize quality samples of the given task over a large dataset when curating your dataset.
Train 5-10 minutes per 100kb or 45-60 minutes per 10MB of your dataset.
Save a number of checkpoints that evenly divides the number of minutes your training. Saving more than 10-15 checkpoints returns diminishing value and makes assessing quality difficult.
Set test prompts that are not included in your dataset.
You can deploy multiple checkpoints and conduct further testing in our Playground. Deployed checkpoints are pro-rated according to time deployed.
For more detailed information on fine-tuning and preparing your dataset, refer to our docs.
These tips are meant as loose guidelines and experimentation is encouraged.
At Forefront, we believe building a simple experience for fine-tuning can increase experimentation with quicker feedback loops so companies and individuals can apply language models to a myriad problems. If you have any ideas on how we can further improve the fine-tuning experience, please get in touch with our team.
More than one year has passed since the public release of OpenAI's API for GPT-3. Since then, thousands of developers and hundreds of companies have started building on the platform to apply the transformer-based language model to a variety of NLP problems.
In its wake, EleutherAI, a team of AI researchers open-sourcing their work, released their first implementation of a GPT-like system, the 2.7B parameter GPT-Neo, and most recently, the 6B parameter GPT-J. Before getting into GPT-J deployments, let's understand why a company or developer would use GPT-J in the first place.
So why would one prefer to use the open-source 6B parameter GPT-J over the 175B parameter GPT-3 Davinci? The answer comes down to cost and performance.
First, let's talk about cost. With GPT-3, you pay per 1000 tokens. For the unacquainted, you can think of tokens as pieces of words, where 1000 tokens are about 750 words. So with GPT-3, your costs scale directly with usage. On the other end, the open-sourced GPT-J can be deployed to cloud infrastructure enabling you to effectively get unlimited usage while only incurring the cost of the cloud hardware hosting the model.
Now let's talk about performance. "Bigger is better" has become an adage for a reason, and transformer-based language models are no exception. While a 100B parameter transformer model will always generally outperform a 10B parameter one, the keyword is generally. Unless you're trying to solve general artificial intelligence, you probably have a specific use case in mind. This is where fine-tuning GPT-J, or specializing the model on a dataset for a specific task, can lead to better performance than GPT-3 Davinci.
Now that we've discussed why one would use GPT-J over GPT-3 to lower costs at scale and achieve better performance on specific tasks, we'll discuss how to deploy GPT-J.
How to deploy GPT-J on Forefront
For this tutorial, we'll be deploying the standard GPT-J-6B.
Create deployment
Once logged in, you can click "New deployment".
Select Vanilla GPT-J
From here, add a name and optional description for your deployment then select "Vanilla GPT-J".
Press "Deploy"
Navigate to your newly created deployment, and press "Deploy" to deploy your Vanilla GPT-J model.
Replica count
From your deployment, you can control the replica count for your deployments as usage increases to maintain fast response speeds at scale.
Inferencing
To begin inferencing, copy the URL under the name and refer to our docs on a full set of instructions for passing requests and receiving responses.
You can expect all the parameters you'd typically use with GPT-3 like response length, temperature, top P, top K, repetition penalty, and stop sequences.
Playground
You can also navigate to Playground to experiment with your new GPT-J deployment without needing to use Postman or implement any code.
Deploying GPT-J on Forefront takes only a few minutes. On top of the simplicity we bring to the deployment process, we've made several low-level machine code optimizations enabling your models to run at a fraction of the cost compared to deploying on Google's TPU v2 with no loss in throughput. Start using GPT-J
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