GPT-JT-6B-v1
Maintainer: togethercomputer
301
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
GPT-JT-6B-v1 is a language model developed by togethercomputer. It is a fork of EleutherAI's GPT-J (6B) model that has been fine-tuned using a new decentralized training algorithm. The resulting model outperforms many 100B+ parameter models on classification benchmarks.
GPT-JT-6B-v1 was trained on a large collection of diverse data, including Chain-of-Thought (CoT), the Public Pool of Prompts (P3) dataset, and the Natural-Instructions (NI) dataset. The model also uses the UL2 training objective, which allows the model to see bidirectional context of the prompt.
Model inputs and outputs
Inputs
- Text prompts of varying lengths
Outputs
- Continued text output based on the input prompt
Capabilities
GPT-JT-6B-v1 has shown strong performance on a variety of classification benchmarks compared to larger 100B+ parameter models. The model is particularly adept at tasks that require reasoning and understanding of context, such as question answering and natural language inference.
What can I use it for?
GPT-JT-6B-v1 can be a powerful tool for a variety of text-based applications, such as:
- Content generation: The model can be used to generate coherent and contextually relevant text, such as stories, articles, or dialogue.
- Question answering: The model can be used to answer questions by drawing upon its broad knowledge base and understanding of language.
- Text classification: The model can be used to classify text into different categories, such as sentiment, topic, or intent.
Things to try
One interesting aspect of GPT-JT-6B-v1 is its use of the UL2 training objective, which allows the model to see bidirectional context of the prompt. This can be particularly useful for tasks that require a deep understanding of the input text, such as summarization or natural language inference. Try experimenting with prompts that require the model to reason about the relationships between different parts of the input text.
Another interesting avenue to explore is the model's performance on few-shot learning tasks. The description mentions that the model performs well on few-shot prompts for both classification and extraction tasks. Try designing a few-shot learning experiment and see how the model performs.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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