RedPajama-INCITE-Chat-3B-v1
Maintainer: togethercomputer
144
⛏️
| 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
The RedPajama-INCITE-Chat-3B-v1 is a 2.8B parameter language model developed by Together and leaders from the open-source AI community. It is fine-tuned on datasets like OASST1 and Dolly2 to enhance its chatting ability. The model is part of the RedPajama-INCITE series, which includes the base model [object Object] and an instruction-tuned version [object Object]. The chat version, RedPajama-INCITE-Chat-3B-v1, is designed to excel at dialog-style interactions.
Model inputs and outputs
The RedPajama-INCITE-Chat-3B-v1 model takes in text prompts in a conversational format, where the human message is prefixed with <human>: and the model's response is prefixed with <bot>:. The model outputs text continuations that continue the dialog.
Inputs
- Text prompts in a conversational format, with the human message prefixed by
<human>:and the model's response prefixed by<bot>:.
Outputs
- Continuation of the dialog, output as text.
Capabilities
The RedPajama-INCITE-Chat-3B-v1 model excels at several tasks out of the box, including:
- Summarization and question answering within context
- Extraction
- Classification
The model also performs well on few-shot prompts, with improved performance on classification and extraction tasks compared to the base model.
What can I use it for?
The RedPajama-INCITE-Chat-3B-v1 model is intended for research purposes, such as safe deployment of models with the potential to generate harmful content, probing and understanding the limitations and biases of dialogue models, and use in educational or creative tools. The maintainer, togethercomputer, provides the model under an Apache 2.0 license.
Things to try
One interesting thing to try with the RedPajama-INCITE-Chat-3B-v1 model is exploring its few-shot capabilities. The model performs better on classification and extraction tasks when provided with a few examples in the prompt, compared to the base model. This suggests the model has learned to effectively leverage in-context information, which could be useful for a variety of applications.
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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