dolly-v2-3b

Maintainer: databricks

Total Score

281

Last updated 5/23/2024

🛸

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The dolly-v2-3b is a 2.8 billion parameter causal language model created by Databricks, a leading cloud data and AI company. It is derived from EleutherAI's pythia-2.8b model and fine-tuned on a ~15K record instruction corpus generated by Databricks employees. This makes dolly-v2-3b an instruction-following model, trained to perform a variety of tasks like brainstorming, classification, QA, generation, and summarization.

While dolly-v2-3b is not a state-of-the-art model, it exhibits surprisingly high-quality instruction following behavior compared to its foundation model. Databricks has also released larger versions of the Dolly model, including dolly-v2-12b and dolly-v2-7b, which leverage larger pretrained models from EleutherAI.

Model inputs and outputs

Inputs

  • Instruction: The model takes a natural language instruction as input, which can cover a wide range of tasks like question answering, text generation, language understanding, and more.

Outputs

  • Generated text: The model generates text in response to the given instruction. The output length and quality will depend on the complexity of the instruction and the model's capabilities.

Capabilities

The dolly-v2-3b model demonstrates strong instruction following behavior across a variety of domains, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. For example, it can generate coherent and relevant responses to prompts like "Write a press release announcing a new underwater research facility" or "Classify these sentences as positive, negative, or neutral in sentiment."

What can I use it for?

The dolly-v2-3b model can be a valuable tool for developers and researchers working on natural language processing applications that require instruction following or generation capabilities. Some potential use cases include:

  • Chatbots and virtual assistants: The model's ability to understand and respond to natural language instructions can be leveraged to build more engaging and capable conversational AI systems.

  • Content generation: dolly-v2-3b can be used to generate a wide range of text-based content, from creative writing to technical documentation, based on high-level instructions.

  • Task automation: The model can be used to automate various text-based tasks, like research, summarization, and data extraction, by translating high-level instructions into concrete actions.

Things to try

One key capability of dolly-v2-3b is its ability to follow complex instructions and generate coherent responses, even for tasks that may be outside the scope of its training data. For example, you can try providing the model with instructions that require reasoning, such as "Explain the difference between nuclear fission and fusion in a way that a 10-year-old would understand." The model's ability to break down technical concepts and explain them clearly is an impressive feature.

Another interesting aspect to explore is the model's performance on open-ended tasks, where the instruction leaves room for creative interpretation. For instance, you could try prompting the model with "Write a short story about a robot who discovers their true purpose" and see how it generates an engaging narrative.



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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