mpt-7b-instruct
Maintainer: mosaicml
461
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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
mpt-7b-instruct is a model for short-form instruction following. It was built by finetuning MPT-7B on a dataset derived from the Databricks Dolly-15k and the Anthropic Helpful and Harmless (HH-RLHF) datasets. This model was trained by MosaicML.
Model Inputs and Outputs
This is a text-to-text model, taking in natural language text and generating new text in response. The model can handle a wide range of input prompts and produce diverse outputs, from succinct factual answers to engaging stories.
Inputs
- Natural language text prompts, which can include instructions, questions, or open-ended requests
Outputs
- Generated text relevant to the input prompt
- Outputs can range from short factual responses to longer narrative pieces
Capabilities
mpt-7b-instruct demonstrates strong performance on a variety of language tasks, including question answering, summarization, and open-ended generation. For example, when given the prompt "What is a quoll?", the model provides a detailed explanation of this Australian marsupial. The model can also generate creative stories and engage in open-ended dialogue when prompted.
What Can I Use It For?
The mpt-7b-instruct model could be useful for a variety of applications that require natural language processing, such as:
- Building chatbots or virtual assistants that can understand and respond to user instructions
- Automating content creation tasks like writing summaries, articles, or creative fiction
- Enhancing search engines or question-answering systems with more natural language understanding
Things to Try
One interesting aspect of the mpt-7b-instruct model is its ability to handle very long input sequences, thanks to the use of ALiBi. You could try providing the model with long passages of text, such as entire books or lengthy articles, and see how it responds to open-ended prompts or generates continuations. The model's capacity for handling long-form content makes it a compelling tool for tasks like story generation or summarization.
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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DeciLM-7B-instruct is a 7 billion parameter language model developed by Deci that has been fine-tuned for short-form instruction following. It is built by LoRA fine-tuning on the SlimOrca dataset. The model leverages an optimized transformer decoder architecture with variable Grouped-Query Attention to achieve strong performance and efficiency. Compared to similar models like DeciLM-6B-instruct and DeciLM-7B, DeciLM-7B-instruct offers enhanced instruction-following capabilities while retaining the speed and accuracy of its base model. Model inputs and outputs DeciLM-7B-instruct is a text generation model that takes prompts as input and generates relevant text outputs. It can be used for a variety of natural language tasks, including question answering, summarization, and open-ended conversation. Inputs Prompts**: Free-form text that the model uses as a starting point to generate relevant output. Outputs Generated text**: The model's response to the input prompt, which can range from a single sentence to multiple paragraphs depending on the task. Capabilities DeciLM-7B-instruct is highly capable at understanding and following instructions provided in natural language. It can break down complex tasks into step-by-step instructions, provide detailed explanations, and generate relevant text outputs. The model's strong performance and efficiency make it a compelling choice for a wide range of applications, from customer service chatbots to task-oriented virtual assistants. What can I use it for? DeciLM-7B-instruct is well-suited for commercial and research use cases that require a language model with strong instruction-following capabilities. Some potential applications include: Customer service**: The model can be used to power chatbots that can provide detailed, step-by-step instructions to assist customers with product usage, troubleshooting, and other queries. Virtual assistants**: By leveraging the model's ability to understand and follow instructions, virtual assistants can be developed to help users with a variety of tasks, from scheduling appointments to providing cooking instructions. Content generation**: The model can be used to generate high-quality, relevant content for websites, blogs, and other digital platforms, with the ability to follow specific instructions or guidelines. Things to try One interesting aspect of DeciLM-7B-instruct is its ability to break down complex tasks into clear, step-by-step instructions. Try providing the model with prompts that involve multi-step processes, such as "How do I bake a cake?" or "Walk me through the process of changing a tire." Observe how the model responds, noting the level of detail and the clarity of the instructions provided. Another interesting experiment would be to explore the model's ability to follow instructions that involve creative or open-ended tasks, such as "Write a short story about a talking giraffe" or "Design a poster for a new music festival." This can help demonstrate the model's flexibility and its capacity for generating diverse and engaging content.
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