---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
- qa
- open data
- opt
metrics:
- accuracy
widget:
- text: "Certainly! Consider a user interface for a complex software application. If the user encounters a lengthy list of commands or prompts, it could lead to confusion and frustration. By limiting the number of tokens, the model can provide concise instructions that are easy to understand, leading to a smoother user experience.\n\n# [UNDERSTANDING]\n"
datasets:
- ccore/open_data_understanding
pipeline_tag: text-generation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# OPT_350_open_data_understanding

## Description

This model has been trained to understand and respond to any content inserted after the `[PAPER]` tag. It uses advanced language modeling techniques to understand the context, structure, and underlying goals of the input text.

## How to use

To interact with this template, place your text after the `[PAPER]` tag. The model will process the text and respond accordingly. For example:

[PAPER]
Your text here...


## Example

[PAPER]
We present a scalable method to build a high-quality instruction-following language model...


The model will understand and respond to your text according to its context and content.

## Comprehension Sections

### [UNDERSTANDING]
This section provides a detailed analysis and decomposition of the inserted text, facilitating the understanding of the content.

### [QUESTIONS AND ANSWERS]
This section addresses questions and answers that could arise based on the text provided.

### [OBJECTION AND REPLY]
This section addresses any objections and responses that could arise from analysis of the text.

## Common questions

- **What can this model do?**
   - This model can understand and respond to any text placed after the `[PAPER]` tag.

- **Is a specific format necessary?**
   - No, the model is quite flexible regarding the text format.

- **How does this model perform?**
   - The model outperforms other LLaMa-based models on the Alpaca leaderboard, demonstrating a highly effective alignment.

## Warnings

- This model was trained on a diverse corpus, but may still have bias or limitations.
- Continuous validation of the model and its output is essential.

## Contact and Support

For more information, visit [Hugging Face](https://huggingface.co/).