README.md

---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
- qa
- open data
- opt
metrics:
- accuracy
model-index:
- name: mini3
  results: []
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/).