---
license: wtfpl
language:
- en
pipeline_tag: text-generation
tags:
- llama
- w++
- meme++
- tiny
---

# Model Card for Model ID

Meme++ generator.

## Model Details

### Model Description

This is a tiny LLaMA model trained from scratch for 31000 steps (253952000 tokens) out of `i forgor :skull:`.

- **Developed by:** mrsteyk
- **Model type:** LLaMA
- **Language(s) (NLP):** English
- **License:** WTFPL

### Model Sources [optional]

- **Repository:** maybe someday

## Uses

This was intended for Meme++ character chard generation, trained a small demo.

### Direct Use

Random Meme++ card generation.

### Out-of-Scope Use

CSAM related stuff.

## Bias, Risks, and Limitations

This model was trained on a randomly scraped DataSet, I tried filtering as much as I could automatically, it might still try to generate kids because people are fucking weirdos.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

Meme++ character definition taken off the internet.

### Training Procedure 

This was trained using `lit-llama` based model code and `pytorch-lightning` CLI based trainer code.


#### Training Hyperparameters

- **Training regime:** fp32
- **Optimizer and LR:** DeepSpeed FusedAdamW with 1e-5

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

[More Information Needed]

#### Summary

[W&B run](https://wandb.ai/mrsteyk/memepp-llama/runs/44e3aut4)

## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** 1050 Ti Mobile
- **Hours used:** ~6
- **Cloud Provider:** Local Machine(C)(TM)
- **Compute Region:** RU
- **Carbon Emitted:** ~~450kg~~

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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