---
license: openrail
language:
- en
datasets:
- HuggingFaceFW/fineweb
---
# Model Card for neoncortex/Haikusian

## Model Details

### Model Description

**This model and associated dataset are a WIP.**

**Comments and contributions are welcome.**

Haikusian is a language model specialized in generating haiku poetry, trained using a custom syllable-based tokenizer and a unique dataset of haikus. The model leverages recent advances in natural language generation to produce creative and structurally valid haiku poems while adhering to the traditional 5-7-5 syllable pattern across three lines.

Developed by RoboApocalypse (Neon Cortex), Haikusian represents an exploration into the artistic capabilities of AI systems. By combining computational linguistics techniques with neural networks, this model aims to generate poetic and culturally enriching content, pushing the boundaries of what AI can achieve in the creative domain.

Key features of Haikusian include:

- Custom syllable tokenizer: A novel tokenization approach that splits text into syllables, allowing the model to learn and generate haikus at the syllable level.
- Haiku-specific training data: A meticulously curated dataset of haiku poems from various sources, ensuring the model captures the nuances and structure of this poetic form.
- Reinforced with synthetic data: Further fine-tuned on a synthetically generated dataset of haikus, enhancing the model's ability to produce coherent and thematically consistent outputs.
- Syllable pattern enforcement: Built-in mechanisms to ensure generated haikus adhere to the 5-7-5 syllable pattern across three lines, a defining characteristic of traditional haiku poetry.
- Open-source and transparent: Released under the OpenRAIL license, promoting open science, responsible development, and potential commercial applications of this creative AI technology.

Whether for artistic exploration, educational purposes, or simply appreciating the intersection of technology and culture, Haikusian offers a unique perspective on the capabilities of language models in the realm of poetry generation.

- **Developed by:** Neon Cortex
- **Funded by:** Neon Cortex
- **Shared by:** RoboApocalypse
- **Model type:** TBA
- **Language(s):** English (en)
- **License:** OpenRAIL

### Model Sources

- **Repository:** https://huggingface.co/RoboApocalypse/Haikusian
- **Paper:** Maybe?
- **Demo:** Eventually

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### 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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### 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 Dataset Card if possible. -->

[More Information Needed]

#### Factors

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

[More Information Needed]

#### Metrics

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

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## 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:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## 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:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

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

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]