# Planck-OpenLAiNN-25M 🤗

Hey there fellow researchers, developers, and AI enthusiasts! Today I'm releasing a new family of Models, Planck LAiNN, These are probably some of the smallest LLMs that are on HF. They aren't super useful but it was a fun expierment!~

These are the GGUF quants of the models. For the original models, you can find them [here](https://huggingface.co/UUFO-Aigis/Planck-OpenLAiNN-25M-gguf).
## Models Overview
- **Panck-OpenLAiNN-10M**: A Truely Tiny model with just 10 Million parameters, this is probably boarderline useless, but it *IS* functional.
- **Panck-OpenLAiNN-25M**: The second smallest model, 25 million parameters, it's not that much better.
- **Panck-OpenLAiNN-50M**: Surprisingly smart, it's 50 Million parameters and could potentially maybe, Possibly even be useful ;)
- **Panck-OpenLAiNN-75M**: The current *""heavy""* weight of the Plank-OpenLAiNN Models.
## Pretraining Details

Plank-OpenLAiNN was trained on 32B tokens of the Fineweb dataset, it's the same one that was used for the Pico-LAiNN family of models. The model was pretrained with a context length of 1024 tokens.

## Other information:

- **Compatibility**: Built to be compatible with existing projects that use LLAMA 2's tokenizer and architecture.
- **Ease of Use**: No need to reinvent the wheel. These models are ready to be plugged into your applications.
- **Open Source**: Fully open source, so you can tweak, tune, and twist them to your heart's content.
## Getting Started

To start using these models, you can simply load them via the Hugging Face `transformers` library:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


MODEL_NAME = "UUFO-Aigis/Panck-OpenLAiNN-25M"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
    inputs = tokenizer.encode(prompt, return_tensors="pt")

    outputs = model.generate(
        inputs,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        do_sample=True
    )


    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

def main():
    # Define your prompt
    prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."

    generated_text = generate_text(prompt, model, tokenizer)

    print(generated_text)

if __name__ == "__main__":
    main()
```
# Benchy

|    Tasks     | Value |   |Stderr|
|--------------|------:|---|-----:|
|arc_challenge | 0.1817|±  |0.0113|
|arc_easy      | 0.3291|±  |0.0096|
|boolq         | 0.6138|±  |0.0085|
|hellaswag     | 0.2700|±  |0.0044|
|lambada_openai| 0.1104|±  |0.0044|
|piqa          | 0.5740|±  |0.0115|
|winogrande    | 0.5170|±  |0.0140|

## Future Plans

- **More Models**: I'm currenetly training the bigger siblings of Pico-OpenLAiNN, including a 1B parameter version and beyond. 2-4 Billion parameter versions are planned. These will be Released as OpenLAiNN.
- **New architecture**: This is still up in the air and I'm still developing it, things are going well and I'll post updates.
- **Paper**: A detailed paper or training data will be posted at some point. 

## Credit Where Credit's Due

If you find these models useful and decide to use these models, a link to this repository would be highly appreciated. I am a one man show running this, Thanks 🤗
## Contact
If you have questions, Please reach out to me at urlsys32dll@gmail.com

<p align="center">
  <img src="UUFO.png" alt="U.U.F.O Research Logo" width="250"/>
</p>