# Pico-OpenLAiNN-100M-GGUF 🤗

Hey there fellow researchers, developers, and AI enthusiasts! Today I'm releasing the full version of Pico-OpenLAiNN-100M, I had previously released a version before this that was trained on only 8B tokens. This LLM was trained on the full 32B tokens that the entire Open-PicoLAiNN family is trained on.

These are the GGUF quants of the models. For the original models, you can find them [here](https://huggingface.co/UUFO-Aigis/Pico-OpenLAiNN-100M).
## Models Overview

- **Pico-OpenLAiNN-100**: The smallest of the bunch, this 100M parameter model is perfect for quick experiments and applications where computational resources are *extremely* limited.
- **Pico-OpenLAiNN-250**: This is the middle child of the PicoLAiNN family, it's still tiny at 250M parameters but is more capable than the 100M parameter model.
- **Pico-OpenLAiNN-500**: My current "Heavyweight" Model, this model has 500M parameters and is the most capable of the Pico-OpenLAiNN models.
## Pretraining Details

This specific version of Pico LAiNN was trained on just 32B tokens of the fineweb dataset.

## 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.

# Benchy :3

|    Tasks     | Value |   |Stderr|
|--------------|------:|---|-----:|
|arc_challenge | 0.1826|±  |0.0113|
|arc_easy      | 0.4007|±  |0.0101|
|boolq         | 0.6012|±  |0.0086|
|hellaswag     | 0.2936|±  |0.0045|
|lambada_openai| 0.2701|±  |0.0062|
|piqa          | 0.6338|±  |0.0112|
|winogrande    | 0.5099|±  |0.0140|

## Future Plans

- **More Models**: I'm currenetly training the bigger siblings of this models, 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, and will release if I deem it to be actually useful, so stay tuned, this will likely be named FLaRE-LAiNN.
- **Paper**: A detailed paper made available for those interested in the details. 

## 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

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