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Planck-OpenLAiNN-25M-gguf
UUFO-Aigis/Planck-OpenLAiNN-25M-gguf
Planck-OpenLAiNN-25M-gguf is a machine learning model from UUFO-Aigis. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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…
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At a glance
- Access
- Public
- Created
- Aug 20, 2024
- Updated
- Jul 15, 2026
- SHA
- d9e8cf37
Model card
From the Hugging Face model README
Planck-OpenLAiNN-25M-GGUF 🤗
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.
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.
Benchy :3
| 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 and I'm doing this for free, Thanks 🤗
- Created
- Aug 20, 2024
- Updated
- Jul 15, 2026