---
library_name: transformers
model_name: multiscreen_psi16_768
license: apache-2.0
datasets:
- roneneldan/TinyStories
tags:
- generated_from_trainer
- sft
- trl
- multiscreen
- tiny-stories
- small-language-model
- experimental
- research
- arxiv:2604.01178
- arxiv:2305.07759
---

# Model Card for multiscreen_psi16_768

This model is an **unofficial** experimental pre-traind model of multiscreen with [	TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) datasets.
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import AutoTokenizer, AutoModelForCausalLM


model_id = "kurogane/tinystorys_multiscreen_vocab768"
cache_dir = r"/media/kurogane/backup/cache"

model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True,
    cache_dir=cache_dir,
    )
model.to("cuda:0")

tokenizer = AutoTokenizer.from_pretrained(
    model_id, 
    padding_side="left", 
    cache_dir=cache_dir,
    )

model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs)

s_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(s_output)
```

### result example
> A list of colors: red, blue, yellow, green, orange. All the people

## Training procedure

 


This model was trained with SFT.

### Framework versions

- TRL: 0.24.0
- Transformers: 5.8.0
- Pytorch: 2.11.0+cu129
- Datasets: 4.3.0
- Tokenizers: 0.22.2
  
## Used archtechture
This model is an experimental tiny language model trained on TinyStories using a Multiscreen-style architecture inspired by the paper *Screening Is Enough* by Ken M. Nakanishi.
This model implementation was developed as an experimental Hugging Face Transformers port, with reference to the unofficial PyTorch implementation `dieOD/multiscreen-pytorch`. This model is not an official implementation released by the author of the Multiscreen paper.
- Multiscreen paper: https://arxiv.org/abs/2604.01178
- Reference implementation: https://github.com/dieOD/multiscreen-pytorch

## Used dataset
The training data is based on the TinyStories dataset by Ronen Eldan and Yuanzhi Li.
- TinyStories paper: https://arxiv.org/abs/2305.07759
- TinyStories dataset: https://huggingface.co/datasets/roneneldan/TinyStories