Tiny CED

Tiny CED is a small custom PyTorch encoder-decoder language model trained from scratch on the English TinyStories dataset. This package contains inference-only weights in Safetensors format.

This is not a Transformers PreTrainedModel. Load it with the included model.py and safetensors.torch.load_model as shown below.

Model details

The token embedding and output head are tied. model.safetensors was exported with safetensors.torch.save_model so the shared tensor is stored only once.

Usage

Install the runtime dependencies:

pip install -r requirements.txt

Run generation on CPU:

python generate.py \
  --device cpu \
  --text "Once upon a time, a little rabbit" \
  --max-new-tokens 100

Use --device cuda when CUDA is available. Set --temperature 0 for greedy decoding.

Training and evaluation

The exported weights come from the existing best.pt checkpoint; no retraining was performed during export.

• Optimizer: AdamW
• Peak learning rate: 3e-4
• Best-checkpoint training tokens: 40,004,782
• Best-checkpoint step: 5,856
• Validation loss: 1.896527
• Validation perplexity: 6.662715

Validation used the TinyStories validation split and the same 8,192-token BPE tokenizer included in this repository.

Files

• model.safetensors: inference weights
• model.py: exact custom PyTorch architecture
• config.json: architecture and token IDs
• tokenizer.json: BPE tokenizer
• generate.py: minimal generation example

Limitations

This is a small research model trained only on synthetic English children's stories. It may generate incorrect, repetitive, biased, unsafe, or otherwise inappropriate text. It is not suitable for factual, safety-critical, or production use without further evaluation.

The source project did not specify a license for its code or weights. Choose an appropriate license and confirm the training-data terms before publishing this package publicly. TinyStories is listed as CDLA-Sharing-1.0 on its dataset page.