| ---
|
| language:
|
| - en
|
| base_model: mikahniehaus/Tern
|
| pipeline_tag: text-generation
|
| tags:
|
| - tinycode
|
| - onnx
|
| - python
|
| - reasoning
|
| ---
|
| # tinycode-generator
|
|
|
| Accepted TinyCode model, based on Tern 124M. English request -> short plan -> Python code. Uses lossless whitespace-separated GPT-2 BPE.
|
|
|
| ## Files
|
| - `model.onnx`: complete FP32 browser inference graph, no external tensor files.
|
| - `manifest.json`: SHA-256, graph dimensions and inference settings.
|
| - `checkpoint.pt`: native PyTorch model (custom Tern architecture, not Transformers AutoModel).
|
| - `runtime/codenarrator/`: Python architecture, tokenizer, preprocessing and decoding implementation.
|
| - `gpt2.tiktoken`: complete GPT-2 mergeable token ranks (base64 token + rank).
|
| - `tinycode_config.json`: architecture and task settings.
|
|
|
| Upload ALL contents of this folder to the ROOT of a public Hugging Face model repository.
|
| Keep filenames unchanged. The website downloads `model.onnx` using
|
| `https://huggingface.co/USERNAME/REPO/resolve/main/model.onnx`.
|
| The native checkpoints are provided for reproducibility; they are not downloaded by the website.
|
| No hosted inference server is needed. Weights execute on the visitor's CPU via ONNX Runtime Web.
|
|
|
| Original pretraining model: https://huggingface.co/mikahniehaus/Tern .
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|
|
|
|
