metadata
language:
- en
base_model: mikahniehaus/Tern
pipeline_tag: text2text-generation
tags:
- tinycode
- onnx
- python
- reasoning
tinycode-narrator
Accepted TinyCode model, based on Tern 124M. Python code -> steps -> summary. The ONNX graph includes the snippet LoRA adapter; native inference needs both checkpoint.pt and snippet_adapter.pt. Functions use the unchanged function checkpoint.
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 .