coedit-base-litert-int8

CoEdIT Base LiteRT INT8

Split encoder/decoder LiteRT (.tflite) conversion of jbochi/coedit-base, intended for fully local text editing in browsers, extensions, and native LiteRT clients. This is an independently produced conversion, not an official release from the upstream model author or Grammarly.

Model details

The encoder state is computed once and reused during autoregressive decoder steps. Tensor names, task prefixes, sizes, and SHA-256 checksums are recorded in manifest.json.

Files

• encoder.tflite: quantized encoder graph
• decoder.tflite: quantized autoregressive decoder graph
• tokenizer.json: tokenizer vocabulary and model
• tokenizer_config.json and special_tokens_map.json: tokenizer metadata
• manifest.json: runtime contract and integrity metadata

Instruction prefixes

Fix grammatical errors in this sentence: <text>
Make the sentence formal: <text>
Change the style to casual: <text>
Make the sentence simpler: <text>
Paraphrase the sentence: <text>

Runtime integration

Load the two graphs with LiteRT, tokenize to the fixed 256-token input shape, run the encoder once, and feed its hidden states plus the decoder token buffer to the decoder for each generated token. Consult manifest.json for the exact tensor contract and EOS/padding IDs.

Limitations

• Browser CPU/WASM autoregressive decoding can be slow, particularly for long rewrites. WebGPU or a native LiteRT runtime is recommended when available.
• Dynamic-range quantization can change outputs relative to the source model.
• Output should be reviewed before use; the model may omit, add, or alter meaning.
• The graphs use fixed-size tensors and are not a Transformers checkpoint.

License and attribution

The source model is published under Apache License 2.0. This converted form is distributed under the same license; see LICENSE and NOTICE.

• Source: https://huggingface.co/jbochi/coedit-base
• Upstream base: https://huggingface.co/google/flan-t5-base
• CoEdIT paper: https://arxiv.org/abs/2305.09857