ONNX Model Zoo

yuntian-deng/paw-interpreter-onnx

By yuntian-deng · ↓ 0 · ♥ 1 · Updated 2025-10-24

What's in this repo

This page lists the files in yuntian-deng/paw-interpreter-onnx, including image_encoder.onnx, interpreter.onnx, text_embeddings.onnx.

  • 3 ONNX models (.onnx files)
  • 11 files, including 3 .onnx, 3 .data, 3 .json, 1 .md
  • Last updated 2025-10-24

ONNX files

File Size Download
image_encoder.onnx — Download
interpreter.onnx — Download
text_embeddings.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

paw-interpreter-onnx

ONNX runtime models for ProgramAsWeights.

Model Files

Core (always downloaded):

  • text_embeddings.onnx - Text tokenizer embeddings
  • interpreter.onnx - Main interpreter model
  • tokenizer/ - Fast tokenizer

Optional (lazy loaded):

  • image_encoder.onnx - Image encoder (CLIP + projection) - only downloaded when using image inputs

Usage

import programasweights as paw

# Text-only program (no Pillow needed!)
fn = paw.function("abc123")
result = fn("Extract emails: contact@example.com")

# With images (requires: pip install programasweights[images])
from PIL import Image
img = Image.open("photo.jpg")
result = fn("Count apples:", img)

Installation

# Base install (text-only, ~70MB)
pip install programasweights

# With image support (~85MB)
pip install programasweights[images]

Model Details

  • Base model: Qwen2-0.5B
  • Runtime: ONNX (lightweight, fast)
  • Input: .paw files (prefix KV cache)
  • Output: Generated text

Model info

programasweights
programasweights onnx neural-programs prefix-tuning license:mit region:us
↓ 0 ♥ 1

Created: 2025-10-22

Updated: 2025-10-24

View on Hugging Face

Files (11)

.gitattributes
README.md
image_encoder.onnx ONNX
image_encoder.onnx.data
image_processor/preprocessor_config.json
interpreter.onnx ONNX
interpreter.onnx.data
model_config.json
text_embeddings.onnx ONNX
text_embeddings.onnx.data
tokenizer/tokenizer.json

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