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 embeddingsinterpreter.onnx- Main interpreter modeltokenizer/- 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 FaceFiles (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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