Instructions to use thomasjvu/alkahest-0.8b-q4-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use thomasjvu/alkahest-0.8b-q4-onnx with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-text-to-text', 'thomasjvu/alkahest-0.8b-q4-onnx');
Experimental model. This repository is an experimental Alkahest/Rally package. It may fail, behave unpredictably, or produce unsuitable output. Use at your own risk; do not rely on it for safety-critical or production decisions without your own validation.
Alkahest 0.8B Q4 ONNX
Public experimental browser-oriented ONNX package for the finalized Alkahest 0.8B direct lane.
Status
- Variant: direct Alkahest 0.8B
- Runtime: Transformers.js / WebGPU
- Preferred dtype map:
{ "embed_tokens": "q4", "decoder_model_merged": "q4", "vision_encoder": "fp16" } - Browser smoke: passed text load, WebGPU session initialization, and coherent text generation
Files
onnx/embed_tokens_q4.onnxonnx/embed_tokens_q4.onnx_dataonnx/decoder_model_merged_q4.onnxonnx/decoder_model_merged_q4.onnx_dataonnx/vision_encoder_fp16.onnxonnx/vision_encoder_fp16.onnx_data
Notes
This package includes Qwen3.5 RMSNorm offset patching during ONNX weight transplant. Older experiment repos without that patch can load but generate corrupted text.
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