Intriguing properties of neural networks (2013)
Explaining and harnessing adversarial examples (2014)
| { | |
| "model_id": "nomic-ai/nomic-embed-text-v1.5", | |
| "canonical_type": "genai", | |
| "primary_metric": "similarity", | |
| "runs": [ | |
| { | |
| "run_id": 1, | |
| "timestamp": "2026-07-13T17:11:03.405866Z", | |
| "mode": "full", | |
| "triggered_by": "optimum-genai", |
Companion to openvinotoolkit/omega#12 and huggingface/optimum-intel#1788.
This gist documents the verified-working way to run this model on OpenVINO today, and the known limitation that is not yet fixable without new upstream work.
Related: optimum-intel PR #1788, omega issue #12, prior investigation gist.
A user testing PR #1788 (adds cohere_asr / CohereLabs/cohere-transcribe-03-2026 OpenVINO export+inference support) reported two problems:
--task automatic-speech-recognition-with-past: MatMul dimension mismatch (1024 vs 1280) in encoder_decoder_proj.optimum to 2.2.0.dev0, export succeeded, but inference via OVModelForSpeechSeq2Seq through the transformers ASR pipeline crashed with AttributeError: 'NoneType' object has no attribute 'shape'.Ticket: openvinotoolkit/omega#66
Reported symptom: INT8 weight-compressed inference of Qwen/Qwen3.6-27B (base model for
prism-ml/Bonsai-27B-gguf) on GPU.1 (Intel Arc Pro B60, discrete, Xe2/"Battlemage") is
~75x slower (~3.35 s/token) than INT4 on the same device (~44.75 ms/token), despite
both being functionally correct (WWB similarity 0.962 for INT8, 0.911 for INT4, both ≥ 0.9
threshold). CPU and GPU.0 (integrated Xe-LPG) show no such anomaly.
Bottom line up front: the dominant root cause is **GPU memory (VRAM) capacity