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mlukasze / accuracy-supervisor__history.json
Created July 13, 2026 17:55
MEAT pipeline diagnostics for nomic-ai/nomic-embed-text-v1.5
{
"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",
@mlukasze
mlukasze / README.md
Created July 14, 2026 07:18
CohereLabs/cohere-transcribe-03-2026 — Working OpenVINO Inference Instructions (companion to omega#12)

CohereLabs/cohere-transcribe-03-2026 — Working OpenVINO Inference Instructions

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.

TL;DR

@mlukasze
mlukasze / gist_summary.md
Created July 15, 2026 07:16
cohere_asr OpenVINO follow-up: two bug reports investigated (PR#1788 / omega#12)

cohere_asr OpenVINO follow-up — bug investigation & fix (2026-07 iteration)

Related: optimum-intel PR #1788, omega issue #12, prior investigation gist.

Task

A user testing PR #1788 (adds cohere_asr / CohereLabs/cohere-transcribe-03-2026 OpenVINO export+inference support) reported two problems:

  1. Export failure with --task automatic-speech-recognition-with-past: MatMul dimension mismatch (1024 vs 1280) in encoder_decoder_proj.
  2. After upgrading 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'.
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mlukasze / GPU_PERF_ANALYSIS_REPORT.md
Created August 28, 2026 07:21
GPU Perf Analysis: Bonsai-27B/Qwen3.6-27B INT8 vs INT4 on Arc Pro B60 (Xe2) — omega#66

GPU Performance Analysis: INT8 vs INT4 on Xe2 (Arc Pro B60) for Bonsai-27B / Qwen3.6-27B

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