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isaacmg/qwen3-vl-8b-hebrew-v18a-ckpt

sourceHugging Faceapache-2.0updated 22d agoView on Hugging Face

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Qwen3-VL-8B Hebrew — v1.8a checkpoints (vision-frozen control)

➡️ Unless you are studying this series' training history, use the flagship instead: [qwen3-vl-8b-hebrew-v19a-ckpt](https://huggingface.co/isaacmg/qwen3-vl-8b-hebrew-v19a-ckpt).

Status: superseded — A/B control arm. v1.8 was a controlled experiment on whether training the vision tower matters for handwriting: v1.8a kept the vision LoRA frozen (the accidental status quo of v1–v1.7, here made deliberate), while its twin v1.8b trained it. v1.8b won the comparison and became the ancestor of every later checkpoint. Keep this repo for reproducing the ablation; do not use it for inference.

The series at a glance

One line per generation — which checkpoint to use and which are historical:

version	repo	status	canonical revision	headline (benchmark)
v1.9a	v19a-ckpt	FLAGSHIP — use this	step 1300 `43e21bd7`	F1 0.816 / CER 0.216 (Genizah religious-140); F1 0.862 / CER 0.196 (frozen PGP-131)
v1.9b	v19b-ckpt	experimental control — not adopted	step 1300 `f8618c28`	merger-LoRA ablation study; loss ≡ v1.9a
v1.8b	v18b-ckpt	superseded; warm-start ancestor of all v1.9	step 700 `c80313f8`	first arm with a live vision-tower LoRA
v1.8a	v18a-ckpt	superseded A/B control (vision frozen)	step 700	control arm for the v1.8 vision experiment
v1.7	v17-ckpt	superseded	step 800	first Genizah-handwriting generation; best VLM on both corpora at its era's benchmarks (Aug 2026)
v1.6	v16-ckpt	superseded; Talmud-print reference	step 1000 (official); step 1100 `b9f47f32` (v1.7 warm start)	Talmud page CER: gemara 0.090 / rashi 0.047 / tosafot 0.099 — Rashi-script 6.7× better than the best closed model we tested (0.315)
v1.5	rashi-ckpt	superseded	final `e050aca8` (step 2000)	proved pure Rashi-glyph perception: CER 0.018 on unmemorizable synthetic text
v1	hebrew-ckpt	superseded (language-only LoRA)	step 3800 `e2f85dde`	first checkpoints to read Vilna gemara from pixels

\* CER over substantive attempts only, alignment-based scorer. Benchmarks differ across generations (Talmud print vs Genizah manuscripts) — compare within a row's named benchmark, not across rows.