GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
mabdulaziz499
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
mabdulaziz499
Model Tree
QCRI/Fanar-2-Oryx-IVU
Input Modalities
Output Modalities
Supported Functionality
| # | Model | Params | CER ↓ | WER ↓ | Exact ↑ | Lines ≤5% CER | Notes |
|---|---|---|---|---|---|---|---|
| 1 | Ours — Fanar + LoRA (generalist mix) | 8.29B | 3.77% | 16.50% | 29.4% | 77.6% | KHATT + Muharaf + wiki-synthetic (built for generality) |
| 2 | Ours — Fanar-2-Oryx-IVU + LoRA | 8.29B | 4.11% | 15.96% | 31.7% | 79.2% | fine-tuned on KHATT train (in-domain) |
| 3 | Arabic-English-handwritten-OCR-v3 | 3.75B | 5.86% | 18.30% | 28.8% | 69.1% | community Arabic/English handwriting model |
| 4 | Arabic-handwritten-OCR-4bit-Qwen2.5-VL-3B-v3 | 3.75B | 5.96% | 18.88% | 28.9% | 70.5% | community model, trained on Muharaf — never saw KHATT |
| 5 | Ours — Qwen2.5-VL-3B + LoRA | 3.75B | 6.12% | 18.43% | 29.3% | 74.1% | fine-tuned on KHATT train (in-domain) |
| 6 | Qwen2.5-VL-7B-Instruct | 8.29B | 50.85% | 82.56% | 0.1% | 0.5% | general-purpose VLM |
| 7 | Fanar-2-Oryx-IVU (base, zero-shot) | 8.29B | 50.98% | 82.58% | 0.1% | 0.5% | our base model, before fine-tuning |
| 8 | Qwen2.5-VL-3B-Instruct | 3.75B | 73.35% | 93.92% | 0.1% | 0.1% | general-purpose VLM |
| 9 | Qari-OCR-v0.3 (Arabic OCR) | 2.21B | 235.55% | 126.10% | 0.0% | 0.0% | Arabic OCR specialist — built for PRINTED text |
CER = character error rate (verbatim: diacritics and punctuation count).
Lower is better. Full raw numbers: benchmark.json in this repo.
Corpus-level CER hides the shape of the errors, so here is the quality profile — what share of lines land under a given error threshold:
Quality profile of this model: median line CER 1.89%, with 77.6% of lines at ≤5% CER, 92.5% at ≤10%, and 29.4% transcribed exactly.
| Ground truth | Prediction | CER | |
|---|---|---|---|
| ذهب نوح مظفر ضرغام بصحبه رؤوف بن لؤي رايق ظافر | ذهب نوح مظفر ضرغام بصحبه رؤوف بن لؤي رايق ظافر | 0.0% | good |
| عطعوط وهلال خازن عفيف للحج . بدأت قوافل | عطعوط وهلال خازن عفيف للحج . بدأت قوافل | 0.0% | good |
| أصحابنا ظ ع ك ، ث خ ض، ب س ش، ص غ هـ أننا في الحج هل تعلم فائدة الكلمات التالية | أصحابنا ظ ع ك ، ث خ ض، ب س ش، ص غ هـ أننا في الحج هل تعلم فائدة الكلمات التالية | 0.0% | good |
| كان جاري في الخيمه بيكلم وهو نائم بكلمات لا أفهمها مثل انقض بغـلس له الضابط | كان جاري في الخيمه يتكلم وهو نائم بكلمات لا أفهمها مثل انقض بغـلس له الضابط |
python
import torchfrom transformers import AutoModelForImageTextToText, AutoProcessorfrom peft import PeftModelfrom PIL import ImageBASE = "QCRI/Fanar-2-Oryx-IVU"ADAPTER = "mabdulaziz499/arabic-htr-fanar-7b-lora"processor = AutoProcessor.from_pretrained(BASE, min_pixels=200704, max_pixels=802816)model = AutoModelForImageTextToText.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, ADAPTER)model.eval()PROMPT = "Transcribe the handwritten Arabic text in this image exactly as written, including any diacritics. Output only the transcription, nothing else."image = Image.open("line.png").convert("RGB")messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": PROMPT}]}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)with torch.inference_mode():out = model.generate(**inputs, max_new_tokens=160, do_sample=False)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0].strip())
Input expectations: one single line of handwritten Arabic per image (as in KHATT). For a paragraph or a form, segment it into lines first and call the model per line — see Limitations.
Output: Arabic text in logical Unicode order (NFC). Use
arabic-reshaper + python-bidi only for terminal display, never for
storage or scoring.
| Base model | QCRI/Fanar-2-Oryx-IVU |
| Method | QLoRA — 4-bit NF4 base, LoRA r=16, α=32, dropout=0.05 |
| Adapted modules | attention + MLP projections of the language model (vision tower frozen) |
| Trainable params | 40.4M (LoRA only) |
| Data | KHATT v1.0 train+validation (16882 lines after leakage filtering) + font-rendered synthetic augmentation |
| Schedule | 3000 steps ≈ 2.8 epochs, effective batch 16, lr 0.0001, cosine, warmup 0.03 |
| Compute | 2.6 h on a single 96 GB GPU |
Data hygiene: KHATT contains fixed paragraphs copied verbatim by many writers, so the same sentence appears in both train and test in different handwriting. Every training line whose exact text occurs in the test split was removed before training (~1,700 lines). Without that filter a model can memorize the test text and report a flattering CER.
All models in the table were scored by the same script under one protocol:
local_khatt:test — 1038 real handwritten lines, held out.max_new_tokens=160.min_pixels=200704, max_pixels=802816).benchmark.json.The adapter is released under Apache-2.0, matching the base model
QCRI/Fanar-2-Oryx-IVU (Apache-2.0). Using it
requires downloading that base model under its own license. It was trained on
KHATT v1.0, distributed for research; check the dataset's terms
before commercial use and cite the KHATT authors in publications.
bibtex
@misc{arabic_htr_fanar_7b_lora,title = {arabic-htr-fanar-7b-lora: a QLoRA adapter for handwritten Arabic line recognition},author = {Abdelaziz, Mahmoud},year = {2026},url = {https://huggingface.co/mabdulaziz499/arabic-htr-fanar-7b-lora}}
Please also cite KHATT (Mahmoud et al., Pattern Recognition, 2014) and the base model Fanar-2-Oryx-IVU.
| 2.7% |
| typical (median) |
| نس . | لكل . | 75.0% | failure case |
| Final train loss | 0.2956 |