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Alsamir
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Model Details
Model Provider
Alsamir
Model Tree
Qwen/Qwen3-VL-4B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This checkpoint stores step 9,750 of 11,000 planned steps. Training was interrupted; this is not a completed 11,000-step run. No verified total of unique training images or accuracy score is claimed. Earlier-stage lineage has not been independently audited.
Pre-merge and post-merge outputs matched on one dataset image. This checks export behavior only, not OCR accuracy. The image may have been used for training. Repetition, omissions, incorrect letters/digits, and layout errors remain possible. Review output against the source, especially names and numbers.
Install PyTorch, Accelerate, Pillow, and a Transformers version supporting Qwen3-VL. Exact export versions are recorded in release_manifest.json.
python
import torchfrom PIL import Imagefrom transformers import AutoProcessor, AutoModelForImageTextToTextrepo = "Alsamir/Abjad-checkpoint-9750"processor = AutoProcessor.from_pretrained(repo)model = AutoModelForImageTextToText.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto").eval()image = Image.open("page.png").convert("RGB")messages = [{"role":"user", "content":[{"type":"image"}, {"type":"text", "text":"Transcribe all visible Arabic text exactly. Preserve Arabic letters and digits. Return only the transcription."}]}]prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)with torch.inference_mode(): result = model.generate(**inputs, max_new_tokens=2048, do_sample=False)print(processor.batch_decode(result[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])Convert PDF pages to images before inference. Long pages may hit the token limit. Word conversion and exact layout reconstruction need a separate document pipeline. Use hardware supporting the selected dtype; CPU inference may be slow.
Includes full model shards, tokenizer, processor, configuration and a checksum/provenance manifest. Training documents, optimizer state, credentials and machine-specific paths are excluded. The original checkpoint is retained separately. The base model is licensed under Apache 2.0. No dataset is redistributed.
The published repository is a standalone Transformers model. It contains no adapter and no machine-specific paths. Windows users can run it with NVIDIA CUDA, Python 3.10 or 3.11, and a CUDA-enabled PyTorch build. OCI users can use the same files in a CUDA Linux image. A GPU with enough VRAM for the BF16 model is recommended.
powershell
py -3.11 -m venv .venv.venv\Scripts\Activate.ps1python -m pip install --upgrade pip# Install the CUDA-enabled PyTorch wheel matching your driver from pytorch.orgpip install -r requirements-windows.txtpython run_ocr_windows.py page.png --output result.txtThe runner selects CUDA when available and falls back to CPU. Rasterize PDFs into PNG or TIFF pages first. Long pages may require cropping. Review names, numbers, tables, stamps, and signatures against the source document.
import torchfrom PIL import Imagefrom transformers import AutoProcessor, AutoModelForImageTextToTextrepo = "Alsamir/Abjad-checkpoint-9750"processor = AutoProcessor.from_pretrained(repo)model = AutoModelForImageTextToText.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto").eval()image = Image.open("page.png").convert("RGB")messages = [{"role":"user", "content":[{"type":"image"}, {"type":"text", "text":"Transcribe all visible Arabic text exactly. Preserve Arabic letters and digits. Return only the transcription."}]}]prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)with torch.inference_mode(): result = model.generate(**inputs, max_new_tokens=2048, do_sample=False)print(processor.batch_decode(result[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])py -3.11 -m venv .venv.venv\Scripts\Activate.ps1python -m pip install --upgrade pip# Install the CUDA-enabled PyTorch wheel matching your driver from pytorch.orgpip install -r requirements-windows.txtpython run_ocr_windows.py page.png --output result.txt