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amad-iq
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Model Details
Model Provider
amad-iq
Model Tree
AhmedZaky1/DIMI-Arabic-OCR-V2
amad-iq/amad-vlm5
Input Modalities
Output Modalities
Supported Functionality
KITAB-Bench ocr-eval, 13 datasets, 3,760 images, final-answer scoring with the
benchmark's unmodified metrics.
| amad-vlm5 | amad-vlm6 | |
|---|---|---|
| CER ↓ (macro) | 0.254 | 0.235 |
| CHrF ↑ (macro) | 81.05 | 81.68 |
| CER ↓ (10 leak-free datasets) | 0.321 | 0.236 |
| CHrF ↑ (10 leak-free datasets) | 77.35 | 82.46 |
Largest per-dataset gains: khattparagraph CER 0.711 → 0.088, evarest 0.291 → 0.142, hindawi 0.241 → 0.137.
What the gain is. Error-type decomposition (see the report) shows the improvement is behavioral: the merge cuts amad-vlm5's thinking rate on page-level inputs from 45–98% to 5–18% and removes its truncated and empty outputs, so insertions and deletions collapse. Reading accuracy itself (substitution rate) is worse on 10 of 13 datasets and better only on adab and evarest. Hallucination is not cancelled but moved: on isippt and muharaf the merged model over-generates where amad-vlm5 did not. Four per-dataset wins and three losses are statistically significant (bootstrap); five are not.
| Repository | Format | Use |
|---|---|---|
| this repo | bf16 safetensors | Transformers, vLLM, further fine-tuning |
| amad-vlm6-GGUF | Q8_0 + mmproj | llama.cpp, LM Studio |
python
import re, torchfrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationrepo = "amad-iq/amad-vlm6"model = Qwen2_5_VLForConditionalGeneration.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")processor = AutoProcessor.from_pretrained(repo)messages = [{"role": "user", "content": [{"type": "image", "image": "page.png"},{"type": "text", "text": "Extract the text in the image. Give me the final text, nothing else."}]}]inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,return_dict=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=4096, do_sample=False, repetition_penalty=1.05)raw = processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0]print(re.sub(r"<think>.*?</think>", "", raw, flags=re.S).strip())
The model inherits a thinking behaviour from one parent: on dense pages it may
emit <think>…</think> before the transcription. Keep only the text after the
last </think>, and allow 4,096 new tokens — a 500-token budget truncates
dense pages.
yaml
merge_method: tiesbase_model: Qwen/Qwen2.5-VL-7B-Instructmodels:- model: amad-iq/amad-vlm5parameters: {weight: 1.0, density: 0.5}- model: AhmedZaky1/DIMI-Arabic-OCR-V2 # V2 only — V1 is subsumed by V2parameters: {weight: 1.0, density: 0.5}parameters: {lambda: 1.0, normalize: false}dtype: bfloat16
Apache 2.0, inherited from the base model and both parents.