Instructions to use Misraj/Baseer__Nakba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Misraj/Baseer__Nakba with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Misraj/Baseer__Nakba")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Misraj/Baseer__Nakba")
model = AutoModelForImageTextToText.from_pretrained("Misraj/Baseer__Nakba")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Misraj/Baseer__Nakba with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Misraj/Baseer__Nakba"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Misraj/Baseer__Nakba",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Misraj/Baseer__Nakba

SGLang

How to use Misraj/Baseer__Nakba with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Misraj/Baseer__Nakba" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Misraj/Baseer__Nakba",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Misraj/Baseer__Nakba" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Misraj/Baseer__Nakba",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Misraj/Baseer__Nakba with Docker Model Runner:
```
docker model run hf.co/Misraj/Baseer__Nakba
```

Baseer-Nakba HTR: A State-of-the-Art VLM for Arabic Handwritten Text Recognition

Overview

This repository contains the model weights and inference pipeline for our submission to the NAKBA NLP 2026 Arabic Handwritten Text Recognition (HTR) competition.

Our approach adapts the 3B-parameter Baseer Vision-Language Model (VLM) to effectively parse and recognize highly cursive, historical Arabic manuscripts. Through a progressive training pipeline, domain-matched data augmentation, and advanced checkpoint merging, this unified model mitigates the challenges of varying writer styles, age-related document degradation, and morphological complexity.

To try our Baseer model for document extraction, please visit: Baseer — Baseer is the SOTA model on Arabic Document Extraction.

🏆 Competition Results

Our final model (Misraj AI) secured 1st place on the official Nakba hidden test set leaderboard.

Rank	Team	CER	WER
🥇 1st	Misraj AI	0.0790	0.2440
🥈 2nd	Oblevit	0.0925	0.3268
🥉 3rd	3reeq	0.0938	0.2996
4th	Latent Narratives	0.1050	0.3106
5th	Al-Warraq	0.1142	0.3780
6th	Not Gemma	0.1217	0.3063
7th	NAMAA-Qari	0.1950	0.5194
8th	Fahras	0.2269	0.5223
—	Baseline	0.3683	0.6905

Training Methodology

Our model was trained using a multi-stage Supervised Fine-Tuning (SFT) curriculum.

Data Augmentation: The Muharaf enhancement dataset was converted to grayscale to match the visual complexity and tonal distribution of the Nakba competition data.
Decoder-Only SFT: We first trained the text decoder autoregressively on the structurally similar Muharaf dataset to condition the language modeling head.
Full Encoder-Decoder Tuning: We subsequently unfroze the vision encoder and trained the full architecture on the Nakba dataset using differential learning rates — a key step that yielded a >5% improvement in WER over decoder-only tuning.
Checkpoint Merging: To stabilize predictions and maximize generalization, we merged our top-performing checkpoints (Epoch 1 and Epoch 5) using SLERP interpolation.

Training Hyperparameters

All supervised experiments were conducted with standardized hyperparameters across configurations.

Parameter	Value
Hardware	2× NVIDIA H100 GPUs
Base Model	3B-parameter Baseer
Epochs	5
Optimizer	AdamW
Weight Decay	0.01
Learning Rate Schedule	Cosine
Batch Size	128
Max Sequence Length	1200 tokens
Input Image Resolution	644 × 644 pixels
Decoder-Only Learning Rate	1e-4
Encoder Learning Rate	9e-6
Decoder Learning Rate (Full Tuning)	1e-4

Image Examples

The model works reliably on images from the Nakba dataset and visually similar historical manuscripts.

Merge Method

This model was merged using the SLERP merge method.

Models Merged

Baseer_Nakba_ep_1
Baseer_Nakba_ep_5

Configuration

merge_method: slerp
base_model: Baseer_Nakba_ep_1
models:
  - model: Baseer_Nakba_ep_1
  - model: Baseer_Nakba_ep_5
parameters:
  t:
    - value: 0.50
dtype: bfloat16

Citation

If you use this model or find our work helpful, please consider citing our paper:

@inproceedings{misrajai2026nakba,
  title     = {Adapting Vision-Language Models for Historical Arabic Handwritten Text Recognition},
  author    = {Misraj AI},
  booktitle = {Nakba OCR Competition, NLP 2026},
  year      = {2026}
}

Paper for Misraj/Baseer__Nakba

Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR

Paper • 2509.18174 • Published Sep 17, 2025 • 134

Misraj
/

Baseer__Nakba