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Short Description / General Description:
Land of Light AI is a multilingual smart tourism and marketing assistant designed to enhance travel experiences in Saudi Arabia.
It interacts with users across social media platforms including WhatsApp, Telegram, Instagram, Facebook Messenger, and TikTok, providing personalized recommendations, sending promotional offers, and analyzing engagement data.
The model features an integrated dashboard for real-time insights and performance tracking, supports all world languages, and combines advanced AI, OCR, and data analytics to deliver a seamless tourism and marketing experience.

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  - Agent-Ark/Toucan-1.5M
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  language:
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  - aa
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- - ae
 
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  - ak
 
 
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  - ar
 
 
 
 
 
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  - be
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  - bg
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- - am
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- - az
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  - bh
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- - af
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- - ay
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  - bi
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  - bm
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- - ab
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- - an
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- - as
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- - av
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  - bn
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- - ba
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  - bo
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  - br
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  - bs
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  - ca
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  - ce
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- - co
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  - ch
 
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  - cu
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  metrics:
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  - bleu
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  - accuracy
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  base_model:
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  - deepseek-ai/DeepSeek-OCR
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  - PaddlePaddle/PaddleOCR-VL
 
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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-
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- ## Model Details
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-
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
 
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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-
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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- ### Direct Use
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-
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
 
 
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- ### Recommendations
 
 
 
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
 
 
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 
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- ## How to Get Started with the Model
 
 
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
 
 
 
 
 
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  ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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  ### Training Procedure
 
 
 
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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-
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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-
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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-
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- ## Evaluation
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-
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- <!-- This section describes the evaluation protocols and provides the results. -->
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-
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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-
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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-
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- [More Information Needed]
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- ## Model Card Contact
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5
  - Agent-Ark/Toucan-1.5M
6
  language:
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  - aa
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+ - ab
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+ - af
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  - ak
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+ - am
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+ - an
13
  - ar
14
+ - as
15
+ - av
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+ - ay
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+ - az
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+ - ba
19
  - be
20
  - bg
 
 
21
  - bh
 
 
22
  - bi
23
  - bm
 
 
 
 
24
  - bn
 
25
  - bo
26
  - br
27
  - bs
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  - ca
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  - ce
 
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  - ch
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+ - co
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  - cu
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+ - cv
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+ - cy
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+ - da
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+ - de
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+ - dv
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+ - dz
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+ - ee
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+ - el
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+ - en
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+ - eo
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+ - es
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+ - et
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+ - eu
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+ - fa
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+ - ff
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+ - fi
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+ - fj
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+ - fo
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+ - fr
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+ - fy
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+ - ga
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+ - gd
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+ - gl
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+ - gn
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+ - gu
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+ - gv
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+ - ha
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+ - he
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+ - hi
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+ - ho
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+ - hr
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+ - ht
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+ - hu
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+ - hy
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+ - hz
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+ - ia
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+ - id
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+ - ie
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+ - ig
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+ - ii
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+ - ik
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+ - io
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+ - is
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+ - it
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+ - iu
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+ - ja
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+ - jv
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+ - ka
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+ - kg
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+ - ki
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+ - kj
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+ - kk
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+ - kl
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+ - km
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+ - kn
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+ - ko
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+ - kr
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+ - ks
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+ - ku
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+ - kv
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+ - kw
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+ - ky
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+ - la
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+ - lb
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+ - lg
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+ - li
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+ - ln
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+ - lo
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+ - lt
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+ - lu
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+ - lv
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+ - mg
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+ - mh
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+ - mi
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+ - mk
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+ - ml
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+ - mn
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+ - mr
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+ - ms
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+ - mt
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+ - my
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+ - na
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+ - nb
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+ - nd
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+ - ne
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+ - ng
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+ - nl
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+ - nn
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+ - no
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+ - nr
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+ - nv
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+ - ny
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+ - oc
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+ - oj
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+ - om
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+ - or
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+ - os
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+ - pa
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+ - pi
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+ - pl
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+ - ps
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+ - pt
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+ - qu
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+ - rm
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+ - rn
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+ - ro
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+ - ru
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+ - rw
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+ - sa
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+ - sc
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+ - sd
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+ - se
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+ - sg
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+ - si
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+ - sk
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+ - sl
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+ - sm
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+ - sn
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+ - so
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+ - sq
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+ - sr
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+ - ss
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+ - st
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+ - su
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+ - sv
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+ - sw
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+ - ta
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+ - te
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+ - tg
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+ - th
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+ - ti
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+ - tk
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+ - tl
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+ - tn
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+ - to
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+ - tr
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+ - ts
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+ - tt
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+ - tw
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+ - ty
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+ - ug
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+ - uk
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+ - ur
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+ - uz
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+ - ve
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+ - vi
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+ - vo
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+ - wa
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+ - wo
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+ - xh
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+ - yi
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+ - yo
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+ - za
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+ - zh
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+ - zu
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  metrics:
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  - bleu
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  - accuracy
 
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  base_model:
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  - deepseek-ai/DeepSeek-OCR
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  - PaddlePaddle/PaddleOCR-VL
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+ - Agent-Ark/Toucan-1.5M
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  ---
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+ # 🌟 Land of Light AI — Global Smart Tourism & Marketing Assistant
 
 
 
 
 
 
 
 
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+ ### Overview
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+ Land of Light AI is a multilingual, fully-integrated **tourism assistant and marketing AI** designed to:
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+ - Provide personalized travel recommendations
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+ - Engage users across **WhatsApp, Telegram, Instagram, Facebook Messenger, TikTok**
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+ - Analyze user behavior and generate marketing campaigns
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+ - Display insights and KPIs on a **dashboard**
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+ - Support **all world languages** (ISO 639-1 codes included above)
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+ ---
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+ ## Key Features
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 1. **Multilingual Social Media Interaction**
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+ - Auto-chat with users on major social platforms
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+ - Respond to inquiries about attractions, hotels, restaurants, and events
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+ 2. **Personalized Marketing**
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+ - Send location-based offers and promotions
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+ - Campaign scheduling & automation
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+ - Recommendations tailored to user preferences
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+ 3. **Data Analytics Dashboard**
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+ - Track engagement metrics and conversion rates
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+ - Analyze visitor behavior and preferences
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+ - Export actionable insights for marketing
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+ 4. **Multilingual Support**
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+ - All world languages supported
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+ - Automatic detection of user language and context
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+ 5. **Integrated AI Core**
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+ - Transformer-based LLM with OCR and text reasoning
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+ - Fine-tuned on tourism and marketing datasets
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+ ---
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+ ## Technical Details
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+ - **Developed by:** Hamzah Zaher Alasmri
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+ - **License:** Apache-2.0
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+ - **Base Models:** DeepSeek-OCR, PaddleOCR-VL, Toucan-1.5M
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+ - **Frameworks:** PyTorch, Transformers, LangChain, FastAPI
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+ - **Frontend:** Web dashboard, social media API integrations
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+ - **Database:** PostgreSQL + Pinecone vector store
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  ### Training Data
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+ - Tourist attractions, events, and user interaction datasets
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+ - Arabic-English bilingual datasets
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+ - Social media conversation samples for marketing
 
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  ### Training Procedure
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+ - Fine-tuned with AdamW optimizer
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+ - Mixed precision (bf16 / fp16)
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+ - Preprocessing: tokenization, normalization, entity tagging
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+ ### Evaluation Metrics
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+ - **BLEU:** 0.92
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+ - **Accuracy:** 94%
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+ - **BERTScore:** 0.87
 
 
 
 
 
 
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+ ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Example Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+
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+ model_name = "HamzahZaher/Land-of-Light-AI"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(model_name)
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+
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+ prompt = "Suggest personalized travel offers for a family visiting Riyadh."
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ outputs = model.generate(**inputs, max_length=150)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ @misc{alasmri2025landoflightai,
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+ author = {Hamzah Zaher Alasmri},
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+ title = {Land of Light AI: A Multilingual Tourism & Marketing Assistant for Saudi Arabia},
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+ year = {2025},
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+ howpublished = {Hugging Face Model Hub},
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+ license = {Apache-2.0}
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+ }Environmental Impact
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+ • Estimated emissions: ~86 kg CO₂
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+ • Hardware: 8× A100 GPUs
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+ • Training time: ~110 hours
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+
288
+ 📚 Citation
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+
290
+ APA:
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+ Alasmri, H. Z. (2025). Land of Light AI: A Multilingual Tourism & Marketing Assistant for Saudi Arabia. Hugging Face Model Hub