Added model card

README.md

@@ -1,199 +1,667 @@
 ---
+license: gemma
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+extra_gated_button_content: Acknowledge license
+tags:
+- conversational
+language:
+- arz
+datasets:
+- MBZUAI-Paris/Egyptian-SFT
+base_model:
+- google/gemma-3-12b-pt
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# JAIS Initiative: Nile-Chat Models
 
+## Model Overview
 
+Nile-Chat is a family of open instruction-tuned models for Egyptian dialect, developed to handle both scripts commonly used in Egypt: Arabic script and Latin-based Arabizi. As part of the [Jais](https://arxiv.org/abs/2308.16149) project for standard Arabic and its extensions to dialectal Arabic, Nile-Chat is designed to support natural language generation in a way that reflects the script-diverse nature of Egyptian communication. These models are effective for a variety of tasks including question answering, translation and transliteration. Their range of sizes ensures accessibility, from lightweight personal deployments to more powerful setups, enabling broader use of AI technologies for Egyptian Arabic speakers. The family includes two versions:
 
-## Model Details
+* [Nile-Chat-4B](https://huggingface.co/MBZUAI-Paris/Nile-Chat-4B): A compact 4B parameter model that balances efficiency and fluency, well-suited for generating Egyptian Arabic in both Arabic and Latin scripts.
+* [Nile-Chat-12B](https://huggingface.co/MBZUAI-Paris/Nile-Chat-12B): A dense 12B parameter model providing high-capacity generation capabilities, ideal for complex, multi-turn interactions and nuanced understanding in both Egyptian scripts.
 
-### Model Description
+**Paper:** [Coming Soon!](#)
 
-<!-- Provide a longer summary of what this model is. -->
+## 👥 Our Team
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The model is developed by MBZUAI France Lab, an AI research center in Paris affiliated with the [Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)](https://mbzuai.ac.ae/) headquartered in Abu Dhabi.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+## Usage
 
-<!-- Provide the basic links for the model. -->
+Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```sh
+pip install -U transformers sentencepiece
+```
+
+Then, copy the snippet from the section below.
+
+#### Running with the `pipeline` API
+
+```python
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation",
+    model="MBZUAI-Paris/Nile-Chat-12B",
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda" # replace with "mps" to run on a Mac device
+)
+
+messages = [
+    {"role": "user", "content": 'اسمك ايه؟'},
+]
+
+outputs = pipe(messages, max_new_tokens=256)
+assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
+print(assistant_response)
+```
+
+- Response:
+
+>اسمي نايل-شات، على اسم نهر النيل، اطول نهر في العالم، اللي من زمان كان عامل مهم في تطور مصر، وبيساعد في معيشة الناس وأثر على التراث والثقافة بتاعتنا. وعشان انا موديل لغة، الباحثين بتوع جامعة محمد بن زايد للذكاء الاصطناعي دربوني باستخدام مجموعة من المصادر المفتوحة، وعشان كده سموني نايل-شات. لو عندك اي سؤال او محتاج مساعدة في حاجة، اتفضل اسأل!
+
+
+## Training Data
+Nile-Chat models were trained on diverse datasets focusing on Egyptian dialect consisting of approximately 3.3B tokens during continual pre-training phase, 1.9M instructions during instruction finetuning and 0.2M samples for DPO, with a maximum length of 2048 tokens, including:
+
+* Web documents: A diverse collection of Egyptian web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary.
+* Instruction samples created from publicly available Egyptian Arabic datasets including translation and transliteration.
+* Translated English and multi-lingual pretraining and instruction-tuning datasets using Claude 3.5 Sonnet (v2).
+
+The dataset covers both Egyptian Arabic and Latin scripts. Our instruction tuning dataset [Egyptian-SFT](https://huggingface.co/datasets/MBZUAI-Paris/Egyptian-SFT) is publicly available.
+
+
+## Implementation Information
+Nile-Chat models are based on Gemma 3 models. The Nile-Chat models were trained using 8 NVIDIA A100 80 GB GPUs in parallel using FSDP on AWS Sagemaker. The model is trained using HuggingFace transformers and parameter-efficient fine-tuning with LoRA rank of 256 for both continual pre-training and instruction finetuning, while performing full finetuning for DPO. The continual pre-training is divided into two phases: (i) general pre-training on 2.8B tokens from the Egyptian web and (ii) annealing phase with 0.5B high quality Egyptian text.
+
+# Evaluation
+Nile-Chat models were evaluated on a comprehensive suite of tasks using various datasets and benchmarks to assess their performance across multiple dimensions. These included tasks such as:
+
+* **EgyptianMMLU:** An Egyptian version of ArabicMMLU and MMLU benchmarks.
+* **EgyptianHellaSwag:** An Egyptian version of HellaSwag (In both scripts Arabic and Latin).
+* **Belebele Arz_Arab:** Belebele is a multiple-choice machine reading comprehension dataset published by Facebook spanning 122 language variants. The Evaluation is done on the Arz_Arab part of Belebele that refers to Egyptian Arabic.
+* **Translation:** Including four directions and three languages: Arabic script Egyptian, MSA and English.
+* **Transliteration:** Transforming a sentence from Egyptian (written in Arabic script) to Arabizi (Written in Latin script) and vice-versa.
+* **EgyptianPIQA:** An Egyptian version of PIQA benchmark (In both scripts Arabic and Latin).
+* **EgyptianWinoGrande:** An Egyptian version of WinoGrande benchmark (In both scripts Arabic and Latin).
+* **EgyptianRACE:** An Egyptian version of RACE benchmark (In both scripts Arabic and Latin).
+* **EgyptianOpenBookQA:** An Egyptian version of OpenBookQA benchmark.
+
+The models were compared against a collection of existing open-source Arabic models to gauge their effectiveness, with a particular focus on performance in Egyptian. All scores are based on zero-shot performance. The prompts are written mainly in Egyptian. We used [Language Model Evaluation Harness](https://github.com/MBZUAI-Paris/lm-evaluation-harness-nile-chat) to conduct these evaluations. All evaluations are done with applying chat template except for EgyptianWinoGrande.
+
+**Benchmarks:**
+<table>
+    <tr>
+        <td>Model</td>
+        <td>Average</td>
+        <td><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianMMLU_dev" target="_blank">EgyptianMMLU</a></td>
+        <td ><a href="https://huggingface.co/datasets/facebook/belebele/viewer/ary_Arab" target="_blank">Belebele Arz</a></td>
+        <td><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianHellaSwag" target="_blank">EgyptianHellaSwag</br>(Arabic Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianPIQA" target="_blank">EgyptianPIQA</br>(Arabic Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianWinoGrande" target="_blank">EgyptianWinoGrande</br>(Arabic Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianOpenBookQA" target="_blank">EgyptianOpenBookQA</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianRACE" target="_blank">EgyptianRACE High</br>(Arabic Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianRACE" target="_blank">EgyptianRACE Middle</br>(Arabic Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianHellaSwag" target="_blank">EgyptianHellaSwag</br>(Latin Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianPIQA" target="_blank">EgyptianPIQA</br>(Latin Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianWinoGrande" target="_blank">EgyptianWinoGrande</br>(Latin Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianRACE" target="_blank">EgyptianRACE High</br>(Latin Script)</a></td>
+        <td ><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianRACE" target="_blank">EgyptianRACE Middle</br>(Latin Script)</a></td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/google/gemma-3-4b-it" target="_blank">gemma-3-4b-it</a></td>
+        <td>41.4</td>
+        <td>46.08</td>
+        <td>38.56</td>
+        <td>42.56</td>
+        <td>60.32</td>
+        <td>56.49</td>
+        <td>35.79</td>
+        <td>33.68</td>
+        <td>40.06</td>
+        <td>30.90</td>
+        <td>52.76</td>
+        <td>48.57</td>
+        <td>25.47</td>
+        <td>26.94</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-family-6p7b-chat" target="_blank">jais-family-6p7b-chat</a></td>
+        <td>43.22</td>
+        <td>42.6</td>
+        <td>57.33</td>
+        <td>49.18</td>
+        <td>62.23</td>
+        <td>57.04</td>
+        <td>33.33</td>
+        <td>34.72</td>
+        <td>37.5</td>
+        <td>30.27</td>
+        <td>53.25</td>
+        <td>52.14</td>
+        <td>24.18</td>
+        <td>28.06</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-adapted-7b-chat" target="_blank">jais-adapted-7b-chat</a></td>
+        <td>41.81</td>
+        <td>40.96</td>
+        <td>55.67</td>
+        <td>40.85</td>
+        <td>56.5</td>
+        <td>54.35</td>
+        <td>32.89</td>
+        <td>34.62</td>
+        <td>42.33</td>
+        <td>30.81</td>
+        <td>51.67</td>
+        <td>50.4</td>
+        <td>24.38</td>
+        <td>28.06</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/Qwen/Qwen2.5-7B-Instruct"  target="_blank">Qwen2.5-7B-Instruct</a></td>
+        <td>43.85</td>
+        <td>45.74</td>
+        <td>64.22</td>
+        <td>45.47</td>
+        <td>58.02</td>
+        <td>56.41</td>
+        <td>38.7</td>
+        <td>35.45</td>
+        <td>41.76</td>
+        <td>30.51</td>
+        <td>51.88</td>
+        <td>50.95</td>
+        <td>24.88</td>
+        <td>26.11</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/ALLaM-AI/ALLaM-7B-Instruct-preview" target="_blank">ALLaM-7B-Instruct-preview</a></td>
+        <td>48.53</td>
+        <td>60.08</td>
+        <td>67.67</td>
+        <td>57.29</td>
+        <td>66.1</td>
+        <td>62.18</td>
+        <td>40.04</td>
+        <td>39.5</td>
+        <td>45.17</td>
+        <td>32.17</td>
+        <td>53.09</td>
+        <td>50.63</td>
+        <td>25.07</td>
+        <td>31.94</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/CohereLabs/c4ai-command-r7b-arabic-02-2025" target="_blank">c4ai-command-r7b-arabic-02-2025</a></td>
+        <td>46.27</td>
+        <td>50.97</td>
+        <td>70.67</td>
+        <td>50.39</td>
+        <td>61.84</td>
+        <td>57.2</td>
+        <td>36.91</td>
+        <td>41.89</td>
+        <td>46.02</td>
+        <td>30.88</td>
+        <td>52.32</td>
+        <td>51.43</td>
+        <td>25.07</td>
+        <td>27.22</td>
+    </tr>
+    <!-- <tr style="border-top: 4px solid;"></tr> -->
+    <tr>
+        <td><a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct" target="_blank">Llama-3.1-8B-Instruct</a></td>
+        <td>42.5</td>
+        <td>42.88</td>
+        <td>55.89</td>
+        <td>43.1</td>
+        <td>57.97</td>
+        <td>54.27</td>
+        <td>35.57</td>
+        <td>34.41</td>
+        <td>40.34</td>
+        <td>31.77</td>
+        <td>53.3</td>
+        <td>50.24</td>
+        <td>24.48</td>
+        <td>28.33</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/FreedomIntelligence/AceGPT-v2-8B-chat" target="_blank">AceGPT-v2-8b-chat</a></td>
+        <td>48.04</td>
+        <td>55.25</td>
+        <td>73.33</td>
+        <td>53.14</td>
+        <td>62.5</td>
+        <td>58.39</td>
+        <td>39.82</td>
+        <td>41.06</td>
+        <td>47.16</td>
+        <td>33.16</td>
+        <td>53.8</td>
+        <td>50.24</td>
+        <td>26.07</td>
+        <td>30.56</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/google/gemma-2-9b-it" target="_blank">gemma-2-9b-it</a></td>
+        <td>45.41</td>
+        <td>50.72</td>
+        <td>49.44</td>
+        <td>49.53</td>
+        <td>61.35</td>
+        <td>61.79</td>
+        <td>35.79</td>
+        <td>40.23</td>
+        <td>48.01</td>
+        <td>33.75</td>
+        <td>53.69</td>
+        <td>50.79</td>
+        <td>26.66</td>
+        <td>28.61</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/google/gemma-3-12b-it" target="_blank">gemma-3-12b-it</a></td>
+        <td>50.22</td>
+        <td>61.55</td>
+        <td>77</td>
+        <td>49.49</td>
+        <td>64.96</td>
+        <td>63.53</td>
+        <td>38.03</td>
+        <td>41.27</td>
+        <td>48.86</td>
+        <td>37.52</td>
+        <td>53.14</td>
+        <td>51.19</td>
+        <td>31.02</td>
+        <td>35.28</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-family-13b-chat" target="_blank">jais-family-13b-chat</a></td>
+        <td>44.66</td>
+        <td>44.85</td>
+        <td>66.33</td>
+        <td>52.99</td>
+        <td>64.85</td>
+        <td>57.91</td>
+        <td>36.91</td>
+        <td>33.26</td>
+        <td>38.64</td>
+        <td>30.46</td>
+        <td>53.09</td>
+        <td>48.18</td>
+        <td>25.28</td>
+        <td>27.78</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-adapted-13b-chat" target="_blank">jais-adapted-13b-chat</a></td>
+        <td>44.63</td>
+        <td>50.03</td>
+        <td>65.33</td>
+        <td>47.53</td>
+        <td>61.3</td>
+        <td>56.72</td>
+        <td>37.14</td>
+        <td>35.45</td>
+        <td>41.76</td>
+        <td>31.14</td>
+        <td>52.87</td>
+        <td>50.79</td>
+        <td>23.98</td>
+        <td>26.11</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/Qwen/Qwen2.5-14B-Instruct"  target="_blank">Qwen2.5-14B-Instruct</a></td>
+        <td>49.39</td>
+        <td>60.81</td>
+        <td>72.33</td>
+        <td>55.84</td>
+        <td>63.97</td>
+        <td>59.97</td>
+        <td>38.26</td>
+        <td>43.25</td>
+        <td>50.28</td>
+        <td>33.49</td>
+        <td>52.87</td>
+        <td>53.41</td>
+        <td>27.35</td>
+        <td>30.28</td>
+    </tr>
+    <tr style="border-top: 4px solid;"></tr>
+    <tr>
+        <td><strong><a href="https://huggingface.co/MBZUAI-Paris/Nile-Chat-4B" target="_blank">Nile-Chat-4B</a></td>
+        <td>53.07</td>
+        <td>50.25</td>
+        <td>68.56</td>
+        <td>55.92</td>
+        <td>67.3</td>
+        <td>61.87</td>
+        <td>40.94</td>
+        <td>42.1</td>
+        <td>46.02</td>
+        <td>50.55</td>
+        <td>65.32</td>
+        <td>60.62</td>
+        <td>37.36</td>
+        <td>43.06</td>
+    </tr>
+    <tr>
+        <td><strong><a href="https://huggingface.co/MBZUAI-Paris/Nile-Chat-12B" target="_blank">Nile-Chat-12B</a></td>
+        <td>57.92</td>
+        <td>62.59</td>
+        <td>79.44</td>
+        <td>64.04</td>
+        <td>70.69</td>
+        <td>63.53</td>
+        <td>42.06</td>
+        <td>48.02</td>
+        <td>53.13</td>
+        <td>53.71</td>
+        <td>65.1</td>
+        <td>59.98</td>
+        <td>41.72</td>
+        <td>48.89</td>
+    </tr>
+</table>
+
+**Translation and Transliteration Tasks:**
+<table>
+    <tr>
+        <td rowspan="2">Model</td>
+        <td colspan="3"><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianBench" target="_blank">Long Translation</a></td>
+        <td colspan="3"><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianBench" target="_blank">Short Translation</a></td>
+        <td colspan="3"><a href="https://huggingface.co/datasets/MBZUAI-Paris/EgyptianBench" target="_blank">Transliteration</a></td>
+    </tr>
+    <tr>
+        <td>BLEU</td>
+        <td>chrF</td>
+        <td>BERTScore</td>
+        <td>BLEU</td>
+        <td>chrF</td>
+        <td>BERTScore</td>
+        <td>BLEU</td>
+        <td>chrF</td>
+        <td>BERTScore</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/google/gemma-3-4b-it" target="_blank">gemma-3-4b-it</a></td>
+        <td>20.67</td>
+        <td>44.75</td>
+        <td>73.03</td>
+        <td>04.76</td>
+        <td>31.15</td>
+        <td>52.98</td>
+        <td>01.44</td>
+        <td>20.36</td>
+        <td>47.54</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-family-6p7b-chat" target="_blank">jais-family-6p7b-chat</a></td>
+        <td>12.71</td>
+        <td>36.53</td>
+        <td>68.07</td>
+        <td>08.73</td>
+        <td>31.52</td>
+        <td>56.78</td>
+        <td>00.70</td>
+        <td>10.64</td>
+        <td>42.51</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-adapted-7b-chat" target="_blank">jais-adapted-7b-chat</a></td>
+        <td>10.61</td>
+        <td>27.56</td>
+        <td>63.48</td>
+        <td>09.19</td>
+        <td>24.85</td>
+        <td>53.52</td>
+        <td>01.11</td>
+        <td>06.14</td>
+        <td>40.45</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/Qwen/Qwen2.5-7B-Instruct"  target="_blank">Qwen2.5-7B-Instruct</a></td>
+        <td>19.89</td>
+        <td>44.80</td>
+        <td>73.64</td>
+        <td>11.34</td>
+        <td>36.31</td>
+        <td>54.96</td>
+        <td>02.74</td>
+        <td>20.63</td>
+        <td>49.32</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/ALLaM-AI/ALLaM-7B-Instruct-preview" target="_blank">ALLaM-7B-Instruct-preview</a></td>
+        <td>26.57</td>
+        <td>52.59</td>
+        <td>78.34</td>
+        <td>25.20</td>
+        <td>48.12</td>
+        <td>65.97</td>
+        <td>02.10</td>
+        <td>18.92</td>
+        <td>49.42</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/CohereLabs/c4ai-command-r7b-arabic-02-2025" target="_blank">c4ai-command-r7b-arabic-02-2025</a></td>
+        <td>25.18</td>
+        <td>50.26</td>
+        <td>77.97</td>
+        <td>23.30</td>
+        <td>45.34</td>
+        <td>65.20</td>
+        <td>03.52</td>
+        <td>24.57</td>
+        <td>50.49</td>
+    </tr>
+    <!-- <tr style="border-top: 4px solid;"></tr> -->
+    <tr>
+        <td><a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct" target="_blank">Llama-3.1-8B-Instruct</a></td>
+        <td>12.90</td>
+        <td>32.58</td>
+        <td>68.76</td>
+        <td>09.06</td>
+        <td>28.56</td>
+        <td>54.19</td>
+        <td>03.26</td>
+        <td>17.55</td>
+        <td>48.71</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/FreedomIntelligence/AceGPT-v2-8B-chat" target="_blank">AceGPT-v2-8b-chat</a></td>
+        <td>24.59</td>
+        <td>49.39</td>
+        <td>77.57</td>
+        <td>22.47</td>
+        <td>44.97</td>
+        <td>66.30</td>
+        <td>04.80</td>
+        <td>23.52</td>
+        <td>49.33</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/google/gemma-2-9b-it" target="_blank">gemma-2-9b-it</a></td>
+        <td>23.09</td>
+        <td>46.98</td>
+        <td>75.42</td>
+        <td>11.73</td>
+        <td>39.00</td>
+        <td>60.42</td>
+        <td>02.68</td>
+        <td>24.28</td>
+        <td>48.26</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/google/gemma-3-12b-it" target="_blank">gemma-3-12b-it</a></td>
+        <td>22.90</td>
+        <td>45.97</td>
+        <td>73.46</td>
+        <td>05.24</td>
+        <td>32.82</td>
+        <td>54.34</td>
+        <td>02.77</td>
+        <td>26.16</td>
+        <td>50.47</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-family-13b-chat" target="_blank">jais-family-13b-chat</a></td>
+        <td>10.41</td>
+        <td>31.98</td>
+        <td>64.15</td>
+        <td>08.64</td>
+        <td>30.10</td>
+        <td>57.00</td>
+        <td>00.84</td>
+        <td>11.35</td>
+        <td>44.71</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/inceptionai/jais-adapted-13b-chat" target="_blank">jais-adapted-13b-chat</a></td>
+        <td>15.53</td>
+        <td>41.48</td>
+        <td>70.86</td>
+        <td>15.96</td>
+        <td>38.81</td>
+        <td>63.52</td>
+        <td>01.00</td>
+        <td>13.33</td>
+        <td>46.08</td>
+    </tr>
+    <tr>
+        <td><a href="https://huggingface.co/Qwen/Qwen2.5-14B-Instruct"  target="_blank">Qwen2.5-14B-Instruct</a></td>
+        <td>21.71</td>
+        <td>45.55</td>
+        <td>73.36</td>
+        <td>09.26</td>
+        <td>34.21</td>
+        <td>53.89</td>
+        <td>04.07</td>
+        <td>25.83</td>
+        <td>51.41</td>
+    </tr>
+    <tr style="border-top: 4px solid;"></tr>
+    <tr>
+        <td><strong><a href="https://huggingface.co/MBZUAI-Paris/Nile-Chat-4B" target="_blank">Nile-Chat-4B</a></td>
+        <td>37.49</td>
+        <td>58.40</td>
+        <td>84.30</td>
+        <td>30.35</td>
+        <td>52.01</td>
+        <td>74.07</td>
+        <td>51.46</td>
+        <td>80.44</td>
+        <td>89.59</td>
+    </tr>
+    <tr>
+        <td><strong><a href="https://huggingface.co/MBZUAI-Paris/Nile-Chat-12B" target="_blank">Nile-Chat-12B</a></td>
+        <td>40.53</td>
+        <td>60.61</td>
+        <td>85.45</td>
+        <td>32.2</td>
+        <td>53.53</td>
+        <td>74.72</td>
+        <td>52.21</td>
+        <td>80.97</td>
+        <td>89.71</td>
+    </tr>
+
+    
+</table>
+
+## Usage and Limitations
+
+These models have certain limitations that users should be aware of.
+<details>
+<summary>Intended Usage</summary>
+
+Open Large Language Models (LLMs) have a wide range of applications across
+various industries and domains. The following list of potential uses is not
+comprehensive. The purpose of this list is to provide contextual information
+about the possible use-cases that the model creators considered as part of model
+training and development.
+
+* Content Creation and Communication
+  * Text Generation: These models can be used to generate creative text formats
+    such as poems, scripts, code, marketing copy, and email drafts.
+  * Chatbots and Conversational AI: Power conversational interfaces for customer
+    service, virtual assistants, or interactive applications.
+  * Text Summarization: Generate concise summaries of a text corpus, research
+    papers, or reports.
+* Research and Education
+  * Natural Language Processing (NLP) Research: These models can serve as a
+    foundation for researchers to experiment with NLP techniques, develop
+    algorithms, and contribute to the advancement of the field.
+  * Language Learning Tools: Support interactive language learning experiences,
+    aiding in grammar correction or providing writing practice.
+  * Knowledge Exploration: Assist researchers in exploring large bodies of text
+    by generating summaries or answering questions about specific topics.
+</details>
+<details>
+<summary>Limitations</summary>
+
+* Training Data
+  * The quality and diversity of the training data significantly influence the
+    model's capabilities. Biases or gaps in the training data can lead to
+    limitations in the model's responses.
+  * The scope of the training dataset determines the subject areas the model can
+    handle effectively.
+* Context and Task Complexity
+  * LLMs perform better on tasks framed with clear prompts and
+    instructions. Open-ended or highly complex tasks might be challenging.
+  * A model's performance can be influenced by the amount of context provided
+    (longer context generally leads to better outputs, up to a certain point).
+* Language Ambiguity and Nuance
+  * Natural language is inherently complex. LLMs might struggle to grasp subtle
+    nuances, sarcasm, or figurative language.
+* Factual Accuracy
+  * LLMs generate responses based on information they learned from their
+    training datasets, but they are not knowledge bases. They may generate
+    incorrect or outdated factual statements.
+* Common Sense
+  * LLMs rely on statistical patterns in language. They might lack the ability
+    to apply common sense reasoning in certain situations.
+</details>
+<details>
+<summary> Ethical Considerations and Risks</summary>
+
+The development of large language models (LLMs) raises several ethical concerns.
+In creating an open model, we have carefully considered the following:
+
+* Bias and Fairness
+  * LLMs trained on large-scale, real-world text data can reflect socio-cultural
+    biases embedded in the training material.
+* Misinformation and Misuse
+  * LLMs can be misused to generate text that is false, misleading, or harmful.
+  * Guidelines are provided for responsible use with the model, see the
+    [Responsible Generative AI Toolkit][rai-toolkit].
+* Transparency and Accountability:
+  * This model card summarizes details on the models' architecture,
+    capabilities, limitations, and evaluation processes.
+  * A responsibly developed open model offers the opportunity to share
+    innovation by making LLM technology accessible to developers and researchers
+    across the AI ecosystem.
+
+Risks identified and mitigations:
+
+* Perpetuation of biases: It's encouraged to perform continuous monitoring
+  (using evaluation metrics, human review) and the exploration of de-biasing
+  techniques during model training, fine-tuning, and other use cases.
+* Generation of harmful content: Mechanisms and guidelines for content safety
+  are essential. Developers are encouraged to exercise caution and implement
+  appropriate content safety safeguards based on their specific product policies
+  and application use cases.
+* Privacy violations: Models were trained on data filtered for removal of PII
+  (Personally Identifiable Information). Developers are encouraged to adhere to
+  privacy regulations with privacy-preserving techniques.
+
+</details>