Update README.md

README.md

@@ -5,16 +5,18 @@ language:
 - en
 pipeline_tag: text-generation
 tags:
-  - pytorch
+- pytorch
 library_name: transformers
+base_model:
+- QCRI/Fanar-1-9B
 ---
 # Fanar-1-9B-Instruct
 
-**Fanar-1-9B-Instruct** is a powerful Arabic-English LLM developed by [Qatar Computing Research Institute (QCRI)](https://www.hbku.edu.qa/en/qcri) at [Hamad Bin Khalifa University (HBKU)](https://www.hbku.edu.qa/), a member of Qatar Foundation for Education, Science, and Community Development. It is the instruction-tuned version of [Fanar-1-9B](). Fanar continually pretrains the `google/gemma-2-9b` model on 1T Arabic and English tokens. Fanar pays particular attention to the richness of the Arabic language by supporting a diverse set of Arabic dialects including Modern Standard Arabic (MSA), Levantine, and Egyptian. Fanar, through meticulous curation of the pretraining and instruction-tuning data, is aligned with Arab cultural values.
+**Fanar-1-9B-Instruct** is a powerful Arabic-English LLM developed by [Qatar Computing Research Institute (QCRI)](https://www.hbku.edu.qa/en/qcri) at [Hamad Bin Khalifa University (HBKU)](https://www.hbku.edu.qa/), a member of Qatar Foundation for Education, Science, and Community Development. It is the instruction-tuned version of [Fanar-1-9B](https://huggingface.co/QCRI/Fanar-1-9B). We continually pretrains the `google/gemma-2-9b` model on 1T Arabic and English tokens. We pay particular attention to the richness of the Arabic language by supporting Modern Standard Arabic (MSA) and a diverse set of Arabic dialects including Levantine and Egyptian. Fanar models, through meticulous curation of the pretraining and instruction-tuning data, are aligned with Islamic values and Arab cultures.
 
 **Fanar-1-9B-Instruct** is a core component within the [Fanar GenAI platform](https://fanar.qa/) that offers a suite of capabilities including image generation, video and image understanding, deep thinking, advanced text-to-speech (TTS) and automatic-speech-recognition (ASR), attribution and fact-checking, Islamic RAG, among several other features.
 
-We have published a comprehensive [report](https://arxiv.org/pdf/2501.13944) with all the details regarding FANAR. We also provide an API to the model and our GenAI platform (request access [here](https://api.fanar.qa/request/en)).
+We have published a comprehensive [report](https://arxiv.org/pdf/2501.13944) with all the details regarding our Fanar GenAI platform. We also provide an API to our models and the GenAI platform (request access [here](https://api.fanar.qa/request/en)).
 
 ---
 
@@ -22,13 +24,13 @@ We have published a comprehensive [report](https://arxiv.org/pdf/2501.13944) wit
 
 | Attribute                  | Value                              |
 |---------------------------|------------------------------------|
-| Developed by              | [QCRI](https://www.hbku.edu.qa/en/qcri) and [HBKU](https://www.hbku.edu.qa/)                      |
-| Sponsored by              | [MCIT](https://www.mcit.gov.qa/en/)
+| Developed by              | [QCRI](https://www.hbku.edu.qa/en/qcri) at [HBKU](https://www.hbku.edu.qa/)                      |
+| Sponsored by              | [Minisitry of Communications and Technology, State of Qatar](https://www.mcit.gov.qa/en/)
 | Model Type                | Autoregressive Transformer         |
 | Parameter Count           | 8.7 Billion                          |
 | Context Length            | 4096 Tokens                        |
-| Input                     | text only                          |
-| Output                    | text only                          |
+| Input                     | Text only                          |
+| Output                    | Text only                          |
 | Training Framework        | [LitGPT](https://github.com/Lightning-AI/litgpt) |
 | Pretraining Token Count   | 1 Trillion (ar + en) |
 | SFT Instructions          | 4.5M                               |
@@ -87,56 +89,45 @@ Fanar-1-9B-Instruct is built for:
 
 - Conversational agents (Arabic only or bilingual)
 - Cultural and dialectal question answering in Arabic
-- Educational, governmental, and civic NLP applications in the Arab world
+- Educational, governmental, and civic NLP applications focussed on the Arab world or Arabic-speaking audiences
 - Research on Arabic natural language generation and understanding
 
 Fanar is intended to be deployed as part of a broader AI system. Developers are encouraged to implement proper safeguards to ensure culturally respectful, accurate, and safe deployment. It should not be used to generate or spread **harmful, illegal, or misleading content**
 
-A version of this model is currently deployed as part of a real-world system at [chat.fanar.qa](chat.fanar.qa).
+A version of this model can be accessed through [Fanar Chat](chat.fanar.qa). We are continuously improving the Fanar’s models and capabilities, and answers can differ from what you get from Fanar-1-9B-Instruct.
 
 --- 
 
 ## Ethical Considerations & Limitations
 
-Fanar is capable of generating fluent and contextually appropriate responses, but as with any generative model there are uncertainities. The model may produce **biased, offensive, or incorrect outputs**. The model should It is **not suitable for high-stakes decision making** (e.g., legal, medical, or financial advice). Though we have extensively tested Fanar and attempted to mitigate these issues, we cannot redress every possible scenario. Thus, we advise developers to implement safety checks and perform domain-specific fine-tuning for sensitive use cases. Kindly refer to our [Terms of Service]( https://chat.fanar.qa/terms-of-service) and [Privacy Policy](https://chat.fanar.qa/privacy-policy).
+Fanar is capable of generating fluent and contextually appropriate responses, but as with any generative model there are uncertainities. The model may produce **biased, offensive, or incorrect outputs**. The model is **not suitable for high-stakes decision making** (e.g., legal, medical, or financial advice). Though we have extensively tested Fanar and attempted to mitigate these issues, we cannot redress every possible scenario. Thus, we advise developers to implement safety checks and perform domain-specific fine-tuning for sensitive use cases. Kindly refer to our [Terms of Service]( https://chat.fanar.qa/terms-of-service) and [Privacy Policy](https://chat.fanar.qa/privacy-policy).
 
-The output generated by this model is not considered a statement of QCRI, HBKU, or any other organization or individual.
+The output generated by this model is not considered a statement of QCRI, HBKU, Qatar Foundation, MCIT or any other organization or individual.
  
 ---
 
 ## Evaluation
 
-Evaluation results for Fanar-1-9B-Instruct will be released soon across Arabic and English benchmarks including:
-
-- Arabic MMLU
-- Ar-IFEval
-- TruthfulQA
-- HellaSwag
-- ARC (Easy/Challenge)
-- OpenBookQA
-
 Evaluation was conducted using a modified version of the LM Evaluation Harness and internal cultural alignment benchmarks.
 
 <div style="overflow-x: auto;">
 
 | Model | MMLU (5-shot) | MMMLU (Arabic) (0-shot) | ArabicMMLU (3-shot) | HellaSwag (0-shot) | PIQA (0-shot) | ARC Challenge (0-shot) | Belebele (Arabic) (3-shot) | ACVA (5-shot) | GSM8k | OALL (0-shot) | OALL v2 (0-shot) | Almieyar Arabic (3-shot) | Arab Cultural MCQ (3-shot) | AraDiCE PIQA (MSA) (0-shot) | AraDiCE PIQA(Egy) (0-shot) | AraDiCE PIQA(Lev) (0-shot) | AraDiCE ArabicMMLU(Egy) (0-shot) | AraDiCE ArabicMMLU(Lev) (0-shot) |
 |-------|----------------|--------------------------|----------------------|--------------------|---------------|-------------------------|------------------------------|---------------|--------|----------------|------------------|---------------------------|-----------------------------|-------------------------------|------------------------------|------------------------------|-----------------------------------|-----------------------------------|
-| Fanar-1-9B-it | 71.53% | **58.89%** | **67.69%** | **83.16%** | 82.54% | **67.15%** | **83.22%** | 80.02% | **74.60%** | **68.32%** | **66.29%** | **78.68%** | 72.40% | **67.68%** | **63.66%** | 59.03% | 59.63% | 60.62% |
-| ALLaM-7B-Instruct-preview | 60.72% | 54.89% | 68.59% | 76.35% | 80.52% | 51.62% | 75.80% | 74.52% | 46.63% | 57.31% | 63.66% | 76.31% | **74.20%** | 67.52% | 63.44% | **60.88%** | **62.50%** | **64.17%** |
-| aya-expanse-8b | 62.85% | 47.14% | 60.10% | 78.54% | **81.18%** | 56.40% | 70.78% | 77.11% | 8.26% | 53.18% | 59.74% | 70.20% | 67.30% | 63.00% | 59.41% | 56.53% | 53.52% | 53.71% |
+| Fanar-1-9B-it | 71.53% | **58.89%** | 67.69% | **83.16%** | **82.54%** | **67.15%** | **83.22%** | 80.02% | **74.60%** | **68.32%** | **66.29%** | **78.68%** | 72.40% | **67.68%** | **63.66%** | 59.03% | 59.63% | 60.62% |
+| ALLaM-7B-Instruct-preview | 60.72% | 54.89% | **68.59%** | 76.35% | 80.52% | 51.62% | 75.80% | 74.52% | 46.63% | 57.31% | 63.66% | 76.31% | **74.20%** | 67.52% | 63.44% | **60.88%** | **62.50%** | **64.17%** |
+| aya-expanse-8b | 62.85% | 47.14% | 60.10% | 78.54% | 81.18% | 56.40% | 70.78% | 77.11% | 8.26% | 53.18% | 59.74% | 70.20% | 67.30% | 63.00% | 59.41% | 56.53% | 53.52% | 53.71% |
 | c4ai-command-r7b-arabic-02-2025 | 66.91% | 49.54% | 63.06% | 74.67% | 78.02% | 49.15% | 72.78% | 79.80% | 30.33% | 49.38% | 64.44% | 73.82% | 69.20% | 62.30% | 60.99% | 56.69% | 54.78% | 56.06% |
 | AceGPT-v2-8B-Chat | 66.45% | 51.16% | 62.61% | 79.21% | 80.58% | 53.50% | 74.56% | 77.66% | 41.77% | 50.16% | 60.40% | 74.31% | 68.90% | 64.58% | 61.32% | 56.91% | 54.53% | 53.91% |
 | gemma-2-9b-it | 71.65% | 57.93% | 64.16% | 79.06% | 79.38% | 63.99% | 78.31% | **80.67%** | 60.95% | 56.11% | 64.21% | 73.69% | 68.60% | 61.26% | 59.96% | 57.24% | 57.95% | 59.25% |
 | jais-adapted-13b-chat | 56.64% | 44.45% | 58.97% | 80.86% | 80.47% | 54.27% | 67.52% | 75.24% | 44.05% | 46.41% | 56.56% | 65.46% | 65.30% | 61.10% | 58.05% | 55.77% | 52.87% | 53.59% |
 | jais-family-6p7b-chat | 49.42% | 41.59% | 55.80% | 72.04% | 74.05% | 44.62% | 65.11% | 72.04% | 53.68% | 48.20% |    54.73%    | 61.72% | 64.10% | 62.51% | 60.12% | 57.24% | 49.11% | 47.49% |
-| Llama-3.1-8B-Instruct | 68.04% | 47.58% | 59.05% | 79.22% | 80.74% | 55.29% | 66.72% | 76.67% | 29.26% | 47.81% |    TBD    | 69.70% | 66.10% | 58.11% | 55.39% | 54.24% | 46.86% | 47.52% |
+| Llama-3.1-8B-Instruct | 68.04% | 47.58% | 59.05% | 79.22% | 80.74% | 55.29% | 66.72% | 76.67% | 29.26% | 47.81% | 55.97% | 69.70% | 66.10% | 58.11% | 55.39% | 54.24% | 46.86% | 47.52% |
 | Qwen2.5-7B-Instruct | **74.21%** | 55.63% | 63.96% | 80.44% | 79.92% | 55.03% | 74.61% | 78.09% | 71.34% | 54.19% | 62.69% | 75.69% | 68.10% | 60.55% | 58.65% | 56.04% | 48.74% | 53.42% |
 
 </div>
 
 
-
-
 ---
 
 ## Citation
@@ -146,7 +137,7 @@ If you use Fanar in your research or applications, please cite:
 ```bibtex
 @misc{fanarllm2025,
       title={Fanar: An Arabic-Centric Multimodal Generative AI Platform}, 
-      author={Fanar Team},
+      author={Fanar Team and Ummar Abbas and Mohammad Shahmeer Ahmad and Firoj Alam and Enes Altinisik and Ehsannedin Asgari and Yazan Boshmaf and Sabri Boughorbel and Sanjay Chawla and Shammur Chowdhury and Fahim Dalvi and Kareem Darwish and Nadir Durrani and Mohamed Elfeky and Ahmed Elmagarmid and Mohamed Eltabakh and Masoomali Fatehkia and Anastasios Fragkopoulos and Maram Hasanain and Majd Hawasly and Mus'ab Husaini and Soon-Gyo Jung and Ji Kim Lucas and Walid Magdy and Safa Messaoud and Abubakr Mohamed and Tasnim Mohiuddin and Basel Mousi and Hamdy Mubarak and Ahmad Musleh and Zan Naeem and Mourad Ouzzani and Dorde Popovic and Amin Sadeghi and Husrev Taha Sencar and Mohammed Shinoy and Omar Sinan and Yifan Zhang and Ahmed Ali and Yassine El Kheir and Xiaosong Ma and Chaoyi Ruan}},
       year={2025},
       url={https://arxiv.org/abs/2501.13944}, 
 }
@@ -156,12 +147,12 @@ If you use Fanar in your research or applications, please cite:
 
 ## Acknowledgements
 
-This project is an effort by the [Qatar Computing Research Institute (QCRI)](https://qcri.org), part of [Hamad Bin Khalifa University (HBKU)](https://hbku.edu.qa). We thank our engineering and research teams for their efforts in advancing Arabic-centric large language models.
-We thank the [MCIT](https://www.mcit.gov.qa/en/) for sponsoring this project and their continued support by providing the compute infrastructure.
+This project is from [Qatar Computing Research Institute (QCRI)](https://qcri.org) at [Hamad Bin Khalifa University (HBKU)](https://hbku.edu.qa), a member of Qatar Foundation. We thank our engineers, researchers, and support team for their efforts in advancing Arabic-centric large language models.
+Special thanks to the [Minisitry of Communications and Technology, State of Qatar](https://www.mcit.gov.qa/en/) for their continued support by providing the compute infrastructure through the Google Cloud Platform.
 
 
 ---
 
 ## License
 
-This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).