Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+base_model: inceptionai/jais-adapted-70b-chat
+language:
+- ar
+- en
+thumbnail: null
+tags:
+- Arabic
+- English
+- LLM
+- Decoder
+- causal-lm
+- jais-family
+license: apache-2.0
+pipeline_tag: text-generation
 ---
+# Jais Family Model Card
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+The Jais family of models is a comprehensive series of bilingual English-Arabic large language models (LLMs). These models are optimized to excel in Arabic while having strong English capabilities. We release two variants of foundation models that include:
 
+- Models **pre-trained from scratch** (`jais-family-*`).
+- Models **pre-trained adaptively from [Llama-2](https://arxiv.org/pdf/2307.09288)** (`jais-adapted-*`).
 
+In this release, we introduce 20 models across 8 sizes, ranging from 590M to 70B parameters, trained on up to 1.6T tokens of Arabic, English, and code data.  *All* pre-trained models in this series are instruction fine-tuned (`*-chat`) for dialog using a curated mix of Arabic and English instruction data.
 
-## Model Details
+We hope this extensive release will accelerate research in Arabic NLP, and enable numerous downstream applications for the Arabic speaking and bilingual community. The training and adaptation techniques we demonstrate successfully for Arabic models are extensible to other low and medium resource languages.
 
-### Model Description
+## Jais Family Details
 
-<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** Inception, Cerebras Systems.
+- **Language(s):** (NLP): Arabic (MSA) and English.
+- **Input:** Text only data.
+- **Output:** Model generates text.
+- **Model Sizes:** 590M, 1.3B, 2.7B, 6.7B, 7B, 13B, 30B, 70B.
+- **Demo:** [Access the live demo here](https://arabic-gpt.ai/)
+- **License:** Apache 2.0
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+| **Pre-trained Model**   | **Fine-tuned Model** | **Size (Parameters)** | **Context length (Tokens)** |
+|:---------------------|:--------|:-------|:-------|
+| [jais-family-30b-16k](https://huggingface.co/inceptionai/jais-family-30b-16k)   | [Jais-family-30b-16k-chat](https://huggingface.co/inceptionai/jais-family-30b-16k-chat) | 30B | 16,384 |
+| [jais-family-30b-8k](https://huggingface.co/inceptionai/jais-family-30b-8k)  | [Jais-family-30b-8k-chat](https://huggingface.co/inceptionai/jais-family-30b-8k-chat) | 30B | 8,192 |
+| [jais-family-13b ](https://huggingface.co/inceptionai/jais-family-13b)  | [Jais-family-13b-chat](https://huggingface.co/inceptionai/jais-family-13b-chat) | 13B | 2,048 |
+| [jais-family-6p7b](https://huggingface.co/inceptionai/jais-family-6p7b)  | [Jais-family-6p7b-chat](https://huggingface.co/inceptionai/jais-family-6p7b-chat) | 6.7B | 2,048 |
+| [jais-family-2p7b](https://huggingface.co/inceptionai/jais-family-2p7b)  | [Jais-family-2p7b-chat](https://huggingface.co/inceptionai/jais-family-2p7b-chat) | 2.7B   | 2,048  |
+| [jais-family-1p3b](https://huggingface.co/inceptionai/jais-family-1p3b)  | [Jais-family-1p3b-chat](https://huggingface.co/inceptionai/jais-family-1p3b-chat) | 1.3B | 2,048 |
+| [jais-family-590m](https://huggingface.co/inceptionai/jais-family-590m)  | [Jais-family-590m-chat](https://huggingface.co/inceptionai/jais-family-590m-chat)  | 590M   | 2,048  |
 
-- **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]
+| **Adapted pre-trained Model**   | **Fine-tuned Model** | **Size (Parameters)** | **Context length (Tokens)** |
+|:---------------------|:--------|:-------|:-------|
+| [jais-adapted-70b](https://huggingface.co/inceptionai/jais-adapted-70b)   | [Jais-adapted-70b-chat](https://huggingface.co/inceptionai/jais-adapted-70b-chat) | 70B | 4,096 |
+| [jais-adapted-13b](https://huggingface.co/inceptionai/jais-adapted-13b)  | [Jais-adapted-13b-chat](https://huggingface.co/inceptionai/jais-adapted-13b-chat) | 13B | 4,096 |
+| [jais-adapted-7b](https://huggingface.co/inceptionai/jais-adapted-7b)  | [Jais-adapted-7b-chat](https://huggingface.co/inceptionai/jais-adapted-7b-chat) | 7B | 4,096 |
 
-### Model Sources [optional]
+### Model Architecture:
+<a name="model-architecture"></a>
 
-<!-- Provide the basic links for the model. -->
+All models in this family are auto-regressive language models that use a transformer-based, decoder-only architecture (GPT-3).
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Jais models (`jais-family-*`) are *trained from scratch*, incorporating the SwiGLU non-linear activation function and ALiBi position encoding. These architectural enhancements allow the models to extrapolate at long sequence lengths, leading to improved context handling and precision.
 
-## Uses
+Jais adapted models (`jais-adapted-*`) are *built on top of Llama-2*, which employs RoPE position embedding and Grouped Query Attention. We introduce tokenizer expansion with Arabic data, which improves fertility and compute efficiency by over 3x. In particular, we add `32,000` new Arabic tokens from the Jais-30b vocabulary into the Llama-2 tokenizer. 
+To initialize these new Arabic token embeddings we first learn a linear projection from the embedding space of Jais-30b to Llama's embedding space, using the set of shared English tokens present in both vocabularies. Next, this learned projection is applied to transform the existing Jais-30b Arabic embeddings into the Llama-2 embedding space.
 
-<!-- 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
+## Getting started
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Below is sample code to use the model. Note that the model requires a custom model class, so users must enable `trust_remote_code=True` while loading the model.
 
-[More Information Needed]
+```python
+# -*- coding: utf-8 -*-
 
-### Downstream Use [optional]
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+model_path = "inceptionai/jais-adapted-70b-chat"
 
-[More Information Needed]
+prompt_eng = "### Instruction:Your name is 'Jais', and you are named after Jebel Jais, the highest mountain in UAE. You were made by 'Inception' in the UAE. You are a helpful, respectful, and honest assistant. Always answer as helpfully as possible, while being safe. Complete the conversation between [|Human|] and [|AI|]:\n### Input: [|Human|] {Question}\n[|AI|]\n### Response :"
+prompt_ar = "### Instruction:اسمك \"جيس\" وسميت على اسم جبل جيس اعلى جبل في الامارات. تم بنائك بواسطة Inception في الإمارات. أنت مساعد مفيد ومحترم وصادق. أجب دائمًا بأكبر قدر ممكن من المساعدة، مع الحفاظ على البقاء أمناً. أكمل المحادثة بين [|Human|] و[|AI|] :\n### Input:[|Human|] {Question}\n[|AI|]\n### Response :"
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True)
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
 
-[More Information Needed]
+def get_response(text, tokenizer=tokenizer, model=model):
+    tokenized = tokenizer(text, return_tensors="pt")
+    input_ids, attention_mask = tokenized['input_ids'].to(device), tokenized['attention_mask'].to(device)
+    input_len = input_ids.shape[-1]
+    generate_ids = model.generate(
+        input_ids,
+        attention_mask=attention_mask,
+        top_p=0.9,
+        temperature=0.3,
+        max_length=2048,
+        min_length=input_len + 4,
+        repetition_penalty=1.2,
+        do_sample=True,
+        pad_token_id=tokenizer.pad_token_id
+    )
+    response = tokenizer.batch_decode(
+        generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
+    )[0]
+    response = response.split("### Response :")[-1].lstrip()
+    return response
 
-### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+ques = "ما هي عاصمة الامارات؟"
+text = prompt_ar.format_map({'Question': ques})
+print(get_response(text))
 
-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]
+ques = "What is the capital of UAE?"
+text = prompt_eng.format_map({'Question': ques})
+print(get_response(text))
+```
 
 ## 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]
+### Pretraining Data
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+The Jais family of models are trained on up to 1.6 Trillion tokens of diverse English, Arabic and Code data. The data consists of the following sources:
 
-[More Information Needed]
+- **Web:** We used publicly available web pages, wikipedia articles, news articles, and social network content in both Arabic and English.
+  
+- **Code:** To enhance the reasoning capability of our model, we include Code data in various programming languages. 
 
-## 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. -->
+- **Books:** We used a selection of publicly available Arabic and English books data, which improves long-range context modelling and coherent storytelling. 
 
-[More Information Needed]
+- **Scientific:** A subset of ArXiv papers were included to improve reasoning and long context abilities.
 
-#### Factors
+- **Synthetic:** We augment the volume of Arabic data by translating English to Arabic using an in-house machine translation system. We restrict this to high quality English resources such as English Wikipedia and English books. 
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+We extensively preprocess and deduplicate the training data. For Arabic, we used a custom preprocessing pipeline to filter for data with high linguistic quality. More information on this pipeline can be found in the [Jais paper](https://arxiv.org/abs/2308.16149).
 
-[More Information Needed]
+- **Jais pre-trained** (`jais-family-*`): Following our previous experimentation with language alignment mixing in [Jais](https://arxiv.org/abs/2308.16149), we used a ratio of 1:2:0.4 of Arabic:English:Code data. This recipe for <u>from scratch pre-training</u> addresses Arabic data scarcity while improving performance in both languages.
+- **Jais adapted pre-trained** (`jais-adapted-*`): For the <u>adapted pre-training of Llama-2</u>, we utilized a larger Arabic dataset of ~334B Arabic tokens mixed with English and Code data. We vary the mixing ratio, at different model sizes, to introduce strong Arabic capabilities while maintaining performance in English.
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+| **Pre-trained model**   | **English data (tokens)** | **Arabic data (tokens)** | **Code data (tokens)** | **Total data (tokens)** |
+|-------------------------|---------------------------|--------------------------|------------------------|------------------------|
+| [jais-family-30b-16k](https://huggingface.co/inceptionai/jais-family-30b-16k) | 980B | 490B | 196B | 1666B |
+| [jais-family-30b-8k](https://huggingface.co/inceptionai/jais-family-30b-8k)   | 882B | 441B | 177B | 1500B | 
+| [jais-family-13b ](https://huggingface.co/inceptionai/jais-family-13b)        | 283B  | 141B | 56B  | 480B | 
+| [jais-family-6p7b](https://huggingface.co/inceptionai/jais-family-6p7b)       | 283B  | 141B | 56B  | 480B | 
+| [jais-family-2p7b](https://huggingface.co/inceptionai/jais-family-2p7b)       | 283B  | 141B | 56B  | 480B | 
+| [jais-family-1p3b](https://huggingface.co/inceptionai/jais-family-1p3b)       | 283B  | 141B | 56B  | 480B | 
+| [jais-family-590m](https://huggingface.co/inceptionai/jais-family-590m)       | 283B  | 141B | 56B  | 480B | 
+| [jais-adapted-70b](https://huggingface.co/inceptionai/jais-adapted-70b)       | 33B   | 334B | 4B   | 371B |
+| [jais-adapted-13b](https://huggingface.co/inceptionai/jais-adapted-13b)       | 127B  | 140B | 13B  | 280B |
+| [jais-adapted-7b](https://huggingface.co/inceptionai/jais-adapted-7b)         | 18B   | 19B  |  2B   | 39B |
 
-[More Information Needed]
+### Finetuning data
 
-### Results
+<!-- This should link to a Data 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]
+All chat models in the Jais family are fine-tuned using Arabic and English prompt-response pairs in both single-turn and multi-turn settings. Data sources include open-source fine-tuning datasets filtered for topic and style diversity. Additionally, internally curated human data is incorporated to enhance cultural adaptation. This data is supplemented with content generated using synthetic methods including machine translation, distillation, and model self-chat. Overall, our updated instruction-tuning dataset comprises ~10M and ~4M prompt-response pairs in English and Arabic respectively.
 
-#### Summary
-
-
-
-## Model Examination [optional]
+### Training Procedure
 
-<!-- Relevant interpretability work for the model goes here -->
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-[More Information Needed]
+During the pre-training of (`jais-family-*`) models, documents are packed into sequences separated by EOS tokens, and the model is trained autoregressively, applying the loss to all tokens. For jais-30b models, the context length is progressively expanded from 2k to 8K to 16K by incorporating curated long-context documents in training. This progressive expansion leverages faster initial training at shorter context lengths, while gradually extending support for larger context lengths towards the end of the training process.
 
-## Environmental Impact
+During the adapted pre-training of the (`jais-adapted-*`) models, we first initialize the new tokenizer and Arabic embeddings as described in [Model Architecture](#model-architecture). In training, we implemented a two-stage approach to overcome observed higher norms of the new Arabic embeddings. In the first stage, the backbone of the model is frozen, and the embeddings are trained using approximately 15 billion tokens from a bilingual corpus of English and Arabic. In the second stage, the backbone is unfrozen, and continuous pretraining is conducted with all parameters.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+During instruction tuning, each training example consists of a single-turn or multi-turn prompt and it's response. Instead of one example per sequence, examples are packed together while the loss is masked on the prompt tokens. This approach speeds up training by allowing more examples to be processed per batch.
 
-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]
+### Training Hyperparameters:
 
-## Technical Specifications [optional]
+#### Jais-adapted-70b-chat
+| Hyperparameter | Value                           |
+|----------------|-------------------------------------------|
+| Precision      | fp32                                      |
+| Optimizer      | AdamW                                     |
+| Learning rate  | 0 to 2.0e-05(<=380 warmup steps)<br>2.0e-05 to 2.0e-06(>380 and <=13175 steps, Cosine Decay) |
+| Weight decay   | 0.1                                       |
+| Batch size     | 264|
+| Context Length | 4096|
+| Steps          | 13175 |
 
-### Model Architecture and Objective
+### Compute Infrastructure
 
-[More Information Needed]
+The training process was performed on the Condor Galaxy (CG) supercomputer platform. A CG contains 64 Cerebras CS-2 Wafer-Scale Engines (WSE-2) with 40 GB of SRAM, and achieves a total of 960 PetaFLOP/s.
 
-### Compute Infrastructure
+## Evaluation
 
-[More Information Needed]
+<!-- This section describes the evaluation protocols and provides the results. -->
 
-#### Hardware
+We conducted a comprehensive evaluation of Jais models focusing on both English and Arabic, using LM-harness in a zero-shot setting. The evaluation criteria spanned various dimensions, including:
+
+- **Knowledge:** How well the model answers factual questions.
+- **Reasoning:** The model's ability to answer questions requiring reasoning.
+- **Misinformation/Bias:** Assessment of the model's susceptibility to generating false or misleading information, and its neutrality.
+
+### Arabic evaluation results:
+
+<style>
+  .table-container {
+    overflow-x: auto;
+    white-space: nowrap;
+  }
+</style>
+
+<div class="table-container">
+
+|     **Models**   |  Avg  | ArabicMMLU*| MMLU  | EXAMS*| LitQA*| agqa | agrc | Hellaswag  | PIQA | BoolQA | Situated QA | ARC-C | OpenBookQA | TruthfulQA | CrowS-Pairs |
+|--------------------------|-------|------------|-------|-------|-------|------|------|------------|------|--------|-------------|-------|------------|------------|-------------|
+| jais-family-30b-16k      | 49.2  |    44.0    | 33.4  | 40.9  | 60    | 47.8 | 49.3 | 60.9       | 68.6 | 70.3   | 41.6        | 38.7  | 31.8       | 45.2       | 57          |
+| jais-family-30b-8k       | 49.7  |    46.0    | 34    | 42    | 60.6  | 47.6 | 50.4 | 60.4       | 69   | 67.7   | 42.2        | 39.2  | 33.8       | 45.1       | 57.3        |
+| jais-family-13b          | 46.1  |    34.0    | 30.3  | 42.7  | 58.3  | 40.5 | 45.5 | 57.3       | 68.1 | 63.1   | 41.6        | 35.3  | 31.4       | 41         | 56.1        |
+| jais-family-6p7b         | 44.6  |    32.2    | 29.9  | 39    | 50.3  | 39.2 | 44.1 | 54.3       | 66.8 | 66.5   | 40.9        | 33.5  | 30.4       | 41.2       | 55.4        |
+| jais-family-2p7b         | 41.0  |    29.5    | 28.5  | 36.1  | 45.7  | 32.4 | 40.8 | 44.2       | 62.5 | 62.2   | 39.2        | 27.4  | 28.2       | 43.6       | 53.6        |
+| jais-family-1p3b         | 40.8  |    28.9    | 28.5  | 34.2  | 45.7  | 32.4 | 40.8 | 44.2       | 62.5 | 62.2   | 39.2        | 27.4  | 28.2       | 43.6       | 53.6        |
+| jais-family-590m         | 39.7  |    31.2    | 27    | 33.1  | 41.7  | 33.8 | 38.8 | 38.2       | 60.7 | 62.2   | 37.9        | 25.5  | 27.4       | 44.7       | 53.3        |
+| jais-family-30b-16k-chat | 51.6  |    59.9    | 34.6  | 40.2  | 58.9  | 46.8 | 54.7 | 56.2       | 64.4 | 76.7   | 55.9        | 40.8  | 30.8       | 49.5       | 52.9        |
+| jais-family-30b-8k-chat  | 51.4  |    61.2    | 34.2  | 40.2  | 54.3  | 47.3 | 53.6 | 60         | 63.4 | 76.8   | 54.7        | 39.5  | 30         | 50.7       | 54.3        |
+| jais-family-13b-chat     | 50.3  |    58.2    | 33.9  | 42.9  | 53.1  | 46.8 | 51.7 | 59.3       | 65.4 | 75.2   | 51.2        | 38.4  | 29.8       | 44.8       | 53.8        |
+| jais-family-6p7b-chat    | 48.7  |    55.7    | 32.8  | 37.7  | 49.7  | 40.5 | 50.1 | 56.2       | 62.9 | 79.4   | 52          | 38    | 30.4       | 44.7       | 52          |
+| jais-family-2p7b-chat    | 45.6  |    50.0    | 31.5  | 35.9  | 41.1  | 37.3 | 42.1 | 48.6       | 63.7 | 74.4   | 50.9        | 35.3  | 31.2       | 44.5       | 51.3        |
+| jais-family-1p3b-chat    | 42.7  |    42.2    | 30.1  | 33.6  | 40.6  | 34.1 | 41.2 | 43         | 63.6 | 69.3   | 44.9        | 31.6  | 28         | 45.6       | 50.4        |
+| jais-family-590m-chat    | 37.8  |    39.1    |  28   |29.5   | 33.1  | 30.8 | 36.4 | 30.3       | 57.8 | 57.2   | 40.5        | 25.9  | 26.8       | 44.5       | 49.3        |
+
+
+
+|     **Adapted Models**   |  Avg  | ArabicMMLU*| MMLU  | EXAMS*| LitQA*| agqa | agrc | Hellaswag  | PIQA | BoolQA | Situated QA | ARC-C | OpenBookQA | TruthfulQA | CrowS-Pairs |
+|--------------------------|-------|------------|-------|-------|-------|------|------|------------|------|--------|-------------|-------|------------|------------|-------------|
+| jais-adapted-70b         | 51.5  |    55.9    | 36.8  | 42.3  | 58.3  | 48.6 | 54   | 61.5       | 68.4 | 68.4   | 42.1        | 42.6  | 33         | 50.2       | 58.3        |
+| jais-adapted-13b         | 46.6  |    44.7    | 30.6  | 37.7  | 54.3  | 43.8 | 48.3 | 54.9       | 67.1 | 64.5   | 40.6        | 36.1  | 32         | 43.6       | 54.00       |
+| jais-adapted-7b          | 42.0  |    35.9    | 28.9  | 36.7  | 46.3  | 34.1 | 40.3 | 45         | 61.3 | 63.8   | 38.1        | 29.7  | 30.2       | 44.3       | 53.6        |
+| jais-adapted-70b-chat    | 52.9  |    66.8    | 34.6  | 42.5  | 62.9  | 36.8 | 48.6 | 64.5       | 69.7 | 82.8   | 49.3        | 44.2  | 32.2       | 53.3       | 52.4        |
+| jais-adapted-13b-chat    | 50.3  |    59.0    | 31.7  | 37.5  | 56.6  | 41.9 | 51.7 | 58.8       | 67.1 | 78.2   | 45.9        | 41    | 34.2       | 48.3       | 52.1        |
+| jais-adapted-7b-chat     | 46.1  |    51.3    | 30    | 37    | 48    | 36.8 | 48.6 | 51.1       | 62.9 | 72.4   | 41.3        | 34.6  | 30.4       | 48.6       | 51.8        |
+
+</div>
+
+Arabic benchmarks are translated using an in-house MT model and reviewed by Arabic linguists. Benchmarks labeled with an asterisk (*) are natively Arabic; for further details, see the [Jais paper](https://arxiv.org/abs/2308.16149). Additionally, we include [ArabicMMLU](https://arxiv.org/abs/2402.12840), a native Arabic benchmark based on regional knowledge.
+
+
+### English evaluation results:
+
+<div class="table-container">
+
+| **Models**                  | Avg      | MMLU | RACE | Hellaswag | PIQA | BoolQA | SIQA | ARC-Challenge | OpenBookQA | Winogrande | TruthfulQA     | CrowS-Pairs |
+|--------------------------|----------|------|------|-----------|------|--------|------|---------------|------------|------------|----------------|-------------|
+| jais-family-30b-16k      | 59.3     | 42.2 | 40.5 | 79.7      | 80.6 | 78.7   | 48.8 | 50.3          | 44.2       | 71.6       | 43.5           | 72.6        |
+| jais-family-30b-8k       | 58.8     | 42.3 | 40.3 | 79.1      | 80.5 | 80.9   | 49.3 | 48.4          | 43.2       | 70.6       | 40.3           | 72.3        |
+| jais-family-13b          | 54.6     | 32.3 | 39   | 72        | 77.4 | 73.9   | 47.9 | 43.2          | 40         | 67.1       | 36.1           | 71.7        |
+| jais-family-6p7b         | 53.1     | 32   | 38   | 69.3      | 76   | 71.7   | 47.1 | 40.3          | 37.4       | 65.1       | 34.4           | 72.5        |
+| jais-family-2p7b         | 51       | 29.4 | 38   | 62.7      | 74.1 | 67.4   | 45.6 | 35.1          | 35.6       | 62.9       | 40.1           | 70.2        |
+| jais-family-1p3b         | 48.7     | 28.2 | 35.4 | 55.4      | 72   | 62.7   | 44.9 | 30.7          | 36.2       | 60.9       | 40.4           | 69          |
+| jais-family-590m         | 45.2     | 27.8 | 32.9 | 46.1      | 68.1 | 60.4   | 43.2 | 25.6          | 30.8       | 55.8       | 40.9           | 65.3        |
+| jais-family-30b-16k-chat | 58.8     | 42   | 41.1 | 76.2      | 73.3 | 84.6   | 60.3 | 48.4          | 40.8       | 68.2       | 44.8           | 67          |
+| jais-family-30b-8k-chat  | 60.3     | 40.6 | 47.1 | 78.9      | 72.7 | 90.6   | 60   | 50.1          | 43.2       | 70.6       | 44.9           | 64.2        |
+| jais-family-13b-chat     | 57.5     | 36.6 | 42.6 | 75        | 75.8 | 87.6   | 54.4 | 47.9          | 42         | 65         | 40.6           | 64.5        |
+| jais-family-6p7b-chat    | 56       | 36.6 | 41.3 | 72        | 74   | 86.9   | 55.4 | 44.6          | 40         | 62.4       | 41             | 62.2        |
+| jais-family-2p7b-chat    | 52.8     | 32.7 | 40.4 | 62.2      | 71   | 84.1   | 54   | 37.2          | 36.8       | 61.4       | 40.9           | 59.8        |
+| jais-family-1p3b-chat    | 49.3     | 31.9 | 37.4 | 54.5      | 70.2 | 77.8   | 49.8 | 34.4          | 35.6       | 52.7       | 37.2           | 60.8        |
+| jais-family-590m-chat    | 42.6     | 27.9 | 33.4 | 33.1      | 63.7 | 60.1   | 45.3 | 26.7          | 25.8       | 50.5       | 44.5           | 57.7        |
+
+</div>
+
+<div class="table-container">
+  
+|**Adapted Models**| Avg      | MMLU | RACE | Hellaswag | PIQA | BoolQA | SIQA | ARC-Challenge | OpenBookQA | Winogrande | TruthfulQA     | CrowS-Pairs |
+|--------------------------|----------|------|------|-----------|------|--------|------|---------------|------------|------------|----------------|-------------|
+| jais-adapted-70b         | 60.1     | 40.4 | 38.5 | 81.2      | 81.1 | 81.2   | 48.1 | 50.4          | 45         | 75.8       | 45.7           | 74          |
+| jais-adapted-13b         | 56       | 33.8 | 39.5 | 76.5      | 78.6 | 77.8   | 44.6 | 45.9          | 44.4       | 71.4       | 34.6           | 69          |
+| jais-adapted-7b          | 55.7     | 32.2 | 39.8 | 75.3      | 78.8 | 75.7   | 45.2 | 42.8          | 43         | 68         | 38.3           | 73.1        |
+| jais-adapted-70b-chat    | 61.4     | 38.7 | 42.9 | 82.7      | 81.2 | 89.6   | 52.9 | 54.9          | 44.4       | 75.7       | 44             | 68.8        |
+| jais-adapted-13b-chat    | 58.5     | 34.9 | 42.4 | 79.6      | 79.7 | 88.2   | 50.5 | 48.5          | 42.4       | 70.3       | 42.2           | 65.1        |
+| jais-adapted-7b-chat     | 58.5     | 33.8 | 43.9 | 77.8      | 79.4 | 87.1   | 47.3 | 46.9          | 43.4       | 69.9       | 42             | 72.4        |
+
+</div>
+
+
+### GPT-4 evaluation
+
+
+In addition to the LM-Harness evaluation, we conducted an open-ended generation evaluation using GPT-4-as-a-judge. We measured pairwise win-rates of model responses in both Arabic and English on a fixed set of 80 prompts from the Vicuna test set. 
+English prompts were translated to Arabic by our in-house linguists. 
+In the following, we compare the models in this release of the jais family against previously released versions: 
+
+<p align="center">
+  <img src="https://huggingface.co/inceptionai/JaisFamilySupplmentary/resolve/main/jais.png" alt="Jais-adapted GPT-4">
+</p>
+<p align="center">
+  <em>GPT-4-as-a-judge evaluation of Jais in Arabic and English. Jais family models are significantly better than previous Jais at generations in both languages. </em>
+</p>
+
+<p align="center">
+  <img src="https://huggingface.co/inceptionai/JaisFamilySupplmentary/resolve/main/jais-adapted.png" alt="Jais-adapted GPT-4">
+</p>
+<p align="center">
+  <em>GPT-4-as-a-judge evaluation of adapted Jais in Arabic and English. The generation quality of Arabic is significantly enhanced, while achieving improvement in English when compared to Llama-2 instruct. </em>
+</p>
+
+Besides pairwise comparison, we also perform MT-bench style single-answer grading on a scale of 1 to 10.
+
+<p align="center">
+  <img src="https://huggingface.co/inceptionai/JaisFamilySupplmentary/resolve/main/mt_bench.png" alt="MT-bench">
+</p>
+<p align="center">
+  <em>MT-bench style single-answer grading evaluation of Jais and adapted Jais in Arabic and English. Comparisons are made between select corresponding models from earlier releases. The quality ratings of responses are generally improved, with significant enhancements in Arabic.</em>
+</p>
+
+
+## Intended use
+
+We release the Jais family of models under a full open-source license. We welcome all feedback and opportunities to collaborate. Spanning sizes from 590M to 70B parameters, this suite of bilingual models accommodates a wide range of use cases.  Some potential downstream applications include:
+
+- **Research**: The Jais family serves Arabic researchers and NLP practitioners, offering both compute-efficient and advanced model sizes
+  - Natural language understanding and generation tasks.
+  - Mechanistic interpretability analyses on cultural alignment in bilingual pre-trained and adapted pre-trained models.
+  - Quantitative studies of Arabic cultural and linguistic phenomena.
 
-[More Information Needed]
+- **Commercial Use**: Jais 30B and 70B chat models are well-suited for direct use in chat applications with appropriate prompting or for further fine-tuning on specific tasks.
+  - Development of chat assistants for Arabic-speaking users.
+  - Sentiment analysis to gain insights into local markets and customer trends.
+  - Summarization of bilingual Arabic-English documents.
 
-#### Software
+Audiences that we hope will benefit from our model:
+- **Academics**: For those researching Arabic Natural Language Processing.
+- **Businesses**: Companies targeting Arabic-speaking audiences.
+- **Developers**: Those integrating Arabic language capabilities in applications.
 
-[More Information Needed]
+### Out-of-Scope Use
 
-## Citation [optional]
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+While the Jais family of models are powerful Arabic and English bilingual models, it's essential to understand their limitations
+and the potential of misuse. It is prohibited to use the model in any manner that violates applicable laws or regulations.
 
-**BibTeX:**
+The following are some example scenarios where the model should not be used.
 
-[More Information Needed]
+- **Malicious Use**: The model should not be used to generate harmful, misleading, or inappropriate content. Thisincludes but is not limited to:
+  - Generating or promoting hate speech, violence, or discrimination.
+  - Spreading misinformation or fake news.
+  - Engaging in or promoting illegal activities.
 
-**APA:**
+- **Sensitive Information**: The model should not be used to handle or generate personal, confidential, or sensitive information.
 
-[More Information Needed]
+- **Generalization Across All Languages**: Jais family of models are bilingual and optimized for Arabic and English. They should not be presumed to have equal proficiency in other languages or dialects.
 
-## Glossary [optional]
+- **High-Stakes Decisions**: The model should not be used to make high-stakes decisions without human oversight. This includes medical, legal, financial, or safety-critical decisions.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Bias, Risks, and Limitations
 
-[More Information Needed]
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-## More Information [optional]
+The Jais family is trained on publicly available data which was in part curated by Inception. We have employed different techniques to reduce bias in the model. While efforts have been made to minimize biases, it is likely that the model, as with all LLM models, will exhibit some bias.
 
-[More Information Needed]
+The fine-tuned variants are trained as an AI assistant for Arabic and English speakers. Chat models are limited to produce responses for queries in these two languages and may not produce appropriate responses to other language queries.
 
-## Model Card Authors [optional]
+By using Jais, you acknowledge and accept that, as with any large language model, it may generate incorrect, misleading and/or offensive information or content. The information is not intended as advice and should not be relied upon in any way, nor are we responsible for any of the content or consequences resulting from its use. We are continuously working to develop models with greater capabilities, and as such, welcome any feedback on the model.
+  
+Copyright Inception Institute of Artificial Intelligence Ltd. JAIS is made available under the Apache License, Version 2.0 (the “License”). You shall not use JAIS except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.
+ 
+Unless required by applicable law or agreed to in writing, JAIS is distributed on an AS IS basis, without warranties or conditions of any kind, either express or implied. Please see the terms of the License for the specific language permissions and limitations under the License.
 
-[More Information Needed]
 
-## Model Card Contact
+#### Summary
 
-[More Information Needed]
+We release the Jais family of Arabic and English bilingual models. The wide range of pre-trained model sizes, the recipe for adapting English-centric models to Arabic, and the fine-tuning of all sizes unlocks numerous use cases commercially and academically in the Arabic setting.
+
+Through this release, we aim to make LLMs more accessible to Arabic NLP researchers and companies, offering native Arabic models that provide better cultural understanding than English centric ones. The strategies we employ for pre-training, fine-tuning and adaptation to Arabic are extensible to other low and medium resource languages, paving the way for language-focused and accessible models that cater to local contexts.
+
+#### Citation info
+
+```bibtex
+@misc{sengupta2023jais,
+      title={Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models}, 
+      author={Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, William Marshall, Gurpreet Gosal, Cynthia Liu, Zhiming Chen, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, Lalit Pradhan, Zain Muhammad Mujahid, Massa Baali, Xudong Han, Sondos Mahmoud Bsharat, Alham Fikri Aji, Zhiqiang Shen, Zhengzhong Liu, Natalia Vassilieva, Joel Hestness, Andy Hock, Andrew Feldman, Jonathan Lee, Andrew Jackson, Hector Xuguang Ren, Preslav Nakov, Timothy Baldwin and Eric Xing},
+      year={2023},
+      eprint={2308.16149},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+
+@article{jaisfamilymodelcard,
+    title={Jais Family Model Card},
+    author={Inception},
+    year={2024},
+    url = {https://huggingface.co/inceptionai/jais-family-30b-16k-chat/blob/main/README.md}
+}
+```