--- library_name: pytorch license: other pipeline_tag: text-generation tags: - llm - generative_ai - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/jais_6p7b_chat_quantized/web-assets/model_demo.png) # JAIS-6p7b-Chat: Optimized for Mobile Deployment ## State-of-the-art large language model useful on a variety of language understanding and generation tasks JAIS 6.7B is a bilingual large language model (LLM) for both Arabic and English developed by Inception, a G42 company in partnership with MBZUAI and Cerebras. This is a 6.7 billion parameter LLM, trained on a dataset containing 141 billion Arabic tokens and 339 billion English/code tokens. The model is based on transformer-based decoder-only (GPT-3) architecture and uses SwiGLU non-linearity. It implements ALiBi position embeddings, enabling the model to extrapolate to long sequence lengths, providing improved context handling and model precision. The JAIS family of models is a comprehensive series of bilingual English-Arabic LLMs. These models are optimized to excel in Arabic while having strong English capabilities. This model is an implementation of JAIS-6p7b-Chat found [here](https://huggingface.co/inceptionai/jais-family-6p7b). More details on model performance accross various devices, can be found [here](https://aihub.qualcomm.com/models/jais_6p7b_chat_quantized). ### Model Details - **Model Type:** Text generation - **Model Stats:** - Input sequence length for Prompt Processor: 128 - Max context length: 2048 - Number of parameters: 6.7B - Precision: w4a16 + w8a16 (a few layers) - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations. - Supported languages: Arabic (MSA) and English. - Minimum QNN SDK version required: 2.27.7 - TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt. The lower bound is for a short prompt (up to 128 tokens, i.e., one iteration of the prompt processor) and the upper bound is for a prompt using the full context length (2048 tokens). - Response Rate: Rate of response generation after the first response token. | Model | Device | Chipset | Target Runtime | Response Rate (tokens per second) | Time To First Token (range, seconds) |---|---|---|---|---|---| | Jais-6p7b-Chat | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 13.33 | 0.238231 - 3.811696 | -- | -- | ## Deploying JAIS-6p7b-Chat on-device Please follow the [LLM on-device deployment]({genie_url}) tutorial. ## References * [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://arxiv.org/abs/2308.16149) * [Source Model Implementation](https://huggingface.co/inceptionai/jais-family-6p7b) ## Community * Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com). ## Usage and Limitations Model may not be used for or in connection with any of the following applications: - Accessing essential private and public services and benefits; - Administration of justice and democratic processes; - Assessing or recognizing the emotional state of a person; - Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics; - Education and vocational training; - Employment and workers management; - Exploitation of the vulnerabilities of persons resulting in harmful behavior; - General purpose social scoring; - Law enforcement; - Management and operation of critical infrastructure; - Migration, asylum and border control management; - Predictive policing; - Real-time remote biometric identification in public spaces; - Recommender systems of social media platforms; - Scraping of facial images (from the internet or otherwise); and/or - Subliminal manipulation