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Baichuan-7B: Optimized for Mobile Deployment

Large language model achieving state-of-the-art performance on Chinese and English language benchmarks

Baichuan-7B is a family of LLMs. It achieves the state-of-the-art performance of its size on standard Chinese and English authoritative benchmarks (C-EVAL/MMLU). 4-bit weights and 16-bit activations making it suitable for on-device The model is quantized to deployment. For Prompt and output length specified below, the time to first token is Llama-PromptProcessor-Quantized's latency and average time per addition token is Llama-TokenGenerator-KVCache-Quantized's latency.

This is based on the implementation of Baichuan-7B found here. More details on model performance accross various devices, can be found here.

Model Details

  • Model Type: Text generation
  • Model Stats:
    • Number of parameters: 7B
    • Model size: 3.9GB
    • Model-1 (Prompt Processor): Baichuan-PromptProcessor-Quantized
    • Max context length: 1024
    • Prompt processor input: 1024 tokens
    • Prompt processor output: 1024 output tokens + KVCache for token generator
    • Model-2 (Token Generator): Baichuan-TokenGenerator-KVCache-Quantized
    • Token generator input: 1 input token + past KVCache
    • Token generator output: 1 output token + KVCache for next iteration
    • Decoding length: 1024 (1 output token + 1023 from KVCache)
    • Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
    • QNN-SDK: 2.19
Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 QNN Model Library 108.059 ms 1 - 107 MB UINT16 NPU Baichuan-TokenGenerator-KVCache-Quantized
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 QNN Model Library 2599.326 ms 0 - 38 MB UINT16 NPU Baichuan-PromptProcessor-Quantized


  • The license for the original implementation of Baichuan-7B can be found here.



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
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