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.
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 |
License
- The license for the original implementation of Baichuan-7B can be found here.
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
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
Inference API (serverless) does not yet support pytorch models for this pipeline type.