Baichuan2-7B: Optimized for Qualcomm Devices

Baichuan2-7B 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 deployment. For Prompt and output length specified below, the time to first token is Baichuan2-PromptProcessor-Quantized's latency and average time per addition token is Baichuan2-TokenGenerator-Quantized's latency.

This is based on the implementation of Baichuan2-7B found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

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Deploying Baichuan2-7B on-device

Please follow the LLM on-device deployment tutorial.

Getting Started

Download pre-exported model assets from Baichuan2-7B on Qualcomm® AI Hub.

Model Details

Model Type: Model_use_case.text_generation

Model Stats:

• Input sequence length for Prompt Processor: 128
• Context length: 4096
• Number of parameters: 7.07B
• Precision: w4a16 + w8a16 (few layers)
• Num of key-value heads: 8
• Information about the model parts: Prompt Processor and Token Generator are split into 5 parts each. Each corresponding Prompt Processor and Token Generator part share weights.
• Prompt processor model size: 5.06 GB
• Prompt processor input (part1): 128 tokens
• Prompt processor output (part1): Embeddings output
• Prompt processor input (other parts): 128 tokens + KVCache initialized with pad token
• Prompt processor output (other parts): 128 output tokens + KVCache for token generator
• Token generator model size: 5.06 GB
• Token generator input (part1): 128 tokens
• Token generator output (part1): Embeddings output
• Token generator input (other parts): 1 input token + past KVCache
• Token generator output (other parts): 1 output token + KVCache for next iteration
• Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
• Supported languages: Chinese 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 (4096 tokens).
• Response Rate: Rate of response generation after the first response token.

Performance Summary

Model	Runtime	Precision	Chipset	Context Length	Response Rate (tokens per second)	Time To First Token (range, seconds)
Baichuan2-7B	QNN_CONTEXT_BINARY	w4a16	Snapdragon® 8 Elite Mobile	4096	7.72	0.208048 - 6.657536

License

• The license for the original implementation of Baichuan2-7B can be found here.

References

• Baichuan 2: Open Large-scale Language Models
• Source Model Implementation

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

This 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