Qwen2.5-7B-Instruct: Optimized for Mobile Deployment

State-of-the-art large language model useful on a variety of language understanding and generation tasks

The Qwen2.5-7B-Instruct is a state-of-the-art multilingual language model with 7 billion parameters, excelling in language understanding, generation, coding, and mathematics. AI Hub provides with four QNN context binaries (shared weights) that can be deployed on Snapdragon 8 Elite with Genie SDK.

This model is an implementation of Qwen2.5-7B-Instruct found here.

More details on model performance across various devices, can be found here.

Model Details

• Model Type: Model_use_case.text_generation
• Model Stats:
  • Input sequence length for Prompt Processor: 128
  • Context length: 4096
  • Number of parameters: 7B
  • Precision: w4a16 + w8a16 (few layers)
  • Num of key-value heads: 4
  • Information about the model parts: Prompt Processor and Token Generator are split into 6 parts each. Each corresponding Prompt Processor and Token Generator part share weights.
  • Prompt processor model size: 4.61 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: 4.61 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.
  • Minimum QNN SDK version required: 2.27.7
  • Supported languages: Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
  • 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.

| Model | Precision | Device | Chipset | Target Runtime | Response Rate (tokens per second) | Time To First Token (range, seconds) |---|---|---|---|---|---| | Qwen2.5-3B-Instruct | w4a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 15.40274 | 0.1356538 - 4.3409216 | -- | Use Export Script | | Qwen2.5-3B-Instruct | w4a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 12.33811 | 0.1749494 - 5.5983808 | -- | Use Export Script |

Deploying Qwen2.5-7B-Instruct on-device

Please follow the LLM on-device deployment tutorial.

License

• The license for the original implementation of Qwen2.5-7B-Instruct can be found here.
• The license for the compiled assets for on-device deployment can be found here

References

• Qwen2.5 Technical Report
• 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

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