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README.md

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+---
+library_name: pytorch
+license: llama3
+pipeline_tag: text-generation
+tags:
+- llm
+- generative_ai
+- quantized
+- android
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/llama_v3_2_3b_chat_quantized/web-assets/model_demo.png)
+
+# Llama-v3.2-3B-Chat: Optimized for Mobile Deployment
+## State-of-the-art large language model useful on a variety of language understanding and generation tasks
+
+Llama 3 is a family of LLMs. The "Chat" at the end indicates that the model is optimized for chatbot-like dialogue. The model is quantized to w4a16 (4-bit weights and 16-bit activations) and part of the model is quantized to w8a16 (8-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 Llama-PromptProcessor-Quantized's latency and average time per addition token is Llama-TokenGenerator-Quantized's latency.
+
+This is based on the implementation of Llama-v3.2-3B-Chat found
+[here]({source_repo}). More details on model performance
+accross various devices, can be found [here](https://aihub.qualcomm.com/models/llama_v3_2_3b_chat_quantized).
+
+### Model Details
+
+- **Model Type:** Text generation
+- **Model Stats:**
+  - Input sequence length for Prompt Processor: 128
+  - Context length: 4096
+  - Number of parameters: 3B
+  - Model size: 2.4G
+  - Precision: w4a16 + w8a16 (few layers)
+  - Num of key-value heads: 8
+  - Model-1 (Prompt Processor): Llama-PromptProcessor-Quantized
+  - Prompt processor input: 128 tokens + position embeddings + attention mask + KV cache inputs
+  - Prompt processor output: 128 output tokens + KV cache outputs
+  - Model-2 (Token Generator): Llama-TokenGenerator-Quantized
+  - Token generator input: 1 input token + position embeddings + attention mask + KV cache inputs
+  - Token generator output: 1 output token + KV cache outputs
+  - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
+  - Minimum QNN SDK version required: 2.27.7
+  - Supported languages: English.
+  - 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 | Device | Chipset | Target Runtime | Response Rate (tokens per second) | Time To First Token (range, seconds)
+|---|---|---|---|---|---|
+| Llama-v3.2-3B-Chat | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 23.4718 | 0.088195 - 2.82225 | -- | -- |
+
+## Deploying Llama 3.2 on-device
+
+Please follow the [LLM on-device deployment](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial.
+
+
+
+## License
+* The license for the original implementation of Llama-v3.2-3B-Chat can be found [here](https://github.com/facebookresearch/llama/blob/main/LICENSE).
+* The license for the compiled assets for on-device deployment can be found [here](https://github.com/facebookresearch/llama/blob/main/LICENSE)
+
+
+
+## References
+* [LLaMA: Open and Efficient Foundation Language Models](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_2/)
+* [Source Model Implementation](https://github.com/meta-llama/llama3/tree/main)
+
+
+
+## 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:[email protected]).
+
+## 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