Update NPU example readme #11931
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Update NPU example readme #11931
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jason-dai
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Aug 27, 2024
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| > To run Qwen2 and Llama2 with IPEX-LLM on Intel NPUs, we recommend using version **32.0.100.2540** for the Intel NPU. | ||
| > | ||
| > Go to https://www.intel.com/content/www/us/en/download/794734/825735/intel-npu-driver-windows.html to download and unzip the driver. Then follow the same steps on [Requirements](#0-requirements). |
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Our troubleshooting (https://github.com/intel-analytics/ipex-llm/blob/4fcffc40503bd92aad164bb2d4a9cbb69c66342d/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md#troubleshooting) involves transpose value setting workaround. And 32.0.100.2540 is mainly verified on MTL.
jason-dai
reviewed
Aug 27, 2024
| @@ -77,45 +76,9 @@ Inference time: xxxx s | |||
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| ## Example 2: Predict Tokens using `generate()` API using multi processes | |||
| ## 4. Run Optimized Models (Experimental) | |||
| In the example [llama2.py](./llama2.py) and [qwen2.py](./qwen2.py), we show an experimental support for a Llama2 / Qwen2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimization and fused decoderlayer optimization on Intel NPUs. | |||
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The example below shows how to run the optimized model implementations on Intel NPU, including
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The example below shows how to run the optimized model implementations on Intel NPU, including
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Description
Update NPU readme