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Update README.md (#11964)

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python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
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# Run Large Language Model on Intel NPU # Run HuggingFace `transformers` Models on Intel NPU
In this directory, you will find examples on how you could apply IPEX-LLM INT4 or INT8 optimizations on LLM models on [Intel NPUs](../../../README.md). See the table blow for verified models. In this directory, you will find examples on how to directly run HuggingFace `transformers` models on Intel NPUs (leveraging *Intel NPU Acceleration Library*). See the table blow for verified models.
## Verified Models ## Verified Models
@ -52,7 +52,7 @@ For optimal performance, it is recommended to set several environment variables.
set BIGDL_USE_NPU=1 set BIGDL_USE_NPU=1
``` ```
## 3. Run models ## 3. Run Models
In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel NPUs. In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel NPUs.
``` ```
@ -77,7 +77,7 @@ done
``` ```
## 4. Run Optimized Models (Experimental) ## 4. Run Optimized Models (Experimental)
The example below shows how to run the **_optimized model implementations_** on Intel NPU, including The examples below show how to run the **_optimized HuggingFace model implementations_** on Intel NPU, including
- [Llama2-7B](./llama.py) - [Llama2-7B](./llama.py)
- [Llama3-8B](./llama.py) - [Llama3-8B](./llama.py)
- [Qwen2-1.5B](./qwen2.py) - [Qwen2-1.5B](./qwen2.py)
@ -92,7 +92,7 @@ Supported models: Llama2-7B, Qwen2-1.5B, Qwen2-7B, MiniCPM-1B, Baichuan2-7B
#### 32.0.101.2715 #### 32.0.101.2715
Supported models: Llama3-8B, MiniCPM-2B Supported models: Llama3-8B, MiniCPM-2B
### Run Models ### Run
```bash ```bash
# to run Llama-2-7b-chat-hf # to run Llama-2-7b-chat-hf
python llama.py python llama.py