ipex-llm/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models

Run HuggingFace transformers Models with Pipeline Optimization on Intel NPU

In this directory, you will find examples on how to directly run HuggingFace transformers models with pipeline optimization on Intel NPUs. See the table blow for verified models.

Verified Models

Model	Model Link
Llama2	meta-llama/Llama-2-7b-chat-hf
Llama3	meta-llama/Meta-Llama-3-8B-Instruct
Llama3.2	meta-llama/Llama-3.2-1B-Instruct, meta-llama/Llama-3.2-3B-Instruct
Qwen2	Qwen/Qwen2-1.5B-Instruct
Qwen2.5	Qwen/Qwen2.5-7B-Instruct, Qwen/Qwen2.5-3B-Instruct
Baichuan2	baichuan-inc/Baichuan2-7B-Chat
MiniCPM	openbmb/MiniCPM-1B-sft-bf16, openbmb/MiniCPM-2B-sft-bf16

0. Prerequisites

For ipex-llm NPU support, please refer to Quick Start for details about the required preparations.

1. Install & Runtime Configurations

1.1 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.11
conda activate llm

:: install ipex-llm with 'npu' option
pip install --pre --upgrade ipex-llm[npu]

:: [optional] for Llama-3.2-1B-Instruct & Llama-3.2-3B-Instruct
pip install transformers==4.45.0 accelerate==0.33.0

Please refer to Quick Start for more details about ipex-llm installation on Intel NPU.

1.2 Runtime Configurations

Please refer to Quick Start for environment variables setting based on your device.

2. Run Optimized Models

The examples below show how to run the optimized HuggingFace model implementations on Intel NPU:

:: to run Llama-2-7b-chat-hf
python llama2.py --repo-id-or-model-path "meta-llama/Llama-2-7b-chat-hf" --save-directory <converted_model_path>

:: to run Meta-Llama-3-8B-Instruct
python llama3.py --repo-id-or-model-path "meta-llama/Meta-Llama-3-8B-Instruct" --save-directory <converted_model_path>

:: to run Llama-3.2-1B-Instruct
python llama3.py --repo-id-or-model-path "meta-llama/Llama-3.2-1B-Instruct" --save-directory <converted_model_path>

:: to run Llama-3.2-3B-Instruct
python llama3.py --repo-id-or-model-path "meta-llama/Llama-3.2-3B-Instruct" --save-directory <converted_model_path>

:: to run Qwen2.5-7B-Instruct
python qwen.py --repo-id-or-model-path "Qwen/Qwen2.5-7B-Instruct" --save-directory <converted_model_path>

:: to run Qwen2-1.5B-Instruct
python qwen.py --repo-id-or-model-path "Qwen/Qwen2-1.5B-Instruct" --low-bit sym_int8 --save-directory <converted_model_path>

:: to run Qwen2.5-3B-Instruct
python qwen.py --repo-id-or-model-path "Qwen/Qwen2.5-3B-Instruct" --low-bit sym_int8 --save-directory <converted_model_path>

:: to run Baichuan2-7B-Chat
python baichuan2.py --repo-id-or-model-path "baichuan-inc/Baichuan2-7B-Chat" --save-directory <converted_model_path>

:: to run MiniCPM-1B-sft-bf16
python minicpm.py --repo-id-or-model-path "openbmb/MiniCPM-1B-sft-bf16" --save-directory <converted_model_path>

:: to run MiniCPM-2B-sft-bf16
python minicpm.py --repo-id-or-model-path "openbmb/MiniCPM-2B-sft-bf16" --save-directory <converted_model_path>

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the model (e.g. meta-llama/Llama-2-7b-chat-hf) to be downloaded, or the path to the huggingface checkpoint folder.
• --prompt PROMPT: argument defining the prompt to be infered. It is default to be What is AI?.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 32.
• --max-context-len MAX_CONTEXT_LEN: Defines the maximum sequence length for both input and output tokens. It is default to be 1024.
• --max-prompt-len MAX_PROMPT_LEN: Defines the maximum number of tokens that the input prompt can contain. It is default to be 512.
• --disable-transpose-value-cache: Disable the optimization of transposing value cache.
• --disable-streaming: Disable streaming mode of generation.
• --save-directory SAVE_DIRECTORY: argument defining the path to save converted model. If it is a non-existing path, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded, otherwise the lowbit model in SAVE_DIRECTORY will be loaded.

Sample Output of Streaming Mode

meta-llama/Llama-2-7b-chat-hf

-------------------- Input --------------------
input length: 28
<s>[INST] <<SYS>>

<</SYS>>

What is AI? [/INST]
-------------------- Output --------------------
 AI (Artificial Intelligence) is a field of computer science and technology that focuses on the development of intelligent machines that can perform

Inference time: xxxx s