LLM

Run HuggingFace transformers Models on Intel NPU

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

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
GLM-Edge	THUDM/glm-edge-1.5b-chat, THUDM/glm-edge-4b-chat
Qwen2	Qwen/Qwen2-1.5B-Instruct, Qwen/Qwen2-7B-Instruct
Qwen2.5	Qwen/Qwen2.5-3B-Instruct, Qwen/Qwen2.5-7B-Instruct
MiniCPM	openbmb/MiniCPM-1B-sft-bf16, openbmb/MiniCPM-2B-sft-bf16
Baichuan2	baichuan-inc/Baichuan2-7B-Chat

Please refer to Quickstart for details about verified platforms.

0. Prerequisites

For ipex-llm NPU support, please refer to Quickstart 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

:: [optional] for glm-edge-1.5b-chat & glm-edge-4b-chat
pip install transformers==4.47.0 accelerate==0.26.0

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

1.2 Runtime Configurations

Please refer to Quickstart 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, including

• Llama2-7B
• Llama3-8B
• Llama3.2-1B
• Llama3.2-3B
• GLM-Edge-1.5B
• GLM-Edge-4B
• Qwen2-1.5B
• Qwen2-7B
• Qwen2.5-3B
• Qwen2.5-7B
• MiniCPM-1B
• MiniCPM-2B
• Baichuan2-7B

Run

:: 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 glm-edge-1.5b-chat
python glm.py --repo-id-or-model-path "THUDM/glm-edge-1.5b-chat" --save-directory <converted_model_path>

:: to run glm-edge-4b-chat
python glm.py --repo-id-or-model-path "THUDM/glm-edge-4b-chat" --save-directory <converted_model_path>

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

:: to run Qwen2-7B-Instruct
python qwen.py --repo-id-or-model-path "Qwen/Qwen2-7B-Instruct" --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 asym_int4 --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 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>

:: to run Baichuan2-7B-Chat
python baichuan2.py --repo-id-or-model-path "baichuan-inc/Baichuan2-7B-Chat" --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 for Llama2-7B) to be downloaded, or the path to the huggingface checkpoint folder.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be "What is AI?" or "AI是什么？".
• --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: argument defining the maximum sequence length for both input and output tokens. It is default to be 1024.
• --max-prompt-len MAX_PROMPT_LEN: argument defining the maximum number of tokens that the input prompt can contain. It is default to be 512.
• --low-bit LOW_BIT: argument defining the low bit optimizations that will be applied to the model. Current available options are "sym_int4", "asym_int4" and "sym_int8", with "sym_int4" as the default.
• --disable-streaming: argument defining whether to disable the streaming mode for 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.

Troubleshooting

Accuracy Tuning

If you enconter output issues when running the examples, you could try the following methods to tune the accuracy:

1. Before running the example, consider setting an additional environment variable IPEX_LLM_NPU_QUANTIZATION_OPT=1 to enhance output quality.

2. If you are using the default LOW_BIT value (i.e. sym_int4 optimizations), you could try to use --low-bit "asym_int4" instead to tune the output quality.

3. You could refer to the Quickstart for more accuracy tuning strategies.

Important

Please note that to make the above methods taking effect, you must specify a new folder for SAVE_DIRECTORY. Reusing the same SAVE_DIRECTORY will load the previously saved low-bit model, and thus making the above accuracy tuning strategies ineffective.

Better Performance with High CPU Utilization

You could enable optimization by setting the environment variable with set IPEX_LLM_CPU_LM_HEAD=1 for better performance. But this will cause high CPU utilization.

Sample Output

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

Inference time: xxxx s
-------------------- Input --------------------
<s><s> [INST] <<SYS>>

<</SYS>>

What is AI? [/INST]
-------------------- Output --------------------
<s><s> [INST] <<SYS>>

<</SYS>>

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