diff --git a/python/llm/example/GPU/HuggingFace/LLM/glm-edge/README.md b/python/llm/example/GPU/HuggingFace/LLM/glm-edge/README.md index a7038c0ef43..85744192261 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/glm-edge/README.md +++ b/python/llm/example/GPU/HuggingFace/LLM/glm-edge/README.md @@ -1,5 +1,5 @@ # GLM-Edge -In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on GLM-Edge models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/glm-edge-1.5b-chat](https://huggingface.co/THUDM/glm-edge-1.5b-chat) and [THUDM/glm-edge-4b-chat](https://huggingface.co/THUDM/glm-edge-4b-chat) as reference GLM-Edge models. +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on GLM-Edge models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/glm-edge-1.5b-chat](https://huggingface.co/THUDM/glm-edge-1.5b-chat) and [THUDM/glm-edge-4b-chat](https://huggingface.co/THUDM/glm-edge-4b-chat) (or [ZhipuAI/glm-edge-1.5b-chat](https://www.modelscope.cn/models/ZhipuAI/glm-edge-1.5b-chat) and [ZhipuAI/glm-edge-4b-chat](https://www.modelscope.cn/models/ZhipuAI/glm-edge-4b-chat) for ModelScope) as reference GLM-Edge models. ## 0. Requirements To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. @@ -17,6 +17,9 @@ pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-exte pip install transformers==4.47.0 pip install accelerate==0.33.0 pip install "trl<0.12.0" + +# [optional] only needed if you would like to use ModelScope as model hub +pip install modelscope==1.11.0 ``` ### 1.2 Installation on Windows @@ -32,6 +35,9 @@ pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-exte pip install transformers==4.47.0 pip install accelerate==0.33.0 pip install "trl<0.12.0" + +# [optional] only needed if you would like to use ModelScope as model hub +pip install modelscope==1.11.0 ``` ## 2. Configures OneAPI environment variables for Linux @@ -102,14 +108,19 @@ set SYCL_CACHE_PERSISTENT=1 ### Example 1: Predict Tokens using `generate()` API In the example [generate.py](./generate.py), we show a basic use case for a GLM-Edge model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs. -``` +```bash +# for Hugging Face model hub python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT + +# for ModelScope model hub +python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --modelscope ``` Arguments info: -- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the GLM-Edge model (e.g. `THUDM/glm-edge-1.5b-chat` or `THUDM/glm-edge-4b-chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/glm-edge-4b-chat'`. +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the GLM-Edge model (e.g. `THUDM/glm-edge-1.5b-chat` or `THUDM/glm-edge-4b-chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/glm-edge-4b-chat'` for **Hugging Face** or `'ZhipuAI/glm-edge-4b-chat'` for **ModelScope**. - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`. - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. +- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**. #### Sample Output #### [THUDM/glm-edge-1.5b-chat](https://huggingface.co/THUDM/glm-edge-1.5b-chat) diff --git a/python/llm/example/GPU/HuggingFace/LLM/glm-edge/generate.py b/python/llm/example/GPU/HuggingFace/LLM/glm-edge/generate.py index b02afa18860..8001345bd21 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/glm-edge/generate.py +++ b/python/llm/example/GPU/HuggingFace/LLM/glm-edge/generate.py @@ -19,21 +19,32 @@ import argparse from ipex_llm.transformers import AutoModelForCausalLM -from transformers import AutoTokenizer if __name__ == '__main__': parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for GLM-Edge model') - parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-edge-4b-chat", - help='The huggingface repo id for the GLM-Edge model to be downloaded' - ', or the path to the huggingface checkpoint folder') + parser.add_argument('--repo-id-or-model-path', type=str, + help='The Hugging Face or ModelScope repo id for the GLM-Edge model to be downloaded' + ', or the path to the checkpoint folder') parser.add_argument('--prompt', type=str, default="AI是什么？", help='Prompt to infer') parser.add_argument('--n-predict', type=int, default=32, help='Max tokens to predict') + parser.add_argument('--modelscope', action="store_true", default=False, + help="Use models from modelscope") + args = parser.parse_args() - model_path = args.repo_id_or_model_path + + if args.modelscope: + from modelscope import AutoTokenizer + model_hub = 'modelscope' + else: + from transformers import AutoTokenizer + model_hub = 'huggingface' + + model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \ + ("ZhipuAI/glm-edge-4b-chat" if args.modelscope else "THUDM/glm-edge-4b-chat") # Load model in 4 bit, # which convert the relevant layers in the model into INT4 format @@ -43,7 +54,8 @@ load_in_4bit=True, optimize_model=True, trust_remote_code=True, - use_cache=True) + use_cache=True, + model_hub=model_hub) model = model.half().to("xpu") # Load tokenizer