glm-edge

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)

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