diff --git a/python/llm/example/GPU/HuggingFace/LLM/baichuan2/README.md b/python/llm/example/GPU/HuggingFace/LLM/baichuan2/README.md index 09e55443fb1..286ddd04762 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/baichuan2/README.md +++ b/python/llm/example/GPU/HuggingFace/LLM/baichuan2/README.md @@ -1,5 +1,5 @@ # Baichuan -In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Baichuan2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [baichuan-inc/Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan-7B-Chat) as a reference Baichuan model. +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Baichuan2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [baichuan-inc/Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan-7B-Chat) (or [baichuan-inc/Baichuan2-7B-Chat](https://www.modelscope.cn/models/[baichuan-inc/Baichuan2-7B-Chat]) for ModelScope) as a reference Baichuan model. ## 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. @@ -16,6 +16,9 @@ conda activate llm pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ pip install transformers_stream_generator # additional package required for Baichuan-7B-Chat to conduct generation + +# [optional] only needed if you would like to use ModelScope as model hub +pip install modelscope==1.11.0 ``` #### 1.2 Installation on Windows @@ -28,6 +31,9 @@ conda activate llm pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ pip install transformers_stream_generator # additional package required for Baichuan-7B-Chat to conduct generation + +# [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 @@ -95,14 +101,19 @@ set SYCL_CACHE_PERSISTENT=1 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. ### 4. Running examples -``` +```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 Baichuan model (e.g `baichuan-inc/Baichuan2-7B-Chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'baichuan-inc/Baichuan2-7B-Chat'`. +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the Baichuan model (e.g `baichuan-inc/Baichuan2-7B-Chat`) to be downloaded, or the path to the checkpoint folder. It is default to be `'baichuan-inc/Baichuan2-7B-Chat'`. - `--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 #### [baichuan-inc/Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) diff --git a/python/llm/example/GPU/HuggingFace/LLM/baichuan2/generate.py b/python/llm/example/GPU/HuggingFace/LLM/baichuan2/generate.py index 68be4b1a7b1..8b4f620220c 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/baichuan2/generate.py +++ b/python/llm/example/GPU/HuggingFace/LLM/baichuan2/generate.py @@ -19,7 +19,6 @@ import argparse from ipex_llm.transformers import AutoModelForCausalLM -from transformers import AutoTokenizer # prompt format referred from https://github.com/baichuan-inc/Baichuan2/issues/227 # and https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/main/generation_utils.py#L7-L49 @@ -29,14 +28,24 @@ if __name__ == '__main__': parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Baichuan model') parser.add_argument('--repo-id-or-model-path', type=str, default="baichuan-inc/Baichuan2-7B-Chat", - help='The huggingface repo id for the Baichuan model to be downloaded' - ', or the path to the huggingface checkpoint folder') + help='The Hugging Face repo id for the Baichuan 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() + + 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 # Load model in 4 bit, @@ -50,7 +59,8 @@ model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True, trust_remote_code=True, - use_cache=True) + use_cache=True, + model_hub=model_hub) model = model.half().to('xpu') # Load tokenizer diff --git a/python/llm/example/GPU/HuggingFace/LLM/llama2/README.md b/python/llm/example/GPU/HuggingFace/LLM/llama2/README.md index 06bae6ac670..62ab5fbfbd3 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/llama2/README.md +++ b/python/llm/example/GPU/HuggingFace/LLM/llama2/README.md @@ -1,5 +1,5 @@ # Llama2 -In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Llama2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) and [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) as reference Llama2 models. +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Llama2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) and [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) (or [shakechen/Llama-2-7b-chat-hf](https://www.modelscope.cn/models/shakechen/Llama-2-7b-chat-hf) and [ydyajyA/Llama-2-13b-chat-hf](https://www.modelscope.cn/models/ydyajyA/Llama-2-13b-chat-hf) for ModelScope) as reference Llama2 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. @@ -14,6 +14,9 @@ conda create -n llm python=3.11 conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# [optional] only needed if you would like to use ModelScope as model hub +pip install modelscope==1.11.0 ``` #### 1.2 Installation on Windows @@ -24,6 +27,9 @@ conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# [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 @@ -91,14 +97,19 @@ set SYCL_CACHE_PERSISTENT=1 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. ### 4. Running examples -``` +```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 Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`. +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'` for **Hugging Face** and `'shakechen/Llama-2-7b-chat-hf'` for **ModelScope**. - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). 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`. +- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**. #### Sample Output #### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) diff --git a/python/llm/example/GPU/HuggingFace/LLM/llama2/generate.py b/python/llm/example/GPU/HuggingFace/LLM/llama2/generate.py index f678dec305f..038a232d3d3 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/llama2/generate.py +++ b/python/llm/example/GPU/HuggingFace/LLM/llama2/generate.py @@ -19,7 +19,6 @@ import argparse from ipex_llm.transformers import AutoModelForCausalLM -from transformers import LlamaTokenizer # you could tune the prompt based on your own model, # here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style @@ -41,16 +40,29 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]], if __name__ == '__main__': parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model') - parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf", - help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) 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 Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded' + ', or the path to the checkpoint folder') parser.add_argument('--prompt', type=str, default="What is AI?", 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 + Tokenizer = AutoTokenizer + model_hub = 'modelscope' + else: + from transformers import LlamaTokenizer + Tokenizer = LlamaTokenizer + model_hub = 'huggingface' + + model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \ + ("shakechen/Llama-2-7b-chat-hf" if args.modelscope else "meta-llama/Llama-2-7b-chat-hf") # Load model in 4 bit, # which convert the relevant layers in the model into INT4 format @@ -60,11 +72,12 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]], 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 - tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True) + tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True) # Generate predicted tokens with torch.inference_mode(): diff --git a/python/llm/example/GPU/HuggingFace/LLM/minicpm/README.md b/python/llm/example/GPU/HuggingFace/LLM/minicpm/README.md index e77fd4b037d..24e399597ac 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/minicpm/README.md +++ b/python/llm/example/GPU/HuggingFace/LLM/minicpm/README.md @@ -1,5 +1,5 @@ # MiniCPM -In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [openbmb/MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) as a reference MiniCPM model. +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [openbmb/MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) (or [openbmb/MiniCPM-2B-sft-bf16](https://www.modelscope.cn/models/openbmb/MiniCPM-2B-sft-bf16) for ModelScope) as a reference MiniCPM model. ## 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. @@ -15,6 +15,9 @@ conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ pip install "transformers>=4.36" + +# [optional] only needed if you would like to use ModelScope as model hub +pip install modelscope==1.11.0 ``` #### 1.2 Installation on Windows @@ -26,6 +29,9 @@ conda activate llm # below command will install intel_extension_for_pytorch==2.1.10+xpu as default pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ pip install "transformers>=4.36" + +# [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 @@ -93,14 +99,19 @@ set SYCL_CACHE_PERSISTENT=1 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. ### 4. Running examples -``` -python ./generate.py --prompt 'What is AI?' +```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 MiniCPM model (e.g. `openbmb/MiniCPM-2B-sft-bf16`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'openbmb/MiniCPM-2B-sft-bf16'`. +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the MiniCPM model (e.g. `openbmb/MiniCPM-2B-sft-bf16`) to be downloaded, or the path to the checkpoint folder. It is default to be `'openbmb/MiniCPM-2B-sft-bf16'`. - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). 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`. +- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**. #### Sample Output #### [openbmb/MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) diff --git a/python/llm/example/GPU/HuggingFace/LLM/minicpm/generate.py b/python/llm/example/GPU/HuggingFace/LLM/minicpm/generate.py index 669162e61a1..29fea13b013 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/minicpm/generate.py +++ b/python/llm/example/GPU/HuggingFace/LLM/minicpm/generate.py @@ -19,22 +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 MiniCPM model') - parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-2B-sft-bf16", - help='The huggingface repo id for the MiniCPM 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 MiniCPM model to be downloaded' + ', or the path to the checkpoint folder') parser.add_argument('--prompt', type=str, default="What is AI?", 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 \ + ("openbmb/MiniCPM-2B-sft-bf16" if args.modelscope else "openbmb/MiniCPM-2B-sft-bf16") + # Load model in 4 bit, # which convert the relevant layers in the model into INT4 format # When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function. @@ -43,9 +53,10 @@ load_in_4bit=True, trust_remote_code=True, optimize_model=True, - use_cache=True) + use_cache=True, + model_hub=model_hub) - model = model.to('xpu') + model = model.half().to('xpu') # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(model_path, diff --git a/python/llm/example/GPU/HuggingFace/LLM/minicpm3/README.md b/python/llm/example/GPU/HuggingFace/LLM/minicpm3/README.md index 56c58d61c07..6419d238212 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/minicpm3/README.md +++ b/python/llm/example/GPU/HuggingFace/LLM/minicpm3/README.md @@ -1,5 +1,5 @@ # MiniCPM3 -In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM3 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [openbmb/MiniCPM3-4B](https://huggingface.co/openbmb/MiniCPM3-4B) as a reference MiniCPM3 model. +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM3 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [openbmb/MiniCPM3-4B](https://huggingface.co/openbmb/MiniCPM3-4B) (or [OpenBMB/MiniCPM3-4B](https://www.modelscope.cn/models/OpenBMB/MiniCPM3-4B) for ModelScope) as a reference MiniCPM3 model. ## 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. @@ -16,6 +16,9 @@ conda activate llm pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ pip install jsonschema datamodel_code_generator + +# [optional] only needed if you would like to use ModelScope as model hub +pip install modelscope==1.11.0 ``` #### 1.2 Installation on Windows @@ -28,6 +31,9 @@ conda activate llm pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ pip install jsonschema datamodel_code_generator + +# [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 @@ -95,14 +101,19 @@ set SYCL_CACHE_PERSISTENT=1 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. ### 4. Running examples -``` -python ./generate.py --prompt 'What is AI?' +```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 MiniCPM3 model (e.g. `openbmb/MiniCPM3-4B`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'openbmb/MiniCPM3-4B'`. +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the MiniCPM3 model (e.g. `openbmb/MiniCPM3-4B`) to be downloaded, or the path to the checkpoint folder. It is default to be `'openbmb/MiniCPM3-4B'` for **Hugging Face** or `'OpenBMB/MiniCPM3-4B'` for **ModelScope**. - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). 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`. +- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**. #### Sample Output #### [openbmb/MiniCPM3-4B](https://huggingface.co/openbmb/MiniCPM3-4B) diff --git a/python/llm/example/GPU/HuggingFace/LLM/minicpm3/generate.py b/python/llm/example/GPU/HuggingFace/LLM/minicpm3/generate.py index 2a8ebed5b9e..6dca736dc40 100644 --- a/python/llm/example/GPU/HuggingFace/LLM/minicpm3/generate.py +++ b/python/llm/example/GPU/HuggingFace/LLM/minicpm3/generate.py @@ -19,21 +19,31 @@ 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 MiniCPM3 model') - parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM3-4B", - help='The huggingface repo id for the MiniCPM3 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 MiniCPM3 model to be downloaded' + ', or the path to the checkpoint folder') parser.add_argument('--prompt', type=str, default="What is AI?", 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 \ + ("OpenBMB/MiniCPM3-4B" if args.modelscope else "openbmb/MiniCPM3-4B") # Load model in 4 bit, # which convert the relevant layers in the model into INT4 format @@ -43,7 +53,8 @@ load_in_4bit=True, trust_remote_code=True, optimize_model=True, - use_cache=True) + use_cache=True, + model_hub=model_hub) model = model.half().to('xpu')