Add --modelscope option for glm-v4 MiniCPM-V-2_6 glm-edge and internvl2 (#12583)
* Add --modelscope option for glm-v4 and MiniCPM-V-2_6 * glm-edge * minicpm-v-2_6:don't use model_hub=modelscope when use lowbit; internvl2 --------- Co-authored-by: ATMxsp01 <shou.xu@intel.com>
This commit is contained in:
Xu, Shuo
GitHub
parent
f3b5fad3be
commit
b0338c5529
8 changed files with 142 additions and 39 deletions
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@ -1,5 +1,5 @@
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# GLM-Edge
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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.
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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.
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## 0. Requirements
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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.
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@ -17,6 +17,9 @@ pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-exte
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pip install transformers==4.47.0
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pip install accelerate==0.33.0
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pip install "trl<0.12.0"
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# [optional] only needed if you would like to use ModelScope as model hub
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pip install modelscope==1.11.0
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```
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### 1.2 Installation on Windows
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@ -32,6 +35,9 @@ pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-exte
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pip install transformers==4.47.0
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pip install accelerate==0.33.0
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pip install "trl<0.12.0"
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# [optional] only needed if you would like to use ModelScope as model hub
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pip install modelscope==1.11.0
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```
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## 2. Configures OneAPI environment variables for Linux
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@ -102,14 +108,19 @@ set SYCL_CACHE_PERSISTENT=1
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### Example 1: Predict Tokens using `generate()` API
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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.
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```
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```bash
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# for Hugging Face model hub
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
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# for ModelScope model hub
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --modelscope
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```
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Arguments info:
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- `--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'`.
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- `--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**.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
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#### Sample Output
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#### [THUDM/glm-edge-1.5b-chat](https://huggingface.co/THUDM/glm-edge-1.5b-chat)
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@ -19,21 +19,32 @@ import time
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import argparse
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for GLM-Edge model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-edge-4b-chat",
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help='The huggingface repo id for the GLM-Edge model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--repo-id-or-model-path', type=str,
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help='The Hugging Face or ModelScope repo id for the GLM-Edge model to be downloaded'
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', or the path to the checkpoint folder')
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parser.add_argument('--prompt', type=str, default="AI是什么？",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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parser.add_argument('--modelscope', action="store_true", default=False,
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help="Use models from modelscope")
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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if args.modelscope:
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from modelscope import AutoTokenizer
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model_hub = 'modelscope'
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else:
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from transformers import AutoTokenizer
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model_hub = 'huggingface'
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model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \
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("ZhipuAI/glm-edge-4b-chat" if args.modelscope else "THUDM/glm-edge-4b-chat")
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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@ -43,7 +54,8 @@ if __name__ == '__main__':
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True)
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use_cache=True,
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model_hub=model_hub)
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model = model.half().to("xpu")
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# Load tokenizer
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@ -1,5 +1,5 @@
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# MiniCPM-V-2_6
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In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2_6 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) as reference MiniCPM-V-2_6 model.
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In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2_6 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) (or [OpenBMB/MiniCPM-V-2_6](https://www.modelscope.cn/models/OpenBMB/MiniCPM-V-2_6) for ModelScope) as reference MiniCPM-V-2_6 model.
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## 0. Requirements
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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.
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@ -16,6 +16,9 @@ conda activate llm
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install transformers==4.40.0 "trl<0.12.0"
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# [optional] only needed if you would like to use ModelScope as model hub
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pip install modelscope==1.11.0
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```
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#### 1.2 Installation on Windows
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@ -28,6 +31,9 @@ conda activate llm
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install transformers==4.40.0 "trl<0.12.0"
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# [optional] only needed if you would like to use ModelScope as model hub
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pip install modelscope==1.11.0
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```
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### 2. Configures OneAPI environment variables for Linux
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@ -96,31 +102,48 @@ set SYCL_CACHE_PERSISTENT=1
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### 4. Running examples
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- chat without streaming mode:
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```
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```bash
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# for Hugging Face model hub
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python ./chat.py --prompt 'What is in the image?'
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# for ModelScope model hub
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python ./chat.py --prompt 'What is in the image?' --modelscope
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```
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- chat in streaming mode:
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```
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```bash
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# for Hugging Face model hub
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python ./chat.py --prompt 'What is in the image?' --stream
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# for ModelScope model hub
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python ./chat.py --prompt 'What is in the image?' --stream --modelscope
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```
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- save model with low-bit optimization (if `LOWBIT_MODEL_PATH` does not exist)
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```
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```bash
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# for Hugging Face model hub
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python ./chat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?'
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# for ModelScope model hub
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python ./chat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?' --modelscope
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```
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- chat with saved model with low-bit optimization (if `LOWBIT_MODEL_PATH` exists):
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```
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```bash
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# for Hugging Face model hub
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python ./chat.py --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?'
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# for ModelScope model hub
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python ./chat.py --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?' --modelscope
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```
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> [!TIP]
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> For chatting in streaming mode, it is recommended to set the environment variable `PYTHONUNBUFFERED=1`.
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MiniCPM-V-2_6 (e.g. `openbmb/MiniCPM-V-2_6`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'openbmb/MiniCPM-V-2_6'`.
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the MiniCPM-V-2_6 (e.g. `openbmb/MiniCPM-V-2_6`) to be downloaded, or the path to the checkpoint folder. It is default to be `'openbmb/MiniCPM-V-2_6'` for **Hugging Face** or `'OpenBMB/MiniCPM-V-2_6'` for **ModelScope**.
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- `--lowbit-path LOWBIT_MODEL_PATH`: argument defining the path to save/load the model with IPEX-LLM low-bit optimization. If it is an empty string, the original pretrained model specified by `REPO_ID_OR_MODEL_PATH` will be loaded. If it is an existing path, the saved model with low-bit optimization in `LOWBIT_MODEL_PATH` will be loaded. If it is a non-existing path, the original pretrained model specified by `REPO_ID_OR_MODEL_PATH` will be loaded, and the optimized low-bit model will be saved into `LOWBIT_MODEL_PATH`. It is default to be `''`, i.e. an empty string.
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- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is in the image?'`.
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- `--stream`: flag to chat in streaming mode
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- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
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#### Sample Output
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@ -22,14 +22,14 @@ import requests
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import torch
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from PIL import Image
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from ipex_llm.transformers import AutoModel
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from transformers import AutoTokenizer, AutoProcessor
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from transformers import AutoProcessor
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for openbmb/MiniCPM-V-2_6 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-V-2_6",
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help='The huggingface repo id for the openbmb/MiniCPM-V-2_6 model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--repo-id-or-model-path', type=str,
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help='The Hugging Face or ModelScope repo id for the MiniCPM-V-2_6 model to be downloaded'
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', or the path to the checkpoint folder')
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parser.add_argument("--lowbit-path", type=str,
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default="",
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help="The path to the saved model folder with IPEX-LLM low-bit optimization. "
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@ -44,9 +44,20 @@ if __name__ == '__main__':
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help='Prompt to infer')
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parser.add_argument('--stream', action='store_true',
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help='Whether to chat in streaming mode')
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parser.add_argument('--modelscope', action="store_true", default=False,
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help="Use models from modelscope")
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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if args.modelscope:
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from modelscope import AutoTokenizer
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model_hub = 'modelscope'
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else:
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from transformers import AutoTokenizer
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model_hub = 'huggingface'
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model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \
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("OpenBMB/MiniCPM-V-2_6" if args.modelscope else "openbmb/MiniCPM-V-2_6")
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image_path = args.image_url_or_path
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lowbit_path = args.lowbit_path
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@ -61,7 +72,8 @@ if __name__ == '__main__':
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True,
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modules_to_not_convert=["vpm", "resampler"])
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modules_to_not_convert=["vpm", "resampler"],
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model_hub=model_hub)
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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# GLM-4V
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In this directory, you will find examples on how you could apply IPEX-LLM FP8 optimizations on GLM-4V models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/glm-4v-9b](https://huggingface.co/THUDM/glm-4v-9b) as a reference GLM-4V model.
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In this directory, you will find examples on how you could apply IPEX-LLM FP8 optimizations on GLM-4V models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/glm-4v-9b](https://huggingface.co/THUDM/glm-4v-9b) (or [ZhipuAI/glm-4v-9b](https://www.modelscope.cn/models/ZhipuAI/glm-4v-9b) for ModelScope) as a reference GLM-4V model.
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## 0. Requirements
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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.
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@ -16,6 +16,9 @@ conda activate llm
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install tiktoken transformers==4.42.4 "trl<0.12.0"
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# [optional] only needed if you would like to use ModelScope as model hub
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pip install modelscope==1.11.0
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```
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#### 1.2 Installation on Windows
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@ -28,6 +31,9 @@ conda activate llm
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install tiktoken transformers==4.42.4 "trl<0.12.0"
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# [optional] only needed if you would like to use ModelScope as model hub
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pip install modelscope==1.11.0
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```
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### 2. Configures OneAPI environment variables for Linux
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@ -95,15 +101,20 @@ set SYCL_CACHE_PERSISTENT=1
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> 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.
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### 4. Running examples
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```
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python ./generate.py --prompt 'What is in the image?'
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```bash
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# for Hugging Face model hub
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH
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# for ModelScope model hub
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH --modelscope
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the GLM-4V model (e.g. `THUDM/glm-4v-9b`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/glm-4v-9b'`.
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the GLM-4V model (e.g. `THUDM/glm-4v-9b`) to be downloaded, or the path to the checkpoint folder. It is default to be `'THUDM/glm-4v-9b'` for **Hugging Face** or `'ZhipuAI/glm-4v-9b'` for **ModelScope**.
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- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is in the image?'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
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#### Sample Output
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#### [THUDM/glm-4v-9b](https://huggingface.co/THUDM/glm-4v-9b)
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@ -22,13 +22,12 @@ import requests
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from PIL import Image
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for THUDM/glm-4v-9b model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-4v-9b",
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help='The huggingface repo id for the THUDM/glm-4v-9b model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--repo-id-or-model-path', type=str,
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help='The Hugging Face or ModelScope repo id for the glm-4v model to be downloaded'
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', or the path to the checkpoint folder')
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parser.add_argument('--image-url-or-path', type=str,
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default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg',
|
||||
help='The URL or path to the image to infer')
|
||||
|
|
@ -36,9 +35,20 @@ if __name__ == '__main__':
|
|||
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-4v-9b" if args.modelscope else "THUDM/glm-4v-9b")
|
||||
image_path = args.image_url_or_path
|
||||
|
||||
# Load model in 4 bit,
|
||||
|
|
@ -49,7 +59,9 @@ if __name__ == '__main__':
|
|||
load_in_4bit=True,
|
||||
optimize_model=True,
|
||||
trust_remote_code=True,
|
||||
use_cache=True).half().to('xpu')
|
||||
use_cache=True,
|
||||
model_hub=model_hub)
|
||||
model = model.half().to('xpu')
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
|
||||
|
|
|
|||
|
|
@ -22,22 +22,32 @@ import requests
|
|||
import torch
|
||||
from PIL import Image
|
||||
from ipex_llm.transformers import AutoModelForCausalLM
|
||||
from transformers import AutoTokenizer, CLIPImageProcessor
|
||||
from transformers import CLIPImageProcessor
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for OpenGVLab/InternVL2-4B model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="OpenGVLab/InternVL2-4B",
|
||||
help='The huggingface repo id for the OpenGVLab/InternVL2-4B model to be downloaded'
|
||||
', or the path to the huggingface checkpoint folder')
|
||||
help='The Hugging Face or ModelScope repo id for the InternVL2 model to be downloaded'
|
||||
', or the path to the checkpoint folder')
|
||||
parser.add_argument('--image-url-or-path', type=str,
|
||||
default='https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg',
|
||||
help='The URL or path to the image to infer')
|
||||
parser.add_argument('--prompt', type=str, default="What is in the image?",
|
||||
help='Prompt to infer')
|
||||
parser.add_argument('--n-predict', type=int, default=64, 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
|
||||
image_path = args.image_url_or_path
|
||||
n_predict = args.n_predict
|
||||
|
|
@ -48,7 +58,8 @@ if __name__ == '__main__':
|
|||
# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,
|
||||
load_in_low_bit="sym_int4",
|
||||
modules_to_not_convert=["vision_model"])
|
||||
modules_to_not_convert=["vision_model"],
|
||||
model_hub=model_hub)
|
||||
model = model.half().to('xpu')
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path,
|
||||
trust_remote_code=True)
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
# InternVL2
|
||||
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on InternVL2 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [OpenGVLab/InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B) as a reference InternVL2 model.
|
||||
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on InternVL2 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [OpenGVLab/InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B) (or [OpenGVLab/InternVL2-4B](https://www.modelscope.cn/models/OpenGVLab/InternVL2-4B) for ModelScope) as a reference InternVL2 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.
|
||||
|
|
@ -17,6 +17,9 @@ pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-exte
|
|||
|
||||
pip install einops timm
|
||||
|
||||
# [optional] only needed if you would like to use ModelScope as model hub
|
||||
pip install modelscope==1.11.0
|
||||
|
||||
```
|
||||
|
||||
#### 1.2 Installation on Windows
|
||||
|
|
@ -30,6 +33,9 @@ pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-exte
|
|||
|
||||
pip install einops timm
|
||||
|
||||
# [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
|
||||
|
|
@ -98,15 +104,20 @@ set SYCL_CACHE_PERSISTENT=1
|
|||
### 4. Running examples
|
||||
|
||||
- chat with specified prompt:
|
||||
```
|
||||
python ./chat.py --prompt 'What is in the image?'
|
||||
```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 --image-url-or-path IMAGE_URL_OR_PATH
|
||||
|
||||
# for ModelScope model hub
|
||||
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH --modelscope
|
||||
```
|
||||
|
||||
Arguments info:
|
||||
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the InternVL2 (e.g. `OpenGVLab/InternVL2-4B`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'OpenGVLab/InternVL2-4B'`.
|
||||
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the InternVL2 (e.g. `OpenGVLab/InternVL2-4B`) to be downloaded, or the path to the checkpoint folder. It is default to be `'OpenGVLab/InternVL2-4B'`.
|
||||
- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg'`.
|
||||
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is in the image?'`.
|
||||
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `64`.
|
||||
- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
|
||||
|
||||
#### Sample Output
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue