Rename MiniCPM-V-2_6 CPU example (#11998)

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@ -319,7 +319,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
| MiniCPM-V | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V) |
| MiniCPM-V-2 | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2) |
| MiniCPM-Llama3-V-2_5 | | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5) |
| MiniCPM-V-2_6 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v) | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6) |
| MiniCPM-V-2_6 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2_6) | [link](python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6) |
## Get Support
- Please report a bug or raise a feature request by opening a [Github Issue](https://github.com/intel-analytics/ipex-llm/issues)

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@ -1,11 +1,11 @@
# MiniCPM-V
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V models. For illustration purposes, we utilize the [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) as a reference MiniCPM-V model.
# MiniCPM-V-2_6
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2_6 models. For illustration purposes, we utilize the [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) as a reference MiniCPM-V-2_6 model.
## 0. Requirements
To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
## Example: Predict Tokens using `chat()` API
In the example [chat.py](./chat.py), we show a basic use case for a MiniCPM-V model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations.
In the example [chat.py](./chat.py), we show a basic use case for a MiniCPM-V-2_6 model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations.
### 1. Install
We suggest using conda to manage environment:
@ -47,7 +47,7 @@ pip install transformers==4.40.0 trl
Arguments info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MiniCPM-V model (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'`.
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MiniCPM-V-2_6 model (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'`.
- `--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'`.
- `--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?'`.
- `--stream`: flag to chat in streaming mode

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@ -26,9 +26,9 @@ from transformers import AutoTokenizer
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for MiniCPM-V model')
parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for MiniCPM-V-2_6 model')
parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-V-2_6",
help='The huggingface repo id for the MiniCPM-V model to be downloaded'
help='The huggingface repo id for the MiniCPM-V-2_6 model to be downloaded'
', or the path to the huggingface checkpoint folder')
parser.add_argument('--image-url-or-path', type=str,
default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg',