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README.md
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# News
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- 2024-06-27: We released our first visual embedding model minicpm-visual-embedding-v0.1 on [huggingface](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0
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- 2024-05-08: We [committed](https://github.com/bokesyo/minicpm-visual-embedding) our training code (full-parameter tuning with GradCache and DeepSpeed, supports large batch size across multiple GPUs with zero-stage1) and eval code.
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# Get started
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First you are suggested to git clone this huggingface repo or download repo with `huggingface_cli`.
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```bash
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git lfs install
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git clone https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0
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```
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or
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```bash
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huggingface-cli download RhapsodyAI/minicpm-visual-embedding-v0
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```
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```python
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return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
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tokenizer = AutoTokenizer.from_pretrained('/local/path/to/minicpm-visual-embedding-v0
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model = AutoModel.from_pretrained('/local/path/to/minicpm-visual-embedding-v0
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image_1 = Image.open('/local/path/to/document1.png').convert('RGB')
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image_2 = Image.open('/local/path/to/document2.png').convert('RGB')
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# News
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- 2024-06-27: We released our first visual embedding model minicpm-visual-embedding-v0.1 on [huggingface](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0).
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- 2024-05-08: We [committed](https://github.com/bokesyo/minicpm-visual-embedding) our training code (full-parameter tuning with GradCache and DeepSpeed, supports large batch size across multiple GPUs with zero-stage1) and eval code.
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# Get started
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Pip install all dependencies:
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```
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Pillow==10.1.0
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timm==0.9.10
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torch==2.1.2
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torchvision==0.16.2
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transformers==4.36.0
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sentencepiece==0.1.99
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```
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First you are suggested to git clone this huggingface repo or download repo with `huggingface_cli`.
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```bash
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git lfs install
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git clone https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0
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```
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or
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```bash
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huggingface-cli download RhapsodyAI/minicpm-visual-embedding-v0
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```
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```python
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return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
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tokenizer = AutoTokenizer.from_pretrained('/local/path/to/minicpm-visual-embedding-v0', trust_remote_code=True)
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model = AutoModel.from_pretrained('/local/path/to/minicpm-visual-embedding-v0', trust_remote_code=True)
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image_1 = Image.open('/local/path/to/document1.png').convert('RGB')
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image_2 = Image.open('/local/path/to/document2.png').convert('RGB')
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