MiniCPMV-Xray-Images-Lora

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Uses

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Inference

Firstly, load model ``python import torch from transformers import AutoTokenizer, AutoModel

torch.manual_seed(0)

model_path = "/path/to/MiniCPM-V-2"
peft_path = "/path/to/checkpoint-xxx"

model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model.load_adapter(peft_path)
model.eval().cuda()

``

Then, inference
``python
import io
import json
import base64
from PIL import Image

def img2base64(file_name):
   with open(file_name, 'rb') as f:
      encoded_string = base64.b64encode(f.read())
              return encoded_string

      im_64 = img2base64('/mnt/workspace/xray/data/images/99_2.png')
      question = [{"role": "user", "content": "描述一下"}]
      inputs = {"image": im_64, "question": json.dumps(question)}

      image = Image.open(io.BytesIO(base64.b64decode(inputs['image']))).convert('RGB')
      msgs = json.loads(inputs['question'])

      answer, context, _ = model.chat(
           image=image,
           msgs=msgs,
           context=None,
           tokenizer=tokenizer,
           sampling=True,
           temperature=0.7)

print(msgs[-1]["content"]+'\n', answer)
```

Direct Use

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Training Details

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Evaluation

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.10.0