Create README.md

README.md

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+---
+license: mit
+---
+
+```python
+
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+import torch
+model_path = 'shilinxu/EFVLM-36200'
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16 ,device_map='cuda:0', trust_remote_code=True)
+
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+
+
+url = 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'
+import requests
+image = Image.open(requests.get(url, stream=True).raw)
+
+
+messages = [
+    {
+        'role':'user', 
+        'content': [
+            {'type':'text', 'text': 'Describe this image in detail.'},
+            {'type':'image'}
+        ]
+    }
+]
+
+
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+inputs = processor(
+    text=text,
+    images=[image],
+    padding=False,
+    return_tensors="pt",
+)
+inputs = inputs.to(model.device, dtype=torch.bfloat16)
+
+generated_ids = model.generate(**inputs, max_new_tokens=128, temperature=1.0, repetition_penalty=1.2)
+generated_ids = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(output_text)
+```